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 The Other Half of the Story: Effect Size Analysis in Quantitative Research

2013 ◽  
Vol 12 (3) ◽  
pp. 345-351 ◽  
Author(s):  
Jessica Middlemis Maher ◽  
Jonathan C. Markey ◽  
Diane Ebert-May
Keyword(s):  
Educational Research ◽  
Effect Size ◽  
Quantitative Research ◽  
Statistical Significance ◽  
The Other ◽  
Practical Significance ◽  
Significance Testing ◽  
Size Analysis ◽  
Significance Tests ◽  
Statistical Significance Testing

Statistical significance testing is the cornerstone of quantitative research, but studies that fail to report measures of effect size are potentially missing a robust part of the analysis. We provide a rationale for why effect size measures should be included in quantitative discipline-based education research. Examples from both biological and educational research demonstrate the utility of effect size for evaluating practical significance. We also provide details about some effect size indices that are paired with common statistical significance tests used in educational research and offer general suggestions for interpreting effect size measures. Finally, we discuss some inherent limitations of effect size measures and provide further recommendations about reporting confidence intervals.

Interpreting Statistical Significance and Meaningfulness in Adapted Physical Activity Research

1998 ◽  
Vol 15 (2) ◽  
pp. 103-118 ◽  
Author(s):  
Vinson H. Sutlive ◽  
Dale A. Ulrich
Keyword(s):  
Physical Activity ◽  
Sample Size ◽  
Recent Literature ◽  
Statistical Significance ◽  
Effect Sizes ◽  
Significance Tests ◽  
Adapted Physical Activity ◽  
Research Designs ◽  
Alpha Level ◽  
Research Findings

The unqualified use of statistical significance tests for interpreting the results of empirical research has been called into question by researchers in a number of behavioral disciplines. This paper reviews what statistical significance tells us and what it does not, with particular attention paid to criticisms of using the results of these tests as the sole basis for evaluating the overall significance of research findings. In addition, implications for adapted physical activity research are discussed. Based on the recent literature of other disciplines, several recommendations for evaluating and reporting research findings are made. They include calculating and reporting effect sizes, selecting an alpha level larger than the conventional .05 level, placing greater emphasis on replication of results, evaluating results in a sample size context, and employing simple research designs. Adapted physical activity researchers are encouraged to use specific modifiers when describing findings as significant.

scholarly journals A Primer on Practical Significance

2019 ◽  
Author(s):  
Miguel Alejandro Silan
Keyword(s):  
Effect Size ◽  
Statistical Significance ◽  
Effect Sizes ◽  
Practical Significance ◽  
Levels Of Analysis ◽  
Theoretical Significance ◽  
Peer Reviewers ◽  
Different Levels

One of the main criticisms of NHST is that statistical significance is not practical significance. And this evaluation of the practical significance of effects often take an implicit but consequential form in the field: from informal conversations among researchers when evaluating findings, to peer reviewers deciding the importance of an article. This primer seeks to make explicit what we mean when we talk about practical significance, organize what we know of it, and assert a framework for how we can evaluate and establish it. The practical significance of effects is appraised by analyzing (i.) along different levels of analysis, (ii.) across different outcomes, (iii.) across time and (iv.) across relevant moderators; which also underlie the conditions of when small effect sizes can be consequential. Practical significance is contrasted with often conflated terms including statistical significance, effect size and effect size benchmarks as well as theoretical significance. Promising directions are then presented.

scholarly journals Are Most Published Research Findings False In A Continuous Universe?

2021 ◽  
Author(s):  
Kleber Neves ◽  
Pedro Batista Tan ◽  
Olavo Bohrer Amaral
Keyword(s):  
Publication Bias ◽  
Effect Size ◽  
Statistical Power ◽  
Statistical Significance ◽  
Significance Test ◽  
Effect Sizes ◽  
Diagnostic Screening ◽  
Research Findings ◽  
Published Research ◽  
Effect Size Distribution

Diagnostic screening models for the interpretation of null hypothesis significance test (NHST) results have been influential in highlighting the effect of selective publication on the reproducibility of the published literature, leading to John Ioannidis’ much-cited claim that most published research findings are false. These models, however, are typically based on the assumption that hypotheses are dichotomously true or false, without considering that effect sizes for different hypotheses are not the same. To address this limitation, we develop a simulation model that overcomes this by modeling effect sizes explicitly using different continuous distributions, while retaining other aspects of previous models such as publication bias and the pursuit of statistical significance. Our results show that the combination of selective publication, bias, low statistical power and unlikely hypotheses consistently leads to high proportions of false positives, irrespective of the effect size distribution assumed. Using continuous effect sizes also allows us to evaluate the degree of effect size overestimation and prevalence of estimates with the wrong signal in the literature, showing that the same factors that drive false-positive results also lead to errors in estimating effect size direction and magnitude. Nevertheless, the relative influence of these factors on different metrics varies depending on the distribution assumed for effect sizes. The model is made available as an R ShinyApp interface, allowing one to explore features of the literature in various scenarios.

