A simulation study to assess the effect of the number of response categories on the power of ordinal logistic regression for differential item functioning analysis in rating scales
type: Item
instance of: scholarly article
title: A simulation study to assess the effect of the number of response categories on the power of ordinal logistic regression for differential item functioning analysis in rating scales
zbMATH Open document ID: 1358.92019
DOI: 10.1155/2016/5080826
author: Elahe Allahyari
author: Peyman Jafari
author: Zahra Bagheri
published in: Computational \& Mathematical Methods in Medicine
publication date: 2017-04-05T00:00:00Z
review text: Summary: Objective. The present study uses simulated data to find what the optimal number of response categories is to achieve adequate power in ordinal logistic regression (OLR) model for differential item functioning (DIF) analysis in psychometric research. Methods. A hypothetical ten-item quality of life scale with three, four, and five response categories was simulated. The power and type I error rates of OLR model for detecting uniform DIF were investigated under different combinations of ability distribution (\(\theta\)), sample size, sample size ratio, and the magnitude of uniform DIF across reference and focal groups. Results. When \(\theta\) was distributed identically in the reference and focal groups, increasing the number of response categories from 3 to 5 resulted in an increase of approximately 8\% in power of OLR model for detecting uniform DIF. The power of OLR was less than 0.36 when ability distribution in the reference and focal groups was highly skewed to the left and right, respectively. Conclusions. The clearest conclusion from this research is that the minimum number of response categories for DIF analysis using OLR is five. However, the impact of the number of response categories in detecting DIF was lower than might be expected.
Mathematics Subject Classification ID: 92B15
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 6701122
zbMATH Keywords: ordinal logistic regression
zbMATH Keywords: differential item functioning analysis
zbMATH Keywords: response categories
Wikidata QID: Q39611094
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/5080826
OpenAlex ID: W2428223156
DBLP publication ID: journals/cmmm/AllahyariJB16
description: scientific article; zbMATH DE number 6701122