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Xi, Nuo; Browne, Michael W. – Journal of Educational and Behavioral Statistics, 2014
A promising "underlying bivariate normal" approach was proposed by Jöreskog and Moustaki for use in the factor analysis of ordinal data. This was a limited information approach that involved the maximization of a composite likelihood function. Its advantage over full-information maximum likelihood was that very much less computation was…
Descriptors: Factor Analysis, Maximum Likelihood Statistics, Data, Computation
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Rouder, Jeffrey N.; Morey, Richard D. – Multivariate Behavioral Research, 2012
In this article, we present a Bayes factor solution for inference in multiple regression. Bayes factors are principled measures of the relative evidence from data for various models or positions, including models that embed null hypotheses. In this regard, they may be used to state positive evidence for a lack of an effect, which is not possible…
Descriptors: Bayesian Statistics, Multiple Regression Analysis, Factor Analysis, Statistical Inference
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Bird, Kevin D.; Hadzi-Pavlovic, Dusan – Psychological Methods, 2005
The authors provide generalizations of R. J. Boik's (1993) studentized maximum root (SMR) procedure that allow for simultaneous inference on families of product contrasts including simple effect contrasts and differences among simple effect contrasts in coherent analyses of data from 2-factor fixed-effects designs. Unlike the F-based simultaneous…
Descriptors: Factor Analysis, Statistical Inference, Effect Size, Comparative Analysis