essay on quantitative research methods

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Quantitative Research Methods

What is quantitative research, about this guide, introduction, quantitative research methodologies.

• Key Resources
• Quantitative Software
• Finding Qualitative Studies

 The purpose of this guide is to provide a starting point for learning about quantitative research. In this guide, you'll find:

• Resources on diverse types of quantitative research.
• An overview of resources for data, methods & analysis
• Popular quantitative software options
• Information on how to find quantitative studies

Research involving the collection of data in numerical form for quantitative analysis. The numerical data can be durations, scores, counts of incidents, ratings, or scales. Quantitative data can be collected in either controlled or naturalistic environments, in laboratories or field studies, from special populations or from samples of the general population. The defining factor is that numbers result from the process, whether the initial data collection produced numerical values, or whether non-numerical values were subsequently converted to numbers as part of the analysis process, as in content analysis.

Citation: Garwood, J. (2006). Quantitative research. In V. Jupp (Ed.), The SAGE dictionary of social research methods. (pp. 251-252). London, England: SAGE Publications. doi:10.4135/9780857020116

Watch the following video to learn more about Quantitative Research:

(Video best viewed in Edge and Chrome browsers, or click here to view in the Sage Research Methods Database)

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y, and Z are also playing a role.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses.

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Quantitative research is a powerful tool used by researchers to collect and analyze numerical data. But what is quantitative research, and how does it work?

This type of research is often used in natural sciences, social sciences, as well as in business and management. Quantitative research can provide valuable insights into various phenomena. 

This guide will provide an overview of quantitative research, its types, and its advantages and limitations. We will also share the steps for conducting quantitative research and examples of its application in different fields. 

Let’s get into it!

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What is Quantitative Research?

Quantitative research is a research method that involves the collection and analysis of numerical data. The data is collected through surveys, experiments, or other standardized methods. It is then quantified and analyzed using different statistical and computational methods. 

Quantitative research aims to identify patterns, relationships, and trends among variables. It is also used to establish co-relation and cause-and-effect relationships between variables. 

The Significance of Quantitative Research 

Quantitative research enables researchers to collect objective and reliable data that can be generalized to a larger population. 

It provides a rigorous and systematic approach to studying phenomena. Moreover, it allows researchers to test hypotheses and theories in a precise and controlled manner. 

Quantitative research is widely used in social sciences, including psychology, sociology, and economics. It is also widely used in business, marketing, and management. Additionally, quantitative research helps in decision-making by providing objective data that can guide policy decisions.

Characteristics of Quantitative Research

Quantitative research can be distinguished due to its following characteristics:

• Objectivity: Quantitative research is objective. That is, it relies on empirical evidence and statistical analysis to draw conclusions.
• Measurability: Quantitative research is characterized by measurability. It is based on numerical data that can be measured and analyzed. The data collected can be quantified and analyzed using statistical methods.
• Control: Quantitative research offers a high level of control over the research environment. Researchers can manipulate variables to test hypotheses and ensure that the data collected is valid and reliable.
• Replicability: Quantitative research is designed to be replicable, meaning that other researchers can repeat the study and obtain similar results. This allows for verification of the findings and increases the reliability of the research.
• Generalizability: Quantitative data is often generalizable. This means that the findings can be applied to a larger population beyond the sample studied. This is possible due to the use of statistical techniques that allow researchers to make valid inferences about the larger population.
• Large sample size: Quantitative research typically involves large sample sizes to ensure that the findings are representative of the larger population. This allows researchers to make more accurate predictions and generalizations.
• Predetermined research design: Quantitative research is based on a pre-determined research design. The research methods and procedures are established before data collection begins. This ensures that the research is conducted in a systematic and standardized way, increasing the reliability of the findings.

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Types of Quantitative Research Methods

Quantitative research takes on several forms depending on the research objectives and design. Here are some of the most common types of quantitative research:

Experimental Research

Experimentation involves the manipulation of variables in a controlled environment to observe their effects on each other. 

Experimental research aims to establish cause-and-effect relationships between variables. It is often conducted in laboratory settings or controlled environments.

Survey Research

This type of research involves the collection of data through questionnaires or surveys administered to a sample of individuals. 

Surveys can be conducted online, over the phone, through mail, or in person. Survey research is often used to measure the attitudes, opinions, behaviors, and demographics of a specific population.

Longitudinal Research

This type of research involves the collection of data over an extended period of time, typically several years or more. Longitudinal research can be used to observe changes in a particular phenomenon over time and to identify patterns and trends.

Cross-sectional Research

This type of research involves the collection of data at a specific point in time from a sample of individuals. Cross-sectional research is used to produce comparisons of different groups or to measure the prevalence of a phenomenon at different times.

Correlational research

This type of research involves the examination of the relationship between two or more variables without manipulating them. Correlational research can help identify relationships between variables and can be used to predict outcomes.

Advantages of Quantitative Research

Quantitative research offers many advantages over other research methods, including:

• Objectivity and Measurability: 

Quantitative research provides objective and measurable data that can be analyzed using statistical methods. This makes it a reliable method of research as it eliminates personal bias and opinions.

• Statistical Analysis: 

Quantitative research allows for statistical analysis, which enables researchers to identify patterns, trends, and relationships in data. This is particularly useful when analyzing large sets of data.

• Control and Precision: 

Quantitative research provides a high level of control and precision in the data collection process. This allows researchers to minimize the influence of extraneous variables and to draw conclusions based on empirical evidence.

• Replicability:

 Quantitative research can be replicated and generalized to a larger population. This enhances its reliability and validity and makes it easier for other researchers to build upon existing research.

Limitations of Quantitative Research

Although quantitative research has its advantages, it also has several limitations due to its structure. Here are some of them:

• Limited ability to capture in-depth information: 

It is limited in its ability to capture in-depth information about a particular phenomenon. It is because it typically relies on standardized instruments and only those variables that can be quantified.

• Can be expensive and time-consuming: 

This type of research can be expensive and time-consuming to conduct. Especially if large samples are required or if complex statistical analyses are necessary.

• It is not suitable for all research questions: 

It may not be suitable for all kinds of research questions. For instance, questions that require a deeper understanding of the subjective experiences of individuals or the context in which phenomena occur.

When to Use Quantitative Research? 

Quantitative research is an appropriate research method to use in a variety of situations, such as:

• To Test Hypotheses: 

Quantitative research is ideal for testing hypotheses or theories. It helps researchers determine whether there is a significant relationship between variables.

• To Measure Attitudes or Behaviors: 

Quantitative research is useful when trying to measure attitudes, opinions, or behaviors. For example, a company may use quantitative research to measure customer satisfaction or to determine the effectiveness of an advertising campaign.

• To Conduct Large-Scale Studies: 

Quantitative research is an efficient method of data collection when conducting large-scale studies. This is because it allows researchers to collect data from a large sample of participants quickly and efficiently.

• To Compare Groups: 

It can be used to compare groups based on certain characteristics or variables. For example, a researcher may compare the effectiveness of different treatment options for a particular condition.

• To Generalize Findings: 

It is useful when the goal is to generalize findings to a larger population. The data can be analyzed using statistical methods to determine how much the findings are representative of the larger population.

Steps to Conduct Good Quantitative Research

Conducting quantitative research involves a series of steps that researchers must follow to ensure the validity and reliability of their findings. Here are the main steps in conducting quantitative research:

• Formulating a Research Question: 

The first step in conducting quantitative research is to formulate a clear research question. The research question should be specific, measurable, and relevant to the research topic.

• Conducting a Literature Review: 

Researchers should conduct a thorough literature review to identify the existing research on the topic. This way, they can identify any gaps or issues in the research that need to be addressed.

• Developing a Research Design: 

Afterward, students have to develop a research design. The research design should include a clear description of the sample, the data collection methods, and the data analysis procedures.

• Collecting Data: 

The next step is to collect data. The data collection methods used will depend on the research question and the research design. Common data collection methods in quantitative research include surveys, experiments, and observational studies.

• Analyzing Data: 

After data has been collected, the next step is to analyze it. Researchers may use statistical methods to analyze the data and to test hypotheses. The data analysis procedures used will depend on the research question and the research design.

• Interpreting Results: 

The final step in conducting quantitative research is to interpret the results. Researchers should consider the findings in relation to the research question and the existing literature on the topic. They should also consider any limitations of the research and the implications of the findings for future research or practice.

Quantitative Research Examples

Here are two examples that demonstrate what quantitative research looks like:

Quantitative Research Paper on Effects of Poverty

Quantitative Research on Effective Teacher Leadership

In Conclusion, 

Quantitative research is a powerful tool for exploring the relationship between variables and generating objective, measurable data. By understanding the characteristics, types, and steps, researchers can conduct studies that contribute to their field. You can try our AI essay generator to get started with your writing.

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Essay on Qualitative vs. Quantitative Research

Both qualitative and quantitative researches are valued in the research world and are often used together under a single project. This is despite the fact that they have significant differences in terms of their theoretical, epistemological, and methodological formations. Qualitative research is usually in form of words while quantitative research takes the numerical approach. This paper discusses the similarities, differences, advantages, and disadvantages of qualitative and quantitative research and provides a personal stand.

Similarities

Both qualitative and quantitative research approaches begin with a problem on which scholars seek to find answers. Without a research problem or question, there would be no reason for carrying out the study. Once a problem is formulated, researchers at their own discretion and depending on the nature of the question choose the appropriate type of research to employ. Just like in qualitative research, data obtained from quantitative analysis need to be analyzed (Miles & Huberman, 1994). This step is crucial for helping researchers to gain a deeper understanding of the issue under investigation. The findings of any research enjoy confirmability after undergoing a thorough examination and auditing process (Miles & Huberman, 1994).

Both types of research approaches require a concise plan before they are carried out. Once researchers formulate the study question, they must come up with a plan for investigating the matter (Yilmaz, 2013). Such plans include deciding the appropriate research technique to implement, estimating budgets, and deciding on the study areas. Failure to plan before embarking on the research project may compromise the research findings. In addition, both qualitative and quantitative research are dependent on each other and can be used for a single research project (Miles & Huberman, 1994). Quantitative data helps the qualitative research in finding a representative study sample and obtaining the background data. In the same way, qualitative research provides the quantitative side with the conceptual development and instrumentation (Miles & Huberman, 1994).

Differences

Qualitative research seeks to explain why things are the way they seem to be. It provides well-grounded descriptions and explanations of processes in identifiable local contexts (Miles & Huberman, 1994). Researchers use qualitative research to dig deeper into the problem and develop a relevant hypothesis for potential quantitative research. On the other hand, Quantitative research uses numerical data to state and quantify the problem (Yilmaz, 2013). Researchers in quantitative research use measurable data in formulating facts and uncovering the research pattern.

Quantitative research approach involves a larger number of participants for the purpose of gathering as much information as possible to summarize characteristics across large groups. This makes it a very expensive research approach. On the contrary, qualitative research approach describes a phenomenon in a more comprehensive manner. A relatively small number of participants take part in this type of research. This makes the overall process cheaper and time friendly.

Data collection methods differ significantly in the two research approaches. In quantitative research, scholars use surveys, questionnaires, and systematic measurements that involve numbers (Yilmaz, 2013). Moreover, they report their findings in impersonal third person prose by using numbers. This is different from the qualitative approach where only the participants’ observation and deep document analysis is necessary for conclusions to be drawn. Findings are disseminated in the first person’s narrative with sufficient quotations from the participants.

Advantages and Disadvantages of Qualitative Research

Qualitative data is based on human observations. Respondent’s observations connect the researcher to the most basic human experiences (Rahman, 2016). It gives a detailed production of participants’ opinions and feelings and helps in efficient interpretation of their actions (Miles & Huberman, 1994). Moreover, this research approach is interdisciplinary and entails a wide range of research techniques and epistemological viewpoints. Data collection methods in qualitative approach are both detailed and subjective (Rahman, 2016). Direct observations, unstructured interviews, and participant observation are the most common techniques employed in this type of research. Researchers have the opportunity to mingle directly with the respondents and obtain first-hand information.

On the negative side, the smaller population sample used in qualitative research raises credibility concerns (Rahman, 2016). The views of a small group of respondents may not necessarily reflect those of the entire population. Moreover, conducting this type of research on certain aspects such as the performance of students may be more challenging. In such instances, researchers prefer to use the quantitative approach instead (Rahman, 2016). Data analysis and interpretation in qualitative research is a more complex process. It is long, has elusive data, and has very stringent requirements for analysis (Rahman, 2016). In addition, developing a research question in this approach is a challenging task as the refining question mostly becomes continuous throughout the research process.

Advantages and Disadvantages of Quantitative Research

The findings of a quantitative research can be generalized to a whole population as it involves larger samples that are randomly selected by researchers (Rahman, 2016). Moreover, the methods used allows for use of statistical software in test taking (Rahman, 2016). This makes the approach time effective and efficient for tackling complex research questions. Quantitative research allows for objectivity and accuracy of the study results. This approach is well designed to provide essential information that supports generalization of a phenomenon under study. It involves few variables and many cases that guarantee the validity and credibility of the study results.

This research approach, however, has some limitations. There is a limited direct connection between the researcher and respondents. Scholars who adopt this approach measure variables at specific moments in time and disregards the past experiences of the respondents (Rahman, 2016). As a result, deep information is often ignored and only the overall picture of the variables is represented. The quantitative approach uses standard questions set and administered by researchers (Rahman, 2016). This might lead to structural bias by respondents and false representation. In some instances, data may only reflect the views of the sample under study instead of revealing the real situation. Moreover, preset questions and answers limit the freedom of expression by the respondents.

Preferred Method

I would prefer quantitative research method over the qualitative approach. Data management in this technique is much familiar and more accessible to researchers’ contexts (Miles & Huberman, 1994). It is a more scientific process that involves the collection, analysis, and interpretation of large amounts of data. Researchers have more control of the manner in which data is collected. Unlike qualitative data that requires descriptions, quantitative approach majors on numerical data (Yilmaz, 2013). With this type of data, I can use the various available software for classification and analyzes. Moreover, researchers are more flexible and free to interact with respondents. This gives an opportunity for obtaining first-hand information and learning more about other behavioral aspects of the population under study.

As highlighted above, qualitative and quantitative techniques are the two research approaches. Both seek to dig deeper into a particular problem, analyze the responses of a selected sample and make viable conclusions. However, qualitative research is much concerned with the description of peoples’ opinions, motivations, and reasons for a particular phenomenon. On the other hand, Quantitative research uses numerical data to state and explain research findings. Use of numerical data allows for objectivity and accuracy of the research results. However structural biases are common in this approach. Data collection and sampling in qualitative research is more detailed and subjective. Considering the different advantages and disadvantages of the two research approaches, I would go for the quantitative over qualitative research.

Miles, M., & Huberman, A. (1994).  Qualitative data analysis  (2nd Ed.). Beverly Hills: Sage.

Rahman, M. (2016). The Advantages and Disadvantages of Using Qualitative and Quantitative Approaches and Methods in Language “Testing and Assessment” Research: A Literature Review.  Journal of Education and Learning , 6(1), 102.

Yilmaz, K. (2013). Comparison of Quantitative and Qualitative Research Traditions: epistemological, theoretical, and methodological differences.  European Journal of Education , 48(2), 311-325.

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Quantitative Research Design (JARS–Quant)

The current JARS–Quant standards, released in 2018, expand and revise the types of research methodologies covered in the original JARS, which were published in 2008.

JARS–Quant include guidance for manuscripts that report

• Primary quantitative research
• Experimental designs
• Nonexperimental designs

Special designs

Analytic methods, meta-analyses.

In addition, JARS–Quant now divides hypotheses, analyses, and conclusions into primary, secondary, and exploratory groups. This should enhance the readability and replicability of the research.

Providing the information specified in JARS–Quant should become routine and minimally burdensome, thereby increasing the transparency of reporting in psychological research.

For more information on how the revised standards were created, read Journal Article Reporting Standards for Quantitative Research in Psychology .

General quantitative reporting standards

• Quantitative Design Reporting Standards (JARS-Quant) (PDF, 137KB) Information recommended for inclusion in manuscripts that report new data collections regardless of research design

Experimental and nonexperimental designs

• Experimental Designs (PDF, 109KB) Reporting standards for studies with an experimental manipulation
• Random Assignment (PDF, 101KB) Reporting standards for studies using random assignment
• Nonrandom Assignment (PDF, 92KB) Reporting standards for studies using nonrandom assignment
• Clinical Trials (PDF, 106KB) Reporting standards for studies involving clinical trials
• Nonexperimental Designs (PDF, 103KB) Reporting standards for studies using no experimental manipulation
• Longitudinal Studies (PDF, 103KB) Reporting standards for longitudinal studies
• N -of-1 Studies (PDF, 102KB) Reporting standards for N -of-1 studies
• Replication Studies (PDF, 95KB) Reporting standards for replication studies
• Structural Equation Modeling (PDF, 111KB) Reporting standards for studies using structural equation modeling
• Bayesian Statistics (PDF, 104KB) Reporting standards for studies using Bayesian techniques
• Quantitative Meta-Analysis Reporting Standards (PDF, 116KB) Information recommended for inclusion in manuscripts that report quantitative meta-analyses
• Qualitative design standards
• Mixed methods standards
• Race, Ethnicity, and Culture standards

Return to Journal Article Reporting Standards homepage

Jars resources

• History of APA’s journal article reporting standards
• APA Style JARS supplemental glossary
• Supplemental resource on the ethic of transparency in JARS
• Frequently asked questions
• JARS-Quant Decision Flowchart (PDF, 98KB)
• JARS-Quant Participant Flowchart (PDF, 98KB)

Jars articles

• Jars –Quant article
• Jars –Qual / Mixed article
• Jars – rec executive summary

Questions / feedback

Email an APA Style Expert if you have questions, feedback, or suggestions for modules to be included in future JARS updates.

APA resources

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Quantitative and Qualitative Research Methods: Similarities and Differences Compare & Contrast Essay

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Introduction

The aim of this paper is to analyze and to compare quantitative and qualitative research methods. The analysis will begin with the definition and description of the two methods. This will be followed by a discussion on the various aspects of the two research methods.

The similarities and differences between quantitative and qualitative research methods can be seen in their characteristics, data collection methods, data analysis methods, and the validity issues associated with them, as well as, their strengths and weaknesses.

Definition and Description

Qualitative research method is a technique of “studying phenomena by collecting and analyzing data in non-numeric form”. It focuses on exploring the topic of the study by finding as much detail as possible. The characteristics of qualitative research include the following.

First, it focuses on studying the behavior of individuals in their natural settings. Thus, it does not use artificial experiments. This helps researchers to avoid interfering with the participants’ normal way of life.

Second, qualitative research focuses on meanings, perspectives, and understandings. It aims at finding out the meanings that the subjects of the study “attach to their behavior, how they interpret situations, and what their perspectives are on particular issues”.

Concisely, it is concerned with the processes that explain why and how things happen.

Quantitative research is “explaining phenomena by collecting numerical data that are analyzed using mathematical techniques such as statistics”.

It normally uses experiments to answer research questions. Control is an important aspect of the experiments because it enables the researcher to find unambiguous answers to research questions.

Quantitative research also uses operational definitions. Concisely, the terms used in a quantitative study must be defined according to the operations employed to measure them in order to avoid confusion in meaning or communication.

Moreover, the results of quantitative research are considered to be reliable only if they are replicable. This means that the same results must be produced if the research is repeated using the same techniques.

Hypothesis testing is also an integral part of quantitative research. Concisely, hypotheses enable the researcher to concentrate on a specific aspect of a problem, and to identify the methods for solving it.

The similarities and differences between quantitative and qualitative research methods can be seen in their characteristics

Quantitative and qualitative studies are similar in the following ways. To begin with, qualitative research is normally used to generate theory. Similarly, quantitative studies can be used to explore new areas, thereby creating a new theory.

Even though qualitative research focuses on generating theory, it can also be used to test hypotheses and existing theories. In this regard, it is similar to quantitative studies that mainly focus on testing theories and hypotheses.

Both qualitative and quantitative studies use numeric and non-numeric data. For instance, the use of statements such as less than normally involves the use of quantitative data in qualitative studies.

Similarly, quantitative studies can use questionnaires with open-ended questions to collect qualitative data.

Despite these similarities, quantitative and qualitative studies differ in the following ways. To begin with, the purpose of qualitative research is to facilitate understanding of fundamental meanings, reasons, and motives.

It also aims at providing valuable insights concerning a problem through determination of common trends in thought and generation of ideas.

