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(Investigative Ophthalmology and Visual Science. 2007;48:1001-1006.)
© 2007 by The Association for Research in Vision and Ophthalmology, Inc.
DOI:  10.1167/iovs.06-0361
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The Impact of Vision Impairment Questionnaire: An Assessment of Its Domain Structure Using Confirmatory Factor Analysis and Rasch Analysis

Ecosse L. Lamoureux,1 Julie F. Pallant,2 Konrad Pesudovs,3 Gwyn Rees,1 Jennifer B. Hassell,1 and Jill E. Keeffe1,4

1From the Centre for Eye Research Australia, The University of Melbourne, Melbourne, Victoria, Australia; the 2Swinburne University of Technology, Hawthorn, Victoria, Australia; the 3NH&MRC (National Health and Medical Research Council) Centre for Clinical Eye Research, Flinders University and Flinders Medical Centre, Adelaide, South Australia, Australia; and the 4Vision CRC (Correction Research Center), Sydney, Australia.


   Abstract
Top
 Abstract
Materials and Methods
 Results
 Discussion
 References
 
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.

The Impact of Vision Impairment (IVI) questionnaire was designed to assess participation in daily activities and determine the outcome of low-vision rehabilitation on quality of life in people with low vision.1 2 3 4 5 The IVI has been validated and shows good discriminative ability and reliability, with consistent results found between different forms of administration.1 It has a good range of items and, as opposed to most vision-specific questionnaires which typically assess visual functioning, has been designed to assess restriction of participation in daily living as well as to provide a tool to determine the effectiveness of low-vision rehabilitation. The original IVI questionnaire contained 32 items grouped under five domains of participation: leisure and work, consumer and social interaction, household and personal care, mobility, and emotional reaction to vision loss.

The grouping of items within domains is important because they can form subscales that allow for the assessment of intervention at more specific levels. This is particularly relevant to low-vision care, as management is typically undertaken on a task-specific basis, and so it is possible that real gains in one or two areas may be obscured within a large scale assessing overall performance. Subscale measurements of outcome provide a more detailed insight into the effectiveness aspects of low-vision rehabilitation. In the initial validation of the IVI questionnaire, principal components analysis (PCA) was used to explore the underlying structure of the scale, but no Rasch analysis was undertaken, making the findings essentially inferential.1 However, the analysis did not confirm the five domains specified a priori by the authors. Rather, it identified a three-factor solution that was supported by an examination of the scree plot. The authors did not either formally reject the five domains identified a priori or recommend the three-factor structure identified by PCA. The equivocal nature of the factors underlying the IVI may confuse potential users in how best to interpret the results and indicates that further scrutiny of IVI structure is needed.

Further examination of the domain structure is also warranted, as the IVI has recently undergone further validation using Rasch analysis which is a sophisticated approach to questionnaire development using modern psychometric methods.6 Rasch analysis converts categorical data into a linear scale, calculates item difficulty in relation to patient ability, and provides estimates of item and person measures on an interval scale.7 8 9 10 11 12 13 14 15 Rather than an exploratory approach as used in the initial study (i.e., PCA),1 the IVI subscale needs to be validated using a confirmatory process, especially if the outcome of low-vision rehabilitation and the sensitivity to change of specific aspects of quality of life (i.e., mobility, emotion, and leisure) are desired. The main objectives of this study were therefore to (1) assess the dimensions of the 28-item IVI using CFA performed on person-item measures derived from the Rasch analysis of the instrument and (2) to use Rasch analysis to validate the factors of the best-fitting model as viable traits for measuring specific aspects of restriction of participation for people with impaired vision.


   Materials and Methods
Top
 Abstract
 Materials and Methods
Results
 Discussion
 References
 
Participants
Individuals referred to low-vision rehabilitation centers across the state of Victoria (Australia) were recruited. An ophthalmologist’s report, providing the cause of vision loss and visual acuities, was obtained for each participant. The eligibility criteria for the study included presenting visual acuity <6/12 (or ≥6/12 with restricted fields), ≥18 years of age and the ability to converse in English. Individuals who agreed to participate signed a consent form that allowed access to low-vision rehabilitation records. Ethical approval was obtained from the Royal Victorian Eye and Ear Hospital’s Human Research and Ethics Committee, and the research adhered to the tenets of the Declaration of Helsinki.

