Identifying items with differential item functioning (DIF) in an assessment is a crucial step for achieving equitable measurement. One critical issue that has not been fully addressed with existing studies is how DIF items can be detected when data are multilevel. In the present study, we introduced a Lord's Wald X[superscript 2] test-based…

The purpose of this study was to evaluate the degree of classification quality in the basic latent class model when covariates are either included or are not included in the model. To accomplish this task, Monte Carlo simulations were conducted in which the results of models with and without a covariate were compared. Based on these simulations,…

We examined the performance of coefficient alpha and its potential competitors (ordinal alpha, omega total, Revelle's omega total [omega RT], omega hierarchical [omega h], greatest lower bound [GLB], and coefficient "H") with continuous and discrete data having different types of non-normality. Results showed the estimation bias was…

Test fairness is critical to the validity of group comparisons involving gender, ethnicities, culture, or treatment conditions. Detection of differential item functioning (DIF) is one component of efforts to ensure test fairness. The current study compared four treatments for items that have been identified as showing DIF: deleting, ignoring,…

The unstructured multiple-attempt (MA) item response data in virtual learning environments (VLEs) are often from student-selected assessment data sets, which include missing data, single-attempt responses, multiple-attempt responses, and unknown growth ability across attempts, leading to a complex and complicated scenario for using this kind of…

Researchers continue to be interested in efficient, accurate methods of estimating coefficients of covariates in mixture modeling. Including covariates related to the latent class analysis not only may improve the ability of the mixture model to clearly differentiate between subjects but also makes interpretation of latent group membership more…

To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data-model fit. However, previous studies have demonstrated poor…

The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…

Response styles, the tendency to respond to Likert-type items irrespective of content, are a widely known threat to the reliability and validity of self-report measures. However, it is still debated how to measure and control for response styles such as extreme responding. Recently, multiprocess item response theory models have been proposed that…

The diagnostic criteria for attention deficit hyperactivity disorder have evolved over time with current versions of the "Diagnostic and Statistical Manual", (4th edition), text revision, ("DSM-IV-TR") suggesting that two constellations of symptoms may be present alone or in combination. The SCALES instrument for diagnosing attention deficit…

The Disruptive Behavior Disorders (DBD) scale includes the "Diagnostic and Statistical Manual of Mental Disorders" (4th ed.) criteria for attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder, and conduct disorder. This study examined only the ADHD items of the DBD scale. This scale is frequently used for assessing parent-…

Because nominal-scale judgments cannot directly be aggregated into meaningful composites, the addition of a second rater is usually motivated by a desire to estimate the quality of a single rater's classifications rather than to improve reliability. When raters agree, the aggregation problem does not arise. Nevertheless, a proportion of this…

Use of the bootstrap method to approximate the sampling variation of eigenvalues is explicated, and its usefulness is amplified by an illustration in conjunction with two commonly used factor criteria. These criteria are eigenvalues larger than one and the Scree test. (TJH)

CUTOFF is a program to identify the most efficient classification prediction cutting score. It ranks the scores of a criterion and a comparison group and organizes them into cumulative frequency tables allowing for immediate display of an index of correct identifications associated with each score. (Author/JKS)