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Harring, Jeffrey R.; Weiss, Brandi A.; Hsu, Jui-Chen – Psychological Methods, 2012
Two Monte Carlo simulations were performed to compare methods for estimating and testing hypotheses of quadratic effects in latent variable regression models. The methods considered in the current study were (a) a 2-stage moderated regression approach using latent variable scores, (b) an unconstrained product indicator approach, (c) a latent…
Descriptors: Structural Equation Models, Geometric Concepts, Computation, Comparative Analysis
Chan, Wai; Chan, Daniel W.-L. – Psychological Methods, 2004
The standard Pearson correlation coefficient is a biased estimator of the true population correlation, ?, when the predictor and the criterion are range restricted. To correct the bias, the correlation corrected for range restriction, r-sub(c), has been recommended, and a standard formula based on asymptotic results for estimating its standard…
Descriptors: Computation, Intervals, Sample Size, Monte Carlo Methods
Serlin, Ronald C.; Harwell, Michael R. – Psychological Methods, 2004
It is well-known that for normally distributed errors parametric tests are optimal statistically, but perhaps less well-known is that when normality does not hold, nonparametric tests frequently possess greater statistical power than parametric tests, while controlling Type I error rate. However, the use of nonparametric procedures has been…
Descriptors: Multiple Regression Analysis, Monte Carlo Methods, Nonparametric Statistics, Error Patterns
Field, Andy P. – Psychological Methods, 2005
One conceptualization of meta-analysis is that studies within the meta-analysis are sampled from populations with mean effect sizes that vary (random-effects models). The consequences of not applying such models and the comparison of different methods have been hotly debated. A Monte Carlo study compared the efficacy of Hedges and Vevea's…
Descriptors: Meta Analysis, Correlation, Effect Size, Models