scholarly journals Forced-Choice Assessment of Work-Related Maladaptive Personality Traits: Preliminary Evidence From an Application of Thurstonian Item Response Modeling

Assessment ◽  
2016 ◽  
Vol 25 (4) ◽  
pp. 513-526 ◽  
Author(s):  
Nigel Guenole ◽  
Anna A. Brown ◽  
Andrew J. Cooper
Keyword(s):  
Item Response ◽  
Goodness Of Fit ◽  
Stimulus Item ◽  
Forced Choice ◽  
Single Stimulus ◽  
Response Model ◽  
Item Response Model ◽  
Choice Data ◽  
Item Response Modeling ◽  
Response Modeling

This article describes an investigation of whether Thurstonian item response modeling is a viable method for assessment of maladaptive traits. Forced-choice responses from 420 working adults to a broad-range personality inventory assessing six maladaptive traits were considered. The Thurstonian item response model’s fit to the forced-choice data was adequate, while the fit of a counterpart item response model to responses to the same items but arranged in a single-stimulus design was poor. Monotrait heteromethod correlations indicated corresponding traits in the two formats overlapped substantially, although they did not measure equivalent constructs. A better goodness of fit and higher factor loadings for the Thurstonian item response model, coupled with a clearer conceptual alignment to the theoretical trait definitions, suggested that the single-stimulus item responses were influenced by biases that the independent clusters measurement model did not account for. Researchers may wish to consider forced-choice designs and appropriate item response modeling techniques such as Thurstonian item response modeling for personality questionnaire applications in industrial psychology, especially when assessing maladaptive traits. We recommend further investigation of this approach in actual selection situations and with different assessment instruments.

Psychology of Computer Use: VIII. Utilizing a Nonparametric Item Response Model to Develop Unidimensional Scales: Mokscal

1989 ◽  
Vol 68 (3) ◽  
pp. 987-1000 ◽  
Author(s):  
Elisabeth Tenvergert ◽  
Johannes Kingma ◽  
Terry Taerum
Keyword(s):  
Item Response ◽  
Goodness Of Fit ◽  
Functional Form ◽  
Search Procedure ◽  
Response Model ◽  
Scale Analysis ◽  
Item Response Model ◽  
Goodness Of Fit Test ◽  
Item Coefficient ◽  
Nonparametric Item Response

MOKSCAL is a program for the Mokken (1971) scale analysis based on a nonparametric item response model that makes no assumptions about the functional form of the item trace lines. The only constraint the Mokken model puts on the trace lines is the assumption of double monotony; that is, the item trace lines must be nondecreasing and the lines are not allowed to cross. MOKSCAL provides three procedures of scaling: a search procedure, an evaluation of the whole set of items, and an extension of an existing scale. All procedures provide a coefficient of scalability for all items that meet the criteria of the Mokken model and an item coefficient of scalability of every item. A test of robustness of the found scale can be performed to analyze whether the scale is invariant across different subgroups or samples. This robustness test may serve as a goodness-of-fit test for the established scale. The program is written in FORTRAN 77 and is suitable for both mainframe and microcomputers.

scholarly journals Flexible Item Response Modeling in R with the Flexmet Package

Psych ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 447-478
Author(s):  
Leah Feuerstahler
Keyword(s):  
Item Response ◽  
Real Data ◽  
Model Parameters ◽  
Item Response Model ◽  
Response Models ◽  
Item Response Models ◽  
Item Response Modeling ◽  
Flexible Response ◽  
Special Cases ◽  
Response Modeling

The filtered monotonic polynomial (FMP) model is a semi-parametric item response model that allows flexible response function shapes but also includes traditional item response models as special cases. The flexmet package for R facilitates the routine use of the FMP model in real data analysis and simulation studies. This tutorial provides several code examples illustrating how the flexmet package may be used to simulate FMP model parameters and data (both for dichotomous and polytomously scored items), estimate FMP model parameters, transform traditional item response models to different metrics, and more. This tutorial serves as both an introduction to the unique features of the FMP model and as a practical guide to its implementation in R via the flexmet package.

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.

Item response modeling of longitudinal personality data

2004 ◽  
Author(s):  
Kate E. Walton ◽  
Brent W. Roberts ◽  
Avshalom Caspi ◽  
Terrie E. Moffitt
Keyword(s):  
Item Response ◽  
Item Response Modeling ◽  
Response Modeling
Sign in / Sign up

Export Citation Format

Share Document