PURPOSE: To assess and validate the subscale structure of the 28-item Impact of Visual Impairment (IVI) Scale by using confirmatory factor analysis (CFA) and Rasch analysis for use as an outcome measure. METHODS: Three hundred nineteen participants completed the IVI questionnaire, and the responses then were subjected to Rasch analysis by RUMM2020 software. With the person estimates for each item, CFA was used to assess two hypothesized structures: three-and four-factor models. The subscales of the model with the best fit were then further validated by Rasch analysis. RESULTS: CFA supported a three-factor model that included items from the emotional well-being, reading and accessing information, and mobility and independence subscales. Almost all the selected goodness-of-fit statistics for the three-factor model were better than the recommended values. The factor loadings of the items on their respective domains were all statistically significant (P < 0.001) and ranged between 0.54 and 0.81. The three subscales individually fitted the Rasch model according to the item-trait interaction test (mobility and independence chi(2) [df] = 45.9 [44], P = 0.39; emotional well-being = 28.4 [32], P = 0.65; and reading and accessing information = 43.5 [36], P = 0.18). The item-fit residuals values of the three subscales were <2.5 and showed mean and standard deviations approximating 0 and 1, respectively. The internal consistency reliability of the subscales (alpha) was substantial, ranging between 0.89 and 0.91. CONCLUSIONS: An examination of the IVI dimension confirmed a three-subscale structure that displays interval measurement characteristics likely to provide a valid and reliable assessment of restriction of participation. The findings provide an opportunity for a more detailed measurement of the effects of different types of low-vision rehabilitation programs.
PURPOSE: To assess and validate the subscale structure of the 28-item Impact of Visual Impairment (IVI) Scale by using confirmatory factor analysis (CFA) and Rasch analysis for use as an outcome measure. METHODS: Three hundred nineteen participants completed the IVI questionnaire, and the responses then were subjected to Rasch analysis by RUMM2020 software. With the person estimates for each item, CFA was used to assess two hypothesized structures: three-and four-factor models. The subscales of the model with the best fit were then further validated by Rasch analysis. RESULTS:CFA supported a three-factor model that included items from the emotional well-being, reading and accessing information, and mobility and independence subscales. Almost all the selected goodness-of-fit statistics for the three-factor model were better than the recommended values. The factor loadings of the items on their respective domains were all statistically significant (P < 0.001) and ranged between 0.54 and 0.81. The three subscales individually fitted the Rasch model according to the item-trait interaction test (mobility and independence chi(2) [df] = 45.9 [44], P = 0.39; emotional well-being = 28.4 [32], P = 0.65; and reading and accessing information = 43.5 [36], P = 0.18). The item-fit residuals values of the three subscales were <2.5 and showed mean and standard deviations approximating 0 and 1, respectively. The internal consistency reliability of the subscales (alpha) was substantial, ranging between 0.89 and 0.91. CONCLUSIONS: An examination of the IVI dimension confirmed a three-subscale structure that displays interval measurement characteristics likely to provide a valid and reliable assessment of restriction of participation. The findings provide an opportunity for a more detailed measurement of the effects of different types of low-vision rehabilitation programs.
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