Your Statistical Consultant: Answers to Your Data Analysis Questions

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Although many graduate students and researchers have had course work in statistics, they sometimes find themselves stumped in proceeding with a particular data analysis question. In fact, statistics is often taught as a lesson in mathematics as opposed to a strategy for answering questions about world[?], leaving beginning researchers at a loss for how to proceed. In these situations, it is common to turn to a statistical expert, the "go to" person when questions regarding appropriate data analysis emerge. Your Statistical Consultant is an authentic alternative resource for describing, explaining, and making recommendations regarding thorny or confusing statistical issues. Written to be responsive to a wide range of inquiries and levels of expertise, this book is flexibly organized so readers can either read it sequentially or turn directly to the sections that correspond to their concerns and questions.

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Your Statistical Consultant: Answers to Your Data Analysis Questions Review

This is an excellent book authored by two of my colleagues at the Fielding Graduate University. It offers a straightforward yet sage, anti-cookbook approach to statistics. It isn't a stats textbook, but rather it contains the sort of pragmatic advice you might get from a good statistics consultant. It doesn't tell you what to do so much as provide the information you need to dig out of holes and do a first-rate analysis.

Of special value is the unusual, informative and well-balanced discussion of limitations and controversies surrounding various statistical approaches. For instance, there has been considerable and longstanding controversy swirling around the use of the null hypothesis significance testing. Most undergraduate, graduate and postgraduates who engage in hypothesis testing are unaware of these controversies, in part because they are rarely covered in popular statistics textbooks. The authors explain the controversy in a masterful chapter titled "Rethinking Traditional Paradigms: Power, Effect Size, and Hypothesis Testing Alternatives." I can imagine embattled researchers consulting this book while arm-wrestling over statistical issues with others, including dissertation committee members and journal reviewers.

The book contains excellent and informative Illustrations. For instance, I think figures 12.1 and 12.2 from a chapter titled "The Big Picture" are especially important because they capture the commonalities among a wide variety of statistical techniques.

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