On the other hand, the purpose of quantitative research is to quantify data and to use the results obtained from a sample to make generalizations on a particular population.

The sample used in qualitative research is often small and non-representative of the population. On the contrary, quantitative research uses large samples that represent the population. In this regard, it uses random sampling techniques to select a representative sample.

Qualitative research uses unstructured or semi-structured data collection techniques such as focus group discussions, whereas quantitative research uses structured techniques such as questionnaires.

Moreover, qualitative research uses non-statistical data analysis techniques, whereas quantitative uses statistical methods to analyze data. Finally, the results of qualitative research are normally exploratory and inconclusive, whereas the results of quantitative research are usually conclusive.

The similarities and differences between quantitative and qualitative research methods can be seen in their data collection methods

The main data collection methods in qualitative research include observations, interviews, content review, and questionnaires. The researcher can use participant or systematic observation to collect data.

In participant observation, the researcher engages actively in the activities of the subjects of the study. Researchers prefer this technique because it enables them to avoid disturbing the natural settings of the study.

In systematic observation, schedules are used to observe the behaviors of the participants at regular intervals. This technique enhances objectivity and reduces bias during data collection.

Most qualitative studies use unstructured interviews in which the interviewer uses general ideas to guide the interview and prompts to solicit more information.

Content review involves reading official documents such as diaries, journals, and minutes of meetings in order to obtain data. The importance of this technique is that it enables the researcher to reconstruct events and to describe social relationships.

Questionnaires are often used when the sample size is too large to be reached through face-to-face interviews. However, its use is discouraged in qualitative research because it normally influences the way participants respond, rather than allowing them to act naturally during data collection.

Quantitative research mainly uses surveys for data collection. This involves the use of questionnaires and interviews with closed-ended questions to enable the researcher to obtain data that can be analyzed with the aid of statistical techniques.

The questionnaires can be mailed or they can be administered directly to the respondents.

Observations are also used to collect data in quantitative studies. For example, the researcher can count the number of customers queuing at a point of sale in a retail shop.

Finally, quantitative researchers use management information systems to collect data. This involves reviewing documents such as financial reports to obtain quantitative data.

The similarities and differences between quantitative and qualitative research methods can be seen in their data analysis methods

Qualitative researchers often start the analysis process during the data collection and preparation stage in order to discover emerging themes and patterns. This involves continuous examination of data in order to identify important points, contradictions, inconsistencies, and common themes.

After this preliminary analysis, qualitative data is usually organized through systematic categorization and concept formation. This involves summarizing data under major categories that appear in the data set.

Data can also be summarized through tabulation in order to reveal its underlying features. The summaries usually provide descriptions that are used to generate theories. Concisely, the data is used to develop theories that explain the causes of the participants’ behavior.

Theories are also developed through comparative analysis. This involves comparing observations “across a range of situations over a period of time among different participants through a variety of techniques”.

Continuous comparisons provide clues on why participants behave in a particular manner, thereby facilitating theory formulation.

Quantitative analysis begins with the identification of the level of measurement that is appropriate for the collected data. After identifying the measurement level, data is usually summarized under different categories in tables by calculating frequencies and percentage distributions.

A frequency distribution indicates the number of observations or scores in each category of data, whereas a percentage distribution indicates the proportion of the subjects of the study who are represented in each category.

Descriptive statistics help the researcher to describe quantitative data. It involves calculating the mean and median, as well as, minimum and maximum values. Other analytical tools include correlation, regression, and analysis of variance.

Correlation analysis reveals the direction and strength of the relationship associated with two variables. Analysis of variance tests the statistical significance of the independent variables. Regression analysis helps the researcher to determine whether the independent variables are predictors of the dependent variables.

The similarities and differences between quantitative and qualitative research methods can be seen in their validity issues

Validity refers to the “degree to which the evidence proves that the interpretations of the data are correct and appropriate”. Validity is achieved if the measurement instrument is reliable. Replicability is the most important aspect of reliability in quantitative research.

This is because the results of quantitative research can only be approved if they are replicable. In quantitative research, validity is established through experiment review, data triangulation, and participant feedback, as well as, regression and statistical analyses.

In qualitative research, validity depends on unobtrusive measures, respondent validation, and triangulation. The validity of the results is likely to improve if the researcher is unobtrusive. This is because the presence of the researcher will not influence the responses of the participants.

Respondent validation involves obtaining feedback from the respondents concerning the accuracy of the data in order to ensure reliability. Triangulation involves collecting data using different methods at different periods from different people in order to ensure reliability.

The similarities and differences between quantitative and qualitative research methods can be seen in their strengths and weaknesses

The strengths of qualitative research include the following. First, it enables the researcher to pay attention to detail, as well as, to understand meanings and complexities of phenomena.

Second, it enables respondents to convey their views, feelings, and experiences without the influence of the researcher.

Third, qualitative research involves contextualization of behavior within situations and time. This improves the researcher’s understanding, thereby enhancing the reliability of the conclusions made from the findings.

Finally, the findings of qualitative research are generalizable through the theory developed in the study.

Qualitative research has the following weaknesses. Participant observation can lead to interpretation of phenomena based only on particular situations, while ignoring external factors that may influence the behavior of participants.

This is likely to undermine the validity of the research. Additionally, conducting a qualitative research is usually difficult due to the amount of time and resources required to negotiate access, to build trust, and to collect data from the respondents.

Finally, qualitative research is associated with high levels of subjectivity and bias.

Quantitative research has the following strengths. First, it has high levels of precision, which is achieved through reliable measures.

Second, it uses controlled experiments, which enable the researcher to determine cause and effect relationships.

Third, the use of advanced statistical techniques such as regression analysis facilitates accurate and sophisticated analysis of data.

Despite these strengths, quantitative research is criticized because it ignores the fact that individuals are able to interpret their experiences, as well as, to develop their own meanings.

Furthermore, control of variables often leads to trivial findings, which may not explain the phenomena that are being studied. Finally, quantitative research cannot be used to study phenomena that are not quantifiable.

The aim of this paper was to analyze quantitative and qualitative research methods by comparing and contrasting them. The main difference between qualitative and quantitative research is that the former uses non-numeric data, whereas the later mainly uses numeric data.

The main similarity between them is that they can be used to test existing theories and hypothesis. Qualitative and quantitative research methods have strengths and weaknesses. The results obtained through these methods can be improved if the researcher addresses their weaknesses.

Gravetter, F., & Forzano, L.-A. (2011). Research methods for the behavioral sciences. New York, NY: McGraw-Hill.

Kothari, C. (2009). Research methodology: Methods adn techniques. London, England: Sage.

McNeill, P., & Chapman, S. (2005). Research methods. London, England: Palgrave.

Rosenthal, R., & Rosnow, R. (2007). Essentials of behavioral research: Methods and data analysis. Upper River Saddle, NJ: Prentice Hall.

Stangor, C. (2010). Research methods for the behavioral sciences. New York, NY: John Wiley and Sons.

Wallnau, L., & Gravetter, F. (2009). Statistics for the behavioral sciences. London, England: Macmillan.

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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Methods for Quantitative Research in Psychology

• Conducting Research

Psychological Research

August 2023

This seven-hour course provides a comprehensive exploration of research methodologies, beginning with the foundational steps of the scientific method. Students will learn about hypotheses, experimental design, data collection, and the analysis of results. Emphasis is placed on defining variables accurately, distinguishing between independent, dependent, and controlled variables, and understanding their roles in research.

The course delves into major research designs, including experimental, correlational, and observational studies. Students will compare and contrast these designs, evaluating their strengths and weaknesses in various contexts. This comparison extends to the types of research questions scientists pose, highlighting how different designs are suited to different inquiries.

A critical component of the course is developing the ability to judge the quality of sources for literature reviews. Students will learn criteria for evaluating the credibility, relevance, and reliability of sources, ensuring that their understanding of the research literature is built on a solid foundation.

Reliability and validity are key concepts addressed in the course. Students will explore what it means for an observation to be reliable, focusing on consistency and repeatability. They will also compare and contrast different forms of validity, such as internal, external, construct, and criterion validity, and how these apply to various research designs.

The course concepts are thoroughly couched in examples drawn from the psychological research literature. By the end of the course, students will be equipped with the skills to design robust research studies, critically evaluate sources, and understand the nuances of reliability and validity in scientific research. This knowledge will be essential for conducting high-quality research and contributing to the scientific community.

Learning objectives

• Describe the steps of the scientific method.
• Specify how variables are defined.
• Compare and contrast the major research designs.
• Explain how to judge the quality of a source for a literature review.
• Compare and contrast the kinds of research questions scientists ask.
• Explain what it means for an observation to be reliable.
• Compare and contrast forms of validity as they apply to the major research designs.

This program does not offer CE credit.

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Introduces applying statistical methods effectively in psychology or related fields for undergraduates, high school students, and professionals.

August 2023 On Demand Training

Introduces the importance of ethical practice in scientific research for undergraduates, high school students, and professionals.

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Unit 6: Qual vs Quant.

27 Quantitative Methods in Communication Research

Quantitative methods in communication research.

In communication research, both quantitative and qualitative methods are essential for understanding different aspects of communication processes and effects. Here’s how quant methods can be applied:

• Collecting data on communication patterns, relationship satisfaction, or conflict resolution strategies among different groups.
• Collecting numerical data on audience demographics, media consumption habits, or attitudes towards specific communication messages.
• Testing hypotheses about the effects of specific communication behaviors (e.g., eye contact, tone of voice) on relationship outcomes.
• Testing the effects of different communication strategies or messages on audience behavior or perception.
• Quantifying the frequency and types of communication behaviors in recorded interactions (e.g., supportive vs. critical comments)
• Quantifying the frequency of certain themes, words, or images in media content to identify patterns or trends.
• Statistical Analysis :  Using statistical tools to analyze data from surveys or experiments, such as correlation or regression analysis to explore relationships between variables.

Communication Research in Real Life Copyright © 2023 by Kate Magsamen-Conrad. All Rights Reserved.

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Quantitative Research Methods: Maximizing Benefits, Addressing Limitations, and Advancing Methodological Frontiers

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Quantitative Methods for Undergraduate Students

This workshop introduces concepts such as descriptive vs. inferential statistics, running exploratory data analysis, testing data for normality, and parametric vs. non-parametric data analysis. Participants will learn about different statistical methods for exploring and analyzing their data.

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• Open access
• Published: 30 August 2024

Medical students in distress: a mixed methods approach to understanding the impact of debt on well-being

• Adrienne Yang 1   na1 ,
• Simone Langness 2   na1 ,
• Lara Chehab 1   na1 ,
• Nikhil Rajapuram 3 ,
• Li Zhang 4 &
• Amanda Sammann 1  

BMC Medical Education volume  24 , Article number:  947 ( 2024 ) Cite this article

Metrics details

Nearly three in four U.S. medical students graduate with debt in six-figure dollar amounts which impairs students emotionally and academically and impacts their career choices and lives long after graduation. Schools have yet to develop systems-level solutions to address the impact of debt on students’ well-being. The objectives of this study were to identify students at highest risk for debt-related stress, define the impact on medical students’ well-being, and to identify opportunities for intervention.

This was a mixed methods, cross-sectional study that used quantitative survey analysis and human-centered design (HCD). We performed a secondary analysis on a national multi-institutional survey on medical student wellbeing, including univariate and multivariate logistic regression, a comparison of logistic regression models with interaction terms, and analysis of free text responses. We also conducted semi-structured interviews with a sample of medical student respondents and non-student stakeholders to develop insights and design opportunities.

Independent risk factors for high debt-related stress included pre-clinical year (OR 1.75), underrepresented minority (OR 1.40), debt $20–100 K (OR 4.85), debt >$100K (OR 13.22), private school (OR 1.45), West Coast region (OR 1.57), and consideration of a leave of absence for wellbeing (OR 1.48). Mental health resource utilization ( p  = 0.968) and counselors ( p  = 0.640) were not protective factors against debt-related stress. HCD analysis produced 6 key insights providing additional context to the quantitative findings, and associated opportunities for intervention.

Conclusions

We used an innovative combination of quantitative survey analysis and in-depth HCD exploration to develop a multi-dimensional understanding of debt-related stress among medical students. This approach allowed us to identify significant risk factors impacting medical students experiencing debt-related stress, while providing context through stakeholder voices to identify opportunities for system-level solutions.

Peer Review reports

Introduction

Over the past few decades, it has become increasingly costly for aspiring physicians to attend medical school and pursue a career in medicine. Most recent data shows that 73% of medical students graduate with debt often amounting to six-Fig [ 1 ]. – an amount that is steadily increasing every year [ 2 ]. In 2020, the median cost of a four-year medical education in the United States (U.S.) was $250,222 for public and $330,180 for private school students [ 1 ] – a price that excludes collateral costs such as living, food, and lifestyle expenses. To meet these varied costs, students typically rely on financial support from their families, personal means, scholarships, or loans. Students are thereby graduating with more debt than ever before and staying indebted for longer, taking 10 to 20 years to repay their student loans regardless of specialty choice or residency length [ 1 ].

Unsurprisingly, higher debt burden has been negatively correlated with generalized severe distress among medical students [ 3 , 4 ], in turn jeopardizing their academic performance and potentially impacting their career choices [ 5 ]. Studies have found that medical students with higher debt relative to their peers were more likely to choose a specialty with a higher average annual income [ 5 ], less likely to plan to practice in underserved locations, and less likely to choose primary care specialties [ 4 ]. However, a survey of 2019 graduating medical students from 142 medical schools found that, when asked to rank factors that influenced their specialty choice, students ranked economic factors, including debt and income, at the bottom of the list. With this inconsistency in the literature, authors Youngclaus and Fresne declare that further studies and analysis are required to better understand this important relationship [ 1 ].

Unfortunately, debt and its negative effects disproportionately impact underrepresented minority (URM) students, including African Americans, Hispanic Americans, American Indian, Native Hawaiian, and Alaska Native [ 6 ], who generally have more debt than students who are White or Asian American [ 1 ]. In 2019, among medical school graduates who identified as Black, 91% reported having education debt, in comparison to the 73% reported by all graduates [ 1 ]. Additionally, Black medical school graduates experience a higher median education debt amount relative to other groups of students, with a median debt of $230,000 [ 1 ]. This inequitable distribution of debt disproportionately places financial-related stress on URM students [ 7 ], discouraging students from pursuing a medical education [ 8 ]. These deterring factors can lead to a physician workforce that lacks diversity and compromises health equity outcomes [ 9 ].

Limited literature exists to identify the impact of moderating variables on the relationship between debt and debt-related stress. Financial knowledge is found to be a strong predictor of self-efficacy and confidence in students’ financial management, leading to financial optimism and potentially alleviating debt stress [ 10 , 11 , 12 ]. Numerous studies list mindfulness practices, exercise, and connecting with loved ones as activities that promote well-being and reduce generalized stress among students [ 13 , 14 , 15 ]. However, to date, no studies have examined whether these types of stress-reducing activities, by alleviating generalized stress, reduce debt-related stress. Studies have not examined whether resources such as physician role models may act as a protective factor against debt-related stress.

Despite the growing recognition that debt burdens medical students emotionally and academically, we have yet to develop systemic solutions that target students’ unmet needs in this space. We performed the first multi-institutional national study on generalized stress among medical students, and found that debt burden was one of several risk factors for generalized stress among medical students [ 3 ]. The goal of this study is to build upon our findings by using a mixed methods approach combining rigorous survey analysis and human-centered design to develop an in-depth understanding of the impact that education debt has on medical students’ emotional and academic well-being and to identify opportunities for intervention.

We conducted a mixed methods, cross-sectional study that explored the impact of debt-related stress on US medical students’ well-being and professional development. This study was conducted at the University of California, San Francisco (UCSF). All activities were approved by the UCSF institutional review board, and informed consent was obtained verbally from participants prior to interviews. We performed a secondary analysis of the quantitative and qualitative results of the Medical Student Wellbeing Survey (MSWS), a national multi-institutional survey on medical student wellbeing administered between 2019 and 2020, to determine risk factors and moderating variables of debt-related stress. To further explore these variables, we used human-centered design (HCD), an approach to problem-solving that places users at the center of the research process in order to determine key pain points and unmet needs, and co-design solutions tailored to their unique context [ 16 ]. In this study, we performed in-depth, semi-structured interviews with a purposefully sampled cohort of medical students and a convenience sample of non-student stakeholders to determine key insights representing students’ unmet needs, and identified opportunities to ameliorate the impact of debt-related stress on medical students.

Quantitative data: the medical student wellbeing survey

The MSWS is a survey to assess medical student wellbeing that was administered from September 2019 to February 2020 to medical students actively enrolled in accredited US or Caribbean medical schools [ 3 ]. Respondents of the MSWS represent a national cohort of > 3,000 medical students from > 100 unique medical school programs. The MSWS utilizes a combination of validated survey questions, such as the Medical Student Wellbeing Index (MS-WBI), and questions based on foundations established from previously validated wellbeing survey methods [ 3 ]. Questions generally focused on student demographics, sources of stress during medical school, specialty consideration, and frequency in activities that promote wellbeing. Some questions ask students to rate physical, emotional, and social domains of wellbeing using a five-point Likert scale. Questions of interest from the MSWS included debt-related stress, generalized stress, intended specialty choice, and utilization of well-being resources and counselors. An additional variable investigated was average school tuition, which was determined by a review of publicly available data for each student’s listed medical school [ 17 ]. All data from the MSWS was de-identified for research purposes.

Stress: debt-related and generalized stress

Debt stress was assessed by the question, “How does financial debt affect your stress level?” Students responded using a five-point Likert scale from − 2 to 2: significant increase in stress (-2), mild increase (-1), no change (0), mild decrease (1), or significant decrease (2). Responses for this question were evaluated as a binary index of ‘high debt stress,’ defined as a response of − 2, versus ‘low debt stress,’ defined as a response of − 1 or 0. In addition, generalized stress from the MSWS was assessed by questions from the embedded MS-WBI, which produced a score. Previous studies have shown that the score can be used to create a binary index of distress: a score ≥ 4 has been associated with severe distress, and a score < 4 has been associated with no severe distress [ 18 ].

Intended specialty

We categorized students’ responses to intended specialty choice by competitiveness, using the 2018 National Resident Match Program data [ 19 ]. ‘High’ and ‘low’ competitiveness were defined as an average United States Medical Licensing Examination (USMLE) Step 1 score of > 240 or ≤ 230, respectively, or if > 18% or < 4% of applicants were unmatched, respectively. ‘Moderate’ competition was defined as any specialty not meeting criteria for either ‘high’ or ‘low’ competitiveness.

Resource utilization

The MSWS assessed the utilization of well-being resources by the question, “At your institution, which of the following well-being resources have you utilized? (Select all that apply)” Students responded by selecting each of the resource(s) they used: Mental Health and Counseling Services, Peer Mentorship, Self-Care Education, Mindfulness/Meditation Classes, Community Building Events, and Other. The number of choices that the student selected was calculated, allowing for placement into a category depending on the amount of resource utilization: 0–20%, 20–40%, 40–60%, 60–80%, 80–100%. Responses for this question were evaluated as a binary index of ‘high resource utilization,’ defined as a response of 80–100% resource utilization, versus ‘low resource utilization,’ defined as a response of < 80% resource utilization. The co-authors collaboratively decided upon this “top-box score approach,” [ 20 ] which is the sum of percentages for the most favorable top one, two or three highest categories on a scale, to assess if the most extreme users (80–100%) of these supportive resources experienced a decrease in debt-related stress. Additionally, use of a counselor for mental health support was assessed by the question, “Which of the following activities do you use to cope with difficult situations (or a difficult day on clinical rotation)? (Select all that apply).” Students responded by selecting the activities that they use from a list (e.g., listen to music, mindfulness practice, meet with a counselor, exercise). Responses for this question were evaluated as a binary index of ‘Meeting with a Counselor,’ defined by selection of that option, versus ‘Not Meeting with a Counselor,’ defined as not selecting that option.

