Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or nonproficiency of specified latent characteristics. These models are well suited for providing diagnostic and actionable feedback to support intermediate and formative assessment efforts. Several DCMs have been developed…

Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or nonproficiency of specified latent characteristics. These models are well suited for providing diagnostic and actionable feedback to support intermediate and formative assessment efforts. Several DCMs have been developed…

As part of the effort to develop an improved oral reading fluency (ORF) assessment system, Kara et al. (2020) estimated the ORF scores based on a latent variable psychometric model of accuracy and speed for ORF data via a fully Bayesian approach. This study further investigates likelihood-based estimators for the model-derived ORF scores,…

As part of the effort to develop an improved oral reading fluency (ORF) assessment system, Kara et al. estimated the ORF scores based on a latent variable psychometric model of accuracy and speed for ORF data via a fully Bayesian approach. This study further investigates likelihood-based estimators for the model-derived ORF scores, including…

Composite scores are an exceptionally important psychometric tool for behavioral science research applications. A prototypical example occurs with self-report data, where researchers routinely use questionnaires with multiple items that tap into different features of a target construct. Item-level missing data are endemic to composite score…

Network models of the WAIS-IV based on regularized partial correlation matrices have been reported to outperform latent variable models based on uncorrected correlation matrices. The present study sought to compare network and latent variable models using both partial and uncorrected correlation matrices with both types of models. The results show…

Criticisms about psychometric paradigms currently used in healthcare professions education include claims of reductionism, objectification, and poor compliance with assumptions. Nevertheless, perhaps the most crucial criticism comes from learners' difficulty in interpreting and making meaningful use of summative scores and the potentially…

The C-test is a type of gap-filling test designed to efficiently measure second language proficiency. The typical C-test consists of several short paragraphs with the second half of every second word deleted. The words with deleted parts are considered as items nested within the corresponding paragraph. Given this testlet structure, it is commonly…

Scholars have lamented that current methods of assessing student performance do not align with contemporary views of learning as situated within students, contexts, and time. Here, we introduce and describe one theoretical--psychometric paradigm--termed "dynamic measurement"--designed to provide a valid representation of the way students…

An assessment of graphic literacy was developed by articulating and subsequently validating a skills-based cognitive model intended to substantiate the plausibility of score interpretations. Model validation involved use of multiple sources of evidence derived from large-scale field testing and cognitive labs studies. Data from large-scale field…

International students constitute an important sojourner group. The current research aimed to test the psychometric characteristics of the Satisfaction with Migration Life Scale (SWMLS) for international students and the role of different psychosocial variables in predicting SWMLS. The sample included 189 Brazilian college students who attended…

Drawing upon multidimensional theories of intelligence, the current paper evaluates if the Geneva Emotional Competence Test (GECo) fits within a higher-order intelligence space and if emotional intelligence (EI) branches predict distinct criteria related to adjustment and motivation. Using a combination of classical and S-1 bifactor models, we…

Testing organizations are faced with increasing demand to provide subscores in addition to the total test score. However, psychometricians argue that most subscores do not have added value to be worth reporting. To have added value, subscores need to meet a number of criteria: they should be reliable, distinctive, and distinct from each other and…

There is an increasing interest in reporting test subscores for diagnostic purposes. In this article, we review nine popular R packages (subscore, mirt, TAM, sirt, CDM, NPCD, lavaan, sem, and OpenMX) that are capable of implementing subscore-reporting methods within one or more frameworks including classical test theory, multidimensional item…

More robust and rigorous psychometric models, such as multidimensional Item Response Theory models, have been advocated for survey applications. However, item responses may be influenced by construct-irrelevant variance factors such as preferences for extreme response options. Through empirical and simulation methods, this study evaluates the use…