Estimation In Generalized Logistic Regression Models For DIF Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00570044" target="_blank" >RIV/67985807:_____/21:00570044 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Estimation In Generalized Logistic Regression Models For DIF Detection

Popis výsledku v původním jazyce

ZÁKLADNÍ ÚDAJE: Estimation In Generalized Logistic Regression Models For DIF Detection. [IMPS 2021: The Annual Meeting of the Psychometric Society. Virtual, 20.07.2021-23.07.2021]. ABSTRAKT: Generalized logistic regression models are extensions of logistic regression method for differential item functioning (DIF) detection among binary data which account for possibility of guessing or inattention when responding. In this talk we will discuss several approaches to estimate item parameters including nonlinear least squares, maximum likelihood method and a newly implemented expectation-maximization algorithm. We will further propose a new algorithm based on parametric link function. Differences in estimation procedures will be illustrated with a simulation study and we will also show their implementation in the statistical software R including its package difNLR (Hladká & Martinková, 2020).

Název v anglickém jazyce

Estimation In Generalized Logistic Regression Models For DIF Detection

Popis výsledku anglicky

ZÁKLADNÍ ÚDAJE: Estimation In Generalized Logistic Regression Models For DIF Detection. [IMPS 2021: The Annual Meeting of the Psychometric Society. Virtual, 20.07.2021-23.07.2021]. ABSTRAKT: Generalized logistic regression models are extensions of logistic regression method for differential item functioning (DIF) detection among binary data which account for possibility of guessing or inattention when responding. In this talk we will discuss several approaches to estimate item parameters including nonlinear least squares, maximum likelihood method and a newly implemented expectation-maximization algorithm. We will further propose a new algorithm based on parametric link function. Differences in estimation procedures will be illustrated with a simulation study and we will also show their implementation in the statistical software R including its package difNLR (Hladká & Martinková, 2020).

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