Differential Item Functioning Analysis With Ordinal Logistic Regression Techniques

Medical Care ◽  
2006 ◽  
Vol 44 (Suppl 3) ◽  
pp. S115-S123 ◽  
Author(s):  
Paul K. Crane ◽  
Laura E. Gibbons ◽  
Lance Jolley ◽  
Gerald van Belle
Keyword(s):  
Logistic Regression ◽  
Differential Item Functioning ◽  
Ordinal Logistic Regression ◽  
Item Functioning ◽  
Differential Item Functioning Analysis ◽  
Regression Techniques

scholarly journals 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

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Elahe Allahyari ◽  
Peyman Jafari ◽  
Zahra Bagheri
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Differential Item Functioning ◽  
Rating Scales ◽  
Error Rates ◽  
Ordinal Logistic Regression ◽  
Type I ◽  
Item Functioning ◽  
Quality Of Life Scale ◽  
The Impact

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 (θ), sample size, sample size ratio, and the magnitude of uniform DIF across reference and focal groups.Results.Whenθwas 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.

A comparison of three sets of criteria for determining the presence of differential item functioning using ordinal logistic regression

2007 ◽  
Vol 16 (S1) ◽  
pp. 69-84 ◽  
Author(s):  
Paul K. Crane ◽  
Laura E. Gibbons ◽  
Katja Ocepek-Welikson ◽  
Karon Cook ◽  
David Cella ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Differential Item Functioning ◽  
Ordinal Logistic Regression ◽  
Item Functioning

scholarly journals A Log-Linear Modeling Approach for Differential Item Functioning Detection in Polytomously Scored Items

2019 ◽  
Vol 80 (1) ◽  
pp. 145-162
Author(s):  
Gonca Yesiltas ◽  
Insu Paek
Keyword(s):  
Logistic Regression ◽  
Differential Item Functioning ◽  
Function Analysis ◽  
Ordinal Logistic Regression ◽  
Categorical Variables ◽  
Type I ◽  
Detection Rates ◽  
Item Functioning ◽  
Log Linear ◽  
Observed Score

A log-linear model (LLM) is a well-known statistical method to examine the relationship among categorical variables. This study investigated the performance of LLM in detecting differential item functioning (DIF) for polytomously scored items via simulations where various sample sizes, ability mean differences (impact), and DIF types were manipulated. Also, the performance of LLM was compared with that of other observed score–based DIF methods, namely ordinal logistic regression, logistic discriminant function analysis, Mantel, and generalized Mantel-Haenszel, regarding their Type I error (rejection rates) and power (DIF detection rates). For the observed score matching stratification in LLM, 5 and 10 strata were used. Overall, generalized Mantel-Haenszel and LLM with 10 strata showed better performance than other methods, whereas ordinal logistic regression and Mantel showed poor performance in detecting balanced DIF where the DIF direction is opposite in the two pairs of categories and partial DIF where DIF exists only in some of the categories.

scholarly journals Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Zahra Sharafi ◽  
Amin Mousavi ◽  
Seyyed Mohammad Taghi Ayatollahi ◽  
Peyman Jafari
Keyword(s):  
Logistic Regression ◽  
Differential Item Functioning ◽  
Hierarchical Structure ◽  
Large Scale ◽  
Intraclass Correlation ◽  
Real Data ◽  
Ordinal Logistic Regression ◽  
Type I ◽  
Multilevel Data ◽  
Item Functioning

Background. The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. Methods. The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. Results. Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. Conclusions. The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed.

scholarly journals Evaluation of Psychometric Properties and Differential Item Functioning of the Dental Anxiety Inventory (DAI-36) Questionnaire using Iterative Hybrid Ordinal Logistic Regression

2021 ◽  
Author(s):  
Elahe Allahyari ◽  
Narges Roustaei
Keyword(s):  
Logistic Regression ◽  
Differential Item Functioning ◽  
Dental Anxiety ◽  
Age Groups ◽  
Ordinal Logistic Regression ◽  
Chi Square ◽  
Item Functioning ◽  
Gender Education ◽  
Education Levels ◽  
The Difference

Abstract Background: Dental anxiety is a major dental problem. The difference of dental anxiety between groups may be reflect of the perception of individual of the items of questionnaire at the same level of the underlying dental anxiety. The propose of this study was to assess whether the Dental Anxiety Inventory (DAI-36) showed differential item functioning (DIF) by gender, age and education levels.Methods: By an iterative hybrid ordinal logistic regression model, we assessed measurement equivalence of DAI-36 items across gender, education, and age groups. All analysis was run by lordif package in R3.1.3 software for 950 participants.Results: The chi-square statistics declared 7, 7, and 4 non-uniform DIF items, and 2, 5, and 4 uniform DIF items across gender, education, and age groups, respectively. ΔR2 was always lower than 0.07 in all uniform and non-uniform DIF items. However, Δβ1 revealed significant uniform DIF in items 1 and 8 across gender ( Δβ1(item 1)=0.0137, Δβ1(item 8)=0.0124) and items 22 and 27 across age categories ( Δβ1(item 22)=0.0110, Δβ1(item 27)=0.0136). Conclusions: DIF items had no large magnitude or cancel out each other, so statements phrased in DAI-36 questionnaire have equivalent meaning across respondents, regardless of their gender, education, and age groups.

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