Statistics in Psychology Using R and SPSS

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Product Description

Statistics in Psychology covers all statistical methods needed in education and research in psychology. This book looks at research questions when planning data sampling, that is to design the intended study and to calculate the sample sizes in advance. In other words, no analysis applies if the minimum size is not determined in order to fulfil certain precision requirements.

The book looks at the process of empirical research into the following seven stages:

• Formulation of the problem
• Stipulation of the precision requirements
• Selecting the statistical model for the planning and analysis
• The (optimal) design of the experiment or survey
• Performing the experiment or the survey
• Statistical analysis of the observed results
• Interpretation of the results.

Statistics in Psychology Using R and SPSS Review

This is a technical overview of statistics relevant for psychology, featuring examples using R and SPSS. The authors are university professors in Vienna, Austria. In this book, the present a somewhat technical overview of statistics used for various aspects of psychology. Topics covered include descriptive statistics, inferential statistics for one "character" [variable], descriptive and inferential statistics for two "characters" [variables], inferential statistics for more than two "characters" [variables], and model generation and theory-generating procedures. The text is illustrated with command line (not GUI) examples from R and screen shots from SPSS. Each chapter closes with a short list of suggested references (some in English and some in German), but no exercises. End material includes examples of "data input" [data entry] methods, standard statistical tables, and a list of symbols and notations.

I got this book in the hopes that it would provide some worked examples of how to go from a real world question in psychology to an analysis in R and then how to interpret and use the results from R. But that isn't really included in this book. Instead, this book is a very dense text aimed at 4 levels of readers at once: undergraduates, master's students, doctoral students, and "lecturers" [professors], with little call-out labels to indicate who is supposed to read which specific bits of text. I found the statistical concepts covered to be about equivalent to those covered in a statistical methods class for third or fourth year undergraduate math majors. That might be a bit more than undergraduate psychology majors are expecting, unless they've already had a few semesters of introductory statistics. On the other hand, there's no calculus in this book, so it isn't quite full-bore statistics, either. But still, there is a bit of matrix algebra, so perhaps a linear algebra course should be considered a prerequisite.

A statistics textbook that shows examples in R is a great concept, since R is becoming the statistics package of choice among statisticians. However, this book provides no real introduction to R, no step-by-step examples of how to get the package going and open your first data set, let alone how to trouble shoot. The book also only uses command line R, with no mention of the GUI interfaces that are available. What would have been nice is a short discussion comparing doing statistics in R to doing statistics in Excel and SPSS, and a description of why R is becoming the standard. What also would have been nice is even a bit of description about the basics of command line syntax in R, or the meanings or origins of the names of the unfamiliar R commands. Or even an appendix of R commands. But none of that is included here.

The text could have used some editing by an academic native speaker of English who is familiar with the subject material. It contains a few punctuation errors and stylistic oddities that don't fit in an academic text. Furthermore, it uses key words that are incorrectly translated, such as "character" instead of variable and "data input" instead of "data entry". Such words could be severe stumbling blocks for some students trying to put these concepts into the context of material they already know. A further cultural issue for any North American considering adopting this text is that the book provides no problem sets. North American students are used to learning by doing homework assignments as well as reading, so a North American instructor would have to come up with a full load of appropriate problem sets to supplement the text.

One consequence of the book being written for 4 separate levels of audience (undergraduate through professors) at once, and exemplified with 2 different software packages is that the text is extremely dense and difficult to comprehend. It would have been far more useful to focus on one level of audience and one software package, and provide more text to explain points in greater detail. Overall, this text might work for advanced undergraduate and graduate students in non-English speaking universities in Europe, but wouldn't be ideal for use in North American universities. And it also doesn't have much of introduction about how to get started in R, if that's what you're looking for.

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