Measures
The IVI questionnaire, sociodemographic, and clinical data were collected. Participants also completed the SF-12 from which the physical and mental component summaries (PCS-12 and MCS-12, respectively) were computed.16 Each summary scale was scored from 0 to 100, where a score of 100 indicates the best possible score and 0 represents the worst possible score. The PCS-12 and MCS-12 scores were included, to validate the IVI subset of items.

The IVI Questionnaire
The 32-item IVI instrument was either self- or interviewer-administered, to measure vision-related restriction of participation in daily living, as described previously.1 2 Responses to the IVI items were rated on a five-category Likert scale: not at all, 0; hardly at all, 1; a little, 2; a fair amount, 3; a lot, 4; and can’t do because of eyesight, 5; with an additional response category, don’t do because of other reasons, for 19 items. The latter response was not included in computing the average overall or domain score. The wording preceding these items was, In the past month, how much has your eyesight interfered with the following activities. For the remaining 13 items, the rating scale used was: not at all, 0; very rarely, 1; a little of the time, 2; a fair amount of the time, 3; a lot of the time, 4; and all the time, 5. The wording preceding these items was, In the past month, how often has your eyesight made you concerned or worried about the following.

Statistical Analysis
Most of the Rasch analyses were performed with RUMM202017 but Winsteps (ver. 3.61)18 was used to generate transformed individual person scores for all items, as this feature is not currently available in RUMM. Individual person measures for all items in Winsteps were estimated by assigning a category threshold for each item and converting raw scores to Rasch category estimates. These data were required for CFA. The compatibility of the results from the two Rasch analysis software programs was tested by comparing person measures. These results were identical with one decimal place for 94% of cases, with greater deviation only occurring for extreme responders. This deviation arises from the different assumptions used to calculate extreme results.

Confirmatory factor analysis (performed with AMOS, ver. 6; SPSS Science, Chicago, IL) was used to confirm the hypothesized structure statistically. CFA allows for assessment of the overall model fit, the statistical significance tests for theorized relations in the model, and the estimation of latent concepts free of measurement error. CFA was undertaken to assess two hypothesized measurement models based on the findings of previous exploratory investigation1 2 and similar work.19 The first, a three-factor model, comprised three latent traits: mobility and independence (11 items), emotional well-being (8 items), and reading and access to information (9 items). The second was a four-factor model that assessed the interrelationship of four latent traits: mobility and safety (6 items), emotional well-being (8 items), independence (8 items), and reading and near-vision activities (6 items).

CFA with the maximum-likelihood estimation was conducted on the calibrated person-item scores to evaluate model fit of each proposed model. A good model fit can be indicated by a nonsignificant item–trait interaction {chi}2 probability value. However, because the {chi}2 test has been criticized for its dependence on sample size, a range of fit statistics were assessed. A relative {chi}2 is usually used (ratio of {chi}2 to degrees of freedom- {chi}2/df) with a recommended range of 1.0 to 2.0.20 The root mean square error of approximation (RMSEA) is the one of the most informative statistics in determining model fit, as it takes into account the number of variables that are estimated in the model.21 22 23 RMSEA values are required to be ≤0.05 to indicate good fit. Values between 0.05 and 0.08 indicate reasonable fit.21 22 23 For the incremental fit statistics (goodness of fit index: GFI; the Tucker-Lewis index: TLI; and the comparative fit index: CFI) <0.90 indicates lack of fit, between 0.90 and 0.95 indicates reasonable fit, and between 0.95 and 1.00 indicates good fit.21 22 23

