Inclusion of irrelevant variables in a cluster analysis adversely affects subgroup recovery. This paper examines using moment-based statistics to screen variables; only variables that pass the screening are then used in clustering. Normal mixtures are analytically shown often to possess negative kurtosis. Two related measures, "m" and…

Two Monte Carlo studies investigated the effects of within-group covariance structure on subgroup recovery by 10 hierarchical clustering methods using 100 bivariate observations from 2 subgroups. Superior recovery was associated with within-group correlation that matched the direction of subgroup separation. (SLD)

This article examines using moment-based statistics to screen variables that are then used in clustering. A Monte Carlo study found that screening variables was a viable alternative to both ultrametric weighting and forward selection of variables. Advantages and disadvantages of screening are discussed. (SLD)

Used Monte Carlo methods to compare several measures of item-parameter drift, manipulating numbers of examinees and items and numbers of drift items. Overall, Lord's chi square (F. Lord, 1968) measure was the most effective in identifying items that exhibited drift. Discusses the usefulness of other methods. (SLD)

Examined the effect of complex sampling of items on the measurement of differential item functioning (DIF) using the Mantel-Haenszel procedure through a Monte Carlo study. Suggests the superiority of the pooled booklet method when items are selected for examinees according to a balanced incomplete block design. Discusses implications for other DIF…

Monte Carlo methods were used to investigate the effect of misspecification of the second level in a two-level hierarchical linear model (HLM). Sample composition, heterogeneity of the group size, level of intraclass correlation, and correlation between second-level predictors were manipulated. Each of 20 generated data sets was analyzed nine…

A Monte Carlo study compared the usefulness of six variable weighting methods for cluster analysis. Data were 100 bivariate observations from 2 subgroups, generated according to a finite normal mixture model. Subgroup size, within-group correlation, within-group variance, and distance between subgroup centroids were manipulated. Of the clustering…

Monte Carlo studies investigated effects of within-group covariance structure on subgroup recovery by several widely used hierarchical clustering methods. In Study 1, subgroup size, within-group correlation, within-group variance, and distance between subgroup centroids were manipulated. All clustering methods were strongly affected by…

This Monte Carlo study examined strategies for forming the matching variable for the Mantel-Haenszel (MH) differential item functioning (DIF) procedure. Data were generated using a three-parameter logistic item response theory model, with common guessing parameters. The number of subjects and test length were manipulated, as were the difficulty,…

Forming the matching variable for the Mantel-Haenszel differential item functioning (DIF) procedure through use of the total score as the matching variable (thin) and forming the matching variable by pooling total score levels (thick) were compared in a Monte Carlo study. Reasons thick matching is superior are discussed. (SLD)

This Monte Carlo study examined the effect of complex sampling of items on the measurement of differential item functioning (DIF) using the Mantel-Haenszel procedure. Data were generated using a three-parameter logistic item response theory model according to the balanced incomplete block (BIB) design used in the National Assessment of Educational…

The validity of the assumptions under which the ordinal true score test theory was derived was examined using (1) simulation based on classical test theory; (2) a long empirical test with data from 321 sixth graders; and (3) an extensive simulation with 480 datasets based on the 3-parameter model. (SLD)