The purpose of this two-part study is to evaluate methods for multiple group analysis when the comparison group is at the within level with multilevel data, using a multilevel factor mixture model (ML FMM) and a multilevel multiple-indicators multiple-causes (ML MIMIC) model. The performance of these methods was evaluated integrally by a series of…

Certain diversity among team members is beneficial to the growth of an organization. Multiple measures have been proposed to quantify diversity, although little is known about their psychometric properties. This article proposes several methods to evaluate the unidimensionality and reliability of three measures of diversity. To approximate the…

This note is concerned with a latent variable modeling approach for the study of differential item functioning in a multigroup setting. A multiple-testing procedure that can be used to evaluate group differences in response probabilities on individual items is discussed. The method is readily employed when the aim is also to locate possible…

Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture…

Proposes the use of the group overlap concept as a basis for determining effect size. Group overlap may be assessed via prediction of group assignment, using predictive discriminant analysis. The effect-size index proposed is that of improvement-over-chance "(I)" classification. Makes some suggestions for cutoffs of "I" values…

Relationships among the noncentrality parameter for the "F" distribution, mean square between and within groups, and effect size are examined. (Author)

The power of the likelihood ratio test in multiple group confirmatory factor analysis under partial measurement invariance was studied in a population study with a six-variable, two-factor model where a specification error existed for one group. Results are discussed in terms of strategies of multiple group modeling. (SLD)

A generalization of the G index of agreement is presented, allowing for its application in multiple ratings. The generalized coefficient is intended for use in the psychiatric clinic. An illustrative example is provided, using hypothetical data. (Author)

Three factors that increase score variability yet can be associated with an increase, a decrease, or no change in Pearson's correlation coefficient (r) are discussed (restriction of range, errors of measurement, and linear transformations of data). The connection between changes in variability and r depends on how changes occur. (SLD)

Describes the MANOVA (multivariate analysis of variance) Computer Program for ascertaining whether k groups differ significantly from one another on p dependent variables. (Author/RC)

A two-factor analysis of variance with multiple measurements on one factor was conducted among the 40 items of the Vocabulary test of the Test of English as a Foreign Language (TOEFL) for six language groups. All sources of variance were found significant beyond the one per cent level. (Author/RC)

Criterion-keying of interest inventories involves selecting items which best distinguish a group of incumbents in a particular occupation from another group intended to represent the population of interest. This practice is questioned here and data are presented to support the author's contention. (Author/JKS)

Empirical estimates of the power and Type I error rate of the test of the classrooms-within-treatments effect in the nested analysis of variance approach are provided for a variety of nominal alpha levels and a range of classroom effect sizes and research designs. (SLD)