Statistical models for detection of differential item functioning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00570033" target="_blank" >RIV/67985807:_____/21:00570033 - isvavai.cz</a>

Výsledek na webu

<a href="https://dspace.cuni.cz/handle/20.500.11956/125037" target="_blank" >https://dspace.cuni.cz/handle/20.500.11956/125037</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Statistical models for detection of differential item functioning

Popis výsledku v původním jazyce

ZÁKLADNÍ ÚDAJE: Disertační práce. Matematicko-fyzikální fakulta, Univerzita Karlova v Praze. Obhájeno: Praha. 17. 3. 2021. ABSTRAKT: This thesis focuses on topic of Differential Item Functioning (DIF), a phenomenon that can arise in various contexts of educational, psychological, or health-related multiitem measurements. We discuss several statistical methods and models to detect DIF among dichotomous, ordinal, and nominal items. In the first part, generalized logistic regression models for DIF detection among dichotomous items are introduced, which account for possibility of guessing and/or inattention. Techniques for estimation of item parameters are presented, including a newly proposed algorithm based on a parametric link function. Two simulation studies are presented. The first compares the generalized logistic regression models to other widely used DIF detection methods. The second illustrates differences between the techniques to estimate item parameters. Implementation of the models into the R software and its difNLR package is illustrated. In the second part, generalized logistic regression models for DIF detection among polytomous items are discussed. Cumulative logit, adjacent category logit, and nominal models are introduced together with the maximum likelihood method to estimate itemparameters and with examples of implementation in the difNLR package. The third part deals with a nonparametric comparison of regression curves for DIF detection based on kernel smoothing. We discuss several settings and we newly propose an estimate of an optimal weight function for a test statistic to identify DIF. Nonparametric approaches are compared to the logistic regression method in a simulation study. In the fourth and last part, further topics of DIF detection are discussed, including item purification, multiple comparison corrections, and DIF effect sizes. Different approaches are compared in a complex simulation study on three of the most used DIF detection methods.

Název v anglickém jazyce

Statistical models for detection of differential item functioning

Popis výsledku anglicky

ZÁKLADNÍ ÚDAJE: Disertační práce. Matematicko-fyzikální fakulta, Univerzita Karlova v Praze. Obhájeno: Praha. 17. 3. 2021. ABSTRAKT: This thesis focuses on topic of Differential Item Functioning (DIF), a phenomenon that can arise in various contexts of educational, psychological, or health-related multiitem measurements. We discuss several statistical methods and models to detect DIF among dichotomous, ordinal, and nominal items. In the first part, generalized logistic regression models for DIF detection among dichotomous items are introduced, which account for possibility of guessing and/or inattention. Techniques for estimation of item parameters are presented, including a newly proposed algorithm based on a parametric link function. Two simulation studies are presented. The first compares the generalized logistic regression models to other widely used DIF detection methods. The second illustrates differences between the techniques to estimate item parameters. Implementation of the models into the R software and its difNLR package is illustrated. In the second part, generalized logistic regression models for DIF detection among polytomous items are discussed. Cumulative logit, adjacent category logit, and nominal models are introduced together with the maximum likelihood method to estimate itemparameters and with examples of implementation in the difNLR package. The third part deals with a nonparametric comparison of regression curves for DIF detection based on kernel smoothing. We discuss several settings and we newly propose an estimate of an optimal weight function for a test statistic to identify DIF. Nonparametric approaches are compared to the logistic regression method in a simulation study. In the fourth and last part, further topics of DIF detection are discussed, including item purification, multiple comparison corrections, and DIF effect sizes. Different approaches are compared in a complex simulation study on three of the most used DIF detection methods.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA21-03658S" target="_blank" >GA21-03658S: Teoretické základy výpočetní psychometrie</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů