December 2010
Volume 51, Issue 12
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Letters to the Editor  |   December 2010
Author Response: Amblyopia and Strabismus Questionnaire
Author Affiliations & Notes
  • Marta Vianya-Estopa
    School of Optometry and Vision Science, University of Bradford, Bradford, United Kingdom.
  • David B. Elliott
    School of Optometry and Vision Science, University of Bradford, Bradford, United Kingdom.
  • Brendan T. Barrett
    School of Optometry and Vision Science, University of Bradford, Bradford, United Kingdom.
Investigative Ophthalmology & Visual Science December 2010, Vol.51, 6899-6900. doi:10.1167/iovs.10-6176
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      Marta Vianya-Estopa, David B. Elliott, Brendan T. Barrett; Author Response: Amblyopia and Strabismus Questionnaire. Invest. Ophthalmol. Vis. Sci. 2010;51(12):6899-6900. doi: 10.1167/iovs.10-6176.

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      © ARVO (1962-2015); The Authors (2016-present)

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We thank Drs. van de Graaf, Simonsz, Kelderman, Felius, and Passchier for their comments and interest in our paper 1 which evaluated their Amblyopia and Strabismus Questionnaire (A&SQ). 
Drs. Van de Graaf and Simonsz commented on the average visual acuity of our sample and suggested that almost all had amblyopia with a visual acuity worse than 0.30 logMAR. In fact, only 61% had worse visual acuity. The general tone of the criticisms raised by Van de Graaf and Simonsz is that our amblyopes had very low visual acuity and thus our sample was not representative of amblyopic populations in developed countries. To support their claim, they misleadingly quote ∼1% as the prevalence of adult amblyopia with visual acuity worse than 0.30 in the general population. Of course, the visual acuity of our sample of amblyopes should be compared with amblyopic populations in the literature. In this regard, we find that our amblyopic sample is similar to many previously published investigations. For example, Attebo et al. 2 (whom van de Graaf and Simonsz cite) found that, in an adult population, 38% had an acuity of worse than 0.60 logMAR and 90% had an acuity of worse 0.18 logMAR. Rahi et al. 3 found that 33% of their amblyopes at age 16 had acuity of 0.50 logMAR or worse, and in the sample in Brown et al. 4 of ∼4700 adults (mean age very similar to ours at 59 years), 54% of amblyopic eyes had VA worse than 0.30 logMAR. We are therefore strongly of the opinion that our sample is representative of adult amblyopes in developed countries. 
Unfortunately, Van de Graaf and Simonsz also appear to misunderstand our findings in regard to the sensitivity of the A&SQ. They suggest that the ceiling effect that we found in our study was due to “a sample dominated by amblyopia with very low visual acuity” and a relatively small sample size. In our paper, we acknowledged the limitations extending from our modest sample size. However, the ceiling effect that we report is attributable to the finding that most of the subjects had little or no difficulty with many of the functional activities that the questionnaire addresses. If it is true that our sample was dominated by amblyopia with very low visual acuity, surely this ceiling effect would not be present. Indeed, the ceiling effect strongly suggests that the A&SQ may perform better in samples of amblyopes with acuity much poorer than that of the sample we tested. 
We agree with Drs. Kelderman, Felius, and Passchier that our modest sample size of 102 amblyopes limits the extent of any interpretation of the Rasch analysis that can be made from our results, and we highlighted that point in our paper. We also agree that the Rasch model is not a panacea, as no model is. It does, however, provide scientific measurement properties of data that are otherwise ordinal. 5 The main problem with the A&SQ does not appear to be whether it is unidimensional, but whether it provides a valid measurement of the quality of life of patients with strabismus and/or amblyopia. In the original Likert-scaled A&SQ, responses to all items (questions) were weighted equally. Unless all items are equally difficult or important, then simply adding them all up to get a final score does not provide a valid measurement. This observation is particularly true of the A&SQ when subjects are arbitrarily given a score of 4 if they are unable to answer a particular question. Our Rasch analysis of the A&SQ highlighted how different some items were from others (see Fig. 2 in our paper), so that responses to item 7, “miss the other person's hand when shaking hands,” are weighted very differently from those to item 21, “squint or shut one eye in bright sunlight,” for example, and the difference seems logical. 
