Investigative Ophthalmology & Visual Science Cover Image for Volume 61, Issue 7
June 2020
Volume 61, Issue 7
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ARVO Annual Meeting Abstract  |   June 2020
Statistics – What do optometrists need to learn and understand?
Author Affiliations & Notes
  • Sally Marwan Alkhawajah
    Optometry and Vision Science, University of New South Wales, Sydney, New South Wales, Australia
    Optometry and Vision Science, King Saud University, Riyadh, Saudi Arabia
  • Karen Wei
    Optometry and Vision Science, University of New South Wales, Sydney, New South Wales, Australia
  • Jeffrey Lee
    Optometry and Vision Science, University of New South Wales, Sydney, New South Wales, Australia
  • Kirsten Challinor
    Optometry and Vision Science, University of New South Wales, Sydney, New South Wales, Australia
    School of Behavioural and Health Sciences, Austalian Catholic University, Sydney, New South Wales, Australia
  • Isabelle Jalbert
    Optometry and Vision Science, University of New South Wales, Sydney, New South Wales, Australia
  • Footnotes
    Commercial Relationships   Sally Alkhawajah, None; Karen Wei, None; Jeffrey Lee, None; Kirsten Challinor, None; Isabelle Jalbert, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 5108. doi:
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      Sally Marwan Alkhawajah, Karen Wei, Jeffrey Lee, Kirsten Challinor, Isabelle Jalbert; Statistics – What do optometrists need to learn and understand?. Invest. Ophthalmol. Vis. Sci. 2020;61(7):5108.

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

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Abstract

Purpose : Critical appraisal of literature requires statistical knowledge. The first aim was to investigate commonly used statistical analyses in the ophthalmic literature. A second aim was to measure the statistical knowledge, attitudes and practices (KAP) of Australian optometrists.

Methods : Statistical methods used in a random sample of 376 (55%) articles published in 2018 in the journals Clinical and Experimental Optometry (CEO), Ophthalmic and Physiological Optics (OPO), Optometry and Vision Science (OVS) and Ophthalmology were determined. Original articles and systematic reviews were included. Two authors independently audited articles, assigned analyses to 37 pre-determined categories, rated the quality of addressing interocular correlations, and whether the research involved humans, animals, or cells. A 35-item KAP survey was mailed to a random sample of 706 Australian optometrists.

Results : The most commonly used method was descriptive statistics, used in 322 (90%) articles. Inferential statistics most commonly used included t-tests in 120 (33%) and contingency tables in 114 (32%). Non-parametric tests featured in 74 (21%) and ANOVA in 62 (17%) articles. Only 15 (4%) articles did not use any statistical method. Knowledge of the four most used statistical methods allows comprehension of 58.2% of the literature. Keywords extracted from a subset of 114 articles from CEO and OPO are shown in the Figure (WordArt.com). Of 376 articles including 17 systemic reviews, 341 (91%), 17 (5%), and 8 (2%) involved humans, cells and animals, respectively. Only a small proportion of articles (48 of 359 (13%)) collected data from both eyes and considered the correlation between eyes. KAP response rate was 12.5% (88 out of 706). Australian optometrists demonstrated moderate knowledge of (49%, SD=3.4) and attitude towards statistics (57%, SD=6.3). Practice of statistics score was low (31%, SD=1.2).

Conclusions : Optometrists are required to understand a large repertoire of statistical methods to successfully appraise the ophthalmic literature. Australian optometrists have moderate knowledge and attitudes towards statistics; however, their practice scores could be improved. This highlights both the need and some gaps in statistics for optometrists. Future continuing education programs should capitalise on the positive attitude towards statistics measured in this study.

This is a 2020 ARVO Annual Meeting abstract.

 

Figure.1: Word cloud of keywords used in ophthalmic literature

Figure.1: Word cloud of keywords used in ophthalmic literature

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