Quantitative data analysis

We performed a secondary analysis of quantitative data from the MSWS to calculate frequencies and odds ratios for the five quantitative variables described above (debt-related stress, generalized stress, intended specialty, resource utilization, and school tuition). Tests performed are summarized in Table  1 (“Secondary Analysis Tests Performed”). Univariate analysis and multivariate logistic regression were performed among students in the high debt stress (-2) and low debt stress (0 or − 1) for select variables, such as clinical phase, URM, debt burden, specialty competitiveness, and average school tuition, to identify risk factors for high debt stress. To determine if ‘high resource utilization’ or ‘meeting with a counselor’ were moderating variables on the relationship between debt burden and debt stress, we applied the logistic regression with the interaction terms of ‘debt’ and ‘resource utilization’ (high vs. low). Then, we performed a similar analysis but replaced the interaction term with ‘debt’ and ‘meeting with a counselor’ (yes vs. no). We also performed Chi-squared tests to determine the degree to which severe distress increases as debt burden increases, if specialty competitiveness varied by debt stress, and if the proportion of students who identified as URM, in comparison to non-URM, differed by debt level. All statistical tests were two-sided and p  < 0.05 was considered significant. Statistical analyses were performed using SAS version 9.4 and R version 4.0.5.

Qualitative data: interviews and MSWS free text responses

Free-text entries.

At the conclusion of the 2019–2020 MSWS, respondents had unlimited text space to provide comments to two prompts. The first prompt read, “What well-being resource(s), if offered at your school, do you feel would be most useful?” The second prompt read “If you have any further comments to share, please write them below.” Answers to either prompt that pertained to debt, cost of medical school, or finances were extracted for the purpose of this study and analyzed with the other qualitative data subsequently described.

Interview selection & purposive sampling

Interview participants were identified from a repository of respondents to the MSWS who had attached their email address and expressed willingness at the time of the survey to be contacted for an interview [ 3 ]. Our recruitment period was between April 19, 2021 to July 2, 2021. The recruitment process involved sending invitations to all of the email addresses in the list to participate in a 45-minute interview on the topic of student debt and wellbeing. The invitation included a brief screening questionnaire asking students to report updates to questions that were previously asked in the MSWS (i.e.: clinical training year, marital status, dependents). Additional novel questions included primary financial support system, estimate of financial support systems’ household income in the last year, estimate of educational financial debt at conclusion of medical school, student’s plan for paying off debt, and degree of stress (using a Likert scale from 0 to 10) over current and future education debt.

Purposeful sampling of medical student stakeholders for interviews allowed us to maximize heterogeneity. We utilized the students’ responses to the brief screening questionnaire with their corresponding responses to demographic questions from the MSWS to select interviewees that varied by gender, race, presence of severe distress, type of medical school (public vs. private), region of school, and tuition level of school. The sampling ensured a diverse representation, in accordance with HCD methodology [ 21 ]. Brief descriptions of participant experiences are listed in Table  2 (“Interviewee Descriptors”). Students who were selected for interviews were sent a confirmation email to participate. Interviews were to be conducted until thematic saturation was reached. In addition, to include representation from the entire ecosystem, we interviewed a financial aid counselor at a medical school and a pre-medical student, chosen through convenience sampling. We directly contacted those two individuals for interviews.

Semi-structured interviews

All interviews were conducted between April 2021 and July 2021 over Zoom. A single researcher conducted interviews over an average of 45 min. Informed consent was obtained verbally from participants prior to interviews; interviews and their recordings only proceeded following verbal consent. The interview guide (S1 File) included open-ended questions about students’ experience of debt-related stress and their reflections on its consequences. The audio recordings were transcribed using Otter.ai, a secure online transcription service that converts audio files to searchable text files. Interview responses were redacted to preserve anonymity of respondent identity.

Qualitative data analysis

Interview data was analyzed using a general inductive approach to thematic analysis. Specifically, two researchers (SL and AY) independently inductively analyzed transcripts from the first three semi-structured interviews to come up with themes relating to the experiences and consequences of debt-related stress. They reconciled discrepancies in themes through discussion to create the codebook (S2 File), which included 18 themes. SL and AY independently coded each subsequent interview transcript as well as the free text responses from the survey, meeting to reach a consensus on representative quotes for applicable themes.

Following the HCD methodology, two researchers met with the core team to discuss the themes from the interviews and translate them into “insight statements”, which reflect key tensions and challenges experienced by stakeholders. Insight statements carefully articulate stakeholders’ unique perspectives and motivations in a way that is actionable for solution development [ 22 ]. As such, these insight statements are reframed into design opportunities, which suggest that multiple solutions are possible [ 23 , 24 ]. For example, discussion about themes 1a and 1b (“Questionable Job Security” and “Disappointing MD salary and Satisfaction Payoff”) revealed that they were related in the way that they led students to wonder whether the investment in medical school would be offset by the salary payoff. This led to the identification of the tension for low-income students in particular, who have to weigh this tradeoff earlier in their medical school journey than other students who are less financially-constrained (insight: “Medical school is a risky investment for low-income students”.) The design opportunity logically translates into a call to action for brainstorming and solution development: “Support low-income students to make values-based tradeoffs when considering a career in medicine.”

MSWS respondents and quantitative analysis

A total of 3,162 students responded to the MSWS and their sociodemographic characteristics have been described previously [ 3 ]. A total of 2,771 respondents (87.6%) responded to our study’s variables of interest, including a response for ‘high debt stress’ (–2) or ‘low debt stress’ (–1 or 0). Table  3 lists the distribution of debt-related stress across different variables for all respondents.

Risk factors for debt-related stress

Factors that were independently associated with higher debt-related stress included being in pre-clinical year (OR 1.75, 95% CI 1.30–2.36, p  < 0.001), identifying as URM (OR 1.40, 95% CI 1.03–1.88), p  = 0.029), having debt $20–100 K (OR 4.85, 95% CI 3.32–7.30, p  < 0.001), debt > 100 K (OR 13.22, 95% CI 9.05–19.90, p  < 0.001), attending a private medical school (OR 1.45, 95% CI 1.06–1.98, p  = 0.019), attending medical school on the West Coast (OR 1.57, 95% CI 1.17–2.13, p  = 0.003), and having considered taking a leave of absence for wellbeing (OR 1.48, 95% CI 1.13–1.93, p  = 0.004) (Table  4 , S1 Table).

Severe distress by debt amount

Levels of generalized severe distress differed across debt burden groups. As debt level increased, the percentage of individuals with “severe” distress increased ( p  < 0.001).

Debt and career decisions

There were significant differences between the high debt stress versus low debt stress groups and plans to pursue highly vs. moderately vs. minimally competitive specialties ( p  = 0.027) (Fig.  1 ) A greater percentage of low debt stress students were pursuing a highly competitive specialty or a minimally competitive specialty. A greater percentage of high debt stress students were pursuing a moderately competitive specialty. As shown in Table  4 , there were no differences in debt-associated stress between students who choose different specialties, such as medical versus surgical versus mixed (medical/surgical).

Debt stress by specialty competitiveness

URM students’ experience of debt

URM identity was an independent risk factor for higher debt-related stress (Table  4 ) In addition, debt levels varied between those who identify as URM versus non-URM ( p  < 0.001). Students identifying as URM tended to have higher debt than those who did not. Although the percentage of non-URM students was higher than that of URM students within the lowest debt burden category (<$20k), among all higher debt burden categories, including $20–100 K, $100–300 K, and >$300K, the percentage of URM students was higher than the percentage of non-URM students.

Moderating factors on the relationship between debt and debt stress

Protective factors such as high degree of mental health resource utilization and meeting with a counselor did not reduce the impact of debt burden on debt stress. Among students who reported a high degree of mental health resource utilization, there was no impact on the relationship between debt and debt stress ( p  = 0.968). Similarly, meeting with a counselor had no impact on the relationship between debt and debt stress ( p  = 0.640).

Interview respondents and qualitative analysis

We conducted in-depth, semi-structured interviews with 11 medical students, who are briefly described in Table  2 . We reached thematic saturation with 11 interviews, a point at which we found recurring themes. Therefore, no further interviews were needed. Among the medical student interviewees, there was representation from all regions, including the Northeast ( n  = 3), West Coast ( n  = 5), Midwest ( n  = 2), and South ( n  = 1). Students were also from all clinical phases, including pre-clinical ( n  = 3), clinical ( n  = 4), gap year/other ( n  = 2), and post-clinical ( n  = 2). Most interviewees were female ( n  = 8) and 5 of the interviewees identified as URM. Financial support systems were diverse, including self ( n  = 3), spouse/partner ( n  = 3), and parents/other ( n  = 5). Most interviewees reported low debt stress ( n  = 8), as opposed to high debt stress ( n  = 3). 55% of interviewees planned to pursue specialties that pay <$300K ( n  = 6), with some pursuing specialties that pay $300–400 K ( n  = 2) and >$400K ( n  = 3).

Among the MSWS free-text responses, to the prompt, “What well-being resource(s), if offered at your school, do you feel would be most useful?” 20 of 118 respondents (16.9%) provided free-text responses that pertained to debt, cost of medical school, or finances. To the prompt “If you have any further comments to share, please write them below” 11 of 342 students (3.2%) provided relevant free-text responses. Analysis of the free-text responses and semi-structured interviews revealed 6 distinct insights (Table  5 ), with each insight translated into an actionable design opportunity.

Medical school is a risky investment for low-income students.

Description

The personal and financial sacrifices required for low-income students to attend medical school and pursue a career in medicine outweigh the benefits of becoming a physician. When considering a career in medicine, students feel discouraged by questionable job security (theme 1a) and reduced financial compensation (theme 1b) – a combination that jeopardizes immediate and long-term job satisfaction. Some students feel hopeful that their decision to pursue medicine will be personally rewarding (1b.6) and their salaries will stabilize (1a.1, 1a.5), but many low-income students experience doubt about whether they made the right career choice (1b.2, 1b.4, 1b.6), and feel stressed that they will be in debt for longer than they expected (1a.3, 1a.4, 1b.1, 1b.5). Support low-income students to make values-based tradeoffs when considering a career in medicine.

Design opportunity

Support low-income students to make values-based tradeoffs when considering a career in medicine.

Medical schools lack the adaptive infrastructure to be welcoming to low-income students.

Students face financial challenges from the moment they apply to medical school (theme 2a), a costly process that limits admissions options for low-income students due to their inability to pay for numerous application fees (2a.1) and expensive test preparation courses (2a.2, 2a.3). Once students begin medical school, they feel unsupported in their varied responsibilities towards their families (theme 2b) and additional financial needs (theme 2c), requiring them to make tradeoffs with their education and personal lives (2b.2, 2c.1).

Design opportunity 2

Develop flexible systems that can recognize and accommodate students’ complex financial needs during medical school.

Students worry about the impact that their medical school debt has on their present and future families, which compounds feelings of guilt and anxiety.

For students who need to take loans, the decision to pursue a career in medicine is a collective investment with their families. Students feel guilty about the sacrifices their families have to make for the sake of their career (theme 3a) and feel pressure to continue to provide financially for their family while having debt (theme 3b). Students are stressed about acquiring more debt throughout their training (3a.1) and the impact that has on loved ones who are dependent on them (3a.4, 3a.5, 3b.2), especially with respect to ensuring their financial security in the future (3b.4).

Design opportunity 3

Create an environment that acknowledges and accounts for the burden of responsibility that students face towards their families.

Without the appropriate education about loans, the stress of debt is exponentially worse.

Students feel the greatest fear around loans when they do not understand them, including the process of securing loans and paying off debt (theme 4a). Students are overwhelmed by their loan amounts (4a.5) and lack the knowledge or resources to manage their debt (4a.1, 4a.2), making them uncertain about how they will become debt-free in the future (4a.3, 4a.4). Students reported that various resources helped to alleviate those burgeoning fears (theme 4b), including financial aid counselors (4b.2, 4b.3) and physician role models (4b.5, 4b.6) that generally increase knowledge and skills related to debt management (4b.1).

Design opportunity 4

Empower students to become experts in managing their debt by making loan-related resources more available and accessible.

The small, daily expenses are the most burdensome and cause the greatest amount of stress.

Students with educational debt are mentally unprepared for the burden of managing their daily living expenses (theme 5a), causing them to make significant lifestyle adjustments in the hopes to ease their resulting anxiety (theme 5b). These costs are immediate and tangible, compared to tuition costs which are more distant and require less frequent management (5a.3) Students learn to temper their expectations for living beyond a bare minimum during medical school (5a.1, 5b.2, 5b.4) and develop strategies to ensure that their necessary expenses are as low as possible (5b.1, 5b.2, 5b.3, 5b.4).

Design opportunity 5

Develop and distribute resources to support both short- and long-term financial costs for medical students.

Students view debt as a dark cloud that constrains their mental health and dictates their career trajectory.

The constant burden of educational debt constrains students’ abilities to control their mental health (theme 6a) and pursue their desired career path in medicine (themes 6b & 6c). Students feel controlled by their debt (6a.3) and concerned that it will impact their [ability] to live a personally fulfilling life (6a.1, 6a.2, 6c.6), especially with respect to pursuing their desired medical specialties (6b.1, 6c.3, 6c.5, 6c.6). Students with scholarships, as opposed to loans, felt more able to choose specialties that prioritized their values rather than their finances (6c.1, 6c.2), an affordance that impacts long-term career growth and satisfaction.

Design opportunity 6

Create a culture of confidence for managing debt and debt-stress among medical students.

This is the first multi-institutional national study to explore the impact of debt-related stress on medical students’ well-being in the United States. We used an innovative, mixed methods approach to better understand the factors that significantly affect debt-related stress, and propose opportunities for improving medical student well-being.

URM students

Analysis of survey results found that students who identify as URM are more likely to experience higher levels of debt-related stress than non-URM students. Our study also found that among all higher debt burden categories, debt levels were higher for URM students, findings consistent with studies that have shown the disproportionate burden of debt among URM students [ 1 ]. Our semi-structured interviews illuminated that students from low-income backgrounds feel unsupported by their medical schools in these varied financial stressors that extend beyond tuition costs (insight 2), leaving their needs unmet and increasing financial stress over time: “We don’t have different socio-economic classes in medicine because there’s constantly a cost that [isn’t] even factored into tuition cost [and] that we can’t take student loans for.” Many URM students feel especially stressed by their financial obligations towards their families (insight 3), and describe the decision to enter into medicine as one that is collective ( “the family’s going to school” ) rather than individual, placing additional pressure on themselves to succeed in their career: “ Being of low SES , the most significant stressor for me is the financing of medical school and the pull of responsibility for my family.” Several other studies from the literature confirm that students who identify as URM and first generation college or medical students are at higher risk for financial stress compared to their counterparts [ 7 ], and report that they feel as though it is their responsibility to honor their families through their educational and career pursuits [ 25 ]. Our study demonstrates and describes how low-income and URM students face numerous financial barriers in medical school, resulting in medical trainees that are less diverse than the patient populations they are serving [ 1 , 8 ].

Debt amount

Our quantitative analysis found that students with debt amounts over $100,000 are at much higher risk for experiencing severe stress than students with debt less than that amount. Although this finding may seem intuitive, it is important to highlight the degree to which this risk differs between these two cohorts. Students with debt amounts between $20,000 and $100,000 are approximately 5 times more likely to experience high stress than students with debt less than $20,000, while students with debt amounts over $100,000 are approximately 13 times more likely to experience severe stress when compared to the same cohort. Interview participants describe that the more debt they have, the less hopeful they feel towards achieving financial security (insight 1): “There are other healthcare professionals that will not accrue the same amount of loans that we will , and then may or may not have the same salary or privileges […] makes me question , did I do the right thing?” Students internalize this rising stress so as not to shift the feelings of guilt onto their families (insight 3), thereby compounding the psychological burden associated with large amounts of debt (insight 6): “As long as you’re in debt , you’re owned by someone or something and the sooner you can get out of it , the better; the sooner I can get started with my life.”

Pre-clinical students

According to our survey analysis, students who are in their pre-clinical years are at higher risk for stress than students in their clinical years. Our interview findings from insight 4 suggest that students feel initially overwhelmed and unsure about what questions to ask ( “One of my fears is that I don’t know what I don’t know”) or how to manage their loans so that it doesn’t have a permanent impact on their lives: “The biggest worry is , what if [the debt] becomes so large that I am never able to pay it off and it ends up ruining me financially.” Pre-clinical students may therefore feel unsure or ill-equipped to manage their loans, making them feel overwhelmed by the initial stimulus of debt. By the time students reach their clinical years, they may have had time to develop strategies for managing stress, acquire more financial knowledge, and/or normalize the idea of having debt.

Medical school characteristics

Our survey analysis found several risk factors related to medical school characteristics. First, we found that students who attended a private school were at higher risk for debt-related stress than students who attended a public school. Not only is the median 4-year cost of attendance in 2023 almost $100,000 higher in private compared to public medical schools [ 26 ], but it is also the case that financial aid packages are more liberally available for public schools due to state government funding [ 27 ]. This not only relieves students from having higher amounts of debt, but it also creates a more inclusive cohort of medical students. Insight 2 from our interviews suggests that private medical schools without the infrastructure to meet students’ varying financial needs force low-income students to make tradeoffs between their education and personal lives.

Another characteristic that was found to be a risk factor for debt stress was attending a medical school on the West Coast (compared to a non-coastal school.) This was a surprising finding given that tuition rates for both private and public schools on the West Coast are no higher than those in other regions [ 17 ]. The distribution of survey respondents did not vary significantly across regional categories, so no bias in sample size is suspected. While these interviews were not designed to address the reasoning behind students’ choice of medical school matriculation, there is a potential explanation for this finding. Historically, students match for residency programs that are in their home state or not far from their home state; [ 28 , 29 ] therefore, we speculate that students may prefer to settle on the West Coast, and may be willing to take on more financial debt in pursuit of their long-term practice and lifestyle goals.

Our quantitative analysis found that students who reported having considered taking a leave of absence for well-being purposes were at higher risk for debt-related stress. This cohort of students likely experience higher levels of stress as they are conscious of the negative impact it has on their life, and have already ruminated on leaving medical school. A study by Fallar et al. found that the period leading up to a leave of absence is particularly stressful for students because they are unfamiliar with the logistics of taking time off, and don’t feel as though leaving medical school is encouraged or normalized for students [ 30 ]. An interview with a student who did a joint MD and PhD program expressed having more time for herself during her PhD program, and described using money for activities that could alleviate stress (“I took figure skating during my PhD”) rather than create more stress by compromising on their lifestyle during medical school (insight 5). More research may be needed to better understand and support students considering taking a leave of absence from medical school.

• Specialty choice

Our study found that students with high debt stress pursue moderately competitive specialties compared to students with low debt stress. This may be explained by the fact that low debt stress gives students the freedom to pursue minimally competitive specialties, which may be more fulfilling to them but typically have lower salaries. Insight 6 further elaborates upon this finding that students with high debt stress deprioritize specialties for which they are passionate in favor of higher paying specialties that might alleviate their debt: “I love working with kids…but being an outpatient pediatrician just wasn’t going to be enough to justify the [private school] price tag.” Students with lower debt stress describe having the freedom to choose specialties that align with their values, regardless of anticipated salary: “Scholarships give me the freedom to do [specialties] that maybe are a little bit less well-paying in medicine.” Interestingly, certain studies examining the relationship between specialty choice and debt stress have found that high debt stress is associated with a higher likelihood of pursuing a more competitive, and presumably higher paying, specialty [ 5 ]. More research investigating the relationship between debt stress and specialty choice could illuminate opportunities for increasing a sense of agency and overall satisfaction among students for their career choices.

In our exploration of potential protective factors against the effects of debt-related stress, our survey analysis found that the two variables measured (high mental health resource utilization and meeting with a counselor) did not have any impact on reducing debt-related stress. This finding is inconsistent with the literature, which considers these activities to promote general well-being among students but has never been studied in the context of debt-related stress [ 13 , 14 , 15 ]. A potential explanation is that the survey questions that assessed these activities were imperfect. For example, the question of meeting with a counselor was not a standalone question, but instead, was at the bottom of a list of other wellbeing activities; therefore, students may have been fatigued by the time they got to the bottom of the list and not selected it. Additionally, our definition of “high” mental health resource utilization may have been perceived as too strict (i.e.: 80–100%) and perhaps we would have seen effects at lower percentages of utilization (i.e.: 40–60%). Despite this finding, students describe in their interviews that having access to certain resources such as financial knowledge and physician role models can help to alleviate stress by helping them feel confident in managing their loans in the immediate and more distant future (insight 4): “I’ve had explicit discussions with physicians who went to med school , had debt , paid it off [.] the debt hasn’t hindered their life in any way. I think that just makes me feel a lot calmer.” This finding aligns with previous studies that suggest that financial knowledge, such as knowledge about loans and a payoff plan, confers confidence in students’ financial management [ 11 , 12 ]. These factors are also aligned with previous studies that suggest financial optimism, such as with a physician role model who successfully paid off loans, is associated with less financial stress [ 10 ].

