data analysis social research methods

Social Data Analysis

Mikaila Mariel Lemonik Arthur, Rhode Island College

Roger Clark, Rhode Island College

Copyright Year: 2021

Last Update: 2023

Publisher: Rhode Island College Digital Publishing

Language: English

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Reviewed by Alice Cheng, Associate Professor, North Carolina State University on 12/19/23

Social Data Analysis: A Comprehensive Guide" truly lives up to its title by offering a comprehensive exploration of both quantitative and qualitative data analysis in the realm of social research. The book provides an in-depth understanding of the... read more

Comprehensiveness rating: 4 see less

Social Data Analysis: A Comprehensive Guide" truly lives up to its title by offering a comprehensive exploration of both quantitative and qualitative data analysis in the realm of social research. The book provides an in-depth understanding of the subject matter, making it a valuable resource for readers seeking a thorough grasp of social data analysis.

The comprehensiveness of the book is evident in several key aspects:

Coverage of Quantitative and Qualitative Methods:

The book effectively covers both quantitative and qualitative data analysis, acknowledging the importance of a balanced approach in social research. Readers benefit from a holistic understanding of various analytical methods, allowing them to choose the most suitable approach for their research questions. Focus on SPSS for Quantitative Analysis:

The dedicated section on quantitative data analysis with SPSS demonstrates the book's commitment to providing practical guidance. Readers are taken through the nuances of using SPSS, from basic functions to more advanced analysis, enhancing their proficiency in a widely used statistical software. Real-World Application Using GSS Data:

The integration of data from the 2021 General Social Survey (GSS) and the modified GSS Codebook adds a practical dimension to the book. Readers have the opportunity to apply their learning to real-world scenarios, fostering a deeper understanding of social data analysis in action. Consideration of Ethical Practices:

The book's mention of survey weights and their exclusion from the learning dataset reflects a commitment to ethical data analysis practices. This attention to ethical considerations enhances the comprehensiveness of the book by addressing important aspects of responsible research. Supplementary Resources and Glossary:

The inclusion of a glossary ensures that readers, especially those new to the field, can easily grasp the terminology used. The availability of supplementary resources, such as a modified GSS Codebook, further supports readers in applying their knowledge beyond theoretical discussions. Recognition of Alternative Tools:

Acknowledging the existence of alternative tools, such as R, demonstrates the book's awareness of the diversity in data analysis approaches. While focusing on SPSS, the book encourages readers to explore other options, contributing to a more nuanced and well-rounded education in social data analysis. Overall, the book's comprehensiveness lies not only in its coverage of various data analysis methods but also in its commitment to providing practical, ethical, and diverse perspectives on social data analysis. It serves as an inclusive and accessible guide for readers at different levels of expertise.

Content Accuracy rating: 4

"Social Data Analysis: A Comprehensive Guide" maintains a commendable level of accuracy throughout its content. The authors demonstrate a meticulous approach to presenting information, ensuring that concepts are explained with precision and clarity. The accuracy is particularly notable in the sections covering quantitative data analysis with SPSS, where step-by-step instructions are provided for readers to follow, minimizing the risk of misinterpretation.

The use of real-world examples from the 2021 General Social Survey enhances the book's accuracy by grounding theoretical discussions in practical applications. The modified GSS Codebook is a thoughtful addition, contributing to the accuracy of the learning experience by providing a clear reference for variables used in the examples.

The authors' acknowledgment of the limitation regarding survey weights in the learning dataset reflects a commitment to transparency and ethical research practices. While the book focuses on a specific statistical software (SPSS), it accurately recognizes alternative tools like R, allowing readers to make informed decisions based on their preferences and requirements.

The glossary aids in maintaining accuracy by providing clear definitions of key terms, ensuring that readers have a precise understanding of the terminology used. Additionally, the reference to external resources, such as IBM's list of resellers and related guides from Kent State, contributes to the accuracy of the book by directing readers to authoritative sources for further information.

In conclusion, "Social Data Analysis: A Comprehensive Guide" upholds a high level of accuracy, presenting information in a manner that is both reliable and accessible. The book's attention to detail, reliance on real-world examples, and commitment to ethical considerations collectively contribute to its overall accuracy as a valuable resource for those engaging in social data analysis.

