Careful data collection and analysis lies at the heart of good research, through which our understanding of psychology is enhanced. Yet the students who will become the next generation of researchers need more exposure to statistics and experimental design than a typical introductory course presents.
Experimental Design and Analysis for Psychology provides a complete course in data collection and analysis for students who need to go beyond the basics.
Acting as a true course companion, the text's engaging writing style leads readers through a range of often challenging topics, blending examples and exercises with careful explanations and custom-drawn figures to ensure even the most daunting concepts can be fully understood.
Opening with a review of key concepts, including probability, correlation, and regression, the book goes on to explore the analysis of variance and factorial designs, before moving on to consider a range of more specialised, but yet powerful, statistical tools, including the General Linear Model, and the concept of unbalanced designs.
Not just a printed book, Experimental Design and Analysis for Psychology is enhanced by a range of online materials, all of which add to its value as an ideal teaching and learning resource.
The Online Resource Centre features :
For registered adopters: Figures from the book, available to download. Answers to exercises featured in the book. Online-only Part III: bonus chapters featuring more advanced material, to extend the coverage of the printed book.
For students: A downloadable workbook, featuring exercises for self-study. SAS, SPSS and R companions, featuring program code and output for all major examples in the book tailored to these three software packages.
I started out with Statistics for the Behavioral Sciences by Gregory J. Privitera, which provided a fantastic introduction into the world of statistics; not to mention the author's powerpoint slides are very useful and well-organized. After that textbook, I read Experimental Design & Analysis for Psychology. IMO, this is the perfect book to crack open after getting your feet wet with one or more intro level stat courses. The reason being, Abdi takes a very mathematical approach and loves breaking everything down into formulas (#ScoreModel). It may seem a little daunting at first for those w/o a background in math but it's straightforward and once you get a peak at what's "under the hood," you'll have a better conceptualization of the material you thought you grasped in undergrad stats.
Highlights for me was being able to look at analyses through the lens of regression (e.g., ANOVA), constructing the score model from scratch (ch. 22), and coding experimental designs (ch. 15 and onward). Definitely check out the author's additional resources on his site.
Downsides are that there's a few errors and typos in the book. Make sure to consult the book's site for a list of them so you can keep a lookout. This shouldn't be regarded as a criticism but I wish Abdi continued on to multivariate analysis just b/c I enjoyed how he expounded and broke down these concepts into digestible bits and pieces. But alas, that probably would have meant doubling the length of the book.
tl;dr - Great stat book. Get into the math and make sure to check out additional online resources.