This study compares the ability of the multiple indicators, multiple causes (MIMIC) confirmatory factor analysis model to correctly identify cases of differential item functioning (DIF) with more established methods. Although the MIMIC model might have application in identifying DIF for multiple grouping variables, there has been little examination of how well the technique works in terms of correct and incorrect identification of DIF. A Monte Carlo methodology is used in this study, with manipulation of the number of items, number of examinees, differences between the mean abilities of the reference and focal groups, level of DIF contamination of the anchor items, and amount of DIF in the target item. Results indicate that the MIMIC model is effective for DIF identification for 50 items or when the two-parameter logistic model underlies the data but has a very high rate of incorrect DIF identification for 20 items with three-parameter logistic data.

Missing data are a common problem in a variety of measurement settings, including responses to items on both cognitive and affective assessments. Researchers have shown that such missing data may create problems in the estimation of item difficulty parameters in the Item Response Theory (IRT) context, particularly if they are ignored. At the same time, a number of data imputation methods have been developed outside of the IRT framework and been shown to be effective tools for dealing with missing data. The current study takes several of these methods that have been found to be useful in other contexts and investigates their performance with IRT data that contain missing values. Through a simulation study, it is shown that these methods exhibit varying degrees of effectiveness in terms of imputing data that in turn produce accurate sample estimates of item difficulty and discrimination parameters.

No abstract

Abstract. Multivariate analysis of variance (MANOVA) is a useful tool for social scientists because it allows for the comparison of response-variable means across multiple groups. MANOVA requires that the observations are independent, the response variables are multivariate normally distributed, and the covariance matrix of the response variables is homogeneous across groups. When the assumptions of normality and homogeneous covariance matrices are not met, past research has shown that the type I error rate of the standard MANOVA test statistics can be inflated while their power can be attenuated. The current study compares the performance of a nonparametric alternative to one of the standard parametric test statistics when these two assumptions are not met. Results show that when the assumption of homogeneous covariance matrices is not met, the nonparametric approach has a lower type I error rate and higher power than the most robust parametric statistic. When the assumption of normality is untenable, the parametric statistic is robust, and slightly outperforms the nonparametric statistic in terms of type I error rate and power.

The purpose of the present study was to create and test a model that (a) illustrated variables influencing the development of perfectionism, and (b) demonstrated how different types of perfectionism may influence the achievement goals of high-ability students. Using a multiple-groups path analysis, the researchers found that parenting style was related to attachment, with authoritative and permissive parenting associated with secure attachment and authoritarian and uninvolved parenting associated with insecure attachment. Attachment, in turn, was related to perfectionism, with insecure attachment associated with either self-oriented or socially prescribed perfectionism. In addition, the model then illustrated that perfectionism would influence achievement goals, with self-oriented perfectionists more likely to set mastery or performance-approach goals, and socially prescribed perfectionists more likely to set performance-approach or performance-avoidance goals. The findings of this study are interpreted in the context of the existing literature, and implications for working with high-ability perfectionistic students are discussed.