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Pusic, Martin V.; Boutis, Kathy; Pecaric, Martin R.; Savenkov, Oleksander; Beckstead, Jason W.; Jaber, Mohamad Y. – Advances in Health Sciences Education, 2017
Learning curves are a useful way of representing the rate of learning over time. Features include an index of baseline performance (y-intercept), the efficiency of learning over time (slope parameter) and the maximal theoretical performance achievable (upper asymptote). Each of these parameters can be statistically modelled on an individual and…
Descriptors: Statistical Analysis, Models, Regression (Statistics), Medical Education
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Fayers, Peter – Advances in Health Sciences Education, 2011
Although many parametric statistical tests are considered to be robust, as recently shown in Methodologist's Corner, it still pays to be circumspect about the assumptions underlying statistical tests. In this paper I show that robustness mainly refers to "[alpha]", the type-I error. If the underlying distribution of data is ignored there…
Descriptors: Statistical Analysis, Tests, Robustness (Statistics), Statistical Distributions
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Norman, Geoff – Advances in Health Sciences Education, 2010
Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The…
Descriptors: Likert Scales, Statistical Analysis, Data, Sample Size