PURPOSE: To assess and re-engineer the Vision Core Measure 1 (VCM1) questionnaire in low vision (LV) and cataract participants using Rasch analysis. METHODS: 295 participants drawn from a low vision clinic and 181 from a cataract surgery waiting list completed the 10-item VCM1. Unidimensionality, item fit to the model, response category performance, differential item functioning (DIF) and targeting of items to patients were assessed. Category collapsing and item removal were considered to improve the questionnaire. RESULTS: The initial fit of the VCM1 (combined populations) to the Rasch model showed lack of fit (chi2 = 83.3, df = 50, p = 0.002). There was evidence of DIF between the two populations which could not be resolved. Consequently, each population was assessed separately. Irrespective of the population, disordering of response category thresholds was evident. However, collapsing categories produced ordered thresholds and resulted in fit to the Rasch model for the LV (Total chi2 = 41.6, df = 30; p = 0.08) and cataract population (Total chi2 = 17.9, df = 20, p = 0.59). Overall, the VCM1 behaved as a unidimensional scale for each population and no item showed evidence of DIF. Item targeting to patients was however sub-optimal particularly for the cataract population. CONCLUSION: The VCM1 questionnaire could be improved by shortening the response scale, although different response categories are required for cataract and LV populations. Calibration of items also differed across populations. While the VCM1 performs well within the Rasch model, in line with its initial purpose, it requires the addition of items to satisfactorily target low vision and cataract populations.
PURPOSE: To assess and re-engineer the Vision Core Measure 1 (VCM1) questionnaire in low vision (LV) and cataractparticipants using Rasch analysis. METHODS: 295 participants drawn from a low vision clinic and 181 from a cataract surgery waiting list completed the 10-item VCM1. Unidimensionality, item fit to the model, response category performance, differential item functioning (DIF) and targeting of items to patients were assessed. Category collapsing and item removal were considered to improve the questionnaire. RESULTS: The initial fit of the VCM1 (combined populations) to the Rasch model showed lack of fit (chi2 = 83.3, df = 50, p = 0.002). There was evidence of DIF between the two populations which could not be resolved. Consequently, each population was assessed separately. Irrespective of the population, disordering of response category thresholds was evident. However, collapsing categories produced ordered thresholds and resulted in fit to the Rasch model for the LV (Total chi2 = 41.6, df = 30; p = 0.08) and cataract population (Total chi2 = 17.9, df = 20, p = 0.59). Overall, the VCM1 behaved as a unidimensional scale for each population and no item showed evidence of DIF. Item targeting to patients was however sub-optimal particularly for the cataract population. CONCLUSION: The VCM1 questionnaire could be improved by shortening the response scale, although different response categories are required for cataract and LV populations. Calibration of items also differed across populations. While the VCM1 performs well within the Rasch model, in line with its initial purpose, it requires the addition of items to satisfactorily target low vision and cataract populations.
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