Our quantitative analysis of risk factors helped us to identify which areas might significantly impact debt-related stress among medical students, while our qualitative analysis provided more in-depth insight into those risk factors for more human-centered intervention design. The HCD process not only provides additional context from the perspective of medical students, but also proposes distinct design opportunities upon which interventions may be designed and tested. Drawing from the six design opportunities outlined in this paper, we propose a solution on a national scale: lowering the cost of the MCAT and medical school applications to reduce the financial barrier to applying to medical school [ 31 ]. We also propose the following solutions that can be implemented at the level of medical schools to better support medical students facing debt-related stress: (1) providing adequate financial aid that prevents low-income students from needing to work while being in medical school [ 32 ], (2) providing targeted financial planning classes and counseling for first-year medical students who have taken loans [ 33 ], and (3) creating mentorship programs that pair medical students with debt with physician role models who had also had debt but successfully paid it off [ 34 ]. We encourage medical schools to consider these suggestions, choosing the ideas from the list that make sense and tailoring them as necessary for their students and their unique needs. Additionally, given that our quantitative portion of the study was a secondary analysis of a survey focused on general medical student well-being, a nationwide study is needed that is specifically designed to explore the topic of debt-related stress among medical students. Furthermore, more research is needed that assesses the impact of activities that promote well-being (e.g., access to therapy, mindfulness practices, exercise) on debt-related stress among medical students.

Limitations

Our study had some notable limitations. One potential limitation is that our data collection occurred between 2019 and 2021 for this publication in 2023. Additionally, as described in the original study [ 3 ], a limitation of the MSWS is the inability to determine a response rate of students due to the survey distribution by medical student liaisons from each medical school; under the reasonable assumption that the survey was distributed to every US allopathic medical student, the response rate was estimated to have been 8.7%. 3 An additional limitation is the potential for response bias [ 3 ]. A limitation of the qualitative interviews is the potential for response bias among the interviewees. Although we purposely sampled, the students who accepted the invitation to interview may have been students with extreme views, either very negative views of debt or very neutral views of debt. Additionally, the interviewees were not representative of all possible financial situations, given that most students were from private schools, which typically have higher tuition rates. Also, all students had debt amounts in the middle and high categories, with none in the low category. Finally, our model of risk factors for debt-related stress suggested the presence of negative confounding factors, which exerted effects on specific variables (i.e.: pre-clinical year, West Coast) for which univariate analysis found no significant associations but multivariate analysis did. We did not perform further analysis to identify which variables served as the negative confounding variables.

In conclusion, our mixed methods, cross-sectional study exploring debt-related stress and its impact on US medical students’ wellbeing and professional development revealed a set of risk factors and design opportunities for intervention. By using a combined quantitative and qualitative HCD approach, we were able to develop a broad, in-depth understanding of the challenges and opportunities facing medical students with education debt. With these efforts to support the well-being and academic success of students at higher risk of debt-related stress, medical education institutions can develop and nurture a more diverse medical field that can best support the needs of future patients.

Data availability

Data is provided within the supplementary information files.

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Acknowledgements

We thank the members of The Better Lab, including Devika Patel, Christiana Von Hippel, and Marianna Salvatori, for their support. We appreciate Pamela Derish (UCSF) for assistance in manuscript editing and the UCSF Clinical and Translational Science Institute (CTSI) for assistance in statistical analysis. This publication was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UCSF-CTSI Grant Number UL1 TR001872. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Funding was not obtained for this project.

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Adrienne Yang, Simone Langness and Lara Chehab contributed equally to this work.

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Department of Surgery, University of California, San Francisco, CA, USA

Adrienne Yang, Lara Chehab & Amanda Sammann

Department of Trauma Surgery, Sharp HealthCare, San Diego, CA, USA

Simone Langness

Department of Pediatrics, Stanford University, Stanford, CA, USA

Nikhil Rajapuram

Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA

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A.Y. and L.C. wrote the main manuscript text and prepared the figures. S.L. created the study design. All authors reviewed the manuscript.

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Yang, A., Langness, S., Chehab, L. et al. Medical students in distress: a mixed methods approach to understanding the impact of debt on well-being. BMC Med Educ 24 , 947 (2024). https://doi.org/10.1186/s12909-024-05927-9

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Cultivating multicultural christian youth ministry team leaders through covenant relationships with youth in kc and stl metro area churches.

Christopher D. Edin , Liberty University Follow

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One significant challenge for today’s Christian churches involves bridging the gap between youth involvement in church after high school graduation and moving on to college (Owens, 2014). Owens (2014) asserts that today’s adolescents experience parental and church support during high school, but once they head off to college, they leave that support and stability at home. Students leave the church for many reasons. This study proposes that one significant reason is that Christian churches do not educate youth ministry team leaders on multiculturalism. This research explored satisfactory multicultural competencies necessary for youth ministry leaders to disciple youth by asking adult youth ministry leaders and adolescent youth ministry attendees to respond to questionnaires, essays, and surveys to develop essential satisfactory multicultural competencies. This study drew theoretical guidance from an explanatory sequential mixed-methods theory, commencing with a quantitative longitudinal study using closed-ended questionnaires and concluding with qualitative essays and surveys to enhance the quantitative findings. This mixed-methods research describes the satisfactory multicultural competencies essential to empower Christian youth ministry leaders to reach the multicultural youth within Kansas City and St. Louis metro area churches. Kite (2015) reports that multicultural competencies in education require valuing students’ racial diversity and racial differences. Awareness of cultural biases and virtues, learning to view others' worlds, and developing culturally appropriate interpersonal skills legitimize cultural competence. Discussing satisfactory multicultural competence helps transfer these traits from one to another (Kite, 2015).

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This paper is in the following e-collection/theme issue:

Published on 30.8.2024 in Vol 26 (2024)

Assessing the Feasibility and Acceptability of Smart Speakers in Behavioral Intervention Research With Older Adults: Mixed Methods Study

Authors of this article:

Original Paper

• Kelly Quinn 1 , MBA, MSLIS, PhD   ; 
• Sarah Leiser Ransom 1 , MA   ; 
• Carrie O'Connell 1 , MA   ; 
• Naoko Muramatsu 2 , PhD   ; 
• David X Marquez 3 , PhD   ; 
• Jessie Chin 4 , PhD  

1 Department of Communication, University of Illinois Chicago, Chicago, IL, United States

2 Division of Community Health Sciences, School of Public Health, University of Illinois Chicago, Chicago, IL, United States

3 Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL, United States

4 School of Information Sciences, University of Illinois Urbana-Champaign, Urbana, IL, United States

Corresponding Author:

Kelly Quinn, MBA, MSLIS, PhD

Department of Communication

University of Illinois Chicago

1007 W Harrison St

1140 BSB, MC 132

Chicago, IL, 60607

United States

Phone: 1 312 996 3187

Email: [email protected]

Background: Smart speakers, such as Amazon’s Echo and Google’s Nest Home, combine natural language processing with a conversational interface to carry out everyday tasks, like playing music and finding information. Easy to use, they are embraced by older adults, including those with limited physical function, vision, or computer literacy. While smart speakers are increasingly used for research purposes (eg, implementing interventions and automatically recording selected research data), information on the advantages and disadvantages of using these devices for studies related to health promotion programs is limited.

Objective: This study evaluates the feasibility and acceptability of using smart speakers to deliver a physical activity (PA) program designed to help older adults enhance their physical well-being.

Methods: Community-dwelling older adults (n=18) were asked to use a custom smart speaker app to participate in an evidence-based, low-impact PA program for 10 weeks. Collected data, including measures of technology acceptance, interviews, field notes, and device logs, were analyzed using a concurrent mixed analysis approach. Technology acceptance measures were evaluated using time series ANOVAs to examine acceptability, appropriateness, feasibility, and intention to adopt smart speaker technology. Device logs provided evidence of interaction with and adoption of the device and the intervention. Interviews and field notes were thematically coded to triangulate the quantitative measures and further expand on factors relating to intervention fidelity.

Results: Smart speakers were found to be acceptable for administering a PA program, as participants reported that the devices were highly usable (mean 5.02, SE 0.38) and had strong intentions to continue their use (mean 5.90, SE 0.39). Factors such as the voice-user interface and engagement with the device on everyday tasks were identified as meaningful to acceptability. The feasibility of the devices for research activity, however, was mixed. Despite the participants rating the smart speakers as easy to use (mean 5.55, SE 1.16), functional and technical factors, such as Wi-Fi connectivity and appropriate command phrasing, required the provision of additional support resources to participants and potentially impaired intervention fidelity.

Conclusions: Smart speakers present an acceptable and appropriate behavioral intervention technology for PA programs directed at older adults but entail additional requirements for resource planning, technical support, and troubleshooting to ensure their feasibility for the research context and for fidelity of the intervention.

Introduction

The use of behavioral intervention technologies (BITs) in research has proven to be feasible and efficacious in a wide variety of settings [ 1 ], extending the range of research into new geographies and populations that were previously difficult to reach and by providing new media with which to develop and deliver interventions and record data [ 2 ]. Advances in artificial intelligence and computational linguistics have created a new class of technologies that can be used for these purposes. Powered by artificial intelligence and made accessible through voice user interfaces (VUIs), smart speakers or voice-activated personal assistants, such as Amazon’s Alexa and Google’s Nest Home, are widely available and readily acceptable to older adults [ 3 ]. Because of their utility and features, smart speaker technologies have become a focal point in gerontological and health research [ 4 , 5 ]. While attention has been placed on the use of websites, software, mobile apps, and sensors as intervention delivery mechanisms [ 6 ], less attention has been placed on the use of smart speakers as a BIT, especially for older adult populations [ 5 ].

Hermes et al [ 7 ] argue that BITs hold unique characteristics that should be evaluated distinctly as part of traditional implementation outcomes, with an emphasis placed on the evaluation of BIT at the consumer or participant level for factors such as acceptability and adoption. We further argue that BITs often comprise both a delivery technology, such as a smart speaker, and an underlying software application, which is the intervention itself. By conducting independent evaluations of intervention hardware and delivery technology and intervention software applications, a more accurate evaluation of implementation outcomes can be made.

This article aims to report on the acceptability and feasibility of using smart speakers to deliver an in-home physical activity (PA) intervention among a sample of older adults aged ≥65 years. Using data collected from surveys and interviews, along with researcher field notes and device logs, we focus on the evaluation of the smart speaker device, and not the intervention application, as a BIT delivery mechanism in a 10-week pilot study that used Google Nest Home Mini smart speakers. The contribution of this study is 2-fold. First, it examines the feasibility and acceptability of smart speakers as an emerging component of BIT delivery systems, independently of an intervention assessment. Second, it examines the appropriateness of smart speaker technology for use in PA interventions for older adults.

Smart Speaker Basics

Smart speakers use a VUI to aid users in navigating everyday tasks, such as finding information, scheduling events, setting timers and alarms, and playing media [ 8 , 9 ]. The VUI language processing system, also called a voice assistant or conversational agent, is the defining characteristic of the smart speaker. To accomplish tasks by voice, smart speakers integrate several different technologies into a single device to leverage dialogue capabilities: these include subsystems for voice recognition, natural language processing (understanding and generation), and cloud-based data processing. Typically activated using a wake word or phrase, such as “Hey Google” or “Alexa,” smart speakers remain in a state of ambient listening or are always “on,” waiting for the users to initiate a conversation or command. Energy-efficient processors passively process, or “listen,” for the wake word, buffering and rerecording within the device without transmitting or storing any information [ 10 ].

As illustrated in Figure 1 , once a wake word is detected, the device is triggered to begin actively recording [ 11 ], transmitting recorded requests to the device maker’s cloud-based service to decipher users’ speech [ 12 ]. Cloud-based data processing and storage alleviates the need for the device to be capable of speech recognition [ 11 ] or file storage [ 13 ], and data are transmitted seamlessly between the device and the cloud. The audio transmission is deciphered into commands using a natural language processing algorithm, and an appropriate response is generated using speech synthesis, then sent back to the smart speaker to be conveyed to the user [ 12 , 14 ].

To enhance device utility, most platforms like Google and Amazon encourage personalization of the activities that can be performed on their devices. Users are urged to create user and voice profiles and to share personal information like home and work addresses, credit card numbers, calendars, account logins, transportation modes, and nicknames. This information is then used to streamline activities that can be facilitated through the device, such as purchasing items, setting calendar reminders, and generating shopping lists. In another form of customization, Google and Amazon provide developers the ability to build and market add-on applications called skills (for Amazon’s Alexa) and actions (for Google Assistant), which augment available native applications. These custom actions can be designed to support research activities by recording data, supporting intervention activities, or reminding participants to pursue specific actions.

Older Adults and Technology Acceptance

Technology has become increasingly important to everyday life, yet older adults can often trail behind in the adoption of new technology because of limited experience and a lack of necessary skills [ 15 , 16 ]. While age-related gaps in internet use narrowed significantly since the pandemic, older adults still lag behind other age groups in both use and access to broadband connections, as compared with those in younger age groups [ 17 , 18 ]. In part, this may be due to experience with technology in the workplace [ 19 ], but declines in physical and cognitive abilities and limitations in the performance of instrumental activities of daily living may also lead to decreased use [ 20 , 21 ]. Although the use of technology by older persons can often enhance perceptions of life quality [ 22 , 23 ], socioeconomic factors, such as lower income and education levels, compound age-related differences [ 24 ].

VUIs represent a class of technologies that are readily accepted by older adults and perceived as easier to learn and use than keyboard interfaces [ 3 , 25 , 26 ]. Because VUIs do not rely on vision or touch, they are accessible to those with visual or fine motor degradation, mobility impairment, and disability [ 27 ]. In addition, older adult users often build companionship with VUIs, which results in positive experiences that may not only lead to reduced loneliness and increased independence [ 28 ] but also may help to overcome frustration with technological errors [ 29 ]. Processes of technology acceptance by older adults often reflect the dynamics of technology adoption and use described by the Technology Acceptance Model and its derivatives [ 30 ], including the widely accepted Unified Theory of the Acceptance and Use of Technology (UTAUT) model [ 31 ].

Older Adults and PA

The health benefits of PA for older adults are well documented [ 32 ], and its importance in supporting healthy aging cannot be overstated. PA slows age-related declines in functional abilities and helps to maintain physical and mental capacities in such diverse areas as muscle strength, cognitive functioning, disease prevention, and anxiety and depression reduction [ 33 ]. Despite these benefits, older adults report high levels of sedentary behavior [ 34 ]. Environmental contexts, such as weather, accessibility because of distance, and cost and affordability, are cited as factors by older adults for not being physically active [ 35 ]. It is often challenging for older adults to adopt and adhere to a PA regimen [ 36 , 37 ], and mobility challenges sometimes limit the ability to regularly participate in community-based programs because of accessibility issues [ 38 ].

Behavioral interventions to increase PA among older persons are largely successful in increasing levels of PA, and studies have shown that older individuals are more likely to continue PA programs in home-based settings [ 39 , 40 ]. Internet-based PA programs are both cost-efficient [ 41 ] and effective in producing behavioral change [ 42 ], and home-based programs have better adherence rates than community-based programs [ 40 ]. Technology-based interventions have been effective in producing a change in PA behaviors when compared with traditional mechanisms, such as usual care, minimal contact, waitlist control groups, in-person, or other nontechnology interventions [ 43 ]. Taken together, these factors suggest that a home-based PA program facilitated by technology, such as a smart speaker, could support the PA readiness of sedentary older adults. This study contributes to the extant literature by reporting on the acceptability and feasibility of smart speakers to deliver PA programming among a sedentary group of older adults.

Smart Speakers as a BIT

Smart speakers are emerging as a locus in behavioral intervention delivery systems [ 5 ]. Smart speakers can be deployed in participants’ local environments and enable interventions to be delivered remotely, thereby reducing barriers to administration and adherence. When evaluating the use of technology in implementation research, Hermes et al [ 7 ] argue that criteria used to evaluate intervention implementations [ 44 ] should be applied to BITs and the strategies used to guide their use apart from processes used to evaluate intervention implementation, as by conducted these separately a more accurate evaluation of implementation outcomes can be made.

It is relevant to note that the Hermes et al [ 7 ] approach ignores potential distinctions between an intervention mechanism, which may be a technology, such as a software program or application through which the intervention is delivered, and its underlying hardware and delivery infrastructure, which may include a device, such as an internet-enabled watch or smart speaker through which the intervention software operates, as well as the Wi-Fi or internet signal on which it is dependent. We argue that distinctions between each of these elements are important to make when evaluating the feasibility and acceptability of a BIT as well, as challenges and successes may occur with any of the components. Moreover, it is relevant to consider a participant’s ability to distinguish between these elements when undertaking this analysis to ensure appropriate identification of evaluation criteria. For example, studies on smart speakers used for general purposes have suggested that some users fail to make distinctions between the various components of service delivery, such as the voice interface and the device [ 45 , 46 ], which leads to conflation between perceptions of each element.

Five criteria in particular are recommended for evaluation from the perspectives of the participant or consumer and providers or researchers [ 7 , 44 ]:

• Acceptability, or the extent to which a technology is useful or satisfactory.
• Adoption, or the intention to use the technology.
• Appropriateness, or perception that the technology fits, is relevant or compatible with the context of its use.
• Feasibility, or the extent to which a technology can be successfully used in a specific context.
• Fidelity, or evidence that technology can be delivered as intended.

When evaluating a BIT at the level of the consumer or research participant, the outcomes of acceptability, feasibility, and adoption are most commonly measured through models of technology adoption [ 7 ]. The UTAUT model [ 31 ] is one of the most widely used theories of technology adoption [ 47 ], connecting the concepts of acceptability and feasibility to a third important criterion from the user’s perspective: adoption. UTAUT argues that a causal relationship exists between users’ perceptions of technology and their intention to use it. It specifically identifies 4 constructs—expectations of a technology’s performance, the ease with which it can be used, social influence, and facilitating conditions—and links these to a user’s intention to use a particular technology, which, in turn, is strongly correlated with its use. UTAUT subscales regarding performance and effort expectancy have been proven reliable with respect to the use of a wide variety of technologies in older adult populations, including email and social media [ 48 ], tablet computers [ 49 ], and remote health care or telehealth applications [ 50 ].

Studies on older adults’ use of technology have found that expectations of a technology’s performance and perceptions of the amount of effort that will be required to use technology are powerful incentives for new technology adoption [ 51 , 52 ]. Although the feasibility of using smart speakers among older adult populations to improve well-being has been examined in several recent studies [ 25 , 28 , 53 ], their use to explore specific interventions is more limited, especially among older adult populations.

Study Design

Participants were engaged in a 10-week, evidence-based, internet-based PA program that used artificial intelligence to guide activities from October 2020 through January 2022. Of note, the time frame of the study coincided with the COVID-19 pandemic and consequent lockdowns. Because all in-person leisure activities had ceased, the use of alternative delivery mechanisms for exercise programming, such as smart speakers, was potentially attractive. Though the study had been initially designed and planned to include an in-person orientation to the smart speaker and PA application, the pandemic necessitated that all participant interactions be carried out in a virtual context because of the particularly vulnerable nature of the target population. Consequently, all participant interactions, including onboarding and offboarding, weekly check-ins, and interviews, were conducted remotely via Zoom when possible and alternatively via phone calls.

A PA application was developed by the research team to run on Google Nest Home Mini speakers, which replicated components of the Healthy Moves for Aging Well program [ 54 ]. A description of the activities included in the PA application can be found in Multimedia Appendix 1 . Initial feasibility and acceptance of the PA application were validated in a pilot user study before field deployment [ 39 ].

To ensure that participants had internet access, we distributed Wi-Fi hot spots together with the smart speakers. The hot spots also enabled the research team to perform the initial speaker setup and installation of the PA application before the first session. This simplified the orientation process for participants, as the devices merely needed to be connected to power for participant use.

Recruitment was aided by a partner senior-living organization located in a large suburban Midwestern county, which disseminated recruitment materials to both their independent living facility residents and via community programming information channels. The research team consisted of 4 individuals (1 principal investigator and 3 graduate research assistants, all adult women), certified by the Collaborative Institutional Training Initiative Program [ 55 ]. The study design is illustrated in Figure 2 .

