Type I error rates for moderated multiple regression analysis
Article Abstract:
The method of moderated multiple regression is increasingly being applied in the search for moderator variables in industrial and organizational psychology. Because of frequent failures of the method in revealing moderator effects in empirical studies - in which such effects are strongly expected - it has been suggested that the procedure may lack statistical power with respect to hypothesis tests about moderating effects and, therefore, is inappropriate for the purpose of conventional moderator analyses. We evaluated this conclusion with computer simulation data. Our study indicated that the method is not overly conservative and that the Type I error rate of moderated multiple regression is approximately .05 at alpha = .05. Moreover, a proposed alternative multivariate procedure, principal component regression, is shown to have a Type I error rate that approaches unity under ordinary conditions when applied to the evaluation of moderator effects. (Reprinted by permission of the publisher.)
Publication Name: Journal of Applied Psychology
Subject: Social sciences
ISSN: 0021-9010
Year: 1988
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Influence of sample size, estimation method, and model specification on goodness-of-fit assessments in structural equation models
Article Abstract:
The problem of assessing fit of structural equation models is reviewed, and two sampling studies are reported that examine the effects of sample size, estimation method, and model misspecification on fit indices. In the first study, the behavior of indices in a known-population confirmatory factor analysis model is considered. In the second study, the same problem in an empirical data set is examined by looking at antecedents and consequences of work motivation. The findings across the two studies suggest that, (a) as might be expected, sample size is an important determinant in assessing model fit; (b) estimator-specific, as opposed to estimator general, fit indices provide more accurate indications of model fit; and (c) studied fit indices are differently sensitive to model misspecification. Some recommendations for the use of structural equation model fit indices are given. (Reprinted by permission of the publisher.)
Publication Name: Journal of Applied Psychology
Subject: Social sciences
ISSN: 0021-9010
Year: 1989
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Averaging correlation coefficients: influence of heterogeneity and set size
Article Abstract:
Silver and Dunlap (1987) recently investigated two methods for averaging correlations: (a) simple average of rs, and (b) average of Fisher's z transformations of r backtransformed to r. The latter method was found to be less positively biased than the simple average of rs was negatively biased. The current study extends these results by demonstrating that the z-transformation method is less susceptible to heterogeneity in the correlations that are averaged, but that the absolute magnitude of bias in the two approaches becomes similar as set size increases. (Reprinted by permission of the publisher.)
Publication Name: Journal of Applied Psychology
Subject: Social sciences
ISSN: 0021-9010
Year: 1988
User Contributions:
Comment about this article or add new information about this topic:
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