Journal ArticleDOI
The seven deadly sins of statistical analysis.
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TLDR
In a pedantic but playful way, some common errors in the use of statistical analysis that are regularly observed in professional plastic surgical literature are discussed.Abstract:
In a pedantic but playful way, we discuss some common errors in the use of 'statistical analysis' that are regularly observed in our professional plastic surgical literature. The seven errors we discuss are (1) the use of parametric analysis of ordinal data; (2) the inappropriate use of parametric analysis in general; (3) the failure to consider the possibility of committing type II statistical error; (4) the use of unmodified t-tests for multiple comparisons; (5) the failure to employ analysis of covariance, multivariate regression, nonlinear regression, and logistical regression when indicated; (6) the habit of reporting standard error instead of standard deviation; and (7) the underuse or overuse of statistical consultation. Confidence and common sense are advocated as a means to balance statistical significance with clinical importance.read more
Citations
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Journal ArticleDOI
Likert scales, levels of measurement and the "laws" of statistics.
TL;DR: It is shown that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions, and parametric methods can be utilized without concern for “getting the wrong answer”.
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Likert scales: how to (ab)use them
TL;DR: I have recently used Likert-type rating scales to measure student views on various educational interventions, providing a range of responses to a given question or statement.
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Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes
James Carifio,Rocco J. Perla +1 more
TL;DR: The authors identifies, analyses and traces many of these aforementioned problems and presents the arguments, counter arguments and empirical evidence that show these many persistent claims and myths about "Likert scales" to be factually incorrect and untrue.
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Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm
TL;DR: A systematic review of research articles published in top physiology journals suggests that, as scientists, the authors urgently need to change their practices for presenting continuous data in small sample size studies.
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How to analyze Likert and other rating scale data
TL;DR: In this paper, the authors provide a review of basic issues surrounding measurement of various phenomena relevant to educational settings, as well as previous empirical studies examining the effects of using parametric analysis approaches on rating scale data.
References
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Book
Nonparametric statistics for the behavioral sciences
TL;DR: This is the revision of the classic text in the field, adding two new chapters and thoroughly updating all others as discussed by the authors, and the original structure is retained, and the book continues to serve as a combined text/reference.
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The scandal of poor medical research.
TL;DR: The authors need less research, better research, and research done for the right reasons, and researchers who use the wrong techniques, use the right techniques wrongly, misinterpret their results, report their results selectively, cite the literature selectively, and draw unjustified conclusions.
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The scandal of poor medical research
TL;DR: To achieve better research, football, cricket, tennis, athletics, industry, writing, and drama (to mention a few activities) should be improved.
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Statistical analysis and study design in plastic and reconstructive surgical research.
TL;DR: Although there were no differences in the types of statistical analysis errors, there were differences inThe types of study design errors, and the causes of these discrepancies may lie in the nature of plastic surgery.