After enrollment, participants were sent a parcel containing written instructions regarding the use of the smart speaker, PA program application, and Wi-Fi hotspot; a smart speaker; a Wi-Fi hot spot for in-home internet connection diary materials; and consent documents. Initial participant meetings were conducted by phone or Zoom, during which baseline PA and technology attitude measures were taken, and verbal instructions were shared on using the smart speaker, PA app, and Wi-Fi hotspot. Participants were randomly assigned to a research team member who assisted with equipment setup and conducted all interviews and weekly check-ins throughout the intervention. Functionally, this meant that each researcher worked with the same 5 to 6 individuals throughout the study. At baseline (T0), participants took part in a brief individual motivational coaching session to determine their PA goals.

The intervention then took place in 2 phases. During phase 1, which lasted 6 weeks, participants were encouraged to use the smart speakers for their purposes (eg, answering questions, playing media, setting timers or alarms), as well as use the PA application for a minimum of 3 sessions per week. Participants were contacted weekly by phone by a trained member of the research team during this phase to troubleshoot, assess PA goal achievement, and set new PA goals for the coming week. Phase 2 lasted 4 weeks, during which participants continued natural use of the smart speaker and the PA application on their own (ie, without weekly contact).

Assessments of the participants’ perceptions of technology acceptability, prior technology experience, and attitudes toward technology were administered by the researchers at 3 points in the study: T0, 6 weeks and end of phase 1 (T1), and 10 weeks and end of phase 2 (T2). Semistructured interviews provided perceptions and use of the smart speakers and the PA application at T1 and T2. Device logs were maintained and reviewed for the entire study period. Participants were compensated after each interview (T0, T1, and T2) and received a completion bonus for completing all visits.

Ethical Considerations

The study protocol was approved by the institutional review board of the University of Illinois Chicago (UIC Protocol 2019-1013), which reviews and approves human subjects research in accordance with the ethical principles outlined in the Belmont Report and the DHHS regulations 45 CFR Part 46. All procedures were carried out as specified in the study protocol. All participants provided oral acknowledgment of informed consent, as a written acknowledgment requirement was waived because of the conditions of the pandemic. All data were deidentified before analysis. Participants were compensated up to US $100 for time spent and were able to keep their smart speaker device on the study’s conclusion. The reporting of the qualitative findings follows the guidelines outlined in the Consolidated Criteria for Reporting Qualitative Research [ 56 ] for reporting health-related studies, as appropriate.

Data Sources

Measures of technology acceptability and feasibility.

Researchers administered a questionnaire with quantitative measures of familiarity with technology, UTAUT, perceived sociability, and social presence during the participant interviews at T0, T1, and T2. The UTAUT subscales consisted of 30 items related to performance expectancy, effort expectancy, attitudes toward technology, social influence, facilitating conditions, smart speaker self-efficacy, anxiety, and behavioral intention to use smart speakers, measured on a 7-point scale and adapted to the context of smart speakers [ 31 , 39 ]. Four items were used to measure the perceived sociability of smart speakers, and 5 items assessed the social presence of smart speakers [ 57 ]. Items for each measure are listed in Multimedia Appendix 2 . To indicate technological competence in everyday living, a familiarity with technology measure was adapted from the Everyday Technology Use Questionnaire [ 58 ] regarding the frequency of use of 11 everyday technologies, such as searching the internet for information, dealing with recorded telephone menus, or sending and receiving emails [ 59 ].

Device log data were collected from the Google accounts attached to the devices; however, data from 2 (11%) of 18 participants was missing because of technical errors. The remaining (16/18, 89%) data logs were analyzed for frequency of engagement with the PA app by calculating the number of times “Healthy Moves” was activated by the participant. To avoid fatigue, the PA program application asked participants if they needed to take a break or stop the exercise activity. Consequently, because participants were able to voluntarily truncate the application, we used its activation as a measure of engagement.

Interviews and Field Notes

A total of 36 interviews were transcribed for analysis, with an average duration of 18:05 minutes for interviews at T1 and 17:06 minutes for interviews at T2. The interview guide is presented as Multimedia Appendix 3 . In addition, 90 sets of field observation notes were recorded after the weekly motivational sessions during phase 1 and examined.

Because recruitment took place during pandemic lockdowns , participants were recruited using social media postings, recruitment emails posted on listservs, and through the use of 2 research recruitment matchmaking portals. Inclusion criteria were those aged ≥65 years and who spoke English, as the smart speaker application was programmed in English. Participants were excluded if they participated in ≥150 minutes of PA per week or scored <7 correct responses on the Short Portable Mental Status Questionnaire [ 60 , 61 ].

A total of 24 participants were enrolled at T0. The size of the sample was largely determined by the availability of respondents and the adequacy of resources to complete the study. Because of interest in maintaining a PA regime and health complications, 4 participants did not complete the T1 interview. An additional 2 participants did not complete the T2 interview because of withdrawal from the study, so the final sample consisted of 18 individuals. The final study completion sample had an average age of 75.9 (SD 7.3) years; 15 (83%) of 18 participants were female, and 17 (94%) of 18 were White. Familiarity with technology was high among these participants, with 14 (78%) of 18 using a range of everyday technologies, such as searching the internet, using email, or sending text messages on a mobile device, on average, more than once per month. Table 1 summarizes the characteristics of both the study completion sample (n=18) and the recruited sample (n=24).

a Adapted from Everyday Technology Use Questionnaire [ 58 , 59 ].

Data were analyzed using concurrent mixed analysis [ 62 ], with log data, interview, and field note data examined for complementarity and completeness [ 63 ] to broaden and enrich the understanding of the quantitative measures of device feasibility and acceptability. Because of this choice of method, only data collected from the study completion sample were used in this analysis. SPSS (version 29) was used for repeated measures ANOVA, and MaxQDA 2022 (version 22.08.0; VERBI GmbH) was used for first-level descriptive and second-level thematic qualitative text analysis of interview and field observation data.

The coding schema for qualitative analysis of the interview and field note date was developed using a hybrid approach, with a priori codes from the UTAUT constructs comprising the initial coding structure and additional codes developed in vivo. First, a descriptive analysis of the texts was performed by a team of 2 researchers, coding approximately 9 transcripts each. Salient words and phrases were highlighted, and special attention was paid to adjectives and adverbs that added emphasis or provided a judgment of value. Next, keywords and phrases were grouped under thematic umbrellas that either aligned with the preestablished categories derived from UTAUT codes or newly identified categories that emerged during the first descriptive coding phase. The collected measures were then grouped and analyzed according to the criteria for assessing BIT [ 7 , 44 ] to provide an overall assessment of acceptability, adoption, appropriateness, and feasibility. Acceptability was assessed using the UTAUT subscale of performance expectancy, along with measures of perceived sociability, pleasantness, and social presence. Adoption was measured using device log data on intervention app use and examination of the subscale on the behavioral intention to use the smart speaker. Appropriateness was evaluated using measures of attitudes toward using smart speakers and smart speaker self-efficacy. Feasibility was appraised using the UTAUT subscale of effort expectancy.

As the data analyses were conducted concurrently using mixed methods, the results are also presented concurrently. In this format, log data, interview data, and field observation data offer complementarity and completeness to the quantitative measures of acceptability and feasibility.

Acceptability

Adults in this study perceived the smart speaker as acceptable, with measures of performance expectancy (ie, perceived usefulness) to be high during the study period (T0: mean 5.81, SE 0.16; T1: mean 5.04, SE 0.31; T2: mean 5.02, SE 0.38). However, perceived usefulness decreased over time ( F 2,34 =3.66; P =.04; partial eta squared=0.18), perhaps reflecting the practicality of actual use once participants integrated the smart speakers into their everyday routines.

Participants expanded on these perceptions in the interviews, noting that specific features of the smart speaker encouraged these perceptions of utility and spurred them to engage in more frequent and routine use of the PA program. One factor they noted was that because the smart speaker was located at home, barriers to PA engagement were reduced:

Convenience. I didn’t have to go out of the home for the exercise. [Participant 8]

Its accessibility and yeah, I guess that would be it. Its accessibility. It’s right there and it’s easy to consult and makes it easier to engage in the [PA] program. [Participant 19]

The smart speaker also afforded participants time flexibility, enabling them to engage in the intervention at times that were convenient or available for them instead of a set or designated time. This time flexibility enabled them to adhere to the protocol with greater success:

I tried unsuccessfully to pick a time of day that worked the best and stick with it... In a way, it was an advantage because I could fit it in whenever it occurred to me or that I was reminded in some way. [Participant 5]

The visual and physical presence of the smart speaker device also encouraged intervention adherence for participants, as it “reminded” participants to engage in the intervention activities, which also improved adherence:

I like the fact that it reminds me, just seeing it reminds me to do it, and I don’t know what I would use in its stead. [Participant 5]

Several participants noted that the VUI interface of the smart speaker added to the device’s convenience by making it expedient to use:

I don’t have to type anything in, I can just talk to it and it gives me the quick answer that I’m looking for… I think it’s faster than the computer. Because it’s quicker to talk than for me to type it. [Participant 9]

Participants perceived the smart as being pleasant to interact with throughout the study period (T0: mean 5.89, SE 0.16; T1: mean 5.27, SE 0.31; T2: mean, 5.03, SE 0.38), but this perception decreased over time ( F 2,34 =3.63; P =.04; partial eta squared=0.18), perhaps because the novelty of interaction diminished over time. As one participant noted:

So I think it’s a novel way of getting one to focus on an exercise program like this, and to be able to look, shall we say, look forward to doing it, more than if it were simply something you were reading from a pamphlet. [Participant 13]

They also perceived low social presence of the device (T0: mean 3.82, SE 0.27; T1: mean 3.52, SE 0.34; T2: mean 3.31, SE 0.35), and the low social presence did not change over time ( F 2,34 =1.27; P =.29; partial eta squared=0.07).

Adoption of the smart speakers, or the intention to use them, was evidenced through actual use of the devices and indication by participants of a willingness to use its functionality for other purposes in addition to the PA intervention. Examination of the device log data revealed that while heterogeneity existed in the PA program engagement, the use of the devices decreased over the study period. For the first 6 weeks (phase 1), participants engaged with the intervention app <2 times per week (mean 10.19, SD 13.26; range 0-42). Activity was higher during this initial period, perhaps because of the accountability provided by the weekly check-in calls from the research team. Engagement with the intervention app dropped off significantly during the second phase of the study, with participants engaging with the intervention app approximately about once per week (mean 4.94, SD 7.85, range 0-29; F 1,15 =9.49; P =.008; partial eta squared=0.39). However, the patterns of use were heterogeneous, with some participants engaging with the device frequently and others only minimally.

About half of the participants engaged with the PA intervention less than one time per week during the intervention and follow-up phases (n=9), but other participants (n=4) were quite active, engaging with the intervention >4 times per week during phase 1, and continued engagement with intervention >2 times per week during the phase 2. The most frequent user engaged with the PA intervention every day during phase 1 and even more than once daily during phase 2. Examination of the interview data reinforced these findings and demonstrated how these 2 clusters differed. It also reinforced the understanding that as expectations of the device met user experiences, regular use of the devices was reinforced. As one participant noted:

Well, at first I just used it for the exercise program, and then I was a little bit more daring and listened to some music, and then jokes, and the weather, and timer and things like that. I became more comfortable using it more often. [Participant 1]

Conversely, when participants did not use the device with any regularity for either exercise or everyday activities, they were less likely to indicate that they would continue using it at all. The lack of engagement with the smart speaker appeared to lead to a lack of adherence to the intervention protocol:

[B]ut I’m a little distressed with myself for not even thinking of it this week, yeah. Well it didn’t seem to be too helpful to me when I was using it, I guess that’s why I forgot about this week. [Participant 2]

Participants demonstrated a strong willingness to continue using the device during and after the study period (T0: mean 6.35, SE 0.11; T1: mean 5.98, SE 0.27; T2: mean 5.90, SE 0.32), and this behavioral intention to use the device did not change over time ( F 2,34 =1.48; P =.24; partial eta squared=0.08). We note that participants who made a concentrated effort to incorporate the device into their daily routine during the study, for both exercise and other activities, demonstrated greater intention to continue using the device, even beyond the scope of the study.

Appropriateness

Appropriateness, or the perception that the technology is compatible with the context of its use, was demonstrated consistently during the study period, though these perceptions were not uniform among participants. Participants held strongly positive attitudes toward smart speakers (T0: mean 6.17, SE 0.09; T1: mean 6.07, SE 0.16; T2: mean 6.00, SE 0.18), and these perceptions were sustained throughout the study ( F 2,34 =.82; P =.45; partial eta squared=0.05). Comments about specific qualities of the smart speakers that enhanced the delivery of the PA intervention elaborated on these positive perceptions. For example, one prominent feature that was frequently mentioned by participants was the VUI. Participants found the auditory instructions provided by the smart speaker to be an appropriate mechanism to deliver the PA intervention, likening it to an individual coach or mentor:

Well, I do like the fact that even though this is not a real person, there is a voice telling you what to do. So you’re not in a gym filled with people or you’re not in a class. You don’t have to go anywhere, but someone is standing there telling you what to do or sitting there telling you what to do. And so that is a, I think for persons who say live by themself or something, it is like another voice that, I guess it’s watching a TV too, but it’s a voice that coaches you on. [Participant 24]

Other participants liked the ability just to listen and follow instructions. The simplicity of following commands enabled them to carry out the activities easily and with minimal cognitive effort:

I don’t have to think about it because I just follow whatever the directions are. When we’re doing the other exercise, I have to use the paper and I have to look at the paper. In here, you just follow the directions and you’re all set. [Participant 7]

However, the VUI may also have presented some hindrances for delivering the PA intervention. Prior work has identified that older adults may experience difficulty with constructing a structured sentence command in smart speaker use [ 29 ]. Participants in this study described how they had to “learn to talk to the device” by rephrasing questions and commands to obtain a desired response:

But every now and then on the [PA application], I think you’re supposed to answer a certain way. If he [the smart speaker voice] says, “Are you ready to go to the next exercise?” Then you go “Uh-huh.” And he’s like, “Did you hear me?” I think maybe you should know the respect that he needs because sometimes I think he doesn’t understand me and I forget to speak clearly. [Participant 11]

Participants consistently perceived that they could effectively use the smart speakers, assessing their self-efficacy in using the smart speakers quite positively (T0: mean 6.03, SE 0.12; T1: mean 5.79, SE 0.26; T2: mean 5.58, SE 0.25). However, this self-efficacy assessment dropped during the study period ( F 2,34 =1.81; P =.18; partial eta squared=0.10) as participants integrated the devices into daily routines. Self-efficacy was also demonstrated by participants’ ability to engage in intuitive workarounds for issues of communication with the smart speakers. These workarounds consisted of repeating or rephrasing commands, adjusting the speaking volume, or articulating the command more clearly:

...I don’t know whether it was the way I asked, ya know, I asked the question and the device said, “I didn’t understand the question.” So I said it a different way and then it was able to answer. [Participant 7]

Well, once in a while, I think maybe I’m not close enough to it and they will say, “I can’t understand you.” And I don’t know whose fault that is, if it’s a Mini Google device or maybe I’m not directly in front of it. But then I try again and I’m able to interact with it. [Participant 12]

However, challenges with the ability to engage with the intervention on the smart speaker may have contributed to participants’ feelings of being less proficient in using the smart speaker as the study progressed. Miscommunication issues with the device appear to be the primary hindrance and sometimes result in the participant ceasing interaction with the smart speaker or taking time away from the device before attempting interaction again. This effectively prevented the participant from carrying out the intervention at the desired or appropriate time:

Sometimes the Google device misses the mark as far as giving me exactly what I’m looking for. So I either have to rephrase what I’m asking or give up. [Participant 13]

Other participants, especially those who used the device irregularly or only for the PA application, were often discouraged when faced with these difficulties. Some participants did not seem comfortable adjusting their language or speaking style to accommodate the device and would disengage from the conversation and walk away:

[B]ut when I tried to get onto the [PA application], I tried it twice and then both times it said it wasn’t responding, but I didn’t get frustrated. I just thought, “I’m not going to do exercises today.” [Participant 1]

Feasibility

Feasibility, or the perception that smart speakers are easy to use, remained high during the study period . Participants reported that they found the smart speakers easy to use (T0: mean 5.93, SE 0.53; T1: mean 5.56, SE 1.16; T2: mean 5.55, SE 1.24), and this ease of use was sustained throughout the study ( F 2,34 =1.49; P =.24; partial eta squared=0.08). As one user summarized in the interviews:

I found it very easy to use. Very, very helpful. It got me to exercise three times a week, more than I could have without it. [Participant 1]

From the perspective of the research team, however, feasibility was not as clear cut. Examination of field note observations revealed that during the weekly contacts in phase 1, members of the research team often assisted participants in effectively using their smart speakers. Though participants reported that their devices were easy to use, researchers noted that they had made suggestions that devices be moved within the participant’s residence to improve the Wi-Fi signal (n=5) and that participants might rephrase commands to the device to gain an appropriate response (n=5), or worked with participants to adjust volume settings (n=2). In addition, researchers suggested and encouraged participants to use the device in alternate ways in addition to the PA program app (n=4), such as seeking information about the weather or setting timers and alarms. The frequency and nature of these observations suggest that providing support for using the devices was a necessary element in the research protocol and required resources from an administrative perspective, including training members of the research team in basic device functionality as well as potential troubleshooting strategies.

Wi-Fi connectivity issues were a major determinant of the problems that participants experienced. When smart speakers have difficulty maintaining a continuous internet connection, especially when deployed over Wi-Fi hotspots, they may encounter limits on bandwidth, which can impair their operation [ 64 ]. The use of Wi-Fi hotspots was a source of some of the connectivity issues experienced by participants and caused frustration in the form of longer-than-anticipated response times or interruptions in the performance of the device. These signals of connectivity issues required patience when experienced:

I would be patient. Sometimes there’s a long pause before it responds. So patience is sometimes necessary, or just repeat my request. And if I get a sense that it’s not being digested by the device, I rephrase it. [Participant 13]

The placement of the smart speaker within the participant’s residence was also a critical factor in ensuring successful interactions with the device. Prior research has identified that older adults strategically position smart speakers to shape and organize daily routines [ 65 ]. Participants frequently placed devices in the area of their residence, which they spent the most time in to optimize accessibility and convenience. Often, this meant that the smart speaker was placed in a living or family room, but the kitchen was also a popular option. However, these spaces were not always optimal for participating in the PA program intervention. As one participant noted:

Now, I’m sitting in a computer room a study. And so, a lot of times I do it [the PA program application] in here. In fact, I think I’ve been doing it in here for the last week or two, but sometimes I put it [the smart speaker] in the area where we eat, the kitchen eating area, so that if I’m working in the kitchen and I’d like to use it for a timer. So, then I, because it’s there, I ended up doing the exercises in the kitchen. [Participant 16]

Privacy considerations also play a role in device location and, again, may run counter to optimal placement for engagement with the intervention activity or a strong Wi-Fi signal. As one participant described:

My husband and I worry about the privacy issues with the Google Home Mini, and several times when I had it plugged in to prepare in order to do exercises, I would mention, “Oh, I have to Google that.” And the Google Mini would come on. And that was very disconcerting because I was in another room and it was listening. [Participant 21]

Ultimately, participants seemed to maximize the opportunity to use the smart speakers and tried to place the device in ways that would facilitate their engagement with the intervention:

I kept it right here in the living room where it was visible to me every day. If I had tucked it away somewhere, that would have been even more problematic on my part because it’s easy to forget. [Participant 5]

Fidelity, or evidence that the technology can deliver the intervention as intended, is a criterion that is perhaps best viewed from the perspective of the research team, but participants noted how the smart speaker enabled them to execute the intervention with greater precision. Participants liked the program’s routine and noted that it provided structure, including PA, in their daily routines.

I like the fact that it times you, in other words, I don’t have to keep track of the time with the clock or anything like that. I like, it just gives a little more structure so that I can rely less upon my own motivation. I would say it serves as a motivator too. [Participant 9]

In addition, some participants noted that the smart speaker introduced accountability into their engagement with the intervention, alluding to its potential to be used as a mechanism to report on activities associated with the study:

And I realized that I’m on my own time. I can do this or I don’t have to do it, but having the device makes me feel responsible that it’s kind of like big brother is watching me, and if I don’t use it, it’s going to know. [Participant 18]

Though the PA application had been tested extensively before deployment in the study, issues with its performance and interoperation with the device were encountered during both phases of the study. Participants reported that they experienced technical difficulties with the PA application, including the application not responding to them in a timely manner, aborting the intervention without warning, or repeating exercises that they had already completed. Minor modifications to the application by the development team during the study resulted in some improvements in performance, but most incidents were attributable to Wi-Fi connectivity issues, which could cause the PA program application to cease functioning and impair the fidelity of the intervention.

