A modular approach for item response theory modeling with the R package flirt

Jeon, Minjeong; Rijmen, Frank

doi:10.3758/s13428-015-0606-z

Cited by 14 publications

(9 citation statements)

References 32 publications

(29 reference statements)

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“…All the syntaxes used in this study are included in online Supplemental Materials, and researchers can modify them if they want to use the same model and software program to identify potentially biased items. Second, it allows researchers who are very familiar with all kinds of IRT models to build their own models by choosing various modeling options (Jeon & Rijmen, 2016). Another major attraction of FLIRT is that it features efficient maximum likelihood estimation, and the computation usually takes several minutes instead of hours (Jeon & Rijmen, 2016).…”

Section: R Package Flirtmentioning

confidence: 99%

“…Previous studies regarding the use of multilevel explanatory IRT models in DIF testing focused on either binary responses or commercial software programs such as SAS (e.g., Jeon & Rijmen, 2016). But as far as creativity research is concerned, many instruments include items with multiple response categories.…”

Section: The Present Studymentioning

confidence: 99%

“…Second, it allows researchers who are very familiar with all kinds of IRT models to build their own models by choosing various modeling options (Jeon & Rijmen, 2016). Another major attraction of FLIRT is that it features efficient maximum likelihood estimation, and the computation usually takes several minutes instead of hours (Jeon & Rijmen, 2016). However, FLIRT has specific requirements regarding installation and data preparation, and the details are provided in online Supplemental Materials.…”

Section: Introductionmentioning

confidence: 99%

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Illustration of Multilevel Explanatory IRT Model DIF Testing with the Creative Thinking Scale

Qian

Wang

2019

Journal of Creative Behavior

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Creativity has been well studied in the past several decades, and numerous measures have been developed to assess creativity. However, validity evidence associated with each measure is often mixed. In particular, the social consequence aspect of validity has received little attention. This is partly due to the difficulty of testing for differential item functioning (DIF) within the traditional classical test theory framework, which still remains the most popular approach to assessing creativity. Hence, this study provides an example of examining differential item functioning using multilevel explanatory item response theory models. The Creative Thinking Scale was tested for DIF in a sample of 1043 10th-12th graders. Results revealed significant uniform and non-uniform DIF for some items. Differentially functioning items are able to produce measurement bias and should be either deleted or modeled. The detailed implications for researchers and practitioners are discussed.

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Section: R Package Flirtmentioning

confidence: 99%

Section: The Present Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Illustration of Multilevel Explanatory IRT Model DIF Testing with the Creative Thinking Scale

Qian

Wang

2019

Journal of Creative Behavior

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“…My intention for using this well-known dataset is to show that the confirmatory models discussed in this paper can be used for standard psychological research. In addition, this dataset has been frequently utilized to illustrate a variety of newly developed IRT models or procedures (e. g., Braeken, Tuerlinckx, & De Boeck, 2007;De Boeck & Wilson, 2004;Jeon & Rijmen, 2016), including various versions of mixture IRT models (e. g., Cho et al, 2016;Choi & Wilson, 2015). Hence, researchers who are interested in new methodology developments in IRT may already be familiar with this dataset and can easily follow the illustrations provided in this paper.…”

Section: Datamentioning

confidence: 99%

A Constrained Confirmatory Mixture IRT Model: Extensions and Estimation of the Saltus model using Mplus

Jeon¹

2018

TQMP

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In this paper, I will discuss applications, extensions, and estimation of the Saltus model, a specialized confirmatory mixture IRT model. The Saltus model is confirmatory because the number/nature of latent classes is pre-specified and prior item information is utilized for class differentiation. Such a confirmatory model has not been fully utilized in applied research due to two main misconceptions: (1) the model is designed for specific purposes only and (2) a specialized software package is required to estimate the model. In this study, I will discuss that (1) such a confirmatory approach is applicable to various applications of mixture IRT modeling, (2) the model can actually be parameterized as a constrained mixture IRT model and (3) the model can be readily extended and estimated with regular, off-the-shelf mixture IRT software packages that allow for linear constraints on model parameters. An application and estimation of the constrained confirmatory IRT model is illustrated with an empirical example. ActingEditor Roland Pfister (Universität Würzburg) ReviewersTwo anonymous reviewers. CitationJeon, M. (2018). A constrained confirmatory mixture IRT model: Extensions and estimation of the Saltus model using

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“…Equation 3 is an IRT model with a unidimensional latent trait expressed in intercept-slope parametrization. The 2PL model is also a generalized nonlinear model since in Equation 3 the loading parameter is multiplied by the person parameter (Jeon & Rijmen, 2015).…”

Section: Specifying the Research Purpose And Questions Under Investigmentioning

confidence: 99%

Introduction to Multilevel Item Response Theory Analysis

Sulis

Toland

2016

The Journal of Early Adolescence

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Item response theory (IRT) models are the main psychometric approach for the development, evaluation, and refinement of multi-item instruments and scaling of latent traits, whereas multilevel models are the primary statistical method when considering the dependence between person responses when primary units (e.g., students) are nested within clusters (e.g., classes). This article introduces multilevel IRT (MLIRT) modeling, and provides the basic information to conduct, interpret, and report results based on an analysis using MLIRT modeling. The procedures are demonstrated using a sample data set based on the National Institute for the Evaluation of School System survey completed in Italy by fifth-grade students nested in classrooms to assess math achievement. The data and command files (Stata, M plus, flexMIRT) needed to reproduce all analyses and plots in this article are available as supplemental online materials at http://jea.sagepub.com/supplemental .

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A modular approach for item response theory modeling with the R package flirt

Cited by 14 publications

References 32 publications

Illustration of Multilevel Explanatory IRT Model DIF Testing with the Creative Thinking Scale

Illustration of Multilevel Explanatory IRT Model DIF Testing with the Creative Thinking Scale

A Constrained Confirmatory Mixture IRT Model: Extensions and Estimation of the Saltus model using Mplus

Introduction to Multilevel Item Response Theory Analysis

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