The best of both worlds: a joint modeling approach for the assessment of change across repeated measurements

The usefulness of Bayesian methods in estimating complex statistical models is undeniable. From a Bayesian standpoint, this paper aims to demonstrate the capacity of Bayesian methods and propose a comprehensive model combining both a measurement model (e.g., an item response model, IRM) and a structural model (e.g., a latent variable model, LVM). That is, through the incorporation of the probit link and Bayesian estimation, the item response model can be introduced naturally into a latent variable model. The utility of this IRM-LVM comprehensive framework is investigated with a real data example and promising results are obtained, in which the data drawn from part of the British Social Attitudes Panel Survey 1983-1986 reveal the attitude toward abortion of a representative sample of adults aged 18 or older living in Great Britain. The application of IRMs to responses gathered from repeated assessments allows us to take the characteristics of both item responses and measurement error into consideration in the analysis of individual developmental trajectories, and helps resolve some difficult modeling issues commonly encountered in developmental research, such as small sample sizes, multiple discretely scaled items, many repeated assessments, and attrition over time.

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Joint modeling of the two-alternative multidimensional forced-choice personality measurement and its response time by a Thurstonian D-diffusion item response model

Behavior Research Methods ◽

10.3758/s13428-019-01302-5 ◽

2020 ◽

Vol 52 (3) ◽

pp. 1091-1107

Author(s):

Kyosuke Bunji ◽

Kensuke Okada

Keyword(s):

Response Time ◽

Item Response ◽

Joint Modeling ◽

Forced Choice ◽

Response Model ◽

Personality Measurement ◽

Item Response Model

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A Zero-Inflated Box-Cox Normal Unipolar Item Response Model for Measuring Constructs of Psychopathology

Applied Psychological Measurement ◽

10.1177/0146621618758291 ◽

2018 ◽

Vol 42 (7) ◽

pp. 571-589 ◽

Cited By ~ 1

Author(s):

Brooke E. Magnus ◽

Yang Liu

Keyword(s):

Item Response ◽

Latent Variable ◽

Model Comparison ◽

Latent Class ◽

Normal Population ◽

Empirical Support ◽

Variable Density ◽

Parameter Estimates ◽

Response Model ◽

Item Response Model

This research introduces a latent class item response theory (IRT) approach for modeling item response data from zero-inflated, positively skewed, and arguably unipolar constructs of psychopathology. As motivating data, the authors use 4,925 responses to the Patient Health Questionnaire (PHQ-9), a nine Likert-type item depression screener that inquires about a variety of depressive symptoms. First, Lucke’s log-logistic unipolar item response model is extended to accommodate polytomous responses. Then, a nontrivial proportion of individuals who do not endorse any of the symptoms are accounted for by including a nonpathological class that represents those who may be absent on or at some floor level of the latent variable that is being measured by the PHQ-9. To enhance flexibility, a Box-Cox normal distribution is used to empirically determine a transformation parameter that can help characterize the degree of skewness in the latent variable density. A model comparison approach is used to test the necessity of the features of the proposed model. Results suggest that (a) the Box-Cox normal transformation provides empirical support for using a log-normal population density, and (b) model fit substantially improves when a nonpathological latent class is included. The parameter estimates from the latent class IRT model are used to interpret the psychometric properties of the PHQ-9, and a method of computing IRT scale scores that reflect unipolar constructs is described, focusing on how these scores may be used in clinical contexts.

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An Item Response Model for Characterizing Test Compromise

Journal of Educational and Behavioral Statistics ◽

10.3102/10769986027002163 ◽

2002 ◽

Vol 27 (2) ◽

pp. 163-179 ◽

Cited By ~ 13

Author(s):

Daniel O. Segall

Keyword(s):

Item Response ◽

Computerized Adaptive Testing ◽

High Stakes Testing ◽

Small Sample ◽

Model Parameters ◽

Response Model ◽

Posterior Distributions ◽

Item Response Model ◽

High Stakes ◽

Score Gain

This article presents an item response model for characterizing test-compromise that enables the estimation of item-preview and score-gain distributions observed in on-demand high-stakes testing programs. Model parameters and posterior distributions are estimated by Markov Chain Monte Carlo (MCMC) procedures. Results of a simulation study suggest that when at least some of the items taken by a small sample of test takers are known to be secure (uncompromised), the procedure can provide useful summaries of test-compromise and its impact on test scores. The article includes discussions of operational use of the proposed procedure, possible model violations and extensions, and application to computerized adaptive testing.

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Bayesian Hierarchical Multidimensional Item Response Modeling of Small Sample, Sparse Data for Personalized Developmental Surveillance

Educational and Psychological Measurement ◽

10.1177/0013164420987582 ◽

2021 ◽

pp. 001316442098758

Author(s):

Patricia Gilholm ◽

Kerrie Mengersen ◽

Helen Thompson

Keyword(s):

Item Response ◽

Sparse Data ◽

Small Sample ◽

Model Parameters ◽

Response Model ◽

Item Response Model ◽

Multidimensional Item Response ◽

Developmental Surveillance ◽

Bayesian Hierarchical ◽

Item Response Modeling

Developmental surveillance tools are used to closely monitor the early development of infants and young children. This study provides a novel implementation of a multidimensional item response model, using Bayesian hierarchical priors, to construct developmental profiles for a small sample of children ( N = 115) with sparse data collected through an online developmental surveillance tool. The surveillance tool records 348 developmental milestones measured from birth to three years of age, within six functional domains: auditory, hands, movement, speech, tactile, and vision. The profiles were constructed in three steps: (1) the multidimensional item response model, embedded in the Bayesian hierarchical framework, was implemented in order to measure both the latent abilities of the children and attributes of the milestones, while retaining the correlation structure among the latent developmental domains; (2) subsequent hierarchical clustering of the multidimensional ability estimates enabled identification of subgroups of children; and (3) information from the posterior distributions of the item response model parameters and the results of the clustering were used to construct a personalized profile of development for each child. These individual profiles support early identification of, and personalized early interventions for, children with developmental delay.

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