Participants found incidents very frustrating and for some, induced nonuse of the intervention application. As one user stated:

I tried to do it with the (PA application), but it doesn’t work right for me, so I just don’t use it then. So, I do a little exercise, but I just don’t use the Google, I just do them on my own. [Participant 20]

As this participant suggests, when encountering technical issues with the PA application, participants often maintained the exercise regimen of the study without the assistance of the smart speaker. Participants had received extensive support materials in their orientation packet, including illustrated descriptions of the activities that were invoked by the PA application. Because of these materials, several participants felt that they could complete the exercises without the guidance of the smart speakers; however, some used the functionality of the device, such as a timer, to assist in carrying out their activities:

[Participant] told me when she gets frustrated with that she will just use the Google Home [smart speaker] to set a timer and go through the exercises on her own. [Field notes, Participant 16]

Participants also appeared to be able to distinguish between the PA application and the hardware of the smart speaker when they reported the challenges they encountered. This was made clear through their use of gendered pronouns when speaking about the various components, identifying the male voice of the PA application and the female voice of the smart speaker interface, as well as specific references to the PA intervention as an “app.”

Well, there were problems with the software where it would abort and I would be shifted over to the regular Google application as opposed to the [name of PA program app]. [Participant 13]

Taken together, these last 2 points suggest that when participants’ challenges with the PA application were technical, they recognized it was a limitation of the intervention delivery mechanism and not the smart speaker or the intervention. Instead, they sometimes resorted to workarounds, such as relying on their memory to complete the exercise or using the support materials as a prompt. This underscores the importance of providing support materials to participants, not only for the delivery mechanism but also for the intervention itself.

Principal Findings

The goal of this paper was to evaluate the use of smart speakers to deliver an in-home PA intervention among a sample of older adults using previously established criteria for the evaluation of BITs. Our focus for this analysis was on the smart speaker as a delivery mechanism and not the PA intervention itself, as these are distinct components of the intervention implementation and should be evaluated separately. The smart speakers in this study were found to be highly rated for a PA program by participants regarding acceptability, appropriateness, and feasibility, criteria essential to quality BITs [ 7 , 44 ]. Functional and technical factors related to the operation of the smart speakers, such as ensuring consistent Wi-Fi connectivity and ensuring participants used appropriate phrasing when interacting with the devices, created a responsibility for the research team to provide basic technical support and troubleshooting resources. In addition, these same factors possess the potential to impair the fidelity of the intervention. In short, while smart speakers provide a novel and acceptable technology for intervention research, their feasibility in a research context comes with limitations.

Smart speakers afforded participants in this study convenience and flexibility for engaging with the intervention activities and served as a visual reminder to reinforce completion of the study protocol, which improved adherence to the intervention. The VUI was well-received by participants, who noted its ease of use and appropriateness for coaching participants through a PA program. The VUI also introduced challenges for the participants, as it required them to learn how to appropriately phrase commands and adjust their speaking volume to communicate with the device. This represents an intriguing intersection of possibility and limitation. On the one hand, the benefits of the device, as articulated by most respondents, are based on its ease of use and convenience, which are associated with being a hands-free interface that is capable of responding quickly and specifically. This presents a range of possibilities for intervention-based research across health, education, and other applications for older adults [ 66 , 67 ].

By contrast, at the current stage of development, the smart speakers used for this study are not capable of accommodating a human user’s natural diction and phrasing beyond stating “I’m sorry” and requesting the user rephrase their question or direction until an acceptable rudimentary keyword or phrase is recognized. Therefore, successful accommodation to miscommunication hinges on the ability of the participant to mold their habits and language to patterns recognizable by the device. When participants were flexible about adjusting their phrasing and behaviors to mitigate technical glitches, they were also more likely to view the device favorably and use it regularly. When considering acceptability, this is particularly meaningful because the limitations of the technology and the adaptiveness of the participant base must be evaluated in tandem. Future development in artificial intelligence–supported health interventions could leverage the advancement in large language models to provide ubiquitous and fluent user experience [ 68 ].

Participants indicated their intention to use a smart speaker through positive attitudes toward its functionality, but there was a high degree of heterogeneity in their adoption. Some participants embraced the use of the devices, whereas others were frustrated and abandoned their efforts easily. Issues related to Wi-Fi connectivity were particularly challenging and interfered with the ability of the device to function appropriately. The feasibility of using smart speakers in an intervention, while positive from the participants’ perspective, was more challenging from the vantage point of the research team. To provide technical and operational support for successful device operation, members of the research team were required to be familiar with device functionality and basic troubleshooting strategies. In addition, because participants had discretion over device placement within their residence, it was more challenging to ensure that the device would be optimized for both execution of the intervention and Wi-Fi connectivity. These connectivity issues impacted intervention fidelity and underscored the need for robust support.

Overall, technical issues, such as glitches with the PA application, device-level technical problems (volume, articulation, etc), and broader critical infrastructure issues, such as a weak Wi-Fi signal, will stymie engagement with intervention activity and broader intentions to use the smart speakers. However, when participants have outlets to seek technical assistance and support when issues arise, such issues can be effectively mitigated. In other words, older adults are not at all resistant to engaging with smart speakers; however, a robust technical and informational support system should be in place.

The privacy considerations for smart speakers are not inconsequential. To be activated, smart speakers typically require an account to be established with the device maker, which is then associated with the smart speaker. Often, these accounts require additional information to be gathered from the user, such as credit card details, causing concern about personal information collection [ 69 ]. Establishing a linkage between the account and the smart speaker enables personalization of the activities that can be performed, such as reminders; however, it also associates this same information with voice recordings and device interactions [ 13 ]. Location-based data, such as time zone and zip code, allow device makers to transmit relevant information, such as weather and traffic news, but these data also become associated with accounts. All of these additional data points may increase participant privacy vulnerability.

One strategy to enhance participant privacy is to use pseudonym accounts to set up the smart speaker and collect data, as was done in this study. This approach provides some ability to shield participants’ identity, but it also reduces the functionality of smart speakers considerably, as devices are unable to be personalized for calendaring and reminding functions. This reduction in utility can be frustrating to study participants, who anticipate a level of functionality frequently advertised by device makers. However, ultimately, privacy concerns are associated with smart speaker use [ 70 ], and those who use them may be qualitatively different than the general population [ 71 ], thereby presenting a form of sample selection bias for research using the devices.

Limitations

Data analyzed in this study were collected from a relatively small, not randomly controlled sample, so the conclusions reached may not be generalizable to a wider older adult population. This study was conducted during the pandemic, which required recruitment to be carried out via the web, and interviews were conducted via telephone or Zoom. The implications of this context are 2-fold. First, this sample may have been more accepting of technology and may have had higher educational attainment than if they had been enrolled in a clinical or face-to-face setting. This might suggest that the issues highlighted with this sample may be even more pronounced with populations with lower technological proficiency or education levels and may require researchers to provide additional support resources when carrying out studies of this nature. Second, the context of the pandemic may have prompted the participants to have greater acceptance of smart speaker technology to engage with PA, as in-person activities were significantly reduced during that time. These factors also limit the generalizability of these findings as they may have introduced a positive bias to perceptions of acceptability and feasibility in this sample. To gain a greater and more nuanced understanding of the acceptability and feasibility of using smart speakers in a research context, additional studies with representative samples in a nonpandemic context are required.

Challenges encountered by participants that were related to the wireless connectivity of the smart speakers may also have influenced perceptions of the smart speaker and the intervention application. Further exploratory work is needed to distinguish how perceptions of applications can be distinguished from their related delivery mechanisms for evaluation of usability, feasibility, and efficacy. In addition, data collection was constrained because of the pandemic, which limited the ability to observe participants engaging with the smart speakers. Further work is needed to expand data collection efforts beyond self-reports, which would offer additional perspectives on the acceptability of smart speakers in intervention research and provide greater detail on evaluation criteria, such as fidelity.

Finally, interviews at T1 and T2 were only conducted with participants still enrolled in the study at those time points. While data were collected from individuals who did not adhere to the intervention, it did not include input from those individuals who did not complete the study. This factor limits the interpretability of these findings, as input from noncompleters may have included perceptions related to a lack of feasibility or acceptability of smart speakers for use in a PA program.

Conclusions

In conclusion, findings from this study found smart speakers to be acceptable and appropriate for PA intervention research involving older adults, with participants indicating a willingness to adopt these delivery mechanisms for the delivery of the intervention program as well as for their everyday use. However, the feasibility of these devices for use in research contexts was mixed as they require specific and specialized attention to technical support and troubleshooting when used with older adults. Finally, applications developed to run on smart speakers must be developed to minimize disruption, whether because of flaws in design or through careful planning related to the overall Wi-Fi infrastructure, as weakness in this capacity may impair the ability of smart speakers to deliver interventions with high fidelity.

This study contributes to intervention research in that it evaluates the acceptability and feasibility of smart speakers as a behavioral intervention delivery infrastructure or the mechanisms through which an intervention is delivered, separately and distinctively from the technology that comprises an intervention, which here was a PA program application designed to enhance the physical well-being of sedentary older adults. Conducting separate evaluations of these intervention delivery elements is necessary to ensure a thorough assessment of intervention outcomes. Results from this study highlighted that older adults perceive smart speakers to be useful and easy to use. Future studies might explore the suitability of smart speakers as a delivery infrastructure for aspects of behavioral interventions requiring smart speaker functionalities, such as the setting or reminders or the streaming of media content. Research on the use of smart speakers in other specialized populations, such as those with visual impairment or limited mobility, may also prove fruitful.

In addition, these findings offer important insight for research practitioners. At the very basic level, it cautions against oversimplifying the implications of using complex delivery infrastructures, especially with a population such as older adults that might lag the general population in the adoption and use of emerging technologies. On one level, such oversimplification may overlook important aspects of technological delivery mechanisms, such as the provision of technical support and troubleshooting, which can often tap into limited research resources. On a more granular level, the same functional and technological issues that prompt the need for support resources, such as ensuring continuous Wi-Fi connectivity, can ultimately negatively impact intervention fidelity and compromise the integrity of the research process. Taken together, smart speakers offer a novel delivery infrastructure for behavioral intervention research but also require careful planning.

Acknowledgments

This work was supported by the University of Illinois Chicago Center for Clinical and Translational Science; the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1TR002003; and the National Institute on Aging of the National Institutes of Health through grant R24AG064191. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Data Availability

The data sets generated during and analyzed during this study are available from the corresponding author on reasonable request.

Conflicts of Interest

None declared.

Activities of the physical activity program.

Measures of technology acceptability and feasibility.

Interview guide.

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Abbreviations

Edited by E Wethington, A Dominello, I Kronish, J Kaye; submitted 22.11.23; peer-reviewed by S Muraki, M Chatzimina, H Li; comments to author 05.04.24; revised version received 01.06.24; accepted 29.06.24; published 30.08.24.

©Kelly Quinn, Sarah Leiser Ransom, Carrie O'Connell, Naoko Muramatsu, David X Marquez, Jessie Chin. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

• Open access
• Published: 26 August 2024

Inter-laboratory comparison of eleven quantitative or digital PCR assays for detection of proviral bovine leukemia virus in blood samples

• Aneta Pluta 1 , 13 ,
• Juan Pablo Jaworski 2 ,
• Casey Droscha 3 ,
• Sophie VanderWeele 3 ,
• Tasia M. Taxis 4 ,
• Stephen Valas 5 ,
• Dragan Brnić 6 ,
• Andreja Jungić 6 ,
• María José Ruano 7 ,
• Azucena Sánchez 7 ,
• Kenji Murakami 8 ,
• Kurumi Nakamura 8 ,
• Rodrigo Puentes 9 ,
• MLaureana De Brun 9 ,
• Vanesa Ruiz 2 ,
• Marla Eliana Ladera Gómez 10 ,
• Pamela Lendez 10 ,
• Guillermina Dolcini 10 ,
• Marcelo Fernandes Camargos 11 ,
• Antônio Fonseca 11 ,
• Subarna Barua 12 ,
• Chengming Wang 12 ,
• Aleksandra Giza 13 &
• Jacek Kuźmak 1  

BMC Veterinary Research volume  20 , Article number:  381 ( 2024 ) Cite this article

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Bovine leukemia virus (BLV) is the etiological agent of enzootic bovine leukosis and causes a persistent infection that can leave cattle with no symptoms. Many countries have been able to successfully eradicate BLV through improved detection and management methods. However, with the increasing novel molecular detection methods there have been few efforts to standardize these results at global scale. This study aimed to determine the interlaboratory accuracy and agreement of 11 molecular tests in detecting BLV. Each qPCR/ddPCR method varied by target gene, primer design, DNA input and chemistries. DNA samples were extracted from blood of BLV-seropositive cattle and lyophilized to grant a better preservation during shipping to all participants around the globe. Twenty nine out of 44 samples were correctly identified by the 11 labs and all methods exhibited a diagnostic sensitivity between 74 and 100%. Agreement amongst different assays was linked to BLV copy numbers present in samples and the characteristics of each assay (i.e., BLV target sequence). Finally, the mean correlation value for all assays was within the range of strong correlation. This study highlights the importance of continuous need for standardization and harmonization amongst assays and the different participants. The results underscore the need of an international calibrator to estimate the efficiency (standard curve) of the different assays and improve quantitation accuracy. Additionally, this will inform future participants about the variability associated with emerging chemistries, methods, and technologies used to study BLV. Altogether, by improving tests performance worldwide it will positively aid in the eradication efforts.

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Introduction

Bovine leukemia virus (BLV) is a deltaretrovirus from the Orthoretrovirinae subfamily of the Retroviridae family. An essential step in the BLV replication cycle is the integration of DNA copy of its RNA genome into the DNA of a host cell [ 1 ]. Once integrated, the proviral DNA is replicated along with the host’s DNA during cellular divisions, as for any cellular gene. The BLV is the etiologic agent of enzootic bovine leukosis (EBL). BLV causes a persistent infection in cattle, and in most cases this infection is asymptomatic [ 2 ]. In one-third of infected animals the infection progresses to a state of persistent lymphocytosis, and in 1 to 10% of infected cattle it develops into lymphosarcoma [ 2 ]. BLV induces high economic losses due to trade restrictions, replacement cost, reduced milk production, immunosuppression, and increased susceptibility to pneumonia, diarrhea, mastitis, and so on [ 3 , 4 , 5 , 6 ]. BLV is globally distributed with a high prevalence, except for Western Europe and Oceania, where the virus has been successfully eradicated through detection and elimination of BLV-infected animals [ 7 , 8 ]. The agar gel immunodiffusion and ELISA for the detection of BLV-specific antibodies in sera and milk are the World Organization for Animal Health (WOAH, founded as OIE) prescribed tests for serological diagnosis but ELISA, due to its high sensitivity and ability to test many samples at a very low cost, is highly recommended [ 9 ]. Despite the advantages of serologic testing, there are some scenarios in which direct detection of the BLV genomic fragment was important to improve BLV detection. The most frequent cases is the screening of calves with maternal antibodies, acute infection, animals without persistent antibody response and animal subproducts (i.e., semen). In this regard, nucleic acid amplification tests such as real-time quantitative PCR (qPCR) allows for a rapid and highly sensitive detection of BLV proviral DNA (BLV DNA) that can be used to test infected and asymptomatic animals, before the elicitation of anti-BLV specific antibodies and when proviral load (PVL) are still low [ 10 ]. Furthermore, qPCR assays can serve as confirmatory tests for the clarification of inconclusive and discordant serological test results usually associated with these cases [ 11 ]. For these reasons, the inclusion of qPCR in combination with other screening tests might increase control programs efficiency. Additionally, qPCR allows the estimation of BLV PVL which is important for studying the dynamics of BLV infection (i.e., basic research). Further, considering that BLV PVL correlates with the risk of BLV transmission, this feature of qPCR can be exploited for developing rational segregation programs [ 12 , 13 ]. The results of Kobayashi et al. suggest that high PVL is also a significant risk factor for progression to EBL and should therefore be used as a parameter to identify cattle for culling from the herd well before EBL progression [ 14 ]. Several qPCRs have been developed globally for the quantitation of BLV DNA. Although most assays have been properly validated by each developer, a proper standardization and harmonization of such tests is currently lacking. Considering that standardization and harmonization of qPCR methods and results are essential for comparisons of data from BLV laboratories around the world, this could directly impact international surveillance programs and collaborative research. We built a global collaborative network of BLV reference laboratories to evaluate the interlaboratory variability of different qPCRs and sponsored a harmonization of assays to hopefully impact international surveillance programs and research going forward.

In 2018 we conducted the first global trial of this kind to assess the interlaboratory variability of six qPCRs for the detection of BLV DNA [ 15 ]. Since this complex process is a continuous rather than a one-time effort, we now started a second study of this type. In this follow up study, we built a more comprehensive sample panel, accounting for a broader geographical diversification. Additionally, we increased the number of participants to ten collaborating laboratories plus one WOAH reference lab and tested novel methodologies including digital PCR (ddPCR) and FRET-qPCR. Finally, we established the next steps towards the international standardization of molecular assays for the detection of BLV DNA.

Materials and methods

Participants.

The eleven laboratories that took part in the study were:(i) the Auburn University College of Veterinary Medicine (Auburn, Alabama, United States): (ii) AntelBio, a division of CentralStar Cooperative (Michigan, United States); (iii) Laboratórios Federais de Defesa Agropecuária de Minas Gerais (LFDA-MG, Pedro Leopoldo, Brasil); (iv) Centro de Investigación Veterinaria de Tandil (CIVETAN, Buenos Aires, Argentina); (v) the Faculty of Agriculture Iwate University (Iwate, Japan); (vi) Universidad de la República de Uruguay (UdelaR, Montevideo, Uruguay); (vii) the Croatian Veterinary Institute (Zagreb, Croatia); (viii) Instituto Nacional de Tecnología Agropecuaria (INTA, Buenos Aires, Argentina); (ix) Laboratorio Central de Veterinaria (LCV, Madrid, Spain); (x) the National Veterinary Research Institute (NVRI, Puławy, Poland) and (xi) the French Agency for Food, Environmental and Occupational Health and Safety (Anses, Niort, France). All European laboratories participating in this study are acting as national reference laboratories for EBL, NVRI acts as WOAH reference laboratory for EBL, while the remaining laboratories are nationally renowned entities for BLV diagnostics. The eleven participating methods are referred to below as qPCR1 – qPCR5, ddPCR6, qPCR7 – qPCR11, respectively.

Sample collection and DNA extraction

A total of 42 DNA samples obtained from blood of naturally BLV-infected dairy cattle from Poland, Moldova, Pakistan, Ukraine, Canada and United States were used for this study. Thirty-six of them were archival DNA samples obtained between 2012–2018 as described in our previous studies on samples from Poland ( n  = 21) [ 16 , 17 ], Moldova ( n  = 4) [ 18 ], Pakistan ( n  = 5) [ 19 ] and Ukraine ( n  = 6) [ 15 , 20 ]. Between 2020–2021 6 peripheral blood and serum samples from naturally BLV-infected cattle were obtained from three dairy farms of Alberta, Canada and two dairy farms of Michigan, US. Serological testing and sample processing were conducted by the laboratories from which the samples originated. The genomic DNA from Canadian and US samples was extracted from whole blood using a Quick DNA Miniprep Plus kit (Zymo Research) and a DNeasy Blood & Tissue Kit (Qiagen), respectively in University of Calgary and Michigan State University and sent to the NVRI in the form of DNA solutions. Additionally, one plasmid DNA sample (pBLV344) was kindly supplied by Luc Willems (University of Liège, Belgium) and DNA extracted from FLK-BLV cells were included as positive controls. Finally, DNA extracted from PBL of a serologically negative cattle was included as negative control. At the NVRI, the DNA concentration in all samples was estimated by spectrophotometry using a NanoPhotometer (Implen). Each sample was divided into eleven identical aliquots containing between 800 and 4,000 ng of lyophilised genomic DNA. Eleven identical sets of these samples were lyophilized (Alpha 1–4 LSC basic, Martin Christ Gefriertrocknungsanlagen GmbH) and distributed to participating laboratories. At the NVRI, all samples were coded (identification [ 21 ] run numbers 1 to 44) to perform a blinded testing. The samples, together with instructions for their preparation (Additional file 1), were shipped by air at room temperature (RT).

