Detection of determinant genes and diagnostic via Item Response Theory

Tavares, Héliton Ribeiro; Andrade, Dalton Francisco de; Pereira, Carlos Alberto de Bragança

doi:10.1590/s1415-47572004000400033

Cited by 26 publications

(22 citation statements)

References 4 publications

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“…Reference to a four‐parameter model (4PM) has appeared sporadically in the literature (Barton & Lord, 1981; Hambleton & Swaminathan, 1985; Linacre, 2004; Rupp, 2003). However, this model has rarely been used in practice and until recently (see Osgood et al , 2002; Reise & Waller, 2003; Tavares, de Andrade, & Pereira, 2004; Waller & Reise, 2009) was often dismissed as offering little practical benefit and as difficult to estimate. We address each of these issues in turn.…”

Section: Irt Modelsmentioning

confidence: 99%

“…Arguments in favour of a 4PM for IRT analyses have also appeared in genetics research. Tavares et al (2004) modelled whether certain genes were activated or deactivated in individuals as a function of some quality of the person, such as a predisposition to an illness. The genes were considered as ‘items’ and, in general, individuals higher in the predisposition were more likely to have the genes activated.…”

Section: Justification and Formulation Of A Four‐parameter Modelmentioning

confidence: 99%

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Estimation of a four‐parameter item response theory model

Loken

Rulison

2010

Brit J Math & Statis

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We explore the justification and formulation of a four-parameter item response theory model (4PM) and employ a Bayesian approach to recover successfully parameter estimates for items and respondents. For data generated using a 4PM item response model, overall fit is improved when using the 4PM rather than the 3PM or the 2PM. Furthermore, although estimated trait scores under the various models correlate almost perfectly, inferences at the high and low ends of the trait continuum are compromised, with poorer coverage of the confidence intervals when the wrong model is used. We also show in an empirical example that the 4PM can yield new insights into the properties of a widely used delinquency scale. We discuss the implications for building appropriate measurement models in education and psychology to model more accurately the underlying response process.

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Section: Irt Modelsmentioning

confidence: 99%

Section: Justification and Formulation Of A Four‐parameter Modelmentioning

confidence: 99%

Estimation of a four‐parameter item response theory model

Loken

Rulison

2010

Brit J Math & Statis

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“…The model represents the probability of a certain response to a determined item in accordance with the parameters of the item and the respondent's latent traits (Andrade et al, 2000;Reise et al, 1993;Tavares et al, 2004). One of the most widely used IRT models for items with dichotomous and cumulative responses is the two-parameter logistic model (2PLM) developed by Birnbaum (1968) and based on Lord (1952), which is represented by the following equation.…”

Section: Contextmentioning

confidence: 99%

Measuring web usability using item response theory: Principles, features and opportunities

Tezza

Bornia

Andrade

2011

Interacting with Computers

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a b s t r a c tUsability is considered a critical issue on the web that determines either the success or the failure of a company. Thus, the evaluation of usability has gained substantial attention. However, most current tools for usability evaluation have some limitations, such as excessive generality and a lack of reliability and validity. The present work proposes the construction of a tool to measure usability in e-commerce websites using item response theory (IRT). While usability issues have only been considered in theoretical or empirical contexts, in this study, we discuss them from a mathematical point of view using IRT. In particular, we develop a standardised scale to measure usability in e-commerce websites. This study opens a new field of research in the ergonomics of interfaces with respect to the development of scales using IRT.

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“…Although the uni-dimensional IRT model has proved to be useful, the limitations caused by the uni-dimensional assumption have been widely recognized [2, p62]. In recent years, IRT models have also become popular in other areas, such as health behavior [3-5], biology[6-8] and engineering [9, 10]. These new applications often require multidimensional latent spaces to accurately capture the latent structure of the measurement domains.…”

Section: Introductionmentioning

confidence: 99%

Nesting Monte Carlo EM for high-dimensional item factor analysis

Bentler

2013

Journal of Statistical Computation and Simulation

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The item factor analysis model for investigating multidimensional latent spaces has proved to be useful. Parameter estimation in this model requires computationally demanding high-dimensional integrations. While several approaches to approximate such integrations have been proposed, they suffer various computational difficulties. This paper proposes a Nesting Monte Carlo Expectation-Maximization (MCEM) algorithm for item factor analysis with binary data. Simulation studies and a real data example suggest that the Nesting MCEM approach can significantly improve computational efficiency while also enjoying the good properties of stable convergence and easy implementation.

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Detection of determinant genes and diagnostic via Item Response Theory

Cited by 26 publications

References 4 publications

Estimation of a four‐parameter item response theory model

Estimation of a four‐parameter item response theory model

Measuring web usability using item response theory: Principles, features and opportunities

Nesting Monte Carlo EM for high-dimensional item factor analysis

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