Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special…

This article uses meta-analyses published in "Psychological Bulletin" from 1995 to 2005 to describe meta-analyses in psychology, including examination of statistical power, Type I errors resulting from multiple comparisons, and model choice. Retrospective power estimates indicated that univariate categorical and continuous moderators, individual…

This article is a methodological-substantive synergy. Methodologically, we demonstrate latent-variable contextual models that integrate structural equation models (with multiple indicators) and multilevel models. These models simultaneously control for and unconfound measurement error due to sampling of items at the individual (L1) and group (L2)…

The population discrepancy between Cronbach's Coefficient Alpha (L. Cronbach, 1951) and scale reliability with fixed congeneric measure, uncorrelated errors, and sampling of subjects was studied. The difference is expressed in terms of the individual component violations of the assumption of equal tau-equivalence that is necessary and sufficient…

Two studies, both using Type 12 sampling, are presented in which the effects of violating the assumption of essential parallelism in setting confidence intervals are studied. Results indicate that as long as data manifest properties of essential parallelism, the two methods studied maintain precise Type I error control. (SLD)

The sampling variability and zeta-values of parameter estimates for misspecified structural equation models were examined. A Monte Carlo study was used. Results are discussed in terms of asymptotic theory and the implications for the practice of structural equation models. (SLD)

Application of the bootstrap method to complex psychological analysis is illustrated using a simulation experiment with two populations with small and large samples. The method provides variance estimates, allows testing of nested competing models, and gives a preliminary idea about parameter variability. (SLD)