Relevance/Longevity rating: 4

"Social Data Analysis: A Comprehensive Guide" stands out for its relevance in the field of social research and data analysis. Several key aspects contribute to the book's contemporary and practical relevance:

Integration of Current Data:

The incorporation of data from the 2021 General Social Survey (GSS) ensures that the book's examples and applications are based on recent and relevant datasets. This contemporary approach allows readers to engage with real-world scenarios and analyze data reflective of current social trends. Focus on SPSS and Alternative Tools:

The book's emphasis on using SPSS for quantitative data analysis aligns with the software's widespread use in the social sciences. This focus enhances the book's relevance for readers in academic and professional settings where SPSS is commonly employed. Moreover, the acknowledgment of alternative tools, such as R, adds relevance by catering to a diverse audience with varying software preferences. Practical Applications:

The inclusion of practical examples, screenshots, and step-by-step instructions in the section on quantitative data analysis with SPSS enhances the book's relevance. Readers can directly apply the concepts learned, fostering a hands-on learning experience that is directly applicable to their research or academic pursuits. Ethical Considerations:

The discussion on ethical considerations, particularly the mention of survey weights and their exclusion from the learning dataset, adds relevance by addressing contemporary concerns in research methodology. This ethical awareness aligns with current discussions surrounding responsible and transparent research practices. Diversity of Analytical Approaches:

The book's acknowledgment of alternative methods, such as qualitative and mixed methods data analysis with Dedoose, contributes to its relevance by recognizing the diversity of approaches within the social sciences. This inclusivity allows readers to explore different analytical methods based on their research needs. Supplementary Resources:

The provision of supplementary resources, including the modified GSS Codebook and references to external guides, enhances the book's relevance. These resources offer readers additional tools and information to extend their learning beyond the book, ensuring that they stay updated on best practices and advancements in social data analysis. In summary, "Social Data Analysis: A Comprehensive Guide" remains relevant by incorporating current data, addressing ethical considerations, and catering to a diverse audience with practical examples and alternative tools. The book's contemporary approach aligns with the evolving landscape of social research and data analysis, making it a valuable and relevant resource for students, researchers, and practitioners alike.

Clarity rating: 4

"Social Data Analysis: A Comprehensive Guide" excels in clarity, offering readers a lucid and accessible journey through the intricate landscape of social data analysis. Several factors contribute to the clarity of the book:

Clear Explanations and Language:

The authors employ clear and concise language, making complex concepts in social data analysis accessible to a broad audience. Technical terms are explained in a straightforward manner, enhancing comprehension for readers regardless of their prior knowledge in the field. Step-by-Step Instructions:

The section on quantitative data analysis with SPSS stands out for its clarity due to the inclusion of step-by-step instructions. Readers are guided through processes, ensuring that they can follow and replicate actions easily. This approach fosters a practical understanding of how to apply the theoretical concepts discussed. Visual Aids and Examples:

The use of visual aids, such as screenshots and examples, enhances clarity by providing readers with visual cues to reinforce textual explanations. Real-world examples from the 2021 General Social Survey help readers connect theoretical concepts to practical applications, furthering their understanding. Logical Organization:

The book follows a logical and well-organized structure, moving from introducing social data analysis to specific tools and methods. This logical progression aids in the clarity of the learning journey, allowing readers to build on their understanding progressively. Glossary for Terminology:

The inclusion of a glossary ensures that readers can easily reference and understand key terminology. This contributes to overall clarity by preventing confusion about specialized terms used in the context of social data analysis. Consideration of Different Audiences:

The book is mindful of different audiences by providing options for both students and faculty. This consideration adds clarity by tailoring content to the specific needs and perspectives of these distinct reader groups. Transparency Regarding Limitations:

The book's transparency regarding limitations, such as the exclusion of survey weights from the learning dataset, contributes to clarity. Readers are made aware of the scope and purpose of the dataset, avoiding potential confusion about its applicability to real-world scenarios. In summary, "Social Data Analysis: A Comprehensive Guide" is characterized by its clarity, achieved through clear explanations, practical examples, logical organization, and thoughtful consideration of the diverse needs of its readership. The book effectively demystifies social data analysis, making it an approachable and enlightening resource for individuals at various levels of expertise.

