difNLR: Generalized Logistic Regression Models for DIF and DDF Detection

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11320/20:10414453 RIV/00216208:11410/20:10414453

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

difNLR: Generalized Logistic Regression Models for DIF and DDF Detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

difNLR: Generalized Logistic Regression Models for DIF and DDF Detection

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

R Journal

ISSN

2073-4859

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

AT - Rakouská republika

Počet stran výsledku

24

Strana od-do

300-323

Kód UT WoS článku

000579493900020

EID výsledku v databázi Scopus

—