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Book Review – Selecting the Right Analysis for Your Data: Quantitative, Qualitative and Mixed Methods

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    Book Review

    By Janet L. Medlock

    Vogt, W.P., Vogt, E.R., Gardner, D.C. & Haeffele, L.M. (2014).  Selecting the right

    analysis for your data: Quantitative, qualitative, and mixed methods.  New York, NY:  The Guilford Press.

     

    ISBN 978-1-4625-1576-9 (paperback)

    500 pages

          Selecting the right analysis for your data: Quantitative, qualitative, and mixed methods (Vogt, Vogt, Gardner & Haeffele, 2014, p. 1), is a book that offers advice about selecting methods to analyze data.  The authors presuppose that the readers who are seeking this advice already have their research questions and designs in mind so the book is written in a somewhat advanced manner.  “But ‘advanced’ does not necessarily mean highly technical” (Vogt, Vogt, Gardner & Haeffele, 2014, p. 1).  The main focus of the book is deciding when to use which method of analysis (Vogt et al., 2014).  The book addresses data display, coding and data analysis.

     

                The authors begin with two parallel questions researchers should ask themselves about organizing data so that the researcher can best formulate their approach to methodology and data interpretation problems that the researcher may encounter.  Those questions are “What method do you use to analyze a specific kind of data?” and “What kind of data can you analyze when using a specific method?” (Vogt et al., 2014, p. 3). The book is broken down into three parts and subdivided into 14 chapters.  Part I is entitled “Coding Data—by Design,” Part II is called “Analysis and Interpretation of Qualitative Data” and Part III is “Analysis and Interpretation of Qualitative and Combined/Mixed Data” (Vogt et al., 2014).

     

         Chapter 1 – “Coding Survey Data” addresses pitfalls of constructing a survey, how to construct an effective questionnaire, coding and measuring respondents’ answers to questions and analysis guidelines for surveys.  Chapter 2 – “Coding Interview Data” provides a breakdown of the steps of the coding process including setting goals, determining the interviewer’s role, sampling, kinds of questions to ask, modes of communication, observing what is not being said, recording data, tools to use and getting assistance with coding.  “Coding Experimental Data” is in Chapter 3, which sets out coding and measurement issues for experimental designs, those issues that vary by type of experimental design and guidelines for analyzing experimental data.  Chapter 4 – “Coding Data from Naturalistic and Participant Observations” introduces observation research and is broken down by phases including observing, recording and coding.  “Coding Archival Data:  Literature Reviews, Big Data, and New Data” is the title of Chapter 5.  This chapter addresses coding for literature reviews, “big data” which has been historically been a reference to archival data but which the authors define as “an amount of information impossible for one individual to code and analyze in less than a year without computer help” (Vogt et al., 2014, p. 159).  However, the authors argue that much data used in contemporary research can be handled in a reasonable amount of time with minimal computer use, by a solo researcher.  New media is defined as data that is found on social media, websites, etc. (Vogt et al., 2014).

     

                Chapter 6—“Describing, Exploring, and Visualizing Your Data” provides an overview of descriptive statistics, it addresses what statistics to use to prepare for further analysis, using correlations, normal distributions and descriptive statistics to apply missing data procedures.  “What Methods of Statistical Inference to Use When” is the title of Chapter 7 which provides information about null hypothesis significance testing, what statistical tests to use, using confidence intervals, reporting power and precision of estimates, using distribution-free, nonparametric significance tests, using resampling methods and approaches to statistical inference.  Chapter 8 – “What Associational Statistics to Use When” provides further statistical analysis information including using correlations, regression analysis and using categorical dependent variables.  In Chapter 9 – “Advanced Associational Methods,” the authors discuss multilevel modeling, path analysis and factor analysis.  In the last chapter of Part II, Chapter 10 – “Model Building and Selection,” building models, constructing theories and using a multi-model approach are discussed.

     

                Part III of the book begins with Chapter 11, “Inductive Analysis of Qualitative Data:  Ethnographic Approaches and Grounded Theory” provides a background of inductive social research, ethnography and grounded theory.  Chapter 12 – “Deductive Analysis of Qualitative Data:  Comparative Case Studies and Qualitative Comparative Analysis,” addresses case studies and deductive analysis, conducting a single-case analysis, conducting comparative case studies with a small and intermediate sample size.  “Coding and Analyzing Data from Combined and Mixed Designs” is the title of Chapter 13, which covers coding and analyzing data for deductive and inductive designs and data transforming and merging in combined or mixed methods design.  Finally, Chapter 14 – “Conclusion:  Common Themes and Diverse Choices” addresses common themes and choices in a researcher’s approach to choosing the right analysis for their data and suggested strategies and tactics.

     

         The reviewer found the book to be very well organized and each chapter had a thorough introduction.  For that reason, it would be easy for a researcher to choose a topic and start reading any chapter.  Some other texts begin with an overview of a topic and move on to distinct subsequent topics which makes it difficult to stop and start reading because you have to go back and re-read terminology in order to effectively pick up where you left off. 

     

         The authors define terms, followed by examples, which makes the terms much easier to understand.  In addition, the detailed footnotes provide extensive suggestions for further reading.  The authors lay out the pros and cons of particular methods of analysis and book also addresses pitfalls that researchers can come across when they are analyzing their data.  This book offers many time and frustration saving tips to organize data and prepare for analysis.

     

         While there is a lot of information contained in this book, summary tables at the end of each chapter provide a great resource for what is contained in each chapter.  When the authors transition into statistical analysis, they again define the terms and provide a comprehensive review relating to data analysis.  For example, on page 82, table 3.1, there is a table of “Common Measures of Reliability and when to Use Them” (Vogt et al., 2014, p. 82).  They offer suggestions for data analysis and tools and list advantages and disadvantages of each. The reviewer also believes this would be an excellent text for advanced statistics classes for graduate research students.  The book is very contemporary and addresses analyzing data contained in blogs, websites and other social medial sources.  There are also very helpful visual displays in the book including tables and charts.

     

         The book says at the onset that the emphasis is on data analysis and that emphasis in other books is more technical.  However, the reviewer found that the book contained both analytical and technical terms to assist the researcher.  While the authors consider the book to be somewhat of an advanced book and would be best read by someone who has already chosen a topic and a research design, the reviewer found that this book was written like a hand-book and believes that this book, particularly Part I, would be useful to researchers even in the early stages of formulating their research question and design.  The book presents pitfalls that may lie before researchers and makes suggestions on how to maximize efficiencies of the research process.  The more preparation that a researcher can do before even beginning their study, the more likely that they can avoid many of the setbacks that the authors address in the book. What the reviewer perhaps liked most about this book is its emphasis on options and alternatives.  “There are many alternatives, and it is important to select wisely among them….Making effective selections requires attention to detail and a panoramic perspective so that you don’t get lost among the details” (Vogt et al., p. 441).

     

         The reviewer found the book to be extremely well organized, easy to refer to and definitely plans to utilize this book before beginning data analysis. The summary tables at the end of each chapter, the definitions of terms and the general flow of the book make it an excellent resource for graduate students and for both researchers who are still deciding on a research topic as well as those who are farther along in the process.  The reviewer plans to keep this book on her bookshelf and use it as a frequent reference during her own research studies.

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