The latent variables or subscales of the model identified by CFA as providing the best fit were then examined with Rasch analysis using RUMM17 with the purpose of assessing how well the subscales fit the Rasch model. Fit was evaluated by using person and item fit residual statistics, which are transformed weighted mean squares. The transformed mean squares are normally distributed with an expected value of 0 and an expected variance of 1. An item–trait interaction score ({chi}2) with a statistically nonsignificant probability (P > 0.05) indicates fit to the model. An estimate of person separation reliability which indicates how well the items of the instrument separate or spread out the subjects in the sample was also reported.24 The unidimensionality of the each subscale after overall fit to the Rasch model was determined using principal components analysis of the residuals available in RUMM. Unidimensionality is tested by allowing the pattern of factor loadings on the first residual to determine subsets of items. If person estimates derived from these subsets of items differ significantly from the estimates derived from the full subscale, local independence is considered to be compromised.25


   Results
Top
 Abstract
 Materials and Methods
 Results
Discussion
 References
 
The personal and clinical characteristics of the 319 participants of the study appear in Table 1 . The mean PCS-12 and MCS-12 components of the SF-12 were 36.7 and 47.7, respectively, and were comparable to mean scores reported for Americans of similar age groups (38.7 and 50.0 for PCS-12 and MCS-12, respectively).16 Compared with the initial validation study,1 the two samples were similar on personal and clinical characteristics, except for distance visual acuity and main cause of vision loss. In the present study, there was a greater percentage of participants with distance visual acuity <6/18 to 6/60 (47% vs. 29%) and age-related macular degeneration (54% vs. 39%) compared with the initial study.


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  		TABLE 1. The Characteristics of the 319 Study Participants

 
Confirmatory Factor Analysis
Before undertaking CFA, the IVI questionnaire was re-examined by using the Rasch analysis. This resulted in a 28-item questionnaire and a four-category response scale for 26 items and a three-category response scale for two items. The revised questionnaire had an excellent internal consistency, as illustrated by the following statistics: Item–trait interaction {chi}2 = 118, P = 0.32; mean ± SD person fit residual values = 0.068 ± 0.85; mean ± SD item fit residual values = –0.203 ± 1.45 and person separation reliability = 0.95. Rasch calibrated person measures across all items were then generated and CFA was used to assess the hypothesized models. The various goodness-of-fit statistics for the two hypothesized models are shown in Table 2 . The indices showed a good fit between the IVI data and proposed measurement models. The fit indices for these two models were almost identical (Table 2) . The ratio of {chi}2 to degrees-of-freedom value ({chi}2/df) is 1.41 in both models and falls well within the recommended range of 1.0 to 2.0.20 The RMSEA, CFI, and TLI in both models averaged 0.5, 0.94, and 0.94, respectively, and were better than the recommended values. For both models, the goodness-of-fit index (GFI) of 0.85 was slightly less than the benchmark of 0.9.


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  		TABLE 2. Comparative Goodness-of-Fit Statistics for Two Hypothesized Models

 
Inspection of the correlations between the four-factor solution, however, indicated there were high values between the independence and reading subscales (0.91) as well as between the independence and mobility subscales (0.92). Given the evidence of strong overlap between these latent traits and in the interest of model parsimony, the best-fitting model for these data was a three-correlated-factor model (Table 3) representing latent trails of mobility and independence (11 items), reading and accessing information (9 items), and emotional well-being (8 items). The ß coefficients of the 28 items were all statistically significant (P < 0.001) and ranged between 0.62 and 0.77, 0.65 and 0.81, and 0.54 and 0.78 for the mobility and independence, emotional well-being and reading and accessing information subscales, respectively. The interfactor correlations were 0.85 (reading and accessing information and mobility and independence), 0.61 (reading and accessing information and emotional well being), and 0.80 (mobility and independence and emotional well being). The items—Finding what you want during shopping? (ß = 0.80); Interfered with your life in general? (ß = 0.81), and Getting about outdoors? (ß = 0.77)—showed the strongest loading for the mobility and independence, emotional well-being and reading and accessing information subscales, respectively.


