Diagnostic classification models (DCMs) are psychometric models that yield probabilistic classifications of respondents according to a set of discrete latent variables. The current study examines the recently introduced one-parameter log-linear cognitive diagnosis model (1-PLCDM), which has increased interpretability compared with general DCMs due…

Diagnostic classification models (DCMs) have grown in popularity as stakeholders increasingly desire actionable information related to students' skill competencies. Longitudinal DCMs offer a psychometric framework for providing estimates of students' proficiency status transitions over time. For both cross-sectional and longitudinal DCMs, it is…

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…

My dissertation research explored responder behaviors (e.g., demonstrating response styles, carelessness, and possessing misconceptions) that compromise psychometric quality and impact the interpretation and use of assessment results. Identifying these behaviors can help researchers understand and minimize their potentially construct-irrelevant…

Despite the increasing demand for diagnostic information, observed subscores have been often reported to lack adequate psychometric qualities such as reliability, distinctiveness, and validity. Therefore, several statistical techniques based on CTT and IRT frameworks have been proposed to improve the quality of subscores. More recently, DCM has…

Rasch and classification analyses on a field-test version of the third edition of the Assessment, Evaluation, and Programming System (AEPS-3), a curriculum-based assessment used to assess young children birth to age 6 years, were conducted. First, an evaluation of the psychometric properties of data from each developmental area of an AEPS-3…

Maintaining equivalent performance standards across forms is a psychometric challenge exacerbated by small samples. In this study, the accuracy of two equating methods (Rasch anchored calibration and nominal weights mean) and four anchor item selection methods were investigated in the context of very small samples (N = 10). Overall, nominal…

Purpose: Researchers discovered that when students were given the opportunity to change their answers, a majority changed their responses from incorrect to correct, and this change often increased the overall test results. What prompts students to modify their answers? This study aims to examine the modification of scientific reasoning test, with…

This article considers psychometric properties of composite raw scores and transformed scale scores on mixed-format tests that consist of a mixture of multiple-choice and free-response items. Test scores on several mixed-format tests are evaluated with respect to conditional and overall standard errors of measurement, score reliability, and…

Recent advances have enabled diagnostic classification models (DCMs) to accommodate longitudinal data. These longitudinal DCMs were developed to study how examinees change, or transition, between different attribute mastery statuses over time. This study examines using longitudinal DCMs as an approach to assessing growth and serves three purposes:…

The NAEP EDM Competition required participants to predict efficient test-taking behavior based on log data. This paper describes our top-down approach for engineering features by means of psychometric modeling, aiming at machine learning for the predictive classification task. For feature engineering, we employed, among others, the Log-Normal…

This report presents results on a parallel implementation of the expectation-maximization (EM) algorithm for multidimensional latent variable models. The developments presented here are based on code that parallelizes both the E step and the M step of the parallel-E parallel-M algorithm. Examples presented in this report include item response…

Developing a diagnostic tool within the diagnostic measurement framework is the optimal approach to obtain multidimensional and classification-based feedback on examinees. However, end users may seek to obtain diagnostic feedback from existing item responses to assessments that have been designed under either the classical test theory or item…

This paper presents an approach to standard setting that combines the prototype group method (PGM; Eckes, 2012) with a receiver operating characteristic (ROC) analysis. The combined PGM-ROC approach is applied to setting cut scores on a placement test of English as a foreign language (EFL). To implement the PGM, experts first named learners whom…