2021
DOI: 10.31234/osf.io/4m2ap
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Analysis of rating scales: A pervasive problem in bilingualism research and a solution with Bayesian ordinal models

Abstract: Research in bilingualism often involves quantifying constructs of interest by the use of rating scales, for example, to measure language proficiency, dominance, or sentence acceptability. However, ratings are a type of ordinal data, which violates the assumptions of the statistical methods that are commonly used to analyse them. As a result, the validity of ratings is compromised and the ensuing statistical inferences can be seriously distorted. In this article, we describe the problem in detail and demonstrat… Show more

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Cited by 3 publications
(3 citation statements)
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“…A related issue concerns the fact that, in our analyses, self-rated second-language speaking proficiency was treated as a continuous rather than an ordinal variable (Veríssimo, 2021). Treating self-rated proficiency as ordinal may have been more appropriate, especially if the distribution of values of self-rated speaking proficiency was somewhat unusual.…”
Section: Discussionmentioning
confidence: 99%
“…A related issue concerns the fact that, in our analyses, self-rated second-language speaking proficiency was treated as a continuous rather than an ordinal variable (Veríssimo, 2021). Treating self-rated proficiency as ordinal may have been more appropriate, especially if the distribution of values of self-rated speaking proficiency was somewhat unusual.…”
Section: Discussionmentioning
confidence: 99%
“…Lists were created following a Latin square design. We analyzed the results using Bayesian ordinal regression (Veríssimo, 2021; for details, see Appendix B). As expected, the preambles constructed to be plausible received systematically higher ratings than the ones constructed to be implausible.…”
Section: Methodsmentioning
confidence: 99%
“…Finally, in my own contribution (Veríssimo, 2021), I apply Bayesian ordinal models to the analysis of rating scales (e.g., of grammatical acceptability or language proficiency). I first show how the commonly-used statistical methods suffer from important flaws, and then illustrate how ordinal models can provide more valid, accurate, and informative inferences about graded constructs such as language proficiency.…”
mentioning
confidence: 99%