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  		TABLE 3. The Item Labels and Factor Loadings of the Three-Factor Model of the IVI Questionnaire

 
Fit of the Three Subscales to the Rasch Model
Subsequent to CFA supporting the three-factor model, its three domains were assessed separately by Rasch analysis. They all showed nonsignificant item–trait interactions, suggesting fit between data and model (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 fit residuals of all the items recorded values <2.5 and the three subscales showed mean and SD values close to 0 and 1, respectively, suggesting no misfit to the model by items and respondents. The person separation reliability scores ranged between 0.89 and 0.91, indicating a substantial ability to distinguish four strata of person ability (Table 4) . The most difficult and easiest items (in logits) for the mobility and independence subscale were: Stopped you doing the things you want to do? (0.75) and Your general safety at home? (–1.42); for emotional well-being: Felt frustrated or annoyed? (1.1) and Have you felt lonely or isolated? (–1.42); and for the reading and accessing information: Reading ordinary-sized print? (1.92), and Generally looking after your appearance? (–1.13).


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  		TABLE 4. The Mean ± SD Person and Item Fit Residual Values, Person Separation Reliability Scores, and Range of Item Fit Residual Values of the Three Domains of the IVI

 
Test of Local Independence Assumption of the Three Subscales
No significant differences were found between the person estimates of the three subscales and their respective positive and negative subsets (t-test; P = 0.45–0.69). This finding suggests no breach of the assumption of local independence supporting the unidimensionality of the subscales.

Criterion-Related Validity
After Rasch analysis, the person measures of each subscale were used to assess the criterion validity of the subscales of the IVI. The reading and accessing information subscale recorded its strongest correlations with visual acuity, as these activities are critically dependent on near and distance vision (Table 5) . The emotional well-being factor recorded its strongest association with the mental component of the SF-12 (–0.56) which includes items pertinent to emotional and mental health. Equally, the mobility and independence domain recorded its strongest association (–0.43) with the physical component of the SF-12 health (PCS-12) which includes items associated with mobility. These correlations overall tend to support the definition of the new IVI subscale structure.


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  		TABLE 5. Correlation between the Participants’ Characteristics and the Person Measures of the New IVI Subscales

 
Internal Consistency Reliability
The internal consistency reliability of the three-factor model was estimated by Cronbach’s {alpha} using the person measures across items. The mobility and independence, emotional well-being, and reading and accessing information subscales recorded {alpha} values ranging between 0.89 and 0.91. These values are above the suggested moderate level of internal consistency among the instrument items26 and indicate that the items under each subscale consistently measure the same construct.

Scoring of the IVI Subscales
Other investigators wishing to use the IVI subscales can use these validation data to convert raw scores into Rasch person measures without having to perform Rasch analysis. This conversion mainly holds for patients with complete data. Raw scores are calculated by first reversing the scores (0, 1, 2, 3, 4, 5) (5, 4, 3, 2, 1, 0) to give better IVI scores to those experiencing less restriction of participation. The categories are then collapsed to 4(3, 2, 2, 1, 1, 0) or 3(2, 1, 1, 1, 1, 0), as described previously.6 Then, for each subscale the average of the items gives the IVI raw score. This score is related to the IVI Rasch person measure, as illustrated in Figure 1 . The relationship is double-asymptotic because the average raw rating has a floor and a ceiling (at 0 and 3). The relationship can be described as double-asymptotic nonlinear regression.27 The equations listed in Table 6 can be used to convert raw scores to Rasch person measures for each subscale.


Figure 1
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  		FIGURE 1. Scatterplots of the person measure estimated from Rasch analysis versus the average rating for each person across items (raw subscale score). The fit lines are generated with double asymptotic nonlinear regression. (A) Mobility and independence, (B) Emotional well-being, and (C) Reading and accessing information.

