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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.

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Modern Statistics for the Social and Behavioral Sciences

A Practical Introduction

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Viac o knihe

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

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Modern Statistics for the Social and Behavioral Sciences

A Practical Introduction

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