Lamoureux Ecosse L, Pallant Julie F, Pesudovs Konrad, Hassell Jennifer B, Keeffe Jill E
Centre for Eye Research Australia, The University of Melbourne, 32 Gisborne Street, Melbourne, Victoria 3002, Australia.
Invest Ophthalmol Vis Sci. 2006 Nov;47(11):4732-41. doi: 10.1167/iovs.06-0220.
To explore the psychometric properties of the Impact of Vision Impairment scale (IVI) by using Rasch analysis.
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.
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.
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.
运用拉施分析探讨视力损害影响量表(IVI)的心理测量特性。
314名首次转诊至低视力诊所的患者完成了包含32个条目的IVI。使用RUMM2020软件(澳大利亚珀斯RUMM实验室),通过部分计分模型对数据进行拉施分析。评估模型的整体拟合度、反应量表、单个条目拟合度、差异条目功能、单维性和人员区分信度。
最初,26个条目显示出无序的阈值。然而,将反应量表合并为三类(4个条目)和四类(28个条目)后,所有条目产生了有序的反应阈值。随后,去除了4个缺失反应比例高、分布差、偏度高且观察到的曲线与预期模型曲线存在偏差的条目。这种调整使模型整体符合拉施模型(条目 - 特质交互卡方 = 118.3;P = 0.32)。最终,人员和条目的平均(标准差)拟合残差分别为0.06(0.85)和 -0.20(1.45)。人员区分信度为0.9,表明该量表能够区分几组不同的参与者。修订后的量表对参与者的针对性良好,条目的平均位置(0.00)和人员的平均位置(0.16)相似。轻度、中度和重度视力损害参与者之间的显著差异(方差分析;P < 0.001)支持了拉施量表化IVI的效标效度。
结果为拉施量表化的28条目版IVI的测量特性及其评估低视力康复结果的潜力提供了支持。提供了原始分数到拉施人员测量的转换方法。