Consistency rating: 4

"Social Data Analysis: A Comprehensive Guide" maintains a high level of consistency throughout its content, ensuring a cohesive and reliable learning experience. The consistency is evident in the uniform and clear language used across chapters, providing a seamless transition for readers as they navigate different sections of the book. The logical organization of topics and the structured approach to quantitative data analysis with SPSS contribute to a consistent learning curve, allowing readers to progressively build on their knowledge. Additionally, the inclusion of real-world examples and visual aids is consistently applied, enhancing the practicality of the book. The authors' commitment to ethical considerations, such as the transparency about the exclusion of survey weights in the learning dataset, reflects a consistent adherence to responsible research practices. Overall, the book's internal coherence, both in language and content, ensures that readers experience a consistent and reliable guide in their exploration of social data analysis.

Modularity rating: 3

"Social Data Analysis: A Comprehensive Guide" excels in modularity, providing a well-organized and modular structure that enhances the learning experience. The book is divided into distinct sections, each focusing on specific aspects of social data analysis. This modular approach allows readers to navigate the content efficiently, catering to different learning preferences and enabling targeted study.

The modularity is evident in the clear demarcation of chapters, from the introduction of social data analysis to the practical application of quantitative data analysis with SPSS and qualitative data analysis with Dedoose. Each section is designed as a standalone module, contributing to a structured and cohesive learning path.

Furthermore, within each module, the book maintains a modular design with sub-sections, ensuring that readers can easily locate and focus on specific topics of interest. The step-by-step instructions provided in the quantitative data analysis section exemplify this modular design, breaking down complex processes into manageable and easily digestible components.

The inclusion of supplementary resources, such as the modified GSS Codebook and glossary, adds to the modularity by offering readers standalone references that complement the main content. This modularity enhances the accessibility of the book, allowing readers to customize their learning experience based on their specific needs and interests.

In conclusion, the modularity of "Social Data Analysis: A Comprehensive Guide" contributes to the book's effectiveness as an educational resource. The well-structured and modular design facilitates a flexible and user-friendly learning experience, making it a valuable tool for readers seeking to navigate the complexities of social data analysis at their own pace.

Organization/Structure/Flow rating: 4

"Social Data Analysis: A Comprehensive Guide" is a well-structured and informative book that serves as an invaluable resource for students and faculty delving into the realm of social data analysis. The authors adeptly navigate readers through the intricacies of both quantitative and qualitative data analysis, placing a specific emphasis on the use of SPSS (Statistical Package for the Social Sciences) for quantitative analysis.

The book begins with a solid foundation, introducing readers to the concept of social data analysis. The initial sections provide a clear understanding of the importance and application of both quantitative and qualitative methods in social research. Notably, the authors strike a balance between theory and practical application, ensuring that readers can grasp the concepts and implement them effectively.

The heart of the book lies in its detailed exploration of quantitative data analysis with SPSS. The authors guide readers through the usage of this powerful statistical software, offering practical insights and step-by-step instructions. The inclusion of screenshots and examples using data from the 2021 General Social Survey enhances the book's accessibility, allowing readers to follow along seamlessly.

Furthermore, the book goes beyond theoretical discussions and provides a modified GSS Codebook for the data used in the text. This resource is invaluable for readers who wish to apply their knowledge to real-world scenarios. The authors' emphasis on the importance of survey weights and their exclusion from the learning dataset demonstrates a commitment to ethical and accurate data analysis practices.

The inclusion of a glossary enriches the learning experience by providing clear definitions of key terms. Additionally, the section on qualitative and mixed methods data analysis with Dedoose broadens the scope of the book, catering to readers interested in a diverse range of analytical approaches.

While the book excels in elucidating complex topics, it does not shy away from acknowledging alternative tools. The authors rightly introduce R as an open-source alternative, recognizing its significance and suggesting that R supplements to the book may be available in the future.

In conclusion, "Social Data Analysis: A Comprehensive Guide" stands out as a comprehensive and accessible resource for individuals venturing into the field of social data analysis. The authors' expertise, coupled with practical examples and supplementary resources, make this book a valuable companion for students, faculty, and anyone keen on mastering the art and science of social data analysis.

Interface rating: 4

The text is free of significant interface issues, including navigation problems, distortion of images/charts, and any other display features that may distract or confuse the reader.

Grammatical Errors rating: 5

The book contains no grammatical errors

Cultural Relevance rating: 5

The text is not culturally insensitive or offensive in any way.

