A Generalized Logistic Regression Procedure to Detect Differential Item Functioning Among Multiple Groups

Magis, David; Raîche, Gilles; Béland, Sébastien; Gérard, Paul

doi:10.1080/15305058.2011.602810

Cited by 36 publications

(38 citation statements)

References 44 publications

(47 reference statements)

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“…As the method of analysis in this study, we briefly highlight the main components of the generalized logistic regression. Interested readers are directed to the original work by Magis et al (2011) for complete details. The generalized logistic regression DIF model, as presented by Magis et al (2011), has the following form:…”

Section: Methods To Examine Item-level Difmentioning

confidence: 99%

“…Over the last several decades, the literature has featured many DIF methods suitable for two groups (one reference and one focal) and reviews of the methods suitable for dichotomous or polytomous items (e.g., Camilli & Shepard 1994;Millsap & Everson 1993;Penfield & Lam 2000;Potenza & Dorans 1995). Furthermore, performance of different DIF methods have been examined (e.g., Li et al 2012;Penfield 2001), including those that allow for multiple groups (i.e., > 2), such as generalized MantelHaenszel (e.g., Fidalgo & Madeira 2008;Fidalgo & Scalon 2010;Penfield 2001), generalized Lord's test (e.g., Kim et al 1995) and generalized logistic regression Magis et al 2011).…”

Section: Methods To Examine Item-level Difmentioning

confidence: 99%

“…According to Magis et al (2011), three types of DIF can be investigated using this framework: a) uniform DIF (UDIF), b) nonuniform DIF (NUDIF), and both types of DIF effects together (DIF). Tested null hypotheses for these three types include:…”

Section: Methods To Examine Item-level Difmentioning

confidence: 99%

“…Given our study design, discussed subsequently, and our goal of studying itemlevel DIF when the number of groups compared is greater than two, we consider the performance of the generalized logistic regression method (Magis et al 2011). The method is an extension of the well-known logistic regression approach to investigate DIF between two groups, as proposed by Swaminathan and Rogers (1990).…”

Section: Methods To Examine Item-level Difmentioning

confidence: 99%

See 3 more Smart Citations

Detecting differential item functioning using generalized logistic regression in the context of large-scale assessments

Svetina

Rutkowski

2014

Large-scale Assess Educ

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Background: When studying student performance across different countries or cultures, an important aspect for comparisons is that of score comparability. In other words, it is imperative that the latent variable (i.e., construct of interest) is understood and measured equivalently across all participating groups or countries, if our inferences regarding performance can be regarded as valid. Relatively fewer studies examined an item-level approach to measurement equivalence, particularly in settings where a large number of groups is included. Methods: This simulation study examines item-level differential item functioning (DIF) in the context of international large-scale assessment (ILSA) using a generalized logistic regression approach. Manipulated factors included the number of groups (10 or 20), magnitude of DIF, percent of DIF items, the nature of DIF, as well as the percent of affected groups with DIF. Results: Results suggested that the number of groups did not have an effect of the performance of the method (high power and low Type I error rates); however, other factors had impacted the accuracy. Specifically, Type I error rates were inflated in non-DIF conditions, while they were very conservative in all of the DIF conditions. Power was generally high, in particular in conditions where DIF magnitude was large, with one exception -in conditions where DIF was introduced in difficulty parameters and the percent of DIF items was 60. Conclusions: Our findings presented a mixed picture with respect to the performance of the generalized logistic regression method in the context of large number of groups with large sample sizes. In the presence of DIF, the method was successful in distinguishing between DIF and non-DIF, as evidenced by low Type I error and high power rates. On the other hand, however, in the absence of DIF, the method yielded increased Type I errors.

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Section: Methods To Examine Item-level Difmentioning

confidence: 99%

Section: Methods To Examine Item-level Difmentioning

confidence: 99%

Section: Methods To Examine Item-level Difmentioning

confidence: 99%

Section: Methods To Examine Item-level Difmentioning

confidence: 99%

See 2 more Smart Citations

Detecting differential item functioning using generalized logistic regression in the context of large-scale assessments

Svetina

Rutkowski

2014

Large-scale Assess Educ

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“…The essential drawback of the MH method and most of the other existing methods is that they are limited to identifying DIF between two subgroups; for example, for male and female participants. Some methods for multiple subgroups have been developed; see Somes (), Penfield (), Magis, Raîche, Béland, and Gérard (), and Kim, Cohen, and Park (). Gonçalves, Gamerman, and Soares () set up a quite general Bayesian multifactor model for the detection of DIF in the 3PL model (Birnbaum, ).…”

Section: Introductionmentioning

confidence: 99%

Detection of differential item functioning in Rasch models by boosting techniques

Schauberger

Tutz

2015

Brit J Math & Statis

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Methods for the identification of differential item functioning (DIF) in Rasch models are typically restricted to the case of two subgroups. A boosting algorithm is proposed that is able to handle the more general setting where DIF can be induced by several covariates at the same time. The covariates can be both continuous and (multi-)categorical, and interactions between covariates can also be considered. The method works for a general parametric model for DIF in Rasch models. Since the boosting algorithm selects variables automatically, it is able to detect the items which induce DIF. It is demonstrated that boosting competes well with traditional methods in the case of subgroups. The method is illustrated by an extensive simulation study and an application to real data.

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Best Practices in Detecting Bias in Cognitive Tests

Maller

Pei

2017

Handbook of Nonverbal Assessment

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A Generalized Logistic Regression Procedure to Detect Differential Item Functioning Among Multiple Groups

Cited by 36 publications

References 44 publications

Detecting differential item functioning using generalized logistic regression in the context of large-scale assessments

Detecting differential item functioning using generalized logistic regression in the context of large-scale assessments

Detection of differential item functioning in Rasch models by boosting techniques

Best Practices in Detecting Bias in Cognitive Tests

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