We also believe that the results from the A&SQ are probably not unidimensional. In their letter, Kelderman et al. criticize the validity of the Rasch analysis for assessing the dimensionality of the A&SQ and favor the use of traditional psychometric analysis (i.e., factor analysis). Comparisons between factor analysis results 6 and Rasch analysis should be performed with caution, as the fundamental intent of each method differs. The principal component analysis identifies factors within a correlation matrix (i.e., factor structure underlying the items of the A&SQ), whereas the Rasch analysis determines whether there are other dimensions left once the initial latent trait (i.e., vision-related quality of life) has been extracted. Once subscales have been demonstrated to exist by factor analysis, they should be assessed for unidimensionality. 7 For example, a questionnaire with several subscales (demonstrated using factor analysis) may be considered unidimensional (according to the Rasch analysis), since all the items in the questionnaire measure a single underlying trait. If both are not used, which one should be chosen? Factor analysis assumes that the data being analyzed are linear measures and not the ordinally labeled stochastic observations that are provided by the A&SQ. Studies that have compared factor analysis and the Rasch analysis have concluded that the Rasch analysis is much better at determining the identification of the core construct, particularly when the data are ordinal and factors correlate highly. 8,9 In our report, not only do the results of the Rasch analysis strongly suggest that there is an important second dimension, but this dimension also includes a relatively large number of items (5 of a total of 23 items) and the inherent qualities of those items (mainly psychosocial) are all similar and seem logically different from most of the remainder, which assess difficulties with functional activities. Thus, we suggest that it may be more appropriate to provide two scores, since the breach of unidimensionality does not allow for appropriate summation of the items within the A&SQE. In turn, our proposed strategy may help to draw meaningful clinical conclusions about the consequences of living with strabismus or amblyopia or both. 
References
Vianya-Estopa M Elliott DB Barrett BT . An Evaluation of the Amblyopia and Strabismus Questionnaire using Rasch analysis. Invest Ophthalmol Vis Sci. 2010;51:2496–2503. [CrossRef] [PubMed]
Attebo K Mitchell P Cumming R Smith W Jolly N Sparkes R . Prevalence and causes of amblyopia in an adult population. Ophthalmology. 1998;105:154–159. [CrossRef] [PubMed]
Rahi JS Cumberland MP Peckham CS . Does amblyopia affect educational, health, and social outcomes?—findings from a 1958 British birth cohort. BMJ. 2006;332:820–825. [CrossRef] [PubMed]
Brown SA Weih LM Fu CL Dimitrov P Taylor HR McCarty CA . Prevalence of amblyopia and associated refractive errors in an adult population in Victoria, Australia. Ophthalmic Epidemiol. 2000;7:249–258. [CrossRef] [PubMed]
Wright BD Linacre JM . Observations are always ordinal—measurements, however, must be interval. Arch Phys Med Rehab. 1989;70:857–860.
Van de Graaf ES Felius J van Kempen-du Saar H . Construct validation of the Amblyopia and Strabismus Questionnaire (A&SQ) by factor analysis. Graefes Arch Clin Exp Ophthalmol. 2009;247:1263–1268. [CrossRef] [PubMed]
Pesudovs K Burr JM Harley C Elliott DB . The development, assessment, and selection of questionnaires. Optom Vis Sci. 2007; 84:663–674. [CrossRef] [PubMed]
Wright BD . Comparing Rasch measurement and factor analysis. Struct Equation Model. 1996;3:3–24. [CrossRef]
Smith RM . A comparison of methods for determining dimensionality in Rasch measurement. Struct Equation Model. 1996;3:25–40. [CrossRef]
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