Modern Statistics for the Social and Behavioral Sciences: A Practical Introduction

In addition to learning how to apply classic statistical methods, students need to understand when these methods perform well, and when and why they can be highly unsatisfactory. Modern Statistics for the Social and Behavioral Sciences illustrates how to use R to apply both standard and modern methods to correct known problems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practical problems with classic methods were missed for so many years, and why modern techniques have practical value.

Designed for a two-semester, introductory course for graduate students in the social sciences, this text introduces three major advances in the field:

• Early studies seemed to suggest that normality can be assumed with relatively small sample sizes due to the central limit theorem. However, crucial issues were missed. Vastly improved methods are now available for dealing with non-normality.
• The impact of outliers and heavy-tailed distributions on power and our ability to obtain an accurate assessment of how groups differ and variables are related is a practical concern when using standard techniques, regardless of how large the sample size might be. Methods for dealing with this insight are described.
• The deleterious effects of heteroscedasticity on conventional ANOVA and regression methods are much more serious than once thought. Effective techniques for dealing heteroscedasticity are described and illustrated.

Requiring no prior training in statistics, Modern Statistics for the Social and Behavioral Sciences provides a graduate-level introduction to basic, routinely used statistical techniques relevant to the social and behavioral sciences. It describes and illustrates methods developed during the last half century that deal with known problems associated with classic techniques. Espousing the view that no single method is always best, it imparts a general understanding of the relative merits of various techniques so that the choice of method can be made in an informed manner.

Relative advantages/disadvantages of various techniques are presented so that the reader can be helped to understand the choices they make on using the techniques. ... A considerable number of illustrations are included and the book focuses on using R for its computer software application. ... A useful text for ... postgraduate students in the social science disciplines.
―Susan Starkings, International Statistical Review, 2012

This is an interesting and valuable book ... By gathering a mass of results on that topic into a single volume with references, alternative procedures, and supporting software, the author has provided a valuable service to those interested in these issues, which should probably include anyone teaching the techniques covered in this book. ... Recommended to those with a solid background in traditional statistical inference who want a highly competent and comprehensive statement of the cases against traditional statistical inference techniques.
―Robert W. Hayden, MAA Reviews, March 2012