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A Framework for Anchor Methods and an Iterative Forward Approach for DIF Detection.

ABSTRACT: In differential item functioning (DIF) analysis, a common metric is necessary to compare item parameters between groups of test-takers. In the Rasch model, the same restriction is placed on the item parameters in each group to define a common metric. However, the question how the items in the restriction-termed anchor items-are selected appropriately is still a major challenge. This article proposes a conceptual framework for categorizing anchor methods: The anchor class to describe characteristics of the anchor methods and the anchor selection strategy to guide how the anchor items are determined. Furthermore, the new iterative forward anchor class is proposed. Several anchor classes are implemented with different anchor selection strategies and are compared in an extensive simulation study. The results show that the new anchor class combined with the single-anchor selection strategy is superior in situations where no prior knowledge about the direction of DIF is available.

SUBMITTER: Kopf J

PROVIDER: S-EPMC5978507 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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A Framework for Anchor Methods and an Iterative Forward Approach for DIF Detection.

Kopf Julia J Zeileis Achim A Strobl Carolin C

Applied psychological measurement 20140825 2

In differential item functioning (DIF) analysis, a common metric is necessary to compare item parameters between groups of test-takers. In the Rasch model, the same restriction is placed on the item parameters in each group to define a common metric. However, the question how the items in the restriction-termed <i>anchor items</i>-are selected appropriately is still a major challenge. This article proposes a conceptual framework for categorizing anchor methods: The <i>anchor class</i> to describ ...[more]

PMID: 29880995

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Project description:BackgroundOutliers, data points that significantly deviate from the norm, can have a substantial impact on statistical inference and provide valuable insights in data analysis. Multiple methods have been developed for outlier detection, however, almost all available approaches fail to consider the spatial dependence and heterogeneity in spatial data. Spatial data has diverse formats and semantics, requiring specialized outlier detection methodology to handle these unique properties. For now, there is limited research exists on robust spatial outlier detection methods designed specifically under the spatial error model (SEM) structure.MethodWe propose the Spatial-Θ-Iterative Procedure for Outlier Detection (Spatial-Θ-IPOD), which utilizes a mean-shift vector to identify outliers within the SEM. Our method enables an effective detection of spatial outliers while also providing robust coefficient estimates. To assess the performance of our approach, we conducted extensive simulations and applied it to a real-world empirical study using life expectancy data from multiple countries.ResultsSimulation results showed that the masking and JD (Joint Detection) indicators of our Spatial-Θ-IPOD method outperformed several commonly used methods, even in high-dimensional scenarios, demonstrating stable performance. Conversely, the Θ-IPOD method proved to be ineffective in detecting outliers when spatial correlation was present. Moreover, our model successfully provided reliable coefficient estimation alongside outlier detection. The proposed method consistently outperformed other models (both robust and non-robust) in most cases. In the empirical study, our proposed model successfully detected outliers and provided valuable insights in the modeling process.ConclusionsOur proposed Spatial-Θ-IPOD offers an effective solution for detecting spatial outliers for SEM while providing robust coefficient estimates. Notably, our approach showcases its relative superiority even in the presence of high leverage points. By successfully identifying outliers, our method enhances the overall understanding of the data and provides valuable insights for further analysis.

Dataset Information

A Framework for Anchor Methods and an Iterative Forward Approach for DIF Detection.

Publications

A Framework for Anchor Methods and an Iterative Forward Approach for DIF Detection.

Similar Datasets

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