Power of Models in Longitudinal Study: Findings From a Full-Crossed Simulation Design.

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3 variance-covariance structures. The results from a full-crossed simulation design suggest that traditional repeated measures have significantly higher power than do hierarchical multivariate linear models for main effects, but they have significantly lower power for interaction effects in most situations. Significant power differences are also exhibited when power is compared across different covariance structures.

A Monte Carlo Power Analysis of Traditional Repeated Measures and Hierarchical Multivariate Linear Models in Longitudinal Data Analysis.

The power properties of traditional repeated measures and hierarchical linear models have not been clearly determined in the balanced design for longitudinal studies in the current literature. A Monte Carlo power analysis of traditional repeated measures and hierarchical multivariate linear models are presented under three variance-covariance structures. Results suggest that traditional repeated measures have higher power than hierarchical linear models for main effects, but lower power for interaction effects. Significant power differences are also exhibited when power is compared across different covariance structures. Results also supplement more comprehensive empirical indexes for estimating model precision via bootstrap estimates and the approximate power for both main effects and interaction tests under standard model assumptions.

Differential person functioning applied to baseball.

The question of whether a baseball player generally hits better against a left-handed or right-handed pitcher is difficult to answer since handedness is only one of many possible attributes of pitchers. The concept of differential functioning from psychometrics is applied, considering both the effect of the handedness of the pitcher and his earned run average (the mean number of runs scored against a pitcher per 9 innings pitched excluding runs due to errors). Two interesting cases are examined, a left-handed batter and a switch-hitter. Suggestions for further research are offered.