Significance tests in clinical research—Challenges and pitfalls

2013 ◽  
Vol 4 (4) ◽  
pp. 220-223 ◽  
Author(s):  
Eva Skovlund
Keyword(s):  
Statistical Analysis ◽  
Confidence Intervals ◽  
Clinical Relevance ◽  
Statistical Significance ◽  
Research Question ◽  
Statistical Analyses ◽  
Practical Significance ◽  
P Value ◽  
Significance Tests ◽  
Number Of Patients

AbstractBackgroundStatistical analyses are used to help understand the practical significance of the findings in a clinical study. Many clinical researchers appear to have limited knowledge onhowto perform appropriate statistical analysis as well as understanding what the results in fact mean.MethodsThis focal review is based on long experience in supervising clinicians on statistical analysis and advising editors of scientific journals on the quality of statistical analysis applied in scientific reports evaluated for publication.ResultsBasic facts on elementary statistical analyses are presented, and common misunderstandings are elucidated. Efficacy estimates, the effect of sample size, and confidence intervals for effect estimates are reviewed, and the difference between statistical significance and clinical relevance is highlighted. The weaknesses of p-values and misunderstandings in how to interpret them are illustrated with practical examples.Conclusions and recommendationsSome very important questions need to be answered before initiating a clinical trial. What is the research question? To which patients should the result be generalised? Is the number of patients sufficient to draw a valid conclusion? When data are analysed the number of (preplanned) significance tests should be kept small and post hoc analyses should be avoided. It should also be remembered that the clinical relevance of a finding cannot be assessed by the p-value. Thus effect estimates and corresponding 95% confidence intervals should always be reported.

scholarly journals Statistical Primer for Athletic Trainers: Using Confidence Intervals and Effect Sizes to Evaluate Clinical Meaningfulness

2016 ◽  
Vol 51 (12) ◽  
pp. 1045-1048 ◽  
Author(s):  
Monica Lininger ◽  
Bryan L. Riemann
Keyword(s):  
Confidence Intervals ◽  
Effect Size ◽  
Research Study ◽  
Statistical Significance ◽  
Effect Sizes ◽  
Population Parameter ◽  
Significant Difference ◽  
Sample Data ◽  
The Difference ◽  
Clinical Meaningfulness

Objective: To describe confidence intervals (CIs) and effect sizes and provide practical examples to assist clinicians in assessing clinical meaningfulness. Background: As discussed in our first article in 2015, which addressed the difference between statistical significance and clinical meaningfulness, evaluating the clinical meaningfulness of a research study remains a challenge to many readers. In this paper, we will build on this topic by examining CIs and effect sizes. Description: A CI is a range estimated from sample data (the data we collect) that is likely to include the population parameter (value) of interest. Conceptually, this constitutes the lower and upper limits of the sample data, which would likely include, for example, the mean from the unknown population. An effect size is the magnitude of difference between 2 means. When a statistically significant difference exists between 2 means, effect size is used to describe how large or small that difference actually is. Confidence intervals and effect sizes enhance the practical interpretation of research results. Recommendations: Along with statistical significance, the CI and effect size can assist practitioners in better understanding the clinical meaningfulness of a research study.

From Bayes to the Just Noticeable Difference to Effect Sizes: A Note to Understanding the Clinical and Statistical Significance of Oenologic Research Findings

2008 ◽  
Vol 3 (2) ◽  
pp. 185-193 ◽  
Author(s):  
Domenic V. Cicchetti
Keyword(s):  
Clinical Significance ◽  
Marginal Utility ◽  
Statistical Significance ◽  
Jel Classification ◽  
Effect Sizes ◽  
Just Noticeable Difference ◽  
Practical Significance ◽  
Research Findings

AbstractThe objectives of this paper are (1) to broaden the concept and importance of differentiating statistical significance from clinical or practical significance that was introduced in a recent oenologie application that appeared in this Journal (Cicchetti, 2007); (2) to highlight the major contribution of Economics to the clinical-statistical significance differentiation; (3) to provide oenologie researchers with the tools to accomplish this objective; and (4) to provide examples of oenologie applications using these biostatistical tools. Results indicate that the terms clinical significance, effect sizes, the just noticeable difference between stimuli, and the economic term marginal utility are conceptually related and when applied to the results of oenologie research, offer a richness of interpretation that levels of statistical significance alone cannot provide. (JEL Classification: C0, C1)

The Meaningfulness of Statistical Significance Tests in the Analysis of Simulation Results

2016 ◽  
Vol 8 (1) ◽  
pp. 18-45
Author(s):  
Klaus G. Troitzsch
Keyword(s):  
Stochastic Process ◽  
Distribution Function ◽  
Simulation Model ◽  
Effect Size ◽  
Statistical Significance ◽  
Effect Sizes ◽  
Significance Tests ◽  
Agent Based ◽  
Input Parameters ◽  
Simulation Results

This article discusses the question of whether significance tests on simulation results are meaningful at all. It is also argued that it is the effect size much more than the existence of the effect is what matters. It is the description of the distribution function of the stochastic process incorporated in the simulation model which is important. This is particularly when this distribution is far from normal, which is particularly often the case when the simulation model is nonlinear. To this end, this article uses three different agent-based models to demonstrate that the effects of input parameters on output metrics can often be made “statistically significant” on any desired level by increasing the number of runs, even for negligible effect sizes. The examples are also used to give hints as to how many runs are necessary to estimate effect sizes and how the input parameters determine output metrics.

Significance tests: Necessary but not sufficient

1998 ◽  
Vol 21 (2) ◽  
pp. 221-222
Author(s):  
Louis G. Tassinary
Keyword(s):  
Effect Size ◽  
Scientific Community ◽  
Statistical Significance ◽  
Significance Tests ◽  
Experimental Control

Chow (1996) offers a reconceptualization of statistical significance that is reasoned and comprehensive. Despite a somewhat rough presentation, his arguments are compelling and deserve to be taken seriously by the scientific community. It is argued that his characterization of literal replication, types of research, effect size, and experimental control are in need of revision.

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