The Effect of Missing Data Treatment on Mantel-Haenszel DIF Detection
Bibliographic Data
| ID | 8158607 |
|---|---|
| Authors | Barnabas C Emenogu (University of Toronto), Olesya Falenchuk (0009-0008-2022-4913, University of Toronto), Ruth A Childs (0000-0003-2687-0955, University of Toronto) |
| Year | 2011 |
| Volume | 56 |
| Issue | 4 |
| Pages | 459-469 |
| Publication date | 2011-02-02 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Alberta Journal of Educational Research (JOURNAL) |
| Journal identifiers | ISSN: 0002-4805 • E-ISSN: 1923-1857 |
| Publisher | University of Alberta Press (PUBLISHER • CA) |
| DOI | 10.55016/ojs/ajer.v56i4.55429 |
| OpenAlex | W95729519 |
| Language | EN |
| References cited | 1 |
Most implementations of the Mantel-Haenszel differential item functioning procedure delete records with missing responses or replace missing responses with scores of 0. These treatments of missing data make strong assumptions about the causes of the missing data. Such assumptions may be particularly problematic when groups differ in their patterns of nonresponse. Using both real data from Ontario students taking the English- and French-language versions of two large-scale assessments and simulated data, this study compared the Mantel-Haenszel DIF indices produced using a total score or a proportion score as matching variables and treating missing data by listwise deletion, analysiswise deletion, or scoring missing data as incorrect. The results suggest that when many responses are missing, differential nonresponse rates may be a source of DIF
Econometrics · Missing data · Statistics · Cognitive Abilities and Testing · Mathematics · Psychology · Psychometric Methodologies and Testing
| Highly cited | No |
|---|