Written in English
Test items that are differentially difficult for groups of examinees that are matched on the ability pose a problem for educational and psychological measurements. Such items are typically detected using differential item functioning (DIF) analyses, the most common of which is the Mantel-Haenszel method. Most implementations of the Mantel-Haenszel delete records from which any responses are missing or replace missing responses with scores of 0. This study examined the effect of these and other treatments for missing data in Mantel-Haenszel DIF analyses using data from the 1995 Trends in International Mathematics and Science Study (TIMSS) and the School Achievement Indicators Program (SAIP) 2001 Mathematics Assessment. Mantel-Haenszel DIF analyses were performed using a total score and a proportion score as matching variables and treating missing data by listwise deletion, analysiswise deletion, and scoring missing data as incorrect.Results of the analyses suggest that in the TIMSS dataset, where there were 41 dichotomously scored items and little missing data, matching based on the proportion score resulted in detecting more items showing significant values of DIF. However, in 80% of items all MDTs resulted in the same decision as to whether or not an item showed DIF. All missing data treatments identified the same magnitude and direction for 33% of the DIF items. In contrast, in the SAIP dataset, which had 75 items and more missing data, matching based on the total score resulted in detecting more items as showing significant values of DIF in favour of the reference group while matching based on proportion score led to detecting more DIF items in favour of the focal group. Of the 24 DIF items, the listwise deletion conditions identified only two while the other four conditions identified 22 with nine of them across all four conditions. However, all MDTs led to similar decisions in 68% of items. The results of this study clearly demonstrate the importance of decisions about how to treat missing data in DIF analyses.
|Statement||by Barnabas Chukwujiebere Emenogu.|
|The Physical Object|
|Pagination||x, 112 leaves :|
|Number of Pages||112|
In this study, it is aimed to investigate the impact of different missing data handling methods on the detection of Differential Item Functioning methods (Mantel Haenszel and Standardization. Emenogu, B. C., Falenchuck, O., & Childs, R. A. (). The effect of missing data treatment on Mantel-Haenszel DIF detection. The Alberta Journal of Educational Author: Kuan-Yu Jin, Yi-Jhen Wu, Hui-Fang Chen. irregular data points. This study applied smoothing techniques to frequency distributions and investigated the impact of smoothed data on the Mantel-Haenszel (MH) DIF detection in small samples. Eight sample-size combinations were randomly drawn from a real data set to make the study realistic and were replicated 80 times to produce stable results. Mantel-Haenszel is the industry-standard DIF statistic, but it expects complete data because it stratifies the data by raw scores. Please Google "Mantel-Haenszel". The Winsteps implementation is slightly different because it stratifies by person measure (same as raw scores for complete data), so it is robust against missing data.
At the Educational Testing Service, the Mantel-Haenszel procedure is used for differential item functioning (DIF) detection, and the standardization procedure is used to describe DIF. This report describes these procedures. First, an important distinction is made between DIF and Impact, pointing out the need to compare the comparable. The Effect of Missing Data Treatment on Mantel-Haenszel DIF Detection. Most implementations of the Mantel-Haenszel differential item functioning procedure delete records with missing responses or replace missing responses with scores of 0. This article describes the results of a simulation study to investigate the impact of missing data. The Mantel -Haenszel procedure is considered by some to be the most p owerful test for uniform DIF for dichotomous items (Holland & Thayer, ) The Mantel Haenszel procedure is easy to conduct, has an effect size measure and test of significance, and works well for small sample sizes However, the Mantel -Haenszel procedure detects uniform DIF. Leadership, Higher and Adult Education Centre for the Study of Canadian and International Higher Education Research Overview Ruth Childs conducts research on the design and equity of large-scale assessments, admissions processes, and other evaluation systems.
effect of sample size, ability distribution and test length on detection of differential item functioning (DIF) using Mantel-Haenszel statistic. Objectives of the Study The objectives of the study were to: (i) Determine the effect of Sample Size, Ability Distribution and Test Length on the Effect Size of DIF items across 3 DIF Types; A, B and C. The Mantel-Haenszel method is an approach for fitting meta-analytic fixed-effects models when dealing with studies providing data in the form of 2x2 tables or in the form of event counts (i.e., person-time data) for two groups (Mantel & Haenszel, ). of DIF or variances in ability distribution) and is an aspect that methodologists should consider in future simulation studies. Keywords: Type I error, statistical power, Mantel-Haenszel, differential item functioning, meta-analysis Standardized measurement instruments or tests have become an. A new approach for differential item functioning detection using Mantel-Haenszel methods. The GMHDIF program. Autores: Ángel Manuel Fidalgo Aliste Localización: The Spanish Journal of Psychology, ISSN , Vol. 14, Nº. 2, , págs. Idioma: español DOI: /rev_sjopvn; Referencias bibliográficas.