A Monte Carlo study compared the statistical performance of standard and robust multilevel mediation analysis methods to test indirect effects for a cluster randomized experimental design under various departures from normality. The performance of these methods was examined for an upper-level mediation process, where the indirect effect is a fixed…

This study uses within-study comparisons to assess the relative importance of covariate choice, unreliability in the measurement of these covariates, and whether regression or various forms of propensity score analysis are used to analyze the outcome data. Two of the within-study comparisons are of the four-arm type, and many more are of the…

It is argued that ordinal statistical methods are often more appropriate than their more common counterparts because conclusions will be unaffected by monotonic transformation of the variables; they are more statistically robust when used appropriately; and they often correspond more closely to the researcher's goals. (SLD)

A Monte Carlo study extended the research of MacKinnon, Lockwood, Hoffman, West, and Sheets (2002) for single-level designs by examining the statistical performance of four methods to test for mediation in a multilevel experimental design. The design studied was a two-group experiment that was replicated across several sites, included a single…

A Monte Carlo study examined the statistical performance of single sample and bootstrap methods that can be used to test and form confidence interval estimates of indirect effects in two cluster randomized experimental designs. The designs were similar in that they featured random assignment of clusters to one of two treatment conditions and…

Examines the recommendations of H. Keselman, K. Carriere, and L. Lix (1993) regarding choice of sample size for obtaining robust tests of the repeated measures main and interaction hypotheses in a one Between-Subjects by one Within- Subjects design with a Welch-James type multivariate test when covariance matrices are heterogeneous. (SLD)

The utility of the three-form design coupled with maximum likelihood methods for estimation of missing values was evaluated. Simulation studies demonstrate that maximum likelihood estimation and multiple imputation methods produce the most efficient and least biased estimates of variances and covariances for normally distributed and slightly…

Benefits and limitations of structural equation models for multivariate experiments with incomplete data are presented. Examples from studies of latent variable path models of cognitive performance illustrate analyses with latent variables, omitted variables, randomly missing data, and nonrandomly missing data. (SLD)

Alternative tests are presented for the between-by-within interaction null hypothesis and for two within-subjects main effects null hypothesis in a split plot design. Estimated Type I error rates for the interaction tests and for several tests of the second null hypothesis are reported. (SLD)

A Monte Carlo study investigated the use of four cross-validation indices with confirmatory factor analysis models. Influences of sample size, loading size, and degree of model misspecification were studied. Larger sample sizes and better specified models result in better cross-validation results. (SLD)

A proposed solution for the age x cohort x period issue in lifespan research uses all data, even with missing cells; can be used for repeated measures designs or designs in which new subjects are measured at each period; and allows assessment of each main effect and two-way interaction. (SLD)

An evaluation of the rules-of-thumb used to determine the minimum number of subjects required to conduct multiple regression analyses suggests that researchers who use a rule of thumb rather than power analyses trade simplicity of use for accuracy and specificity of response. Insufficient power is likely to result. (SLD)