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Rasch fit statistics and sample size considerations for polytomous data

BMC Medical Research Methodology. 2008;8(1):33 DOI 10.1186/1471-2288-8-33


Journal Homepage

Journal Title: BMC Medical Research Methodology

ISSN: 1471-2288 (Online)

Publisher: BMC

LCC Subject Category: Medicine: Medicine (General)

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML



Fallowfield Lesley J

Rush Robert

Smith Adam B

Velikova Galina

Sharpe Michael


Open peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 23 weeks


Abstract | Full Text

<p>Abstract</p> <p>Background</p> <p>Previous research on educational data has demonstrated that Rasch fit statistics (mean squares and t-statistics) are highly susceptible to sample size variation for dichotomously scored rating data, although little is known about this relationship for polytomous data. These statistics help inform researchers about how well items fit to a unidimensional latent trait, and are an important adjunct to modern psychometrics. Given the increasing use of Rasch models in health research the purpose of this study was therefore to explore the relationship between fit statistics and sample size for polytomous data.</p> <p>Methods</p> <p>Data were collated from a heterogeneous sample of cancer patients (n = 4072) who had completed both the Patient Health Questionnaire – 9 and the Hospital Anxiety and Depression Scale. Ten samples were drawn with replacement for each of eight sample sizes (n = 25 to n = 3200). The Rating and Partial Credit Models were applied and the mean square and t-fit statistics (infit/outfit) derived for each model.</p> <p>Results</p> <p>The results demonstrated that t-statistics were highly sensitive to sample size, whereas mean square statistics remained relatively stable for polytomous data.</p> <p>Conclusion</p> <p>It was concluded that mean square statistics were relatively independent of sample size for polytomous data and that misfit to the model could be identified using published recommended ranges.</p>