scholarly journals Effect Size

Psychology ◽  
2019 ◽  
Author(s):  
David B. Flora
Keyword(s):  
Effect Size ◽  
Null Hypothesis ◽  
Power Analysis ◽  
Meta Analysis ◽  
Significance Test ◽  
Effect Sizes ◽  
Research Reporting ◽  
The Difference ◽  
Strength Of Association ◽  
True Population

Simply put, effect size (ES) is the magnitude or strength of association between or among variables. Effect sizes (ESs) are commonly represented numerically (i.e., as parameters for population ESs and statistics for sample estimates of population ESs) but also may be communicated graphically. Although the word “effect” may imply that an ES quantifies the strength of a causal association (“cause and effect”), ESs are used more broadly to represent any empirical association between variables. Effect sizes serve three general purposes: research results reporting, power analysis, and meta-analysis. Even under the same research design, an ES that is appropriate for one of these purposes may not be ideal for another. Effect size can be conveyed graphically or numerically using either unstandardized metrics, which are interpreted relative to the original scales of the variables involved (e.g., the difference between two means or an unstandardized regression slope), or standardized metrics, which are interpreted in relative terms (e.g., Cohen’s d or multiple R2). Whereas unstandardized ESs and graphs illustrating ES are typically most effective for research reporting, that is, communicating the original findings of an empirical study, many standardized ES measures have been developed for use in power analysis and especially meta-analysis. Although the concept of ES is clearly fundamental to data analysis, ES reporting has been advocated as an essential complement to null hypothesis significance testing (NHST), or even as a replacement for NHST. A null hypothesis significance test involves making a dichotomous judgment about whether to reject a hypothesis that a true population effect equals zero. Even in the context of a traditional NHST paradigm, ES is a critical concept because of its central role in power analysis.

scholarly journals Effect size: a statistical basis for clinical practice

2018 ◽  
Vol 33 (1) ◽  
pp. 84
Author(s):  
José Valladares-Neto
Keyword(s):  
Effect Size ◽  
Risk Estimation ◽  
Meta Analysis ◽  
Scientific Evidence ◽  
P Value ◽  
Conceptual Basis ◽  
Statistical Measure ◽  
The Difference ◽  
Strength Of Association ◽  
Calculate Sample Size

OBJECTIVE: Effect size (ES) is the statistical measure which quantifies the strength of a phenomenon and is commonly applied to observational and interventional studies. The aim of this review was to describe the conceptual basis of this measure, including its application, calculation and interpretation.RESULTS: As well as being used to detect the magnitude of the difference between groups, to verify the strength of association between predictor and outcome variables, to calculate sample size and power, ES is also used in meta-analysis. ES formulas can be divided into these categories: I – Difference between groups, II – Strength of association, III – Risk estimation, and IV – Multivariate data. The d value was originally considered small (0.20 > d ≤ 0.49), medium (0.50 > d≤ 0.79) or large (d ≥ 0.80); however, these cut-off limits are not consensual and could be contextualized according to a specific field of knowledge. In general, a larger score implies that a larger difference was detected.CONCLUSION: The ES report, in conjunction with the confidence interval and P value, aims to strengthen interpretation and prevent the misinterpretation of data, and thus leads to clinical decisions being based on scientific evidence studies.

The Ouroboros of Psychological Methodology: The Case of Effect Sizes (Mechanical Objectivity vs. Expertise)

2018 ◽  
Vol 22 (4) ◽  
pp. 469-476 ◽  
Author(s):  
Ian J. Davidson
Keyword(s):  
Effect Size ◽  
Null Hypothesis ◽  
Significance Test ◽  
Effect Sizes ◽  
Circular Path ◽  
Bottom Up ◽  
Psychological Science ◽  
Null Hypothesis Significance Test ◽  
Theoretical Tool ◽  
Mechanical Objectivity

The reporting and interpretation of effect sizes is often promoted as a panacea for the ramifications of institutionalized statistical rituals associated with the null-hypothesis significance test. Mechanical objectivity—conflating the use of a method with the obtainment of truth—is a useful theoretical tool for understanding the possible failure of effect size reporting ( Porter, 1995 ). This article helps elucidate the ouroboros of psychological methodology. This is the cycle of improved tools to produce trustworthy knowledge, leading to their institutionalization and adoption as forms of thinking, leading to methodologists eventually admonishing researchers for relying too heavily on rituals, finally leading to the production of more new improved quantitative tools that may follow along this circular path. Despite many critiques and warnings, research psychologists’ superficial adoption of effect sizes might preclude expert interpretation much like in the null-hypothesis significance test as widely received. One solution to this situation is bottom-up: promoting a balance of mechanical objectivity and expertise in the teaching of methods and research. This would require the acceptance and encouragement of expert interpretation within psychological science.

Meta-analysis, power analysis, and the null-hypothesis significance-test procedure

1998 ◽  
Vol 21 (2) ◽  
pp. 216-217 ◽  
Author(s):  
Joseph S. Rossi
Keyword(s):  
Null Hypothesis ◽  
Power Analysis ◽  
Meta Analysis ◽  
Verbal Learning ◽  
Test Procedure ◽  
Significance Test ◽  
Size Estimation ◽  
Null Hypothesis Significance Test ◽  
Effect Size Estimation ◽  
History Of

Chow's (1996) defense of the null-hypothesis significance-test procedure (NHSTP) is thoughtful and compelling in many respects. Nevertheless, techniques such as meta-analysis, power analysis, effect size estimation, and confidence intervals can be useful supplements to NHSTP in furthering the cumulative nature of behavioral research, as illustrated by the history of research on the spontaneous recovery of verbal learning.

On the Surprising Longevity of Flogged Horses: Why There Is a Case for the Significance Test

1997 ◽  
Vol 8 (1) ◽  
pp. 12-15 ◽  
Author(s):  
Robert P. Abelson
Keyword(s):  
Null Hypothesis ◽  
Goodness Of Fit ◽  
Meta Analysis ◽  
Significance Test ◽  
Effect Sizes ◽  
Significance Tests ◽  
Frequent Use ◽  
Alternative Procedures ◽  
Mean Differences ◽  
New Research

Criticisms of null-hypothesis significance tests (NHSTs) are reviewed Used as formal, two-valued decision procedures, they often generate misleading conclusions However, critics who argue that NHSTs are totally meaningless because the null hypothesis is virtually always false are overstating their case Critics also neglect the whole class of valuable significance tests that assess goodness of fit of models to data