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Serious Stats: A guide to advanced statistics for the behavioral sciences Paperback – 25 Jun 2012
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Bridges the gap between the undergraduate and postgraduate levels, providing readers with a refresher of the skills they have learnt andthen progressing to more advanced statistical methods
About the Author
Thomas Baguley is Professor of Experimental Psychology at Nottingham Trent University, UK. He is an experimental psychologist working particularly on the statistical or mathematical modelling of long-term memory and spatial cognition. He has over 20 years of teaching and research experience and is Editor of the British Journal of Mathematical and Statistical Psychology.
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Most helpful customer reviews on Amazon.com
This book is an 800 page tour de force of statistical methods. The good news is that the author is a psychologist so the presentation of key conceps and examples are well written. The bad news is that the author is a psychologist (and the book is already 800+ pages) so presentations are short and to the point. I had a problem at work that involved proportional odds logistic regression, and indeed, an example is given. One example. Proportional odds using logistic regression. Are there other methods? Yes, but these are not discussed. Does the example show / interpret odds ratios? No.... confidence intervals for the coefficients? Yes, Wald method, Confidence intervals for odds ratios? No.. Discussion of the ever (de)pressing issue "when can categorical data be analyzed as though it were from an interval scale" meticulously avoided.
Bayesian methods are presented; no mention of current trends in Markov Chain Monte Carlo estimation. Bayesian discussion is limited to analyses that have tractable (prior * likelihood) functions. Current applications of Bayesian techniques are more along the lines of "please step away from the conjugate prior," but the concepts are clear. Bayes factors are presented as well as likelihood estimation, and use of the AIC, BIC.
In fairness, the book covers so many topics that no one topic can get much depth. The book also addresses effect size, confidence and power of a test- all important aspects of inference. Also kudos to the author for providing code snippets in R. R ubiquitous and (free) so this a welcome aspect ot the book. I recommend the book to anyone looking to use statistical methods in the behavorial (and other) sciences. One would benefit from consultation with a statistician having once expored the book for the various means and methods for analysis and design of an experiment.