 

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  		TABLE 6. Equations Converting Raw Score to the Rasch Person Measure

 

   Discussion
Top
 Abstract
 Materials and Methods
 Results
 Discussion
References
 
In the present study, CFA was used to assess the dimension of the IVI and provide evidence of the factorial structure and reliability of the recently Rasch-scaled IVI instrument.6 Two models were proposed a priori and a resultant three-factor model provided the best-fit statistics and most parsimonious measure of restriction of participation in quality of life in patients with low vision. The confirmed dimensions of the IVI represent items from the emotional well-being, reading and accessing information, and mobility and independence, respectively. Our finding supports the recent work of Massof et al.,19 who also used person measures estimated from subsets of functionally grouped items to assess the dimensionality of a vision disability questionnaire. They confirmed a two-factor structure: reading and mobility. Massof et al. did not find an emotional well-being subscale, as their questionnaire, the Activity Breakdown Structure (ABS), did not have such content, because it assesses the difficulty undertaking daily tasks. On the other hand, a critical component of the IVI is the assessment of the emotional impact of visual impairment in people with low vision. With the confirmation of an emotional subscale, it is now possible to provide a more specific assessment of the emotional consequences associated with vision loss as well as to evaluate the effect of rehabilitation programs on emotional health.

Only one factor identified a priori in the initial validation study1 (i.e., emotional well-being) was confirmed in the study. Discrepancy between the initial and current studies could be linked to different factor analysis techniques. The initial study used exploratory methods, whereas the current one used a confirmatory approach. Also, in the present study, Rasch-calibrated person measures were used compared with raw scores used in the initial study. Factor analysis of raw scores can lead to item grouping based on item difficulty. The difference in sample size could also explain the dissimilarity between the two studies. Only 86 participants were included initially, compared with 319 in the present study. It is likely that a bigger sample size provided a better capacity to undertake factor analysis, as it has been suggested that small data sets tend not to generalize as well as those derived from large samples. Tabachnick and Fidell28 have suggested that a ratio of five cases to one item is adequate. In the present study, our ratio was 10.3 compared with 3.1 in the initial study.

In this study, the use of Rasch analysis has enabled a detailed examination of the operation of the subscales. Our findings show that the three domains possess viable measurement characteristics to assess specific aspects of restriction of participation in daily activities for individuals with impaired vision. The domains also possess demonstrated reliability and validity and show no evidence of multidimensionality. Because it had been shown that the Rasch scoring method had greater precision compared with standard Likert scoring and plays an important role in improving sensitivity to change,14 29 30 our findings suggest that the greater accuracy of the Rasch-analyzed subscales could result in improved measurement of the specific outcomes of low-vision rehabilitation trials.30 31 Future studies, however, are needed to substantiate this claim.

One important finding of the new IVI domain structure is the identification of a reading and accessing information domain. Activities related to near and distance vision have consistently been associated with increased difficulty for people with low vision8 32 33 and with this revised IVI domain structure, the impact of these critical activities of daily living could be assessed individually and collectively. Of importance, a significant component of most low-vision rehabilitation programs includes the prescription of low-vision devices as well as strategies intended to improve visual functioning, and the Rasch-assessed domain structure of the IVI can now potentially assess the outcome of low-vision rehabilitation specific to reading and ability to undertake vision-dependent activities.

Finally, moderate correlations were found between the mental component of the SF-12 and the emotional well-being subscale; the physical component of the SF-12 and mobility and independence subscale; and visual acuity and the reading and access to information subscale. This finding provides further support of the new factorial model of the IVI, as the MCS-12 and PCS-12 contain several items specific to emotional well-being and mobility, respectively, and visual acuity functions are critical to distance reading and near vision performance.

In conclusion, through a confirmatory factor analysis and Rasch analysis, our examination of the dimensionality of the IVI questionnaire supported a three-subscale structure with interval level measurement characteristics likely to provide a reliable assessment of specific aspects of restriction of participation in daily living and effectiveness of rehabilitation in people with low vision. This new structure of the IVI opens the door to the exploration of three components of restriction of participation in daily living and a better understanding of the effects of different types of intervention. Future work should evaluate the sensitivity of the IVI subscales to measure outcomes of low-vision rehabilitation.


   Footnotes
 
ELL is a recipient of a Australian National Health and Medical Research Council Public Health Fellowship.

