Applications of polytomous IRT models in applied fields (e.g., health, education, psychology) are abound. However, little is known about the impact of the number of categories and sample size requirements for precise parameter recovery. In a simulation study, we investigated the impact of the number of response categories and required sample size…

Purpose: This article provides a tutorial introduction to ordinal pattern analysis, a statistical analysis method designed to quantify the extent to which hypotheses of relative change across experimental conditions match observed data at the level of individuals. This method may be a useful addition to familiar parametric statistical methods…

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,…

Diagnostic classification models (DCMs) are psychometric models that can be used to estimate the presence or absence of psychological traits, or proficiency on fine-grained skills. Critical to the use of any psychometric model in practice, including DCMs, is an evaluation of model fit. Traditionally, DCMs have been estimated with maximum…

Behavioral and emotional disorders in childhood can have lasting impacts in areas such as education and future employment, often extending into adulthood. Identifying the potential disorders in children's early grades is beneficial to provide proactive assistance. In this study, we employed a well-validated scale - the Strengths and Difficulties…

Current recommended cutoffs for determining measurement invariance have typically derived from simulation studies that have focused on multigroup confirmatory factor analysis, often using continuous data. These cutoffs may be inappropriate for ordered categorical data in a longitudinal setting. This study conducts two Monte Carlo studies that…

The application of diagnostic classification models (DCMs) in the field of educational measurement is getting more attention in recent years. To make a valid inference from the model, it is important to ensure that the model fits the data. The purpose of the present study was to investigate the performance of the limited information…

In this ITEMS module, we introduce the generalized deterministic inputs, noisy "and" gate (G-DINA) model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models. The module contains a nontechnical introduction to diagnostic measurement, an introductory overview of the G-DINA…

The Learning and Study Strategies Inventory (LASSI; Weinstein et al., "Learning and study strategies inventory." H&H Publishing, 1987) is a prominent instrument used in thousands of institutions worldwide as an educational and research tool. Despite its widespread prevalence, there are inconsistencies regarding the underlying latent…

Although Diagnostic Classification Models (DCMs) were introduced to education system decades ago, it seems that these models were not employed for the original aims upon which they had been designed. Using DCMs has been mostly common in analyzing large-scale non-diagnostic tests and these models have been rarely used in developing Cognitive…

In this study, the correct classification level of whether forgiving oneself, others and the situation is abusing their partners was determined by logistic regression analysis. There are 221 young adults ranging from 19-30 in this study, which was designed in the scanning model. Heartland Forgiveness Scale and Information Form were used in the…

The current study compared the model fit indices, skill mastery probabilities, and classification accuracy of six Diagnostic Classification Models (DCMs): a general model (G-DINA) against five specific models (LLM, RRUM, ACDM, DINA, and DINO). To do so, the response data to the grammar and vocabulary sections of a General English Achievement Test,…

Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1)…

Advanced modeling of item response data through the item response theory (IRT) or item factor analysis frameworks is becoming increasingly popular. In the social and behavioral sciences, the underlying structure of tests/assessments is often multidimensional (i.e., more than 1 latent variable/construct is represented in the items). This review…

Collapsing categories is a commonly used data reduction technique; however, to date there do not exist principled methods to determine whether collapsing categories is appropriate in practice. With ordinal responses under the partial credit model, when collapsing categories, the true model for the collapsed data is no longer a partial credit…