Examination of DNA quality/stability

Since different extraction methods and lyophilization process were employed for the preparation of the DNA samples, it was necessary to test the quality of the DNA at the NVRI laboratory. For that purpose, one complete set of samples ( n  = 44) was tested by Fragment Analyzer (Agilent Technologies), before and after freeze-drying, to assess DNA quality by calculating a Genomic Quality Number (GQN) for every sample. Low GQN value (< 2.5) represents sheared or degraded DNA. A high GQN (> 9) represents undegraded DNA. In addition, quality of DNA was assessed by determination of copy number of the histone H3 family 3A ( H3F3A ) housekeeping gene using quantitative real-time PCR (qPCR) [ 22 ]. The qPCR results were expressed as the number of H3F3A gene copies per 300 ng of DNA in each sample. Grubbs´ test was performed to determine outliers. To test the stability of DNA, samples were stored for 20 days at RT (10 days) and at + 4 °C (10 days) and were retested by Fragment Analyzer and qPCR 21 days later. A Mann–Whitney U-test was used to compare the median values between fresh and stored samples (time 0 and time 1), respectively.

Description of BLV qPCR protocols used by participating laboratories

All participating laboratories performed their qPCR or ddPCR using a variety of different equipment, reagents, and reaction conditions, which had been set up, validated, and evaluated previously and are currently used as working protocols. The specific features of each of these protocols are described below and summarized in Table  1 .

All laboratories applied standard procedures for avoiding false-positive results indicative of DNA contamination, such as the use of separate rooms for preparing reaction mixtures, adding the samples, and performing the amplification reaction. One of the ten BLV qPCRs used LTR region and the remaining nine qPCRs used the pol gene as the target sequence for amplification, while the ddPCR amplified the env gene.

Method qPCR1

The BLV qPCR amplifying a 187-bp pol gene was performed according to a previously published methods [ 23 , 24 ]. A real-time fluorescence resonance energy transfer (FRET) PCR was carried out in a 20-μl PCR mixture containing 10 μl handmade reaction master mix and 10 μl genomic DNA. The PCR buffer was 4.5 mM MgCl2, 50 mM KCl, 20 mM Tris–HCl, pH 8.4, supplemented with 0.05% each Tween20 and Non-idet P-40, and 0.03% acetylated BSA (Roche Applied Science). For each 20 μl total reaction volume, the nucleotides were used at 0.2 mM each and 1.5 U Platinum Taq DNA polymerase (Invitrogen, Carlsbad, CA, USA) was used. Primers were used at 1 μM, LCRed640 probe was used at 0.2 μM, and 6-FAM probe was used at 0.1 μM. Amplification was performed in the Roche Light Cycler 480 II (Roche Molecular Biochemicals) using 10 min denaturation step at 95 °C, followed by 18 high-stringency step-down thermal cycles and 30 low-stringency fluorescence acquisition cycles.

A plasmid containing the BLV-PCR amplicon region was diluted ten-fold from 1 × 10 5 copies to 10 copies per 10 µl and was used as a standard to measure the BLV copy numbers.

Method qPCR2

A BLV proviral load qPCR assay developed by AntelBio, a division of CentralStar Cooperative Inc. on Applied Biosystems 7500 Real-Time PCR system [ 25 , 33 ]. This multiplex assay amplifies the BLV pol gene along with the bovine β-actin gene and an internal amplification control, “Spike”. A quantitative TaqMan PCR was carried out in a 25-μl PCR mixture containing 12.5 µl of 2X InhibiTaq Multiplex HotStart qPCR MasterMix (Empirical Bioscience), 16 nM each BLV primer, 16 nM each β-actin primer, 8 nM each spike primer, 8 nM BLV FAM-probe, 8 nM β-actin Cy5-probe, 4 nM spike JOE-probe, 1 µl of an internal spike-in control (10,000 copies per µl), 7.25 µl of nuclease-free water and 4 µl of DNA sample for each qPCR reaction. The thermal PCR protocol was as follows: 95 °C for 10 min, 40 × (95 °C for 15 s, 60 °C for 1 min). Copy numbers of both the BLV pol gene and bovine β-Actin were derived using a plasmid containing target sequences, quantified by ddPCR, diluted 1 × 10 6 copies per µl to 10 copies per µl in tenfold dilutions. DNA concentrations of each sample were measured using a Qubit 4 Fluorometer and used in combination with the qPCR copy numbers to calculate BLV copies per 100 ng.

Method qPCR3

The qPCR assays for the BLV LTR gene were performed according to a previously published methods [ 26 ]. Genomic DNA was amplified by TaqMan PCR with 10 μl of GoTaq Probe qPCR Master Mix × 2 (Promega), 0.6 pmol/μl each primer, 0.3 pmol/µl double-quenched probe and 100 ng genomic DNA. Amplification was performed in the CFX96 cycler (BioRad) according to the protocol: 5 min denaturation at 95°C followed by 45 cycles (60 s at 94°C and 60 s at 60°C). The efficiency of each reaction was calculated from the serial dilution of DNA extracted from BLV persistently infected fetal lamb kidney (FLK) cells, starting at a concentration of 100 ng/µl [ 21 ]. The detection limit was tested using a plasmid containing the target of the qPCRs, starting at 10 3 ng/µl.

Method qPCR4

The quantitative real-time PCR was done with the primers for the BLV pol gene as previously described [ 34 ]. The qPCR reaction mix contained 1 × PCR Master Mix with SYBR Green (FastStart Universal SYBR Green Master Rox, Roche), 0.3 μM each primer and 30 ng of extracted genomic DNA. Amplification was performed in QuantStudio 5 Real-Time PCR System (Applied Biosystems) under the following conditions: 2 min at 50 °C, 10 min at 95 °C, 40 cycles of 15 s at 95 °C and 60 s at 60 °C. A standard curve of six tenfold serial dilutions of pBLV, containing 1 × 10 6 to 10 BLV copies, was built and run 3 times for validation of the method. The number of provirus copies per reaction (100 ng) was calculated.

Method qPCR5

BLV PVLs were determined by using qPCR kit, RC202 (Takara Bio, Shiga, Japan) [ 28 , 35 ]. This qPCR assay amplifies the BLV pol gene along with the bovine RPPH1 gene as an internal control. Briefly, 100 ng genomic DNA was amplified by TaqMan PCR with four primers for pol gene and RPPH1 gene according to the manufacturer’s instructions: 30 s denaturation at 95 °C followed by 45 cycles (5 s at 95 °C and 30 s at 60 °C). The qPCR was performed on a QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific K.K., Tokyo, Japan). Standard curve was generated by creating tenfold serial dilutions of the standard plasmid included in the kit. The standards for calibration ranged from 1 to 10 6 copies/reaction and were run in duplicate. The number of provirus copies per 100 ng was calculated.

Method ddPCR6

The digital droplet PCR (ddPCR) assay for the env gene of the BLV was performed using the protocol previously described by [ 28 , 29 ]. An absolute quantification by TaqMan ddPCR was performed in a typical 20-μl assay, 1 μl of DNA sample was mixed with 1 μl of each primer (10 μM), 0.5 μl of probe (10 μM), and 2 × Supermix emulsified with oil (Bio-Rad). The droplets were transferred to a 96-well plate (Eppendorf). The PCR assay was performed in a thermocycler (C1000 touch cycler; Bio-Rad) with the following parameters: initial denaturation of 10 min at 95 °C, then 40 cycles of 30 s at 94 °C, and 1 min at 58 °C, with final deactivation of the enzyme for 10 min at 98 °C. The presence of fluorescent droplets determined the number of resulting positive events that were analyzed in the software (QuantaSoft v.1.7.4; Bio-Rad), using dot charts. The number of provirus copies per 100 ng were calculated. Each sample was run in duplicate, and results were averaged.

Method qPCR7

This qPCR method for the BLV pol gene is a modified option of widely available quantitative TaqMan qPCR described by Rola-Łuszczak et al. [ 11 ], using the same primers and standards. A quantitative TaqMan PCR was performed in a 20 μl PCR mix containing 10 μl of 2 × ORA qPCR Probe ROX L Mix (highQu, Kraichtal, Germany), 2 μl primer/probe mix (final concentration 400 nM of each of the primers, 200 nM of BLV probe), and 3 μl extracted genomic DNA. Amplification was performed in the Rotor-Gene Q system (Qiagen) with an initial denaturation step and polymerase activation at 95 °C for 3 min, followed by 45 cycles of 95 °C for 5 s and 60 °C for 30 s. As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were made from 1 × 10 10 copies to 1 × 10 1 copies per reaction and used to generate the standard curve and estimate BLV copy number per 100 ng.

Method qPCR8

Proviral load quantification was assessed by SYBR Green real-time quantitative PCR (qPCR) using the pol gene as the target sequence [ 36 ]. Briefly, 12-μl PCR mixture contained Fast Start Universal SYBR Green Master Mix (Roche), 800 nM each BLV pol primers and 1 µl DNA as template. The reactions were incubated at 50 °C for 2 min and 95 °C for 10 min, followed by 40 cycles at 95 °C for 15 s, 55 °C for 15 s and 60 °C for 1 min. All samples were tested in duplicate on a StepOne Plus machine (Applied Biosystems). A positive and negative control, as well as a no-template control, were included in each plate. After the reaction was completed, the specificity of the amplicons was checked by analyzing the individual dissociation curves. As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were made from 1 × 10 6 to 10 copies per µl and used to generate the standard curve and estimate BLV copy number per 100 ng.

Method qPCR9

This qPCR method is a modified option of widely available quantitative TaqMan qPCR described by Rola-Łuszczak et al. [ 11 ], using the same primers and standards. The detection of BLV genome was combined with an endogenous control system (Toussaint 2007) in a duplex assay. Briefly, 20-µl qPCR reaction contained AhPath ID™ One-Step RT-PCR Reagents with ROX (Applied Biosystems, CA, USA) – 10 µl of 2 × RT-PCR buffer and 0.8 µl of 25 × RT-PCR enzyme mix, 400 nM each primer for pol gene, 100 nM BLV specific probe, 40 nM each β-actin primer, 40 nM β-actin specific probe and 2 µl DNA sample. All samples were tested in ABI7500 Real-Time PCR System (Applied Biosystems) according to the following protocol: 10 min at 48 °C (reverse transcription), 10 min at 95 °C (inactivation reverse transcriptase / activation Taq polymerase) followed by 45 cycles (15 s at 95 °C and 60 s at 60 °C). As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were made from 1 × 10 4 copies to 0.1 copies per μl and used to generate the standard curve and estimate BLV copy number per 100 ng.

Method qPCR10

The BLV qPCR was performed as published previously [ 11 ]. A quantitative TaqMan PCR was carried out in a 25-μl PCR mixture containing 12.5 μl of 2 × QuantiTect Multiplex PCR NoROX master mix (Qiagen), 0.4 μM each primer, 0.2 μM specific BLV probe, and 500 ng of extracted genomic DNA. Amplification was performed in the Rotor-Gene Q system (Qiagen) using an initial denaturation step and polymerase activation at 95 °C for 15 min, followed by 50 cycles of 94 °C for 60 s and 60 °C for 60 s. All samples were amplified in duplicate. As a standard, the pBLV1 plasmid (NVRI, Pulawy, PL), containing a 120-bp BLV pol fragment, was used. Tenfold dilutions of this standard were made from 1 × 10 6 copies per μl to 100 copies per μl and were used to estimate the BLV copy numbers per 100 ng.

Method qPCR11

This qPCR method for the BLV pol gene is a modified option of widely available quantitative TaqMan qPCR described by Rola-Łuszczak et al. [ 11 ], using the same primers and standards. The reaction mixture contained 400 nM of each primer, 200 nM of probe, 10 µl of 2 × SsoFast probes supermix (Bio-Rad), 5 µl of DNA sample and H 2 O up to 20 µl of the final volume. PCR assays were carried out on a CFX96 thermocycler (Bio-Rad) under the following amplification profile: 98 °C for 3 min, followed by 45 cycles of 95 °C for 5 s and 60 °C for 30 s. As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were used to generate the standard curve and estimate BLV copy number per 100 ng.

Analysis of BLV pol, env and LTR sequences targeted by particular qPCR/ddPCR assays

In order to assess full-length pol , env and LTR sequence variability among BLV genotypes, all BLV sequences ( n  = 2191) available on 30 September 2023 in GenBank ( https://www.ncbi.nlm.nih.gov/GenBank/ ) repository were retrieved. From the collected sequences, 100 pol , env and LTR sequences, which were characterized by the highest level of sequence variability and divergence, were selected for the further analysis. A pol -based, env -based and LTR-based maximum likelihood (ML) phylogenetic trees (see Additional file 6) was constructed to assign genotypes to the unassigned BLV genomes [ 37 , 38 , 39 ]. For all genes and LTR region the Tamura-Nei model and Bootstrap replications (1,000) were applied. In this analysis, pol sequences were assigned to 7 BLV genotypes (G1, G2, G3, G4, G6, G9, and G10), while env and LTR sequences were assigned to 10 BLV genotypes (G1, G2, G3, G4, G5, G6, G7, G8, G9, and G10). Phylogeny of the same isolates assigned to particular genotypes by ML method was confirmed by Mr. Bayes analysis [ 40 , 41 , 42 ] (data not shown). From this analysis, a total of 100 full-length pol, env and LTR sequences were used for multiple-sequence alignment (MSA) using ClustalW algorithm, implemented in MEGA X. For all sequences, nucleotide diversity (π), defined as the average number of nucleotide differences per site between two DNA sequences in all possible pairs in the sample population, was estimated using MEGA X. To measure the relative variation in different positions of aligned genes and LTR region the Shannon’s entropy (a quantitative measure of diversity in the alignment, where H = 0 indicates complete conservation) was estimated using BioEdit v. 7.2.5 software 64. The statistical analyses were performed using DATAtab e.U. Graz, Austria and GraphPad Software by Dotmatics, Boston.

Examination of the quality and stability of DNA samples

To test the quality of DNA samples, the H3F3A copy number of each individual sample was assessed by qPCR at the NVRI. Copy numbers were normalized to DNA mass input and results were expressed as copy numbers per 300 ng of total DNA. The respective values were tested by Grubbs' test. The results for 43 DNA samples (sample ID: 42 with BLV genome plasmid was excluded) followed a normal distribution (Shapiro–Wilk 0.97; P  = 0.286), with a mean value of 35,626 copies (95% confidence interval [ 43 ] 33,843 to 37,408 copies), a minimum value of 19,848 copies and a maximum value of 46,951 copies (see Additional file 2). Despite a low value for sample ID: 40 no significant outlier was detected in the dataset ( P  > 0.05). Therefore, it can be assumed that the DNA quality was acceptable for all samples present in the panel. Next, DNA stability was assessed by retesting the H3F3A copy numbers in each sample ( n  = 43) after a combined storage consisting in 10 days at RT and 10 days at + 4°C. A Mann–Whitney U-test was used to compare the median values between fresh and stored samples (time 0 and time 1, respectively), and no significant difference was observed at the 5% level ( P  = 0.187) (Fig.  1 A).

Assessment of the stability of DNA samples. A Shown are copy numbers of the H3F3A housekeeping gene in 43 DNA samples that were stored in 10 days at RT and 10 days at + 4°C and tested twice with a 21-day interval. A Mann–Whitney U-test was used to compare the median values between two groups ( P  = 0.187); B Shown are GQN values ( n  = 43) tested twice with a 21-day interval: `before freeze-drying` and `after freeze-drying`. A Mann–Whitney U-test results between two groups ( P  = 0.236)

In addition, the quality of DNA samples after lyophilization was analyzed. DNA from individual samples ( n  = 43) was assessed with the genomic DNA quality number on the Fragment Analyzer system. The GQN from all lyophilized samples ranged from 4.0 to 9.7—that represented undegraded DNA. There was no significant difference in GQN values between `before freeze-drying` and `after freeze-drying` groups with respect to the corresponding DNA samples ( P  = 0.236) (Fig.  1 B). Altogether, these results suggested that sample storage, lyophilization and shipping has a minimal impact in DNA stability and further testing during the interlaboratory trial.

Detection of BLV proviral DNA by different qPCR assays

A total of 44 DNA samples, including two positive (ID: 42 and 43) and one negative (ID: 32) controls, were blinded and independently tested by eleven laboratories using their own qPCR methods (Table  2 ). All laboratories measured the concentration of DNA in samples (Additional file 3). BLV provirus copy number was normalized to DNA concentration and expressed per 100 ng of genomic DNA for each test.

Except for the positive (pBLV344 and FLK cell line) and the negative controls, all samples had previously shown detectable levels of BLV-specific antibodies (BLV-Abs) by enzyme-linked immunosorbent assays (ELISA). During the current interlaboratory study, both the positive and negative controls were assessed adequately by all eleven PCR tests. Of all 43 positive samples, 43, 35, 37, 36, 40, 32, 40, 42, 42, 42 and 41 samples were detected as positive by the qPCR1, qPCR2, qPCR3, qPCR4, qPCR5, ddPCR6, qPCR7, qPCR8, qPCR9, qPCR10 and qPCR11 methods, respectively. Based on these observations, the most sensitive method was the qPCR1, and the method with the lowest sensitivity was the ddPCR6. Twenty-nine out of 44 samples were identified correctly by all qPCRs. The remaining 15 samples gave discordant results. Comparison of qualitative results (positive versus negative) from all eleven methods revealed 87.33% overall agreement and a kappa value of 0.396 (Cohen's kappa method adapted by Fleiss) [ 44 , 45 ]. The levels of agreement among the results from the eleven methods are represented in Table  3 . The maximum agreement was seen between two methods (qPCR9 and qPCR10 [100% agreement and a Cohen's kappa value of 1.000]) that used similar protocols and targeted the same region of BLV pol .

Analysis of BLV pol, env and LTR sequences targeted by particular PCR assays

Due to differences in performance observed among the pol -based qPCR assays (the qPCR1, qPCR2, qPCR4, qPCR5 and qPCR7- qPCR11 methods), and considering that the env -based ddPCR6 and LTR-based qPCR3 assay showed the lowest sensitivity and the poorest agreement with the other assays, the degree of sequence variability between the pol , env and LTR genes was addressed. From the MSAs for pol , env and LTR, the nucleotide diversity (π) was calculated. The π value for pol gene was lower than that for LTR and env gene (π pol , 0.023 [standard deviation {SD}, 0.018]; π LTR , 0.024 [SD, 0.011]; π env , 0.037 [SD, 0.013]). From this analysis, pol sequences appeared to be less variable than env and LTR sequences. In addition, we performed a Shannon entropy-based per-site variability profile of the pol , env and LTR sequences used in this study (Fig.  2 A-C).

Sequence variability measured as per-site entropy. A Multiple alignment of the pol gene showing the locations of qPCR fragments in regions of the pol gene for the qPCR1 (highlighted in pink), qPCR4 (highlighted in yellow) and for the qPCR7, qPCR8, qPCR9, qPCR10 and qPCR11 assays (highlighted in orange). B Multiple alignment of the env gene targeted by ddPCR6 (highlighted by blue rectangle). C Multiple alignment of the LTR region by qPCR3 (highlighted in mint)

The all-observed entropy plots were homogeneous along the whole sequences. Considering the three regions of pol gene, the highest entropy (4.67) occurred in the region targeted by the qPCR1 primers, whereas the entropy for qPCR7—qPCR11 and qPCR4 primers were 1.57 and 0.38, respectively. For the LTR region targeted by qPCR3 primers and for env gene targeted by ddPCR6, the total entropy was equal to 4.46 and 7.85, respectively. This analysis showed a marked region of variability for LTR and env fragments. Interestingly, we noted that the qPCR7—qPCR11 targeted the most conserved regions of reverse transcriptase and qPCR4 primers targeted the most-conserved region of virus integrase (Fig.  2 A-C; see also Additional file 7).

Quantitation of BLV proviral DNA by different qPCR/ddPCR assays

To analyze whether the range of copy numbers detected by each qPCR was comparable to those of the others, Kruskal–Wallis one-way analysis of variance (ANOVA) was used. The violin plots were used to visualize the ANOVA results (Fig.  3 A-B).

Comparison of detection of BLV proviral DNA copy numbers by eleven testing methods. Shown is a box plot of data from Kruskal–Wallis ANOVA, a rank test. The DNA copy numbers for 41 samples, determined independently by each of the 11 qPCRs, were used for the variance analysis. In this analysis, the positive controls (sample ID 42 and ID 43) and negative control (sample ID 32) were excluded. A Violin plot for graphical presentation of the ANOVA of proviral copy number values. B Violin plot for ANOVA analysis of variance, copy number values are presented on a logarithmic scale (Log1.2) for better illustration of copy number differences between PCR methods

The grouping variable revealed significant differences among the distributions of proviral DNA copy numbers with the various qPCRs ( P  < 0.001). These results showed that the abilities of qPCRs/ddPCR to determine the proviral DNA copy number differed. A Dunn-Bonferroni test was used to compare the groups in pairs to find out which was significantly different. The Dunn-Bonferroni test revealed that the pairwise group comparisons of qPCR2—qPCR4, qPCR3—ddPCR6, qPCR4—qPCR5, qPCR4—ddPCR6, qPCR4—qPCR9, qPCR4—qPCR10, qPCR5—qPCR11, ddPCR6—qPCR11 and qPCR9—qPCR11 have an adjusted P value less than 0.05 and thus, it can be assumed that these groups were significantly different in each pair (see Additional file 4). The Pareto chart was used to show the average copy number values of all methods in descending order. These Pareto charts were prepared based on 80–20 rule, which states that 80% of effects come from 20% of the various causes [ 46 ]. The methods that generated the highest copy numbers was qPCR3 and qPCR4, on the other hand the lowest copy numbers and/or highest negative results were generated by ddPCR6 (Fig.  4 ).

A Pareto chart with the proviral BLV copy mean values for eleven PCR assay arranged in descending order. Pareto charts was prepared based on 80–20 rule, which states that 80% of effects come from 20% of the various causes

The correlations between copy numbers detected by different qPCRs and ddPCR assays were calculated. The Kendall's Tau correlation coefficient measured between each pair of the assays was shown in the Additional file 5 and in Fig.  5 as a correlation heatmap. The average correlation for all qPCRs and ddPCR assays was strong (Kendall's tau = 0.748; P  < 0.001).

The heatmap of Kendall’s tau correlation coefficients between copy numbers detected by ten qPCRs and one ddPCR. Statistically significant differences in the distribution of copy numbers, a moderate, strong and very strong correlation between particular qPCRs/ddPCR was observed. The strength of the association, for absolute values of r, 0–0.19 is regarded as very weak, 0.2–0.39 as weak, 0.40–0.59 as moderate, 0.6–0.79 as strong and 0.8–1 as very strong correlation

Since the differences between PCR tests may be influenced by the number of BLV proviral copies present in each sample, we compared the average number of BLV copies between a group of genomic DNA samples that gave concordant results (group I [ n  = 28]) and a group that gave discordant results (group II [ n  = 15]). The mean number of copies was 73,907 (minimum, 0; maximum, 4,286,730) in group I, and 3,479 (minimum, 0; maximum, 218,583) in group II, and this difference was statistically significant ( P  < 0.001 by a Mann–Whitney U- test) (Fig.  6 ).

Impact of BLV proviral copy numbers on the level of agreement. Violin plot for graphical presentation of Mann–Whitney U test. The test was performed to compare BLV provirus copy number in two groups of samples: 28 samples with fully concordant results from all eleven qPCR/ddPCR assays (left) and 15 samples with discordant results from different qPCR/ddPCR assays (right) ( P  < 0.001). Sample ID 42 was excluded from the statistical analysis

The results show that the concordant results group had considerably higher copy numbers (median, 5,549.0) than the discordant results group (median, 6.3).

BLV control and eradication programs consist of correct identification and subsequent segregation/elimination of BLV-infected animals [ 47 ]. Detection of BLV- infected cows by testing for BLV-specific antibodies in serum by agar gel immunodiffusion and ELISA is the key step and standard to be implemented of EBL eradication programs according to WOAH ( https://www.woah.org/en/disease/enzootic-bovine-leukosis/) [ 9 ]. Despite the low cost and high throughput of serological tests, there are several scenarios where highly specific and sensitive molecular assays for the detection of BLV DNA might improve detection and program efficiency.

In this perspective, qPCR assays can detect small quantities of proviral DNA during acute infection, in which animals show very low levels of anti-BLV antibodies [ 43 , 48 , 49 , 50 ]. qPCR methods can also work as confirmatory tests to clarify ambiguous and inconsistent serological test results [ 11 ]. Such quantitative features of qPCRs are crucial when eradication programs progress and prevalence decreases. Moreover, qPCR allows not only the detection of BLV infection but also estimation of the BLV PVL, which directly correlates with the risk of disease transmission [ 51 , 52 ]. This feature of qPCR allows for a rational segregation of animals based on the stratified risk of transmission. These considerations allow for greater precision in the management of BLV within large herds with a high prevalence of BLV ELISA-positive animals to effectively reduce herd prevalence [ 13 , 53 ]. BLV is a global burden and the lack of technical standardization of molecular detection systems remains a huge obstacle to compare surveillance data globally based on the first interlaboratory trial performed in 2018 [ 15 ]. In the 2018 study we observed an adjusted level of agreement of 70% comparing qualitative qPCR results; however, inconsistencies amongst methods were larger when low number of copies of BLV DNA were compared. Samples with low copies of BLV DNA (< 20 copies per 100 ng) accounted for the higher variability and discrepancies amongst tests. We concluded from the first interlaboratory trial that standardizing protocols to improve sensitivity of assays with lower detection rates was necessary.

In this follow up study, we re-tested the TaqMan BLV qPCR developed and validated by NVRI (acting as reference WOAH laboratory) and the one adapted from this original protocol to be used with SYBR Green dye, allowing a significant reduction in costs [ 11 ]. Another 3 laboratories also performed NVRI´s qPCR with slight modifications (i.e., Spain performed a multiplex assay for internal normalization). The remaining 6 labs introduced novel methodologies to the trial including one ddPCR (UY).

To compare different qPCR methods, a more comprehensive sample panel, accounting for a more geographical diversification was used in this trial. The amounts of BLV DNA in these samples were representative of the different BLV proviral loads found in field samples (from 1 to > 10,000 copies of BLV proviral DNA). Of note, 34% of reference samples had less than 100 copies of BLV DNA per 100 ng; samples were lyophilized to grant better preservation and reduced variability during distribution to participants around the globe.

The panel included a single negative control and two positive controls. Diagnostic sensitivity (DxSn) was estimated for each qPCR. Considering the 43 positive samples, the DxSn for the different qPCRs were: qPCR1 = 100%, qPCR2 = 82%, qPCR3 = 86%, qPCR4 = 84%, qPCR5 = 93%, ddPCR6 = 74%, qPCR7 = 93%, qPCR8 = 98%, qPCR9 = 98%, qPCR10 = 98% and qPCR11 = 95%. The most sensitive method was the qPCR1, and the method with the lowest sensitivity was the ddPCR6 method. Twenty-nine out of 44 samples were identified correctly by all qPCRs. The remaining 15 samples gave discordant results. The comparison of qualitative qPCR results among all raters revealed an overall observed agreement of 87%, indicating strong interrater reliability (Cohen´s kappa = 0.396) [ 54 , 55 ].

There are several factors that contribute to variability in qPCR results (i.e., number of copies of target input, sample acquisition, processing, storage and shipping, DNA purification, target selection, assay design, calibrator, data analysis, etc.). For that reason and as expected, the level of agreement among sister qPCRs (qPCR7, qPCR9-11) sharing similar protocols was higher compared to the rest of assays; this was also true for qPCR8 which targets the same region of BLV pol gene (shares same primers) but has a particular set-up to be used with SYBR Green chemistry. Oppositely, lower sensitivity and larger discrepancy against other tests was observed for the ddPCR6 and qPCR2-4.

Based on these observations we investigated which factors might have accounted for larger assessment variability amongst tests. In the first place, we observed that the use of different chemistries was not detrimental for the sensitivity and agreement among tests; similar DxSn and comparable level of agreement were obtained comparing TaqMan (qPCR7, 10, 11) vs SYBR Green (qPCR8) chemistries while targeting identical BLV sequence and using same standards. Also, when a multiplex qPCR (TaqMan) targeting the same BLV sequence and using the same standard was compared to previous ones, agreement was kept high, indicating that the lower sensitivity described for some multiplex qPCRs did not take place in this comparison. The use of an international calibrator and the efficiency estimation (standard curve) might inform variability associated with different chemistries. In contrast, another multiplex assay targeting another region of BLV pol (qPCR2) showed much lower sensitivity and agreement. As qPCR2 is performed as service by private company and oligonucleotide sequences were not available, we were not able to investigate in which proportion each of these two variables contributed to the lower performance of this assay, but we note the addition of 4 µl genomic DNA to this assay that would have an impact the DxSn. In this regard, there is substantial evidence showing that the variability of target sequence among strains from different geographical areas, might affect the sensitivity of BLV qPCRs. Previous studies comparing the pol , gag , tax and env genes reported that the pol gene was the most suitable region to target for diagnostic purposes, since it provided the most-sensitive assays [ 11 , 15 , 56 , 57 , 58 , 59 ]. This might be due in part to higher sequence conservation of pol among strains from different geographical areas. Supporting this observation, it is noticeable how JPN qPCR improved their performance in the current trial, by targeting pol in place of tax , as it did in the previous interlaboratory trial. Since it is a commercial test, we cannot exclude other factors contributing for the performance upgrade observed for this qPCR. In the current study, qPCR3 and ddPCR6 targeting LTR and env sequences, showed lower performances than other assays. Standardization of DNA input into each qPCR would have likely resulted in higher concordance in results. For instance, qPCR1 added 10 µl of genomic DNA per reaction and ddPCR6 added 1 µl of genomic DNA, impacting the resulting sensitivity differences.

Since the sensitivity of each assay and, consequently, the level of agreement among assays might also be influenced by the number of BLV DNA copies present in each sample [ 48 ], we compared the average number of BLV DNA copies between a group of genomic DNA samples that gave concordant results and a group that gave discordant results, and observed that samples that gave discordant results had significantly lower numbers of BLV DNA copies than samples that gave concordant results. Related to this point, the degradation of target DNA during lyophilization, shipment and resuspension, could have been more significant in low-copy compared to high-copy samples. Consequently, the degradation of target DNA in samples with low copies of BLV DNA might have accounted for the greater level of discrepancy within this subset of samples. The rational of adding a large proportion of such samples (34% samples with less than 100 BLV copies per 100 ng of total DNA) was to mimic what is frequently observed in surveillance programs (i.e., hyperacute infection, chronic asymptomatic infection, etc.).

Quantitative methods for the detection of BLV DNA copies are important for segregation programs based on animal level of BLV PVL, as well as for scientific research and the study of BLV dynamics. When the numbers of copies of BLV DNA detected by different assays were compared, in the present study, we observed that although the ability to quantify BLV DNA differed among qPCRs/ddPCR and there were statistically significant differences in the distribution of copy numbers among assays, a strong average correlation was found for the eleven qPCRs/ddPCR. In this regard, the lack of an international calibrator (standard curve) could be a major contributor to the increment of quantitative variation amongst laboratories. For that reason, plasmid pBLV1 containing pol 120 bp sequence was originally constructed for use as standard for quantification and shared with some collaborators (i.e., qPCR7, qPCR8, qPCR 9, qPCR10 and qPCR11). Remarkably, the laboratories used pBLV1 standard in the current trial obtained the most comparable results, indicating that the use of an international standard may have significant impact on the convergence of results; such standard reference material should be prepared under identical conditions. To avoid further variability a detailed protocol for lyophilized DNA sample resuspension, quantitation and template input into each qPCR should be shared with all participants.

Conclusions

BLV DNA was detected with different level of sensitivity in serologically positive samples from different origin and classified into different BLV genotypes. Overall agreement was high; however, we found significant differences in results for the samples with low BLV DNA copy numbers. This second interlaboratory study demonstrated that differences in target sequence, DNA input and calibration curve standards can increase interlaboratory variability considerably. Next steps should focus on (i) standard unification (international gold standard) to estimate individual test efficiency and improve quantitative accuracy amongst tests; (ii) building a new panel of samples with low BLV DNA copy numbers to re-evaluate sensitivity and quantitation of molecular methods. Since no variation was observed in samples from different genotypes, all samples will be collected in Poland to standardize the collection, purification, lyophilization and shipping steps with precise instructions for suspension and constant input volume for the PCR reaction. Finally, we believe that following this standardization approach we will be able to improve overall agreement amongst tests, improving the diagnostic of BLV around the world.

Availability of data and materials

Not applicable.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

One-way analysis of variance

Bovine leukemia virus

BLV-specific antibodies

Digital PCR

Diagnostic sensitivity

Enzootic bovine leukosis

Enzyme-linked immunosorbent assays

Real-time fluorescence resonance energy transfer PCR

Genomic quality number

Histone H3 family 3A housekeeping gene

Maximum likelihood phylogenetic tree

Multiple-sequence alignment

Peripheral blood leukocytes

Phosphate-buffered saline

Proviral load

Quantitative real-time PCR

Room temperature

World Organisation for Animal Health

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Acknowledgements

The authors thank Luc Willems (University of Liège, Belgium) for plasmid DNA sample pBLV344; Marlena Smagacz and Eliza Czarnecka (National Veterinary Research Institute, Poland) for lyophilizing DNA samples and DNA analysis, respectively; Ali Sakhawat (Animal Quarantine Department, Pakistan), Vitaliy Bolotin (National Scientific Center IECVM, Ukraine), Frank van der Meer and Sulav Shrestha (University of Calgary, Canada) for sharing material.

The APC was funded by the National Veterinary Research Institute, Puławy, Poland.

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Department of Biochemistry, National Veterinary Research Institute, Puławy, 24-100, Poland

Aneta Pluta & Jacek Kuźmak

Instituto de Virología E Innovaciones Tecnológicas (IVIT), Centro de Investigaciones en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA) - CONICET, Buenos Aires, Argentina

Juan Pablo Jaworski & Vanesa Ruiz

CentralStar Cooperative, 4200 Forest Rd, Lansing, MI, 48910, USA

Casey Droscha & Sophie VanderWeele

Department of Animal Science, College of Agriculture and Natural Resources, Michigan State University, East Lansing, Michigan, 48824, USA

Tasia M. Taxis

Niort Laboratory, Unit Pathology and Welfare of Ruminants, French Agency for Food, Environmental and Occupational Health and Safety (Anses), Ploufragan-Plouzané, Niort, France

Stephen Valas

Croatian Veterinary Institute, Savska Cesta 143, Zagreb, 10000, Croatia

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Laboratorio Central de Veterinaria (LCV), Ministry of Agriculture, Fisheries and Food, Carretera M-106 (Km 1,4), Madrid, Algete, 28110, Spain

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Department of Veterinary Sciences, Faculty of Agriculture, Iwate University, 3-18-8 Ueda, Morioka, 020-8550, Japan

Kenji Murakami & Kurumi Nakamura

Departamento de Patobiología, Facultad de Veterinaria, Unidad de Microbiología, Universidad de La República, Ruta 8, Km 18, Montevideo, 13000, Uruguay

Rodrigo Puentes & MLaureana De Brun

Laboratorio de Virología, Departamento SAMP, Centro de Investigación Veterinaria de Tandil-CIVETAN (CONICET/UNCPBA/CICPBA), Buenos Aires, Argentina

Marla Eliana Ladera Gómez, Pamela Lendez & Guillermina Dolcini

Laboratório Federal de Defesa Agropecuária de Minas Gerais, Pedro Leopoldo, Brazil

Marcelo Fernandes Camargos & Antônio Fonseca

Department of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, AL, 36849-5519, USA

Subarna Barua & Chengming Wang

Department of Omics Analyses, National Veterinary Research Institute, 24-100, Puławy, Poland

Aneta Pluta & Aleksandra Giza

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Contributions

Proposed the conception and design of the study, A.P.; data curation, A.P., J.P.J., C.D., S.V., D.B., A.S., K.M., R.P., G.D., M.F.C. and CH.W.; investigation, A.P., V.R., S.VW., S.V., A.J., M.J.R., K.N., M.L.B., M.L.G., P.L., A.F., A.G. and S.B., formal analysis, A.P.; statistical analysis, A.P.; database analysis, A.P., visualization of the results, A.P.; resources, A.P., T.M.T. and J.K; writing—original draft preparation, A.P., J.P.J.; writing—review and editing, A.P., J.P.J., C.D., S.VW., T.M.T. and J.K; project administration, A.P. All authors read and approved the submitted version.

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Correspondence to Aneta Pluta .

Ethics declarations

Ethics approval and consent to participate.

The study was approved by the Veterinary Sciences Animal Care Committee No. AC21-0210, Canada; the Institutional Animal Care and Use Committee No. PROTO202000096 from 4/13/2020 to 4/14/2023, Michigan State University, United States and the Ethics Review Board, COMSATS Institute of Information Technology, Islamabad, Pakistan, no. CIIT/Bio/ERB/17/26. Blood samples from Polish, Moldovan and Ukrainian cattle, naturally infected with BLV, were selected from collections at local diagnostic laboratories as part of the Enzootic bovine leukosis (EBL) monitoring program between 2012 and 2018 and sent to the National Veterinary Research Institute (NVRI) in Pulawy for confirmation study. The approval for collection of these samples from ethics committee was not required according to Polish regulation (“Act on the Protection of Animals Used for Scientific or Educational Purposes”, Journal of Laws of 2015). All methods were carried out in accordance with relevant guidelines and regulations. The owners of the cattle herds from which the DNA samples originated, the district veterinarians caring for these farms and the ministries of agriculture were informed and consented to the collection of blood from the animals for scientific purposes and the sending of samples to NVRI.

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The authors declare no competing interests.

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Supplementary Information

12917_2024_4228_moesm1_esm.pdf.

Additional file 1. Copy of the instruction included with the panel of 44 DNA samples sent to participating laboratories for dilution of the lyophilisates

12917_2024_4228_MOESM2_ESM.png

Additional file 2. Detection of the H3F3A gene copy number in 43 DNA samples; no outlier was found for any samples ( P <0.05) (two-sided).

12917_2024_4228_MOESM3_ESM.docx

Additional file 3. Concentration values of 44 DNA samples measured by the 11 participating laboratories (given in ng per µl)

12917_2024_4228_MOESM4_ESM.pdf

Additional file 4. Post hoc - Dunn-Bonferroni-Tests. The Dunn-Bonferroni test revealed that the pairwise group comparisons of qPCR2 - qPCR4, qPCR3 - ddPCR6, qPCR4 - qPCR5, qPCR4 - ddPCR6, qPCR4 - qPCR9, qPCR4 - qPCR10, qPCR5 - qPCR11, ddPCR6 - qPCR11 and qPCR9 - qPCR11 have an adjusted p-value less than 0,05

12917_2024_4228_MOESM5_ESM.docx

Additional file 5. Kendall's Tau correlation coefficient values measured between each pair of assays. The numbers 1 to 11 in the first column and last row of the table indicate the names of the assays qPCR1-qPCR5, ddPCR6, qPCR7-qPCR11 respectively

12917_2024_4228_MOESM6_ESM.png

Additional file 6. Maximum-likelihood phylogenetic analysis of full-length BLV-pol gene sequences representing 7 BLV genotypes (G1, G2, G3, G4, G6, G9, and G10) (A); (B) env-based sequences assigned to 10 BLV genotypes (G1, G2, G3, G4, G5, G6, G7, G8, G9, and G10); (C) LTR-based sequences representing 10 BLV genotypes (G1-G10). For all genes and LTR region the Tamura-Nei model and Bootstrap replications (1,000) were applied in MEGA X

12917_2024_4228_MOESM7_ESM.pdf

Additional file 7. Multiple sequence alignment of reverse transcriptase, integrase, envelope and LTR sequences in the context of the specific primers used by different qPCR assays. (A) Multiple sequence alignment of reverse transcriptase (pol gene) sequences in the context of qPCR7, qPCR8, qPCR9, qPCR10 and qPCR11 assay primers. (B) Multiple sequence alignment of integrase (pol gene) sequences in the context of qPCR4 assay primers. (C) Multiple sequence alignment of env gene sequences in the context of ddPCR6. (D) Sequence alignment of LTR region sequences in the context of qPCR3 method primers

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Pluta, A., Jaworski, J.P., Droscha, C. et al. Inter-laboratory comparison of eleven quantitative or digital PCR assays for detection of proviral bovine leukemia virus in blood samples. BMC Vet Res 20 , 381 (2024). https://doi.org/10.1186/s12917-024-04228-z

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Received : 24 November 2023

Accepted : 09 August 2024

Published : 26 August 2024

DOI : https://doi.org/10.1186/s12917-024-04228-z

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• Bovine leukemia virus ( BLV)
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