scholarly journals The Substantive and Practical Significance of Citation Impact Differences Between Institutions: Guidelines for the Analysis of Percentiles Using Effect Sizes and Confidence Intervals

2014 ◽  
pp. 259-281 ◽  
Author(s):  
Richard Williams ◽  
Lutz Bornmann
Keyword(s):  
Confidence Intervals ◽  
Citation Impact ◽  
Effect Sizes ◽  
Practical Significance

Effect Size and Effect Uncertainty in Organizational Research Methods

2021 ◽  
Author(s):  
Scott B. Morris ◽  
Arash Shokri
Keyword(s):  
Confidence Intervals ◽  
Effect Size ◽  
Sampling Error ◽  
Statistical Significance ◽  
Scientific Progress ◽  
Effect Sizes ◽  
Practical Significance ◽  
Significance Tests ◽  
Wide Range ◽  
Research Findings

To understand and communicate research findings, it is important for researchers to consider two types of information provided by research results: the magnitude of the effect and the degree of uncertainty in the outcome. Statistical significance tests have long served as the mainstream method for statistical inferences. However, the widespread misinterpretation and misuse of significance tests has led critics to question their usefulness in evaluating research findings and to raise concerns about the far-reaching effects of this practice on scientific progress. An alternative approach involves reporting and interpreting measures of effect size along with confidence intervals. An effect size is an indicator of magnitude and direction of a statistical observation. Effect size statistics have been developed to represent a wide range of research questions, including indicators of the mean difference between groups, the relative odds of an event, or the degree of correlation among variables. Effect sizes play a key role in evaluating practical significance, conducting power analysis, and conducting meta-analysis. While effect sizes summarize the magnitude of an effect, the confidence intervals represent the degree of uncertainty in the result. By presenting a range of plausible alternate values that might have occurred due to sampling error, confidence intervals provide an intuitive indicator of how strongly researchers should rely on the results from a single study.

scholarly journals The use of effect size indices to determine practical significance

2006 ◽  
Vol 25 (3) ◽  
Author(s):  
H. S. Styn ◽  
S. M. Ellis
Keyword(s):  
Effect Size ◽  
Statistical Significance ◽  
Empirical Studies ◽  
Research Literature ◽  
Effect Sizes ◽  
Practical Significance ◽  
Significance Tests ◽  
Statistical Application ◽  
Significant Difference

The determination of significance of differences in means and of relationships between variables is of importance in many empirical studies. Usually only statistical significance is reported, which does not necessarily indicate an important (practically significant) difference or relationship. With studies based on probability samples, effect size indices should be reported in addition to statistical significance tests in order to comment on practical significance. Where complete populations or convenience samples are worked with, the determination of statistical significance is strictly speaking no longer relevant, while the effect size indices can be used as a basis to judge significance. In this article attention is paid to the use of effect size indices in order to establish practical significance. It is also shown how these indices are utilized in a few fields of statistical application and how it receives attention in statistical literature and computer packages. The use of effect sizes is illustrated by a few examples from the research literature.

scholarly journals STATISTICAL REPORT REFORM IN SECOND LANGUAGE RESEARCH: A CASE OF EXPERIMENTAL DESIGNS

2021 ◽  
Vol 8 (2) ◽  
pp. 1-27
Author(s):  
Eka Fadilah
Keyword(s):  
Second Language ◽  
Confidence Intervals ◽  
Experimental Studies ◽  
Experimental Designs ◽  
Effect Sizes ◽  
Language Research ◽  
Statistical Report ◽  
Reported Effect ◽  
Power Analyses

This survey aims to review statisical report procedures in the experimental studies appearing in ten SLA and Applied Linguistic journals from 2011 to 2017. We specify our study on how the authors report and interprete their power analyses, effect sizes, and confidence intervals. Results reveal that of 217 articles, the authors reported effect sizes (70%), apriori power and posthoc power consecutively (1.8% and 6.9%), and confidence intervals (18.4%). Additionally, it shows that the authors interprete those statistical terms counted 5.5%, 27.2%, and 6%, respectively. The call for statistical report reform recommended and endorsed by scholars, researchers, and editors is inevitably echoed to shed more light on the trustworthiness and practicality of the data presented.

The P Value Problem in Otolaryngology: Shifting to Effect Sizes and Confidence Intervals

2016 ◽  
Vol 156 (6) ◽  
pp. 978-980 ◽  
Author(s):  
Peter M. Vila ◽  
Melanie Elizabeth Townsend ◽  
Neel K. Bhatt ◽  
W. Katherine Kao ◽  
Parul Sinha ◽  
...  
Keyword(s):  
General Population ◽  
Confidence Intervals ◽  
Clinical Significance ◽  
Paradigm Shift ◽  
Biomedical Literature ◽  
Effect Sizes ◽  
P Value ◽  
P Values ◽  
Population Mean

There is a lack of reporting effect sizes and confidence intervals in the current biomedical literature. The objective of this article is to present a discussion of the recent paradigm shift encouraging the use of reporting effect sizes and confidence intervals. Although P values help to inform us about whether an effect exists due to chance, effect sizes inform us about the magnitude of the effect (clinical significance), and confidence intervals inform us about the range of plausible estimates for the general population mean (precision). Reporting effect sizes and confidence intervals is a necessary addition to the biomedical literature, and these concepts are reviewed in this article.

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