Submitted for publication April 3, 2006; revised September 20, 2006; accepted January 5, 2007.

Disclosure: E.L. Lamoureux, None; J.F. Pallant, None; K. Pesudovs, None; G. Rees, None; J.B. Hassell, None; J.E. Keeffe, None

The publication costs of this article were defrayed in part by page charge payment. This article must therefore be marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact.

Corresponding author: Ecosse L. Lamoureux, Centre for Eye Research Australia, Department of Ophthalmology, University of Melbourne, 32 Gisborne St., East Melbourne Victoria, 3002, Australia; ecosse{at}unimelb.edu.au.


   References
Top
 Abstract
 Materials and Methods
 Results
 Discussion
 References
 

  1. Weih LM, Hassell JB, Keeffe JE. Assessment of the impact of vision impairment. Invest Ophthalmol Vis Sci. 2002;43:927–935.[Abstract/Free Full Text]
  2. Lamoureux EL, Hassell JB, Keeffe JE. The determinants of participation in activities of daily living in people with impaired vision. Am J Ophthalmol. 2004;137:265–270.[CrossRef][ISI][Medline][Order article via Infotrieve]
  3. Lamoureux EL, Hassell JB, Keeffe JE. The impact of diabetic retinopathy on participation in daily living. Arch Ophthalmol. 2004;122:84–88.[Abstract/Free Full Text]
  4. Hassell JB, Lamoureux EL, Keeffe JE. Impact of age-related macular degeneration on quality of life. Br J Ophthalmol. 2006;90:593–596.[Abstract/Free Full Text]
  5. Noë G, Ferraro J, Lamoureux E, Rait J, Keeffe JE. Associations between glaucomatous visual field loss and participation in activities of daily living. Clin Exp Ophthalmol. 2003;31:482–486.[CrossRef][ISI][Medline][Order article via Infotrieve]
  6. Lamoureux E, Pallant J, Pesudovs K, Hassell J, Keeffe J. The Impact of Vision Impairment Questionnaire: an evaluation of its measurement properties using Rasch analysis. Invest Ophthalmol Vis Sci. 2006;47:4732–4741.[Abstract/Free Full Text]
  7. Norquist JM, Fitzpatrick R, Dawson J, Jenkinson C. Comparing alternative Rasch-based methods vs raw scores in measuring change in health. Med Care. 2004;42:125–136.[Medline][Order article via Infotrieve]
  8. Pesudovs K, Garamendi E, Keeves JP, Elliott DB. The Activities of Daily Vision Scale for cataract surgery outcomes: re-evaluating validity with Rasch analysis. Invest Ophthalmol Vis Sci. 2003;44:2892–2899.[Abstract/Free Full Text]
  9. Fisher WP, Jr, Eubanks R, Marier RL. Equating the MOS SF36 and the LSU HSI Physical Functioning Scales. J Outcome Meas. 1997;1:329–362.[Medline][Order article via Infotrieve]
  10. Fisher WPJ. The Rasch debate: validity and revolution in educational measurement. Wilson M eds. Objective Measurement: Theory into Practice. 1994;36–72. Ablex Norwood, NJ.
  11. Massof RW. The measurement of vision disability. Optom Vis Sci. 2002;79:516–552.[ISI][Medline][Order article via Infotrieve]
  12. Wright BD, Linacre JM. Observations are always ordinal, measurements must be interval. Arch Phys Med Rehabil. 1989;70:857–860.[ISI][Medline][Order article via Infotrieve]
  13. White LJ, Velozo CA. The use of Rasch measurement to improve the Oswestry classification scheme. Arch Phys Med Rehabil. 2002;83:822–831.[CrossRef][ISI][Medline][Order article via Infotrieve]
  14. Raczek AE, Ware JE, Bjorner JB, et al. Comparison of Rasch and summated rating scales constructed from SF-36 physical functioning items in seven countries: results from the IQOLA Project. International Quality of Life Assessment. J Clin Epidemiol. 1998;51:1203–1214.[CrossRef][ISI][Medline][Order article via Infotrieve]
