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The effect of person misfit on item parameter estimation: A simulation study Open Access


Other title
Person fit
Item response Theory
Parameter estimation
Type of item
Degree grantor
University of Alberta
Author or creator
Mousavi, Seyed Amin
Supervisor and department
Ying Cui (Educational Psychology)
Examining committee member and department
Todd Rogers (Educational Psychology)
Ruth Childs (Leadership, Higher and Adult Education, University of Toronto)
Ken Cor (Pharmacy and Pharmaceutical Sciences)
Michael Carbonaro (Educational Psychology)
George Buck (Educational Psychology)
Department of Educational Psychology
Measurement,Evaluation and Cognition
Date accepted
Graduation date
Doctor of Philosophy
Degree level
The validity and reliability of a test may be compromised because of the presence of misfitting response patterns in test data. Consequently, the purpose of the present study was to investigate the effects of the inclusion and exclusion of misfitting response patterns on item parameter estimates under using simulated data. The four factors considered included test length (20, 40, and 60 items), item parameter estimation method (MLE and Baysian Modal), percentage of students who responded misfittingly in the sample (10%, 20%, and 30%), and percentage of items susceptible to misfitting responses (25% and 50%). Two person fit indices (lz and HT) were used to remove misfitting response patterns from the data set with misfitting response patterns. The 2-PL IRT model used to analyze the data. The dependent variables were the bias in the estimated b and a parameters, the standard error of the estimated parameters, and the classification accuracy of placing students into one of two performance categories. The results showed that 1) there was no difference between the two item parameter estimators, 2) the item difficulty parameter (b) was less affected by the presence of misfitting response patterns than the item discrimination parameter (a), 3) item parameters with large true b and a parameter values were affected, 4) an increase in the percentage of misfitting response patterns led to larger bias for both the b and a parameters, 5) the standard errors of estimates of the b and a parameters were small across all conditions, 6) the classification accuracy was higher for the low performing students for the fitting data set and lower and essentially constant across the data sets with all the misfitting response patterns, and the data sets with misfitting response patterns removed by lz and by HT, 7) the classification accuracy was lower for the high performing students than the low performing students for the fitting data set and lower and essentially constant for the remaining three data sets. Implications for practice and recommendations for future research are provided.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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