Multiway contingency tables analysis for the social sciences
This book describes the principles and techniques needed to analyze data that form a multiway contingency table. Wickens discusses the description of association in such data using log-linear and log-multiplicative models and defines how the presence of association is tested using hypotheses of independence and quasi-independence. The application of the procedures to real data is then detailed. This volume does not presuppose prior experience or knowledge of statistics beyond basic courses in fundamentals of probability and statistical inference. It serves as an ideal reference for professionals or as a textbook for graduate or advanced undergraduate students involved in statistics in the social sciences.
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Models for Threeway Tables
The Statistical Basis of Sampling and Testing
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algorithm analysis of variance apply association model association parameters calculated cells Chapter choice classification coefficient collapsing combination conditional independence conditional probabilities Consider constraints correct correlation covariance matrix data in Table degrees of freedom denoted depends diagonal discussed effects Equation error example expected frequencies factors functions gives hierarchical homogeneity independence model indicate interpretation least-squares levels likelihood-ratio test linear model log-frequency model log-linear model logarithm logit model marginal distributions marginal frequencies matrix maximum-correlation model maximum-likelihood estimates measure model fits multinomial multinomial distribution multiplicative-association model normal distribution notation null hypothesis observations obtained odds ratio pair Poisson population predictions predictor-outcome predictors probability problem procedure proportion quasi-independence model quasi-symmetry rejected relationship restrictions ridits rows and columns sampling models saturated model scores set of data single subjects subpopulation subscripts subtables test statistic three-way table tion two-factor two-way table uniform-association model unrelatedness variable x2 distribution zero