Robust Haebara Linking for Many Groups: Performance in the Case of Uniform DIF

Robitzsch, Alexander

doi:10.3390/psych2030014

Cited by 13 publications

(10 citation statements)

References 73 publications

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“…Due to the presence of outliers, the average of DIF effects will typically differ from zero. For this kind of DIF effects, robust linking methods must be employed for removing these outlying items from group comparisons [65,71,89,94,107].…”

Section: Discussionmentioning

confidence: 99%

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A Comparison of Linking Methods for Two Groups for the Two-Parameter Logistic Item Response Model in the Presence and Absence of Random Differential Item Functioning

Robitzsch

2021

Foundations

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This article investigates the comparison of two groups based on the two-parameter logistic item response model. It is assumed that there is random differential item functioning in item difficulties and item discriminations. The group difference is estimated using separate calibration with subsequent linking, as well as concurrent calibration. The following linking methods are compared: mean-mean linking, log-mean-mean linking, invariance alignment, Haberman linking, asymmetric and symmetric Haebara linking, different recalibration linking methods, anchored item parameters, and concurrent calibration. It is analytically shown that log-mean-mean linking and mean-mean linking provide consistent estimates if random DIF effects have zero means. The performance of the linking methods was evaluated through a simulation study. It turned out that (log-)mean-mean and Haberman linking performed best, followed by symmetric Haebara linking and a newly proposed recalibration linking method. Interestingly, linking methods frequently found in applications (i.e., asymmetric Haebara linking, recalibration linking used in a variant in current large-scale assessment studies, anchored item parameters, concurrent calibration) perform worse in the presence of random differential item functioning. In line with the previous literature, differences between linking methods turned out be negligible in the absence of random differential item functioning. The different linking methods were also applied in an empirical example that performed a linking of PISA 2006 to PISA 2009 for Austrian students. This application showed that estimated trends in the means and standard deviations depended on the chosen linking method and the employed item response model.

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Section: Discussionmentioning

confidence: 99%

“…In this joint Haebara (HAE-joint) linking approach, distribution parameters are simultaneously estimated with common item parameters. The linking function in HAEjoint is defined as [65,93,94]:…”

Section: Haebara Linking Methods (Hae-asymm Hae-symm Hae-joint)mentioning

confidence: 99%

A Comparison of Linking Methods for Two Groups for the Two-Parameter Logistic Item Response Model in the Presence and Absence of Random Differential Item Functioning

Robitzsch

2021

Foundations

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“…In this situation, groups constitute countries. We included 26 countries (see Table 2) that participated in 2006 and focused on the reading test (see [31] and [32] for other studies using this dataset).…”

Section: Methodsmentioning

confidence: 99%

About the Equivalence of the Latent D-Scoring Model and the Two-Parameter Logistic Item Response Model

Robitzsch¹

2021

Preprint

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This article shows that the recently proposed latent D-scoring model of Dimitrov is statistically equivalent to the two-parameter logistic item response model. An analytical derivation and a numerical illustration are employed for demonstrating this finding. Hence, estimation techniques for the two-parameter logistic model can be used for estimating the latent D-scoring model. In an empirical example using PISA data, differences of country ranks are investigated when using different metrics for the latent trait. In the example, the choice of the latent trait metric matters for the ranking of countries. Finally, it is argued that an item response model with bounded latent trait values like the latent D-scoring model might have advantages for reporting results in terms of interpretation.

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“…• Common-item non-equivalent group linkage: transforming scales of parameters into common scales using common items such as substituting the means and deviations of the item parameter estimates of common items [41][42][43][44][45][46][47][48][49].…”

Section: Item Response Theorymentioning

confidence: 99%

Deep Item Response Theory as a Novel Test Theory Based on Deep Learning

2021

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Item Response Theory (IRT) evaluates, on the same scale, examinees who take different tests. It requires the linkage of examinees’ ability scores as estimated from different tests. However, the IRT linkage techniques assume independently random sampling of examinees’ abilities from a standard normal distribution. Because of this assumption, the linkage not only requires much labor to design, but it also has no guarantee of optimality. To resolve that shortcoming, this study proposes a novel IRT based on deep learning, Deep-IRT, which requires no assumption of randomly sampled examinees’ abilities from a distribution. Experiment results demonstrate that Deep-IRT estimates examinees’ abilities more accurately than the traditional IRT does. Moreover, Deep-IRT can express actual examinees’ ability distributions flexibly, not merely following the standard normal distribution assumed for traditional IRT. Furthermore, the results show that Deep-IRT more accurately predicts examinee responses to unknown items from the examinee’s own past response histories than IRT does.

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Robust Haebara Linking for Many Groups: Performance in the Case of Uniform DIF

Cited by 13 publications

References 73 publications

A Comparison of Linking Methods for Two Groups for the Two-Parameter Logistic Item Response Model in the Presence and Absence of Random Differential Item Functioning

A Comparison of Linking Methods for Two Groups for the Two-Parameter Logistic Item Response Model in the Presence and Absence of Random Differential Item Functioning

About the Equivalence of the Latent D-Scoring Model and the Two-Parameter Logistic Item Response Model

Deep Item Response Theory as a Novel Test Theory Based on Deep Learning

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