When selecting a multilevel model to fit to a dataset, it is important to choose both a model that best matches characteristics of the data's structure, but also to include the appropriate fixed and random effects parameters. For example, when researchers analyze clustered data (e.g., students nested within schools), the multilevel model can be…

Despite the popularity of propensity score (PS) techniques they are not yet well studied for matching multilevel data where selection into treatment takes place among level-one units within clusters. This paper suggests a PS matching strategy that tries to avoid the disadvantages of within- and across-cluster matching. The idea is to first…

Peer selection based on the similarity of a couple of institutional parameters, by itself, is insufficient. Several other considerations, including clarity of purpose, alignment of institutional information to that purpose, identification of appropriate statistical procedures, review of preliminary peer sets, and the application of additional…

One useful and increasingly popular method of classifying students is known commonly as cluster analysis. The variety of techniques that comprise the cluster analytic family are intended to sort observations (for example, students) within a data set into subsets (clusters) that share similar characteristics and differ in meaningful ways from other…

Background: Because of the educational reform and decreasing birth rate in Taiwan over the past 20 years, higher technological and vocational Education (TVE) in Taiwan faces a severe student recruitment competition. Dailey (2007) indicates the need to develop marketing strategies in higher education is evident. TVE institutes are beginning to…

Multivariate selection can be represented as a linear transformation in a geometric framework. In this note this approach is extended to describe the effects of selection on regression analysis and to adjust for the effects of selection using the inverse of the linear transformation. (Author/BW)

The purpose of this study was to examine the effect of instrument completion instructions on univariate and multivariate distributional characteristics and relationships among variables. Instructions allowed free-choice allotment of ratings (unforced instructions) or requested the subject to assign a certain number of ratings to either the highest…

This examination of data selection preceding multivariate analysis compares results grained with "gentle" and "draconian" variable elimination. To acquire comparable results, two stages of statistical exploration into an integrated model of motivation, learning strategies, and quality of teaching were used. The goal of the…

Effects of different weighting schemes on selecting the optimal number of observations in multivariate-multifacet generalizability designs are studied when cost constraints are imposed. Comparison of four schemes through simulation indicates that all four produce similar optimal values and that reliability should be similar. (SLD)

This study examined the data selection process preceding multivariate analysis for a data set measuring student motivation and self-regulated learning. Data were 138 responses to a questionnaire on motivation and self-regulated learning, adapted for Finnish students. The first goal was to compare the results gained with "gentle" and…

Although there is a healthy trend toward employment of multivariate statistical methods in the analysis of data from experiments in psychology and education which involve multiple dependent variables, there remains a need to demonstrate the wide variety of data analytic techniques available for post hoc analysis of statistically significant…

Purpose: This article reports on a within-family study to identify factors that lead mothers to expect that a particular child will serve in the role of primary caregiver. Design and Methods: Data for this study were collected by in-person interviews with a representative sample of 566 mothers between the ages of 65 and 75 years residing in the…

Cudeck and Browne (1983) proposed using cross-validation as a model selection technique in structural equation modeling. The purpose of this study is to examine the performance of eight cross-validation indices under conditions not yet examined in the relevant literature, such as nonnormality and cross-validation design. The performance of each…