PURPOSE: To explore the psychometric properties of the Impact of Vision Impairment scale (IVI) by using Rasch analysis. METHODS: Three hundred fourteen first-time referrals to low-vision clinics completed the 32-item IVI. The data were Rasch-analyzed with a partial credit model using RUMM2020 software (RUMM Laboratory, Perth, WA, Australia). The overall fit of the model, response scale, individual item fit, differential item functioning, unidimensionality, and person-separation reliability were assessed. RESULTS: Initially, 26 items displayed disordered thresholds. However, collapsing the response scale to three categories (4 items) and four categories (28 items) produced ordered response thresholds for all items. Four items with high proportions of missing responses, poor spread, high skewness, and deviation between observed and expected model curves were then removed. This adjustment produced overall fit to the Rasch model (item-trait interaction chi(2) = 118.3; P = 0.32). The final mean (SD) person and item fit residuals ere 0.06 (0.85) and -0.20 (1.45), respectively. The person-separation reliability was 0.9, indicating that the scale was able to discriminate between several different groups of participants. The revised scale was well targeted to the participants, with similar mean locations for items (0.00) and persons (0.16). A significant difference between participants of mild, moderate, and severe visual impairment (ANOVA; P 0.001) supported the criterion validity of the Rasch-scaled IVI. CONCLUSIONS: The results provide support for the measurement properties of the Rasch-scaled 28-item version of the IVI and of its potential for assessing outcomes of low-vision rehabilitation. A raw score-to-Rasch person measure conversion is supplied.
PURPOSE: To explore the psychometric properties of the Impact of Vision Impairment scale (IVI) by using Rasch analysis. METHODS: Three hundred fourteen first-time referrals to low-vision clinics completed the 32-item IVI. The data were Rasch-analyzed with a partial credit model using RUMM2020 software (RUMM Laboratory, Perth, WA, Australia). The overall fit of the model, response scale, individual item fit, differential item functioning, unidimensionality, and person-separation reliability were assessed. RESULTS: Initially, 26 items displayed disordered thresholds. However, collapsing the response scale to three categories (4 items) and four categories (28 items) produced ordered response thresholds for all items. Four items with high proportions of missing responses, poor spread, high skewness, and deviation between observed and expected model curves were then removed. This adjustment produced overall fit to the Rasch model (item-trait interaction chi(2) = 118.3; P = 0.32). The final mean (SD) person and item fit residuals ere 0.06 (0.85) and -0.20 (1.45), respectively. The person-separation reliability was 0.9, indicating that the scale was able to discriminate between several different groups of participants. The revised scale was well targeted to the participants, with similar mean locations for items (0.00) and persons (0.16). A significant difference between participants of mild, moderate, and severe visual impairment (ANOVA; P 0.001) supported the criterion validity of the Rasch-scaled IVI. CONCLUSIONS: The results provide support for the measurement properties of the Rasch-scaled 28-item version of the IVI and of its potential for assessing outcomes of low-vision rehabilitation. A raw score-to-Rasch person measure conversion is supplied.
Authors: Eva K Fenwick; Bao Sheng Loe; Jyoti Khadka; Ryan E K Man; Gwyn Rees; Ecosse L Lamoureux Journal: Qual Life Res Date: 2019-11-09 Impact factor: 4.147
Authors: Eva K Fenwick; Peng Guan Ong; Charumathi Sabanayagam; Gwyn Rees; Jing Xie; Edith Holloway; Ching-Yu Cheng; Tien Y Wong; Blanche Lim; Pok Chien Tan; Ecosse L Lamoureux Journal: Qual Life Res Date: 2015-09-29 Impact factor: 4.147
Authors: Eva K Fenwick; Ryan E K Man; Gwyn Rees; Jill Keeffe; Tien Y Wong; Ecosse L Lamoureux Journal: Qual Life Res Date: 2016-08-24 Impact factor: 4.147
Authors: Lauren N Ayton; Joseph F Rizzo; Ian L Bailey; August Colenbrander; Gislin Dagnelie; Duane R Geruschat; Philip C Hessburg; Chris D McCarthy; Matthew A Petoe; Gary S Rubin; Philip R Troyk Journal: Transl Vis Sci Technol Date: 2020-07-16 Impact factor: 3.283