A Handbook for Data Analysis in the Behaviorial Sciences: Volume 1: Methodological Issues Volume 2: Statistical Issues
Statistical methodology is often conceived by social scientists in a technical manner; they use it for support rather than for illumination. This two-volume set attempts to provide some partial remedy to the problems that have led to this state of affairs. Both traditional issues, such as analysis of variance and the general linear model, as well as more novel methods like exploratory data analysis, are included. The editors aim to provide an updated survey on different aspects of empirical research and data analysis, facilitate the understanding of the internal logic underlying different methods, and provide novel and broader perspectives beyond what is usually covered in traditional curricula.
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alternative analysis applications assumptions axioms Bayesian behavioral sciences bias chapter cognitive Cohen confidence interval confirmation bias considered correlation criterion decision deviation dimensions distributions E. S. Pearson effect size effect sizes empirical epistemology Equation estimate example experiment experimental Fisher frequency frequentist function good-enough heavy-tailed distributions hybrid logic hypothesis testing ideal picture inference interpretation Journal judgment level of significance mathematical Mathematical Psychology matrix mean measurement Meehl methodology methods multidimensional scaling Neyman nonparametric null hypothesis observations obtained pairs parameters possible posterior probability prediction probability problem procedures properties psychological research psychology random numbers ranks reﬂect rejected replication response Rosenthal sample scale values scientific sequence significance level significance testing solution space standard statistical statistical power stimulus studies subjects Table theoretical epistemology theories(T theory tion transformation Tversky Type I error variables variance vector Wilcoxon test within-subjects design York