Dataset Information

Outlier Detection Using t-test in Rasch IRT Equating under NEAT Design.

ABSTRACT: In equating practice, the existence of outliers in the anchor items may deteriorate the equating accuracy and threaten the validity of test scores. Therefore, stability of the anchor item performance should be evaluated before conducting equating. This study used simulation to investigate the performance of the t-test method in detecting outliers and compared its performance with other outlier detection methods, including the logit difference method with 0.5 and 0.3 as the cutoff values and the robust z statistic with 2.7 as the cutoff value. The investigated factors included sample size, proportion of outliers, item difficulty drift direction, and group difference. Across all simulated conditions, the t-test method outperformed the other methods in terms of sensitivity of flagging true outliers, bias of the estimated translation constant, and the root mean square error of examinee ability estimates.

SUBMITTER: Liu C

PROVIDER: S-EPMC9679927 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Outlier Detection Using t-test in Rasch IRT Equating under NEAT Design.

Liu Chunyan C Jurich Daniel D

Applied psychological measurement 20220906 1

In equating practice, the existence of outliers in the anchor items may deteriorate the equating accuracy and threaten the validity of test scores. Therefore, stability of the anchor item performance should be evaluated before conducting equating. This study used simulation to investigate the performance of the <i>t</i>-test method in detecting outliers and compared its performance with other outlier detection methods, including the logit difference method with 0.5 and 0.3 as the cutoff values a ...[more]

PMID: 36425288

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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

Outlier Detection Using t-test in Rasch IRT Equating under NEAT Design.

Publications

Outlier Detection Using t-test in Rasch IRT Equating under NEAT Design.

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