Examining Differential Item Functioning (DIF) in Self-Reported Health Survey Data: Via Multilevel Modeling
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916636051636224 |
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| author | Kaptur, Dandan Chen Liu, Yiqing Kaptur, Bradley Peterman, Nicholas Zhang, Jinming Kern, Justin Anderson, Carolyn |
| author_facet | Kaptur, Dandan Chen Liu, Yiqing Kaptur, Bradley Peterman, Nicholas Zhang, Jinming Kern, Justin Anderson, Carolyn |
| contents | Few health-related constructs or measures have received a critical evaluation in terms of measurement equivalence, such as self-reported health survey data. Differential item functioning (DIF) analysis is crucial for evaluating measurement equivalence in self-reported health surveys, which are often hierarchical in structure. Traditional single-level DIF methods in this case fall short, making multilevel models a better alternative. We highlight the benefits of multilevel modeling for DIF analysis, when applying a health survey data set to multilevel binary logistic regression (for analyzing binary response data) and multilevel multinominal logistic regression (for analyzing polytomous response data), and comparing them with their single-level counterparts. Our findings show that multilevel models fit better and explain more variance than single-level models. This article is expected to raise awareness of multilevel modeling and help healthcare researchers and practitioners understand the use of multilevel modeling for DIF analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13702 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Examining Differential Item Functioning (DIF) in Self-Reported Health Survey Data: Via Multilevel Modeling Kaptur, Dandan Chen Liu, Yiqing Kaptur, Bradley Peterman, Nicholas Zhang, Jinming Kern, Justin Anderson, Carolyn Applications Few health-related constructs or measures have received a critical evaluation in terms of measurement equivalence, such as self-reported health survey data. Differential item functioning (DIF) analysis is crucial for evaluating measurement equivalence in self-reported health surveys, which are often hierarchical in structure. Traditional single-level DIF methods in this case fall short, making multilevel models a better alternative. We highlight the benefits of multilevel modeling for DIF analysis, when applying a health survey data set to multilevel binary logistic regression (for analyzing binary response data) and multilevel multinominal logistic regression (for analyzing polytomous response data), and comparing them with their single-level counterparts. Our findings show that multilevel models fit better and explain more variance than single-level models. This article is expected to raise awareness of multilevel modeling and help healthcare researchers and practitioners understand the use of multilevel modeling for DIF analysis. |
| title | Examining Differential Item Functioning (DIF) in Self-Reported Health Survey Data: Via Multilevel Modeling |
| topic | Applications |
| url | https://arxiv.org/abs/2408.13702 |