Hidden Markov Item Response Theory Models for Responses and Response Times

Molenaar, Dylan; Oberski, Daniel L.; Vermunt, Jeroen K.; Boeck, Paul De

doi:10.1080/00273171.2016.1192983

Cited by 61 publications

(75 citation statements)

References 59 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Mislevy and Verhelst (1990) investigated examinee's two types of item solution strategies (guessing and ability-based strategy) during tests (also see e.g., Schnipke & Scrams 1997;Yamamoto & Everson 1997;Boughton & Yamamoto 2007). Molenaar, Oberski, Vermunt, and De Boeck (2016) hypothesized two types of intelligence (slow and fast) as latent classes and investigated whether and how examinees adopt different types of intelligence during tests. Tijmstra, Bolsinova, and Jeon (in press) assumed two response styles as latent classes and studied examinee's differential item solution behavior.…”

Section: Exploratory Vs Confirmatory Mixture Modelingmentioning

confidence: 99%

Investigation of adolescents’ developmental stages in deductive reasoning: An application of a specialized confirmatory mixture IRT approach

et al. 2019

View full text Add to dashboard Cite

In this paper, we propose a specialized confirmatory mixture IRT model to analyze complex cognitive assessment data that is designed to evaluate adolescents' developmental stages in deductive reasoning. The model is specified for the following purposes: (1) to measure multiple deductive reasoning traits, (2) to identify adolescents' differential developmental stages based on their ability levels in the multiple dimensions, (3) to quantify the differences in dimension-specific performance between developmental stages, and (4) to examine the difficulty levels of test design factors. A Bayesian estimation of the model is described. The overall goodness-of-fit of the model is assessed as well as its parameter recovery to validate the application of the model to the data. performance between developmental stages, and (4) to examine the cognitive complexity of the test design factors.In the following "Specialized confirmatory mixture IRT model", we first describe our motivating data example. In the subsequent "Data and analysis", we describe the rationale, formulation, and features of the proposed model for analyzing the data. We also discuss relations with other existing models and the benefits of a confirmatory approach in mixture IRT modeling. In "Results", we then describe the Bayesian estimation of the described model and present the results from the data analysis in "Model validation". In "Discussion", we provide the evaluation of the goodnessof-fit of the model as well as its parameter recovery. We conclude the paper with a summary and a discussion on contributions and other applications.

show abstract

Section: Exploratory Vs Confirmatory Mixture Modelingmentioning

confidence: 99%

Investigation of adolescents’ developmental stages in deductive reasoning: An application of a specialized confirmatory mixture IRT approach

et al. 2019

View full text Add to dashboard Cite

show abstract

“…A similar line of research has been conducted to investigate examinees' rapid guessing strategy during speeded tests (e. g., Schnipke & Scrams, 1997). Recently, Molenaar, Oberski, Vermunt, and De Boeck (2016) utilized a confirmatory mixture IRT modeling approach, to investigate how examinees utilize different types of intelligence (either slow or fast one) depending on test items. Their model includes two latent classes that represent slow and fast responses, respectively.…”

Section: Exploratory Vs Confirmatory Mixture Irt Analysismentioning

confidence: 99%

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

Jeon¹

2018

TQMP

View full text Add to dashboard Cite

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

show abstract

“…(). One could also use the general mixture models of Molenaar and de Boeck () or Molenaar, Oberski, Vermunt, and De Boeck () for this purpose, although they were proposed in a different context. A general overview of the different strategies has been given by Wise ().…”

Section: Introductionmentioning

confidence: 99%

“…The responses and response times in the irregular response mode are assumed to be faster and are modelled differently; for specific variants of such mixture models see Meyer (2010), Molenaar, Bolsinova, and Vermunt (2018), Wang and Xu (2015) and Wang et al (2018). One could also use the general mixture models of Molenaar and de Boeck (2018) or Molenaar, Oberski, Vermunt, and De Boeck (2016) for this purpose, although they were proposed in a different context. A general overview of the different strategies has been given by Wise (2017).…”

Section: Introductionmentioning

confidence: 99%

Robust estimation of the hierarchical model for responses and response times

Ranger

Wolgast

Kuhn

2018

Brit J Math & Statis

View full text Add to dashboard Cite

Van der Linden's (2007, Psychometrika, 72, 287) hierarchical model for responses and response times in tests has numerous applications in psychological assessment. The success of these applications requires the parameters of the model to have been estimated without bias. The data used for model fitting, however, are often contaminated, for example, by rapid guesses or lapses of attention. This distorts the parameter estimates. In the present paper, a novel estimation approach is proposed that is robust against contamination. The approach consists of two steps. In the first step, the response time model is fitted on the basis of a robust estimate of the covariance matrix. In the second step, the item response model is extended to a mixture model, which allows for a proportion of irregular responses in the data. The parameters of the mixture model are then estimated with a modified marginal maximum likelihood estimator. The modified marginal maximum likelihood estimator downweights responses of test-takers with unusual response time patterns. As a result, the estimator is resistant to several forms of data contamination. The robustness of the approach is investigated in a simulation study. An application of the estimator is demonstrated with real data.

show abstract

Hidden Markov Item Response Theory Models for Responses and Response Times

Cited by 61 publications

References 59 publications

Investigation of adolescents’ developmental stages in deductive reasoning: An application of a specialized confirmatory mixture IRT approach

Investigation of adolescents’ developmental stages in deductive reasoning: An application of a specialized confirmatory mixture IRT approach

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

Robust estimation of the hierarchical model for responses and response times

Contact Info

Product

Resources

About