difNLR: Generalized Logistic Regression Models for DIF and DDF Detection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00532886" target="_blank" >RIV/67985807:_____/20:00532886 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/20:10414453 RIV/00216208:11410/20:10414453
Result on the web
<a href="http://hdl.handle.net/11104/0311264" target="_blank" >http://hdl.handle.net/11104/0311264</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.32614/RJ-2020-014" target="_blank" >10.32614/RJ-2020-014</a>

Result language
angličtina
Original language name
difNLR: Generalized Logistic Regression Models for DIF and DDF Detection
Original language description
Differential item functioning (DIF) and differential distractor functioning (DDF) are important topics in psychometrics, pointing to potential unfairness in items with respect to minorities or different social groups. Various methods have been proposed to detect these issues. The difNLR R package extends DIF methods currently provided in other packages by offering approaches based on generalized logistic regression models that account for possible guessing or inattention, and by pro viding methods to detect DIF and DDF among ordinal and nominal data. In the current paper, we describe implementation of the main functions of the difNLR package, from data generation, through the model fitting and hypothesis testing, to graphical representation of the results. Finally, we provide a real data example to bring the concepts together.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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