A Primer Using SPSS
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Department of economics, bi norwegian business school, oslo, norway.
Part of the book series: Classroom Companion: Business (CCB)
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Front matter, designing the study, research methods and philosophy of science.
Research design, data collection, secondary data and observation, qualitative methods, questionnaire surveys, quantitative data analysis, simple analysis techniques, hypothesis testing, regression analysis, cluster analysis and segmentation, factor analysis, reporting findings, back matter.
This introductory textbook presents research methods and data analysis tools in non-technical language. It explains the research process and the basics of qualitative and quantitative data analysis, including procedures and methods, analysis, interpretation, and applications using hands-on data examples in QDA Miner Lite and IBM SPSS Statistics software. The book is divided into four parts that address study and research design; data collection, qualitative methods and surveys; statistical methods, including hypothesis testing, regression, cluster and factor analysis; and reporting. The intended audience is business and social science students learning scientific research methods, however, given its business context, the book will be equally useful for decision-makers in businesses and organizations.
James E. Sallis
Geir Gripsrud, Ragnhild Silkoset
Ulf Henning Olsson
James Sallis is a Professor at the Department of Business Studies at Uppsala University, Sweden. He teaches marketing, research methods, and statistics in the undergraduate, graduate, and executive education programs. He is statistical advisor for the department's faculty and students and is a frequent guest lecturer at business schools worldwide.
Geir Gripsrud is Professor Emeritus at the Department of Marketing at BI Norwegian Business School in Oslo, Norway, where he has acted as Dean of both Bachelor and Master Programs. An experienced teacher of marketing, marketing research, distribution channels, and international marketing, he is a co-author of widely used Norwegian textbooks on Research Methods as well as on Distribution Channels and Supply Chains.
Ulf H. Olsson is a Professor at the Department of Economics at BI Norwegian Business School in Oslo, Norway. He has held the position as Provost with responsibility for research and academic resources. Working mainly on structural equation modeling, statistical modeling and psychometrics, he has published research articles in leading statistics and psychometric journals and has also authored textbooks on statistics and mathematics.
Ragnhild Silkoset is a Professor of Marketing at BI Norwegian Business School in Oslo, Norway. She has held the position as the Head of the Department of Marketing, as well as Dean for the Executive Programs. Her areas of interest include marketing, marketing research, pricing strategy, network analysis and blockchain technology.
Book Title : Research Methods and Data Analysis for Business Decisions
Book Subtitle : A Primer Using SPSS
Authors : James E. Sallis, Geir Gripsrud, Ulf Henning Olsson, Ragnhild Silkoset
Series Title : Classroom Companion: Business
DOI : https://doi.org/10.1007/978-3-030-84421-9
Publisher : Springer Cham
eBook Packages : Mathematics and Statistics , Mathematics and Statistics (R0)
Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
Hardcover ISBN : 978-3-030-84420-2 Published: 31 October 2021
Softcover ISBN : 978-3-030-84423-3 Published: 01 November 2022
eBook ISBN : 978-3-030-84421-9 Published: 30 October 2021
Series ISSN : 2662-2866
Series E-ISSN : 2662-2874
Edition Number : 1
Number of Pages : XI, 258
Number of Illustrations : 26 b/w illustrations, 112 illustrations in colour
Topics : Statistics for Business, Management, Economics, Finance, Insurance , Management Education , Statistics, general , Statistics for Social Sciences, Humanities, Law , Statistics and Computing/Statistics Programs
Policies and ethics
| DOWNLOAD PDF | Bachelor Level | Business Research Methods MGT-221 | Question Paper | 2077-2020 | BBS 4 Years Program | IV (4th) Year MGMT| Tribhuvan University (TU)
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1. Define research problem. 2. What does management research mean? 3. State any four features of research design.
4. Differentiate between null and alternative hypothesis used in management research.
5. What is thematic analysis?
Attempt any two questions from Q. No. 6 to 8 and Q. No. 9 is compulsory. [2x10=20]
6. Describe the basic principles of research design. Also mention the criterion of good research design.
7. State and explain about research proposal. Discuss the essential steps of proposal writing.
8. Describe the methods of primary data collection. Also write down the sources of secondary data.
9. Write short notes on any ONE: [5]
a. Role of hypothesis in research works.
b. Likert scale of measurement.
Attempt any ONE question.
10. Explain the purposes and types of research report. Also mention the procedures of writing research report.
11. Explain the qualitative and quantitative research. Discuss the various methods of qualitative research.
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Download "business research methods" question paper pdf of bba 4th semester previous years paper for students..
Time Allowed: Three Hours] [Maximum Marks: 80
Note: (1) Section-A is compulsory. Attempt any four questions. Each question carries 2 marks in Section A.