Table of Contents

• Acknowledgements
• How to Use This Book
• Section I. Introducting Social Data Analysis
• Section II. Quantitative Data Analysis
• Section III. Qualitative Data Analysis
• Section IV. Quantitative Data Analysis with SPSS
• Section V. Qualitative and Mixed Methods Data Analysis with Dedoose
• Modified GSS Codebook for the Data Used in this Text
• Works Citied
• About the Authors

Ancillary Material

About the book.

Social data analysis enables you, as a researcher, to organize the facts you collect during your research. Your data may have come from a questionnaire survey, a set of interviews, or observations. They may be data that have been made available to you from some organization, national or international agency or other researchers. Whatever their source, social data can be daunting to put together in a way that makes sense to you and others.

This book is meant to help you in your initial attempts to analyze data. In doing so it will introduce you to ways that others have found useful in their attempts to organize data. You might think of it as like a recipe book, a resource that you can refer to as you prepare data for your own consumption and that of others. And, like a recipe book that teaches you to prepare simple dishes, you may find this one pretty exciting. Analyzing data in a revealing way is at least as rewarding, we’ve found, as it is to cook up a yummy cashew carrot paté or a steaming corn chowder. We’d like to share our pleasure with you.

About the Contributors

Mikaila Mariel Lemonik Arthur is Professor of Sociology at Rhode Island College, where she has taught a wide variety of courses including Social Research Methods, Social Data Analysis, Senior Seminar in Sociology, Professional Writing for Justice Services, Comparative Law and Justice, Law and Society, Comparative Perspectives on Higher Education, and Race and Justice. She has written a number of books and articles, including both those with a pedagogical focus (including Law and Justice Around the World, published by the University of California Press) and those focusing on her scholarly expertise in higher education (including Student Activism and Curricular Change in Higher Education, published by Routledge). She has expertise and experience in academic program review, translating research findings for policymakers, and disability accessibility in higher education, and has served as a department chair and as Vice President of the RIC/AFT, her faculty union. Outside of work, she enjoys reading speculative fiction, eating delicious vegan food, visiting the ocean, and spending time with amazing humans.

Roger Clark is Professor Emeritus of Sociology at Rhode Island College, where he continues to teach courses in Social Research Methods and Social Data Analysis and to coauthor empirical research articles with undergraduate students. He has coauthored two textbooks, An Invitation to Social Research (with Emily Stier Adler) and Gender Inequality in Our Changing World: A Comparative Approach (with Lori Kenschaft and Desirée Ciambrone). He has been ranked by the USTA in its New England 60- and 65-and-older divisions, shot four holes in one on genuine golf courses, and run multiple half and full marathons. Like the Energizer Bunny, he keeps on going and going, but, given his age, leaves it to your imagination where

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The SAGE Handbook of Applied Social Research Methods

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The Second Edition of The SAGE Handbook of Applied Social Research Methods provides students and researchers with the most comprehensive resource covering core methods, research designs, and data collection, management, and analysis issues. This thoroughly revised edition continues to place critical emphasis on finding the tools that best fit the research question given the constraints of deadlines, budget, and available staff. Each chapter offers guidance on how to make intelligent and conscious tradeoffs so that one can refine and hone the research question as new knowledge is gained, unanticipated obstacles are encountered, or contextual shifts take place.

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• New chapters on mixed methods, qualitative comparative analysis, concept mapping, and internet data collection.
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Ch.1 - Applied Research Design

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Data Analysis in Quantitative Research

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• First Online: 13 January 2019
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• Yong Moon Jung 2  

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Quantitative data analysis serves as part of an essential process of evidence-making in health and social sciences. It is adopted for any types of research question and design whether it is descriptive, explanatory, or causal. However, compared with qualitative counterpart, quantitative data analysis has less flexibility. Conducting quantitative data analysis requires a prerequisite understanding of the statistical knowledge and skills. It also requires rigor in the choice of appropriate analysis model and the interpretation of the analysis outcomes. Basically, the choice of appropriate analysis techniques is determined by the type of research question and the nature of the data. In addition, different analysis techniques require different assumptions of data. This chapter provides introductory guides for readers to assist them with their informed decision-making in choosing the correct analysis models. To this end, it begins with discussion of the levels of measure: nominal, ordinal, and scale. Some commonly used analysis techniques in univariate, bivariate, and multivariate data analysis are presented for practical examples. Example analysis outcomes are produced by the use of SPSS (Statistical Package for Social Sciences).

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Jung, Y.M. (2019). Data Analysis in Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_109

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Published : 13 January 2019

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