The authors examined the dimensionality of the VARK learning styles inventory. The VARK measures four perceptual preferences: visual (V), aural (A), read/write (R), and kinesthetic (K). VARK questions can be viewed as testlets because respondents can select multiple items within a question. The correlations between items within testlets are a type of method effect. Four multitrait—multimethod confirmatory factor analysis models were compared to evaluate the dimensionality of the VARK. The correlated trait—correlated method model had the best fit to the VARK scores. The estimated reliability coefficients were adequate. The study found preliminary support for the validity of the VARK scores. Potential problems related to item wording and the scale’s scoring algorithm were identified, and cautions with respect to using the VARK with research were raised.

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This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and traditionally used methods of item selection is also presented. It is shown that the ACO algorithm outperforms the largest factor loadings and maximum test information item selection methods. The results demonstrate the capabilities of using ACO for creating short-form scales.

The LCM and TPM with a log-transformed dependent variable are superior to other approaches in handling data with ceiling effects.

The Marlowe-Crowne Social Desirability Scale (MCSDS), the most commonly used social desirability bias (SDB) assessment, conceptualizes SDB as an individual’s need for approval. The Balanced Inventory of Desirable Responding (BIDR) measures SDB as two separate constructs: impression management and self-deception. Scores on SDB scales are commonly used to validate other measures although insufficiently validated themselves. This study used college students’ responses to the MCSDS and the BIDR to investigate their factorial validity. Using confirmatory factor analysis, neither a one-nor a two-factor model was found to be strongly supported. It is recommended that researchers be cautious when using scores on these SDB scales until their dimensionality is better understood.

This study tested five confirmatory factor analytic (CFA) models of the Positive Affect Negative Affect Schedule (PANAS) to provide validity evidence based on its internal structure. A sample of 223 club sport athletes indicated their emotions during the past week. Results revealed that an orthogonal two-factor CFA model, specifying error correlations according to Zevon and Tellegen’s mood content categories, provided the best fit to our data. In addition, parameter estimates for this model suggest that PANAS scores are reliable and explain large proportions of item variance. Taken together with previous research, the findings further suggest that the PANAS may be a higher-order measure of affect and includes several consistently problematic items. The authors recommend that affect researchers attempt to improve the PANAS by (a) revising consistently problematic items, (b) adding new items to better capture mood content categories, and (c) providing additional internal structure validity evidence through a diagonally weighted least squares estimation of a second-order PANAS CFA model.

A reliability generalization (RG) study was conducted for the Marlowe-Crowne Social Desirability Scale (MCSDS). The MCSDS is the most commonly used tool designed to assess social desirability bias (SDB). Several short forms, consisting of items from the original 33-item version, are in use by researchers investigating the potential for SDB in responses to other scales. These forms have been used to measure a wide array of populations. Using a mixed-effects model analysis, the predicted score reliability for male adolescents was .53 and the reliability for men's responses was lower than that for women's. Suggestions are made concerning the necessity for further psychometric evaluations of the MCSDS.

Cognitive diagnosis models are diagnostic models used to classify respondents into homogenous groups based on multiple categorical latent variables representing the measured cognitive attributes. This study aims to present longitudinal models for cognitive diagnosis modeling, which can be applied to repeated measurements in order to monitor attribute stability of individuals and to account for respondent dependence. Models based on combining latent transition analysis modeling and the DINA and DINO cognitive diagnosis models were developed and then evaluated through a Monte Carlo simulation study. The study results indicate that the proposed models provide adequate convergence and correct classification rates.