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.
A Practical Introduction: Modern Statistics for the Social and Behavioral Sciences by Rand Wilcox Overview
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:
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
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
A Practical Introduction: Modern Statistics for the Social and Behavioral Sciences by Rand Wilcox Review
This book is an outstanding choice for a graduate-level introduction to statistics. It should be mandatory reading for anyone taking such a course. There have been many major advances and insights regarding the inadequacy of classic, routinely used methods for analyzing data. This book covers the classic methods, but unlike the typical text, it explains why standard methods can be highly unsatisfactory, particularly in terms of power, it explains why problems were missed for so many years, and it covers modern methods that deal effectively with known problems. There are extensive illustrations for a very broad range of topics, including robust ANOVA, MANOVA, robust measures of effect size, rank-based techniques, robust regression, smoothers, modern ANCOVA techniques, categorical data and some multivariate methods. The graphical methods that are included are invaluable. The book is timely because of the growing awareness that under general conditions, the better-known data analysis techniques can be woefully inaccurate.
The book assumes no prior training and is aimed at a two-semester course. It covers basics in a manner that forms a conceptual foundation for understanding the relative merits of competing techniques. Technical issues that were rather unimportant not too many years ago are now highly important, because without a proper understanding of how to deal with non-normality and outliers, technically invalid techniques are likely to be used. Indeed, journal articles routinely use strategies that violate basic principles. Wilcox illustrates that using a theoretically correct method can yield a highly different conclusion compared to one that is theoretically incorrect. One of the more common mistakes is discarding outliers and applying some classic method to the remaining data. There are methods that deal effectively with outliers, which are covered in this book, but they are not obvious based on standard training.
The software used is R in conjunction with an R package, written for this book, that covers a vast array of techniques. R is arguably the most important software development during the last quarter of century for analyzing data. It obviously dominates among books written by statisticians and is beginning to replace SPSS among non-statisticians analyzing data. (Applying modern methods with SPSS is difficult at best and generally impossible.) If you are using SPSS, dump it and learn R. Initially, analyses will take longer, but with a little practice, analyses can be done much more quickly. Perhaps more importantly, the access to a much broader range of techniques is invaluable. This book is a testament to the power and practical advantages of R and modern methods.
The book assumes no prior training and is aimed at a two-semester course. It covers basics in a manner that forms a conceptual foundation for understanding the relative merits of competing techniques. Technical issues that were rather unimportant not too many years ago are now highly important, because without a proper understanding of how to deal with non-normality and outliers, technically invalid techniques are likely to be used. Indeed, journal articles routinely use strategies that violate basic principles. Wilcox illustrates that using a theoretically correct method can yield a highly different conclusion compared to one that is theoretically incorrect. One of the more common mistakes is discarding outliers and applying some classic method to the remaining data. There are methods that deal effectively with outliers, which are covered in this book, but they are not obvious based on standard training.
The software used is R in conjunction with an R package, written for this book, that covers a vast array of techniques. R is arguably the most important software development during the last quarter of century for analyzing data. It obviously dominates among books written by statisticians and is beginning to replace SPSS among non-statisticians analyzing data. (Applying modern methods with SPSS is difficult at best and generally impossible.) If you are using SPSS, dump it and learn R. Initially, analyses will take longer, but with a little practice, analyses can be done much more quickly. Perhaps more importantly, the access to a much broader range of techniques is invaluable. This book is a testament to the power and practical advantages of R and modern methods.
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