(i) From Sections B and C two questions each are compulsory.
Each question carries 15 marks.
(i) What is it important to define research problem appropriately?
(ii) "For any research to be successful it must be well designed". Justify this statement.
(iii) What is the criteria for evaluating the quality of secondary data? Differentiate between internal and external secondary data.
(iv) "Scaling describes the procedure by which numbers are assigned to various degrees of opinion, attitude and other concepts". Discuss.
(v) Why would a researcher want to use multivariate analysis rather than univariate or bivariate?
(vi) What characteristics must it possess in order to be a good research hypothesis?
II. How does primary data differ from secondary data? Discuss advantages and disadvantages of various methods of primary data collection.
III. What are the major decisions involved in constructing an itemized rating scale? What is a semantic differential scale? For what purpose is this scale used? What are the differences between the staple scale and the semantic deferential? Which scale is more popular?
IV. Explain the difference between collection of data through questionnaires and schedules. Design a Questionnaire to study Customer's Satisfaction towards services provided by State Bank of India. (Make necessary assumptions).
V. Write short notes on :
(a) Exploratory and descriptive research.
(b) Ethics in business research.
(c) Qualitative and quantitative research.
VI. Discuss various steps in the procedure of testing hypothesis.
VII. Explain the characteristics of a good research report. Explain the layout of research report.
VIII. Write short notes on :
(i) Chi-square test
(ii) t-test.
IX. Distinguish between:
(a) Restricted and unrestricted sampling (b) Convenience and purposive sampling (c) Systematic and stratified sampling (d) Cluster and area sampling.
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Numbers, Facts and Trends Shaping Your World
Read our research on:
Full Topic List
Read Our Research On:
Which social media platforms are most common, who uses each social media platform, find out more, social media fact sheet.
Many Americans use social media to connect with one another, engage with news content, share information and entertain themselves. Explore the patterns and trends shaping the social media landscape.
To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.
Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A.
Here are the questions used for this analysis , along with responses, and its methodology .
A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms Twitter and X are both used in this report to refer to the same platform.
YouTube and Facebook are the most-widely used online platforms. About half of U.S. adults say they use Instagram, and smaller shares use sites or apps such as TikTok, LinkedIn, Twitter (X) and BeReal.
Year | YouTube | TikTok | Snapchat | Twitter (X) | BeReal | Nextdoor | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
8/5/2012 | 54% | 9% | 10% | 16% | 13% | |||||||
8/7/2012 | 14% | |||||||||||
12/9/2012 | 11% | 13% | 13% | |||||||||
12/16/2012 | 57% | |||||||||||
5/19/2013 | 15% | |||||||||||
7/14/2013 | 16% | |||||||||||
9/16/2013 | 57% | 14% | 17% | 17% | 14% | |||||||
9/30/2013 | 16% | |||||||||||
1/26/2014 | 16% | |||||||||||
9/21/2014 | 58% | 21% | 22% | 23% | 19% | |||||||
4/12/2015 | 62% | 24% | 26% | 22% | 20% | |||||||
4/4/2016 | 68% | 28% | 26% | 25% | 21% | |||||||
1/10/2018 | 73% | 68% | 35% | 29% | 25% | 22% | 27% | 24% | ||||
2/7/2019 | 73% | 69% | 37% | 28% | 27% | 20% | 24% | 22% | 11% | |||
2/8/2021 | 81% | 69% | 40% | 31% | 21% | 28% | 23% | 25% | 23% | 18% | 13% | |
9/5/2023 | 83% | 68% | 47% | 35% | 33% | 30% | 29% | 27% | 22% | 22% | 3% |
Note: The vertical line indicates a change in mode. Polls from 2012-2021 were conducted via phone. In 2023, the poll was conducted via web and mail. For more details on this shift, please read our Q&A . Refer to the topline for more information on how question wording varied over the years. Pre-2018 data is not available for YouTube, Snapchat or WhatsApp; pre-2019 data is not available for Reddit; pre-2021 data is not available for TikTok; pre-2023 data is not available for BeReal. Respondents who did not give an answer are not shown.
Source: Surveys of U.S. adults conducted 2012-2023.
Usage of the major online platforms varies by factors such as age, gender and level of formal education.
