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.
|Publisher:||Taylor & Francis|
|Edition description:||New Edition|
|Product dimensions:||6.00(w) x 9.00(h) x 0.47(d)|
Table of Contents
Contents: Introduction. Statistics, Probability, and Decision. Basic Concepts. Contrasts on Means. Testing a Statistical Hypothesis. Simple Analysis of Variance. Regression and ANOVA in the Linear Model Framework. Two-Way Factorial Designs. Repeated-Measures Designs. Unbalanced Designs and Nonorthogonality.