A simulation study provided sample size guidance for differential item functioning (DIF) studies using short scales.
OBJECTIVE
Differential item functioning (DIF) analyses are increasingly used to evaluate health-related quality of life (HRQoL) instruments, which often include relatively short subscales. Computer simulations were used to explore how various factors including scale length affect analysis of DIF by ordinal logistic regression.


METHODS
Simulated data, representative of HRQoL scales with four-category items, were generated. The power and type I error rates of the DIF method were then investigated when, respectively, DIF was deliberately introduced and when no DIF was added. The sample size, scale length, floor effects (FEs) and significance level were varied.


RESULTS
When there was no DIF, type I error rates were close to 5%. Detecting moderate uniform DIF in a two-item scale required a sample size of 300 per group for adequate (>80%) power. For longer scales, a sample size of 200 was adequate. Considerably larger sample sizes were required to detect nonuniform DIF, when there were extreme FEs or when a reduced type I error rate was required.


CONCLUSIONS
The impact of the number of items in the scale was relatively small. Ordinal logistic regression successfully detects DIF for HRQoL instruments with short scales. Sample size guidelines are provided.
Scott
Neil W
NW
Department of Public Health, University of Aberdeen, Aberdeen, UK.
Fayers
Peter M
PM
Aaronson
Neil K
NK
Bottomley
Andrew
A
de Graeff
Alexander
A
Groenvold
Mogens
M
Gundy
Chad
C
Koller
Michael
M
Petersen
Morten A
MA
Sprangers
Mirjam A G
MA
D003198:Computer Simulation; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011594:Psychometrics / Q000379:methods; D011788:Quality of Life / Q000523:psychology*; D018401:Sample Size; D014894:Weights and Measures