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A Mixed Stochastic Approximation EM (MSAEM) Algorithm for the Estimation of the Four-Parameter Normal Ogive Model
In recent years, the four-parameter model (4PM) has received increasing attention in item response theory. The purpose of this article is to provide...
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A 2PLM-RANK multidimensional forced-choice model and its fast estimation algorithm
High-stakes non-cognitive tests frequently employ forced-choice (FC) scales to deter faking. To mitigate the issue of score ipsativity derived, many...
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A two-step estimator for multilevel latent class analysis with covariates
We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in...
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A Note on Improving Variational Estimation for Multidimensional Item Response Theory
Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure...
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Using EM Algorithm for Finite Mixtures and Reformed Supplemented EM for MIRT Calibration
This study revisits the parameter estimation issues in multidimensional item response theory more thoroughly and investigates some computation...
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Joint Latent Space Model for Social Networks with Multivariate Attributes
In social, behavioral and economic sciences, researchers are interested in modeling a social network among a group of individuals, along with their...
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Accelerating item factor analysis on GPU with
Python packagexifa Item parameter estimation is a crucial step when conducting item factor analysis (IFA). From the view of frequentist estimation, marginal maximum...
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Adapting to the algorithm: how accuracy comparisons promote the use of a decision aid
In three experiments, we sought to understand when and why people use an algorithm decision aid. Distinct from recent approaches, we explicitly...
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A Diagnostic Facet Status Model (DFSM) for Extracting Instructionally Useful Information from Diagnostic Assessment
Modern assessment demands, resulting from educational reform efforts, call for strengthening diagnostic testing capabilities to identify not only the...
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The impact of ordinal scales on Gaussian mixture recovery
Gaussian mixture models (GMMs) are a popular and versatile tool for exploring heterogeneity in multivariate continuous data. Arguably the most...
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Dynamical Non-compensatory Multidimensional IRT Model Using Variational Approximation
Multidimensional item response theory (MIRT) is a statistical test theory that precisely estimates multiple latent skills of learners from the...
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Regularized Variational Estimation for Exploratory Item Factor Analysis
Item factor analysis (IFA), also known as Multidimensional Item Response Theory (MIRT), is a general framework for specifying the functional...
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Variational Estimation for Multidimensional Generalized Partial Credit Model
Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for...
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Estimating Finite Mixtures of Ordinal Graphical Models
Graphical models have received an increasing amount of attention in network psychometrics as a promising probabilistic approach to study the...
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Using Penalized EM Algorithm to Infer Learning Trajectories in Latent Transition CDM
Cognitive diagnostic models (CDMs) have arisen as advanced psychometric models in the past few decades for assessments that intend to measure...
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Variational Bayes Inference Algorithm for the Saturated Diagnostic Classification Model
Saturated diagnostic classification models (DCM) can flexibly accommodate various relationships among attributes to diagnose individual attribute...
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Semiparametric Factor Analysis for Item-Level Response Time Data
Item-level response time (RT) data can be conveniently collected from computer-based test/survey delivery platforms and have been demonstrated to...
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Evaluating FIML and multiple imputation in joint ordinal-continuous measurements models with missing data
Missing data is a common occurrence in confirmatory factor analysis (CFA). Much work had evaluated the performance of different techniques when all...
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A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis
Marginal maximum likelihood (MML) estimation is the preferred approach to fitting item response theory models in psychometrics due to the MML...
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Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models
Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models that are widely used in educational and psychological...
