Conducting Mixed-Methods Research: From Classical Social Sciences to the Age of Big Data and Analytics

Reviewed by Sahnya Thom, Assistant Professor, University of Wisconsin-Stout on 7/23/24

Comprehensiveness rating: 4 see less

The textbook covers various aspects of mixed-methods research and thoughtfully includes practical exercises to reinforce learning. These exercises encourage readers to apply mixed-methods research principles to real-world scenarios, fostering deeper understanding and hands-on practice. I also appreciated the summaries and references at the end of chapters. Chapter 11 is particularly useful, offering paper templates for mixed-methods research with various purposes. However, the textbook could benefit from a deeper dive into ethical considerations and biases. Expanding on topics such as informed consent, data privacy, ethical use of data, potential biases, ethical dilemmas, and cultural sensitivity would enhance its comprehensiveness. More case studies focused on ethical issues, especially in the big data chapter, would be helpful. Chapter 9, which discusses Big Data, seems to lack depth on ethical issues. Including discussions on data privacy, referencing the Belmont Report, and mitigating bias in algorithms would improve this section. Additionally, addressing emerging topics like artificial intelligence would significantly contribute to the book’s relevance and utility.

Content Accuracy rating: 5

The text provided a balanced, accurate, and unbiased view of mixed-methods research, highlighting its strengths and weaknesses while presenting various paradigms objectively. The authors discuss the appropriate use of mixed-methods, various design options and decisions, and detailed data collection strategies and analysis with precision and accuracy. For example, Chapter 6 accurately details mixed-methods data collection strategies, including the criteria for selecting qualitative samples. Table 6-4 comprehensively and unbiasedly details the strengths and weaknesses of qualitative research methods. For future revisions, I suggest adding more strengths and weaknesses to the table. For example, another weakness of focus groups is the challenge of coordinating schedules, and another is that dominant voices can influence others, leading to conformity. Overall, the content is free from bias, including guidance on how to present and publish research impartially. Each chapter is well-referenced, with techniques accurately described and supported by examples. No significant errors were found, and the content is presented objectively, offering balanced views on different research methods and techniques.

Relevance/Longevity rating: 4

I find the textbook's content to be up-to-date and thoughtfully structured, ensuring it doesn't become outdated quickly. The modular design makes updates easy and straightforward - essential for long-term relevance. However, I think the book could benefit from including more exciting emerging trends like artificial intelligence and machine learning. While it already addresses important trends like big data, adding sections on AI in mixed-methods research would be valuable. For example, a section on integrating AI tools and case studies on the impact of remote work or student performance using AI, AI in social media, and other social science topics would significantly enhance its relevance. Additionally, including social science research trends like online community interactions, automated coding of qualitative data, evaluating health interventions and policies, ethics and data privacy, sustainability, and digital transformation would keep the content fresh and applicable. Writing a book like this is a large undertaking—a true beast of a topic—and I applaud the authors for their efforts in navigating the ever-changing landscape of research and data analysis.

Clarity rating: 5

I consider the textbook to be clear and easy to understand, making complex concepts accessible for readers. The authors provide context for technical terminology and explain jargon when first introduced. For example, terms like "pragmatism" and "critical realism" are well-explained with relevant context in Chapter 2. The visual aids (e.g., tables and figures) significantly helps to clarify complex information. For future revisions, some sections, like the advanced data analysis techniques in Chapter 7, could benefit from more foundational context or resource links to ensure all readers can follow along easily. Additional introductory material on basic statistical concepts could be beneficial for readers less familiar with or who need a refresher in quantitative methods. Given that this book's target audience includes graduate and PhD students who intend to use mixed-methods research in their theses and dissertations, the overall rating remains a 5. This level of student is expected to have a solid foundation in quantitative methods, which justifies the 5 rating.

Consistency rating: 5

The textbook is a reliable and user-friendly resource for instructors and graduate/PhD students. I appreciated how the authors maintain a coherent and systematic approach to presenting mixed-methods research. They introduce clear definitions and terms, which are then used consistently across different sections. For example, "pragmatism" and "critical realism" are defined in Chapter 2 and re-appear in subsequent chapters. The same is true for "data triangulation" and "mixed-methods design," which are introduced in the early chapters and referenced logically throughout the book. Visual aids, tables and figures, are also consistently formatted, enhancing the reader's ability to follow complex ideas. This internal consistency makes the textbook a dependable and accessible resource for more advanced students.

Modularity rating: 5

I appreciated how well-structured and modular the textbook is, making it easy to assign specific sections at different points in a course. Each chapter is divided into smaller subsections, allowing for targeted reading assignments. For example, Chapters 6 and 7 on data collection and analysis can be taught independently without confusing students. Chapter 11 on “Mixed-Methods Research Paper Templates” could have been overwhelming with all the different template examples, but the authors wisely provided a summary of key concepts at the end of the chapter, enhancing its standalone readability. Overall, the textbook’s modular design makes it a highly valuable resource.

Organization/Structure/Flow rating: 5

I consider the textbook well-organized and logically structured. Each section builds on the previous one, creating a clear progression of ideas. Section 1 introduces the fundamentals of mixed-methods research, with Chapter 1 discussing its characteristics and Chapter 2 covering philosophical foundations. Section 2 focuses on design and data strategies, with Chapters 4, 5, 6, and 7 providing detailed approaches to designing, sampling, data collection, and analysis. Section 3 addresses publishing and practical applications, with Chapters 9, 10, and 11 covering big data integration, meta-inferences, and research paper templates. The clear headings, summaries, and visual aids enhance comprehension and navigation. I also appreciate the overviews given before each section, which set the stage for the chapters that follow.

Interface rating: 4

I consider the textbook’s interface to be strong and free from significant issues that could impede navigation or comprehension. The layout is clean and intuitive, making it easy to locate and navigate through sections Images, charts, and tables are well-integrated and free from distortion, effectively complementing the text without causing distractions. Clear headings, summaries, and well-placed visual aids like tables and figures significantly enhance comprehension. Each chapter concludes with a summary, exercises, and references, providing clear endpoints and transitions. However, adding more hyperlinks, incorporating interactive features (e.g., quizzes, clickable case studies, interactive figures), ensuring a consistent layout for all visual aids, enhancing the readability of tables and charts, and using sidebars or callouts could further improve the overall reading experience.

Grammatical Errors rating: 5

Overall, the textbook is well-written and free from significant spelling or grammatical errors. The content is presented clearly and professionally. I especially appreciated how the authors effectively explain complex concepts without grammatical errors.

Cultural Relevance rating: 4

While the textbook is inclusive and culturally sensitive. It could benefit from more diverse examples. I appreciated the discussion in Chapter 2 about healthcare inequities, highlighting the importance of considering diverse social factors in research. Similarly, Chapter 4 includes a study incorporating diverse perspectives. However, the text would be enhanced by including more case studies from various cultural backgrounds. Additionally, expanding on ethical considerations in diverse settings and addressing potential biases would improve the content. Integrating these aspects would provide a richer view of mixed-methods research, making the textbook an even more valuable resource.

Comments

I applaud the authors for their impressive work on the complex topic of mixed-methods research - essential for both the social sciences and many other disciplines and industries. I will be adopting this book!