% of U.S. adults who say they ever use __ by …
Ages 18-29 | 30-49 | 50-64 | 65+ | |
---|---|---|---|---|
67 | 75 | 69 | 58 | |
78 | 59 | 35 | 15 | |
32 | 40 | 31 | 12 | |
Twitter (X) | 42 | 27 | 17 | 6 |
45 | 40 | 33 | 21 | |
Snapchat | 65 | 30 | 13 | 4 |
YouTube | 93 | 92 | 83 | 60 |
32 | 38 | 29 | 16 | |
44 | 31 | 11 | 3 | |
TikTok | 62 | 39 | 24 | 10 |
BeReal | 12 | 3 | 1 | <1 |
Men | Women | |
---|---|---|
59 | 76 | |
39 | 54 | |
31 | 29 | |
Twitter (X) | 26 | 19 |
19 | 50 | |
Snapchat | 21 | 32 |
YouTube | 82 | 83 |
27 | 31 | |
27 | 17 | |
TikTok | 25 | 40 |
BeReal | 2 | 5 |
White | Black | Hispanic | Asian* | |
---|---|---|---|---|
69 | 64 | 66 | 67 | |
43 | 46 | 58 | 57 | |
30 | 29 | 23 | 45 | |
Twitter (X) | 20 | 23 | 25 | 37 |
36 | 28 | 32 | 30 | |
Snapchat | 25 | 25 | 35 | 25 |
YouTube | 81 | 82 | 86 | 93 |
20 | 31 | 54 | 51 | |
21 | 14 | 23 | 36 | |
TikTok | 28 | 39 | 49 | 29 |
BeReal | 3 | 1 | 4 | 9 |
Less than $30,000 | $30,000- $69,999 | $70,000- $99,999 | $100,000+ | |
---|---|---|---|---|
63 | 70 | 74 | 68 | |
37 | 46 | 49 | 54 | |
13 | 19 | 34 | 53 | |
Twitter (X) | 18 | 21 | 20 | 29 |
27 | 34 | 35 | 41 | |
Snapchat | 27 | 30 | 26 | 25 |
YouTube | 73 | 83 | 86 | 89 |
26 | 26 | 33 | 34 | |
12 | 23 | 22 | 30 | |
TikTok | 36 | 37 | 34 | 27 |
BeReal | 3 | 3 | 3 | 5 |
High school or less | Some college | College graduate+ | |
---|---|---|---|
63 | 71 | 70 | |
37 | 50 | 55 | |
10 | 28 | 53 | |
Twitter (X) | 15 | 24 | 29 |
26 | 42 | 38 | |
Snapchat | 26 | 32 | 23 |
YouTube | 74 | 85 | 89 |
25 | 23 | 39 | |
14 | 23 | 30 | |
TikTok | 35 | 38 | 26 |
BeReal | 3 | 4 | 4 |
Urban | Suburban | Rural | |
---|---|---|---|
66 | 68 | 70 | |
53 | 49 | 38 | |
31 | 36 | 18 | |
Twitter (X) | 25 | 26 | 13 |
31 | 36 | 36 | |
Snapchat | 29 | 26 | 27 |
YouTube | 85 | 85 | 77 |
38 | 30 | 20 | |
29 | 24 | 14 | |
TikTok | 36 | 31 | 33 |
BeReal | 4 | 4 | 2 |
Rep/Lean Rep | Dem/Lean Dem | |
---|---|---|
70 | 67 | |
43 | 53 | |
29 | 34 | |
Twitter (X) | 20 | 26 |
35 | 35 | |
Snapchat | 27 | 28 |
YouTube | 82 | 84 |
25 | 33 | |
20 | 25 | |
TikTok | 30 | 36 |
BeReal | 4 | 4 |
This fact sheet was compiled by Research Assistant Olivia Sidoti , with help from Research Analyst Risa Gelles-Watnick , Research Analyst Michelle Faverio , Digital Producer Sara Atske , Associate Information Graphics Designer Kaitlyn Radde and Temporary Researcher Eugenie Park .
Follow these links for more in-depth analysis of the impact of social media on American life.
Find more reports and blog posts related to internet and technology .
1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 | Media Inquiries
ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .
© 2024 Pew Research Center
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Title: lora: low-rank adaptation of large language models.
Abstract: An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency. We also provide an empirical investigation into rank-deficiency in language model adaptation, which sheds light on the efficacy of LoRA. We release a package that facilitates the integration of LoRA with PyTorch models and provide our implementations and model checkpoints for RoBERTa, DeBERTa, and GPT-2 at this https URL .
Comments: | Draft V2 includes better baselines, experiments on GLUE, and more on adapter latency |
Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
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This introductory textbook presents research methods and data analysis tools in non-technical language. It explains the research process and the basics of qualitative and quantitative data analysis, including procedures and methods, analysis, interpretation, and applications using hands-on data examples in QDA Miner Lite and IBM SPSS Statistics software.
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LoRA: Low-Rank Adaptation of Large Language Models. An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible.
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