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Beckstead, Jason W. – Multivariate Behavioral Research, 2012
The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…
Descriptors: Multiple Regression Analysis, Predictor Variables, Factor Analysis, Structural Equation Models

Harris, Elizabeth L.; And Others – Multivariate Behavioral Research, 1974
One hundred and eighteen subjects participated in an 18-trial four-choice probability learning task to determine if probability learning data could be described by a family of learning curves and to identify ability and personality correlates of these curves. (Author)
Descriptors: Cognitive Ability, Cognitive Processes, Conditioning, Factor Analysis

Farley, John U.; And Others – Multivariate Behavioral Research, 1974
Evaluation of attributes of a subcompact car were combined in linear regressions predicting liking and purchase intention. Of two forms--raw scales and scales weighted by the importance attached to each attribute by each subject--unweighted evaluations proved more consistent and important predictors than those weighted by their saliency. (Author)
Descriptors: Attitudes, Decision Making, Design Preferences, Design Requirements

Olsson, Ulf – Multivariate Behavioral Research, 1979
The paper discusses the consequences for maximum likelihood factor analysis which may follow if the observed variables are ordinal with only a few scale steps. Results indicate that classification may lead to a substantial lack of fit of the model--an erroneous indication that more factors are needed. (Author/CTM)
Descriptors: Classification, Factor Analysis, Goodness of Fit, Maximum Likelihood Statistics