Varied the number of items and categories per item to explore the effects on estimation of item parameters in the nominal response model. Simulation results show that increasing the number of items had little effect on item parameter recovery, but increasing the number of categories increased the error variance of the parameter estimates. (SLD)

Examined 20 goodness-of-fit indexes based on the minimum fit function using a simulation under the 2-group situation. Results support the use of the delta comparative fit index, delta Gamma hat, and delta McDonald's Noncentrality Index to evaluation measurement invariance. These three approaches are independent of model complexity and sample size.…

Examined the effects of ignoring multilevel data structures in nonhierarchical covariance modeling using a Monte Carlo simulation. Results suggest that when the magnitudes of intraclass correlations are less than 0.05 and the group size is small, the consequences of ignoring the data dependence within the multilevel data structures seem to be…

Studied sample sizes for confidence intervals on the increase in the squared multiple correlation coefficient using simulation. Discusses predictors and actual coverage probability and provides sample-size guidelines for probability coverage to be near the nominal confidence interval. (SLD)

Two unresolved implementation issues with logistic regression (LR) for differential item functioning (DIF) detection include ability purification and effect size use. Purification is suggested to control inaccuracies in DIF detection as a result of DIF items in the ability estimate. Additionally, effect size use may be beneficial in controlling…

Reporting methods used in large-scale assessments such as the National Assessment of Educational Progress (NAEP) rely on latent regression models. To fit the latent regression model using the maximum likelihood estimation technique, multivariate integrals must be evaluated. In the computer program MGROUP used by the Educational Testing Service for…

The Johnson-Neyman (J-N) technique (P. Johnson and N. Neyman, 1936) is used to determine areas of significant difference in a criterion variable between two or more groups in situations of linear regression. In using this technique, researchers have encountered difficulties with results, possibly related to the J-N technique's sensitivity to…

This study presents and evaluates a method of standardization that may be used by test practitioners to standardize classical item statistics when sample sizes are small. The effectiveness of this standardization approach was compared through simulation with the one-parameter logistic (1PL) and three parameter logistic (3PL) models based on the…

Studied two procedures for detecting potentially flawed items in translated tests with small samples: (1) conditional item "p" value comparisons; and (2) delta plots. Varied several factors in this simulation study. Findings show that the two procedures can be valuable in identifying flawed test items, especially when the size of the…

The underlying statistical models for multiple regression analysis are typically attributed to two types of modeling: fixed and random. The procedures for calculating power and sample size under the fixed regression models are well known. However, the literature on random regression models is limited and has been confined to the case of all…

The jackknife and bootstrap methods are becoming more popular in research. Although the two approaches have similar goals and employ similar strategies, information is lacking with regard to the comparability of their results. This study systematically investigated the issue for a canonical correlation analysis, using data from four random samples…

When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

Simulation results show that the power to detect small mean differences when fitting a model with free residual variances across groups decreases as the difference in R squared increases. This decrease is more pronounced in the presence of correlated errors and if group sample sizes differ. (SLD)

Studied the use of different weighting techniques in structural equation modeling and found, through simulation, that the use of an effective sample size weight provides unbiased estimates of key parameters and their sampling variances. Also discusses use of a popular normalization technique of scaling weights. (SLD)

Type I error rates and rejection rates for three-dimensionality assessment procedures were studied with data sets simulated to reflect short tests and small samples. Results show that the G-squared difference test (D. Bock, R. Gibbons, and E. Muraki, 1988) suffered from a severely inflated Type I error rate at all conditions simulated. (SLD)