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ORIGINAL ARTICLE
Year : 2018  |  Volume : 21  |  Issue : 10  |  Page : 1296-1303

Primary principles in developing scale with Rasch analysis: Portfolio anxiety assessment


1 Department of Biostatistics and Medical Informatics, Ondokuz Mayis University, Samsun, Turkey
2 Department of Medical Education, Medical Faculty, Ondokuz Mayis University, Samsun, Turkey

Correspondence Address:
Dr. L Tomak
Department of Biostatistics and Medical Informatics, Medical Faculty, Ondokuz Mayis University, Samsun
Turkey
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/njcp.njcp_275_17

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Background: Rasch model is a useful method for developing a new scale. This study aims to determine the fitting between data obtained from answers for a portfolio anxiety scale and Rasch model and describes how the scale can be modified to increase the fitting through different steps. Materials and Methods: A portfolio scale was applied to 171 students of the Faculty of Medicine, Ondokuz Mayis University. The partial credit model was used, and fit statistics were assessed to determine the fitting of the data to Rasch model. Person separation index (PSI) was used for reliability. Results: For a satisfaction subscale, the average item fit residual value was 0.47 and the average person fit residual value was −0.29. For the item–trait χ2 interaction, P = 0.655 and PSI = 0.81. For a writing anxiety subscale, the average item fit residual value was 0.08 and the average person fit residual value was −0.24. For the item–trait χ2 interaction, P = 0.698 and PSI = 0.73. For a reflection anxiety subscale, the average item fit residual value was 0.64 and the average item fit residual value was 0.64. For the item–trait χ2 interaction, P = 0.195 and PSI = 0.73. Conclusion: The validity and reliability of Rasch analysis portfolio scale were analyzed, and items that worked well were included in the study. The results show that Rasch model provides a more accurate analysis for developing and adapting scales. Both the fit statistics and fit graphs help improve the analyses.


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