Conference Highlights Conference Highlights: The 2001 Annual Meeting of the Mid-Western Educational Research Association

Conference Highlight

Mid-Western Educational Research Association 2001 Annual Meeting Call for Proposals

Call for Proposal

Investigation of Power Using F Approximations for the Hotelling-Lawley Trace and Pillai\u27s Trace

Differences among treatment groups in terms of which variable or linear combination of variables causes a significant multivariate analysis of variance (MANOVA) are often difficult to determine. This study is an attempt to develop a means by which a significant MANOVA can be followed by a discriminant analysis for the purpose of finding a significant contrast which can determine which variable or linear combination of variables is causing differences in which treatment groups. Significance of the contrast was tested using Roy-Bose simultaneous confidence intervals. These intervals traditionally have been considered conservative as a hypothesis-testing procedure. Of concern in any hypothesis-testing procedure is type I error and power. This study investigated type I error and power of the procedure in numerous situations which used many combinations of number of groups, number of variables, nominal alpha, and group size. Included are situations which involved no violation of MANOVA assumptions, as well as situations involving a violation of normality or a violation of the assumption of a homogeneous covariance structure. Results show that the proposed procedure needs great improvement when the assumptions of MANOVA are not met. When the assumptions are met, the procedure works fairly well in terms of power and type I error for a small number of groups or variables. As the number of groups or variables reaches six, the procedure begins to lose power, but type I error is acceptable

Precision Power and Its Application to the Selection of Regression Sample Sizes

Because of contradictions among the various methods, sample size selection in multiple regression has been problematic. For example, how does one reconcile the difference between a 15: 1 subject-to-variable rule and a 30: 1 rule? The purpose of this paper is to analyze the advantages and disadvantages of the various methods of selecting sample sizes in regression. A discussion of the importance of cross-validity to prediction studies will be followed by descriptions of the three categories of sample size methods: cross-validation approaches, rules-of-thumb, and statistical power methods. A rationale will then be developed for the application of precision power to multiple regression, leading to the presentation, through multiple examples, of the precision power method for sample size selection in prediction studies

Presidential Address: Undergraduate and Graduate Preparation in Educational Research Methods

The advent of high stakes state testing in K-12 education and The No Child Left Behind Act of 2001, with its focus on scientifically-based research (SBR), has opened new challenges for both undergraduate and graduate preparation programs in education. This address will report on how we are currently preparing our undergraduate and graduate students in educational research methods and then offer specific recommendations that would allow our graduates to better understand and use the information generated by these new public policies

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

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