Polytomous item explanatory IRT models with random item effects: Concepts and an application

Measurement ◽  
2020 ◽  
Vol 151 ◽  
pp. 107062
Author(s):  
Jinho Kim ◽  
Mark Wilson
Keyword(s):  
Polytomous Item ◽  
Irt Models ◽  
Explanatory Irt ◽  
Item Effects

Cross-Classification Multilevel Logistic Models in Psychometrics

2003 ◽  
Vol 28 (4) ◽  
pp. 369-386 ◽  
Author(s):  
Wim Van den Noortgate ◽  
Paul De Boeck ◽  
Michel Meulders
Keyword(s):  
Random Sample ◽  
Multilevel Model ◽  
Multilevel Models ◽  
Population Distribution ◽  
Logistic Models ◽  
Educational Measurement ◽  
Random Parameters ◽  
Irt Models ◽  
Cross Classification ◽  
Item Effects

In IRT models, responses are explained on the basis of person and item effects. Person effects are usually defined as a random sample from a population distribution. Regular IRT models therefore can be formulated as multilevel models, including a within-person part and a between-person part. In a similar way, the effects of the items can be studied as random parameters, yielding multilevel models with a within-item part and a between-item part. The combination of a multilevel model with random person effects and one with random item effects leads to a cross-classification multilevel model, which can be of interest for IRT applications. The use of cross-classification multilevel logistic models will be illustrated with an educational measurement application.

scholarly journals Estimating Explanatory Extensions of Dichotomous and Polytomous Rasch Models: The eirm Package in R

Psych ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 308-321
Author(s):  
Okan Bulut ◽  
Guher Gorgun ◽  
Seyma Nur Yildirim-Erbasli
Keyword(s):  
Item Response ◽  
Rating Scale ◽  
Real Data ◽  
R Package ◽  
Explanatory Models ◽  
Scale Model ◽  
Test Model ◽  
Partial Credit Model ◽  
Irt Models ◽  
Explanatory Irt

Explanatory item response modeling (EIRM) enables researchers and practitioners to incorporate item and person properties into item response theory (IRT) models. Unlike traditional IRT models, explanatory IRT models can explain common variability stemming from the shared variance among item clusters and person groups. In this tutorial, we present the R package eirm, which provides a simple and easy-to-use set of tools for preparing data, estimating explanatory IRT models based on the Rasch family, extracting model output, and visualizing model results. We describe how functions in the eirm package can be used for estimating traditional IRT models (e.g., Rasch model, Partial Credit Model, and Rating Scale Model), item-explanatory models (i.e., Linear Logistic Test Model), and person-explanatory models (i.e., latent regression models) for both dichotomous and polytomous responses. In addition to demonstrating the general functionality of the eirm package, we also provide real-data examples with annotated R codes based on the Rosenberg Self-Esteem Scale.

New Evidence on the Construct Validity of the ASI-3 and the Dimensional Conceptualization of Trait Anxiety Sensitivity From IRT Modeling

2017 ◽  
Vol 33 (3) ◽  
pp. 181-189 ◽  
Author(s):  
Christoph J. Kemper ◽  
Michael Hock
Keyword(s):  
Construct Validity ◽  
Anxiety Sensitivity ◽  
Latent Class ◽  
Dimensional Structure ◽  
Sensitivity Index ◽  
Response Scale ◽  
Established Risk Factor ◽  
Irt Models ◽  
Level Data ◽  
Extreme Response

Abstract. Anxiety Sensitivity (AS) denotes the tendency to fear anxiety-related sensations. Trait AS is an established risk factor for anxiety pathology. The Anxiety Sensitivity Index-3 (ASI-3) is a widely used measure of AS and its three most robust dimensions with well-established construct validity. At present, the dimensional conceptualization of AS, and thus, the construct validity of the ASI-3 is challenged. A latent class structure with two distinct and qualitatively different forms, an adaptive form (normative AS) and a maladaptive form (AS taxon, predisposing for anxiety pathology) was postulated. Item Response Theory (IRT) models were applied to item-level data of the ASI-3 in an attempt to replicate previous findings in a large nonclinical sample (N = 2,603) and to examine possible interpretations for the latent discontinuity observed. Two latent classes with a pattern of distinct responses to ASI-3 items were found. However, classes were indicative of participant’s differential use of the response scale (midpoint and extreme response style) rather than differing in AS content (adaptive and maladaptive AS forms). A dimensional structure of AS and the construct validity of the ASI-3 was supported.

Obsession with Covid-19 in Peruvian police and armed forces: Validation of the obsession with Covid-19 Scale in Spanish using SEM and IRT models

Death Studies ◽  
2021 ◽  
pp. 1-11
Author(s):  
Tomás Caycho-Rodríguez ◽  
Lindsey W. Vilca ◽  
Carlos Carbajal-León ◽  
José Heredia-Mongrut ◽  
Miguel Gallegos ◽  
...  
Keyword(s):  
Armed Forces ◽  
Irt Models
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