scholarly journals Linking Item Response Model Parameters

Psychometrika ◽  
2015 ◽  
Vol 81 (3) ◽  
pp. 650-673 ◽  
Author(s):  
Wim J. van der Linden ◽  
Michelle D. Barrett
Keyword(s):  
Item Response ◽  
Model Parameters ◽  
Response Model ◽  
Item Response Model

DSM criteria for major depression: evaluating symptom patterns using latent-trait item response models

2004 ◽  
Vol 35 (4) ◽  
pp. 475-487 ◽  
Author(s):  
STEVEN H. AGGEN ◽  
MICHAEL C. NEALE ◽  
KENNETH S. KENDLER
Keyword(s):  
Major Depression ◽  
Item Response ◽  
Latent Trait ◽  
Risk Measurement ◽  
Measurement Properties ◽  
Model Parameters ◽  
Response Model ◽  
Item Response Model ◽  
Response Models ◽  
Item Response Models

Background. Expert committees of clinicians have chosen diagnostic criteria for psychiatric disorders with little guidance from measurement theory or modern psychometric methods. The DSM-III-R criteria for major depression (MD) are examined to determine the degree to which latent trait item response models can extract additional useful information.Method. The dimensionality and measurement properties of the 9 DSM-III-R criteria plus duration are evaluated using dichotomous factor analysis and the Rasch and 2 parameter logistic item response models. Quantitative liability scales are compared with a binary DSM-III-R diagnostic algorithm variable to determine the ramifications of using each approach.Results. Factor and item response model results indicated the 10 MD criteria defined a reasonably coherent unidimensional scale of liability. However, person risk measurement was not optimal. Criteria thresholds were unevenly spaced leaving scale regions poorly measured. Criteria varied in discriminating levels of risk. Compared to a binary MD diagnosis, item response model (IRM) liability scales performed far better in (i) elucidating the relationship between MD symptoms and liability, (ii) predicting the personality trait of neuroticism and future depressive episodes and (iii) more precisely estimating heritability parameters.Conclusions. Criteria for MD largely defined a single dimension of disease liability although the quality of person risk measurement was less clear. The quantitative item response scales were statistically superior in predicting relevant outcomes and estimating twin model parameters. Item response models that treat symptoms as ordered indicators of risk rather than as counts towards a diagnostic threshold more fully exploit the information available in symptom endorsement data patterns.

scholarly journals Erratum to: Linking Item Response Model Parameters

Psychometrika ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. 273-273 ◽  
Author(s):  
Wim J. van der Linden ◽  
Michelle D. Barrett
Keyword(s):  
Item Response ◽  
Model Parameters ◽  
Response Model ◽  
Item Response Model

An Item Response Model for Characterizing Test Compromise

2002 ◽  
Vol 27 (2) ◽  
pp. 163-179 ◽  
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.

A Mixture Item Response Model for Multiple-Choice Data

2001 ◽  
Vol 26 (4) ◽  
pp. 381-409 ◽  
Author(s):  
Daniel M. Bolt ◽  
Allan S. Cohen ◽  
James A. Wollack
Keyword(s):  
Item Response ◽  
Multiple Choice ◽  
Test Construction ◽  
Item Parameter ◽  
Monte Carlo Algorithm ◽  
Choice Test ◽  
Model Parameters ◽  
Response Model ◽  
Item Response Model ◽  
Choice Data

A mixture item response model is proposed for investigating individual differences in the selection of response categories in multiple-choice items. The model accounts for local dependence among response categories by assuming that examinees belong to discrete latent classes that have different propensities towards those responses. Varying response category propensities are captured by allowing the category intercept parameters in a nominal response model ( Bock, 1972 ) to assume different values across classes. A Markov Chain Monte Carlo algorithm for the estimation of model parameters and classification of examinees is described. A real-data example illustrates how the model can be used to distinguish examinees that are disproportionately attracted to different types of distractors in a test of English usage. A simulation study evaluates item parameter recovery and classification accuracy in a hypothetical multiple-choice test designed to be diagnostic. Implications for test construction and the use of multiple-choice tests to perform cognitive diagnoses of item response patterns are discussed.

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

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.

scholarly journals Extensions of the skew-normal ogive item response model

2014 ◽  
Vol 28 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Jorge Luis Bazán ◽  
Márcia D. Branco ◽  
Heleno Bolfarine
Keyword(s):  
Item Response ◽  
Response Model ◽  
Item Response Model ◽  
Skew Normal

Are Teachers' Unions Really to Blame? Collective Bargaining Agreements and Their Relationships with District Resource Allocation and Student Performance in California

2011 ◽  
Vol 6 (3) ◽  
pp. 354-398 ◽  
Author(s):  
Katharine O. Strunk
Keyword(s):  
Resource Allocation ◽  
Collective Bargaining ◽  
Student Performance ◽  
Item Response ◽  
Response Model ◽  
Item Response Model ◽  
Teachers Unions ◽  
Achievement Growth ◽  
Unique Measure ◽  
Level Student

Increased spending and decreased student performance have been attributed in part to teachers' unions and to the collective bargaining agreements (CBAs) they negotiate with school boards. However, only recently have researchers begun to examine impacts of specific aspects of CBAs on student and district outcomes. This article uses a unique measure of contract restrictiveness generated through the use of a partial independence item response model to examine the relationships between CBA strength and district spending on multiple areas and district-level student performance in California. I find that districts with more restrictive contracts have higher spending overall, but that this spending appears not to be driven by greater compensation for teachers but by greater expenditures on administrators' compensation and instruction-related spending. Although districts with stronger CBAs spend more overall and on these categories, they spend less on books and supplies and on school board–related expenditures. In addition, I find that contract restrictiveness is associated with lower average student performance, although not with decreased achievement growth.

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