Statistical Models in Behavioral Research

Statistical Models in Behavioral Research

Statistical Models in Behavioral Research

Statistical Models in Behavioral Research

Synopsis

This book presents in compact form a framework based in probability theory and the general linear model family for students and researchers using regression and analysis of variance methods. Special emphasis is placed on problems of properly using statistical computer programs. The relation between regression and analysis of variance is developed by means of the theory of linear contrasts for the benefit of students and users not versed in matrix algebra. Much attention is given to choosing proper error estimates, calculating proper estimates of standard errors in a variety of designs, and dealing with the problems of unbalanced designs. Having taught research design and quantitative methods in psychology for many years, Estes has developed ways of simplifying the presentation of concepts and derivations so as to make the substance of important statistical results available to students and investigators who lack much mathematical background and/or much taste for doing derivations.

Designed to supplement standard texts used in graduate courses in intermediate and advanced statistics, research methods, and experimental design for psychologists or other behavioral scientists, this text also has something to offer experienced investigators: material on model testing and related topics not covered in textbooks or other readily available sources.

Excerpt

This book evolved during my teaching of courses in statistics and quantitative methods to advanced undergraduate and graduate students in psychology and social science off and on for nearly 40 years. Apologies are due the students who happened to fall in earlier classes, before I had learned how to teach this kind of material effectively, and appreciation is due to many collaborators and assistants. in the latter connection, I owe a special debt to Robert R. Rosenthal, with whom I shared a graduate course at Harvard University for 8 years and from whom I have learned a good deal about how to help students get across the gap between theory and application. I also want to mention a succession of outstanding teaching assistants at Harvard, including Beverly Chew, Jean MacMillan, and most notably, Kris Kirby, who caught seemingly innumerable glitches in draft chapters of this volume and brought my attention to many possibilities for improving communicability. Finally, I wish to thank Nancy Rury, who accomplished the all but impossible task of converting my handwritten drafts into neat typescript, Kay Estes, who prepared the index, and my longtime friend and publisher, Lawrence Erlbaum, who personally supervised the final transition from typescript into print.

W. K. E.

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