  15. Pesudovs K, Garamendi E, Elliott DB. The Quality of Life Impact of Refractive Correction (QIRC) Questionnaire: development and validation. Optom Vis Sci. 2004;81:769–777.[ISI][Medline][Order article via Infotrieve]
  16. Ware J, Kosinski M, Keller S. SF-12: How to Score the SF-12 Physical and Mental Health Summary Scales. 1998; Quality Metric Lincoln, RI.
  17. Andrich D, Lyne A, Sheridan B, Luo G. RUMM 2020. 2003; RUMM Laboratory Perth, Australia.
  18. Linacre J. A User’s Guide to Winsteps: Rasch-Model Computer Program. 2002; Mesa Press Chicago.
  19. Massof RW, Hsu CT, Baker FH, et al. Visual disability variables. II: The difficulty of tasks for a sample of low-vision patients. Arch Phys Med Rehabil. 2005;86:954–967.[CrossRef][ISI][Medline][Order article via Infotrieve]
  20. Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL. Multivariate Data Analysis. 2005; 6th ed. Prentice Hall Englewood Cliffs, NJ.
  21. Byrne B. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. 2001; Lawrence Erlbaum Associates Mahwah, NJ.
  22. Bentler PM. Comparative fit indexes in structural models. Psychol Bull. 1990;107:238–246.[CrossRef][ISI][Medline][Order article via Infotrieve]
  23. Kline R. Principles and Practice of Structural Equation Modeling. 2005; 2nd ed. Guilford Press New York.
  24. Mallinson T, Stelmack J, Velozo C. A comparison of the separation ratio and coefficient alpha in the creation of minimum item sets. Med Care. 2004;42:117–124.
  25. Smith EV. Detecting and evaluation the impact of multidimensionality using item fit statistics and principal component analysis of residuals. J Appl Meas. 2002;3:205–231.[Medline][Order article via Infotrieve]
  26. Nunnally J. Psychometric Theory. 1978; McGraw-Hill New York.
  27. Massof RW. Application of stochastic measurement models to visual function rating scale questionnaires. Ophthalmic Epidemiol. 2005;12:103–124.[CrossRef][ISI][Medline][Order article via Infotrieve]
  28. Tabachnick B, Fidell L. Using Multivariate Statistics. 2001; 4th ed. HarperCollins New York.
  29. McHorney CA, Haley SM, Ware JE, Jr. Evaluation of the MOS SF-36 Physical Functioning Scale (PF-10): II. Comparison of relative precision using Likert and Rasch scoring methods. J Clin Epidemiol. 1997;50:451–461.[CrossRef][ISI][Medline][Order article via Infotrieve]
  30. Fitzpatrick R, Norquist JM, Jenkinson C, et al. A comparison of Rasch with Likert scoring to discriminate between patients’ evaluations of total hip replacement surgery. Qual Life Res. 2004;13:331–338.[CrossRef][ISI][Medline][Order article via Infotrieve]
  31. Hays RD, Morales LS, Reise SP. Item response theory and health outcomes measurement in the 21st century. Med Care. 2000;38:1128–1142.
  32. Hart P, Stevenson M, Montgomery A, Muldrew K, Chakravarthy U. Further validation of the Daily Living Tasks Dependent on Vision: identification of domains. Br J Ophthalmol. 2005.1127–1130.
  33. Stelmack J, Szlyk J, Stelmack T, et al. Use of Rasch person-item map in exploratory data analysis: a clinical perspective. J Rehabil Res Dev. 2004;41:233–241.[CrossRef][ISI][Medline][Order article via Infotrieve]



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E. L. Lamoureux, J. F. Pallant, K. Pesudovs, G. Rees, J. B. Hassell, and J. E. Keeffe
The Effectiveness of Low-Vision Rehabilitation on Participation in Daily Living and Quality of Life
Invest. Ophthalmol. Vis. Sci., April 1, 2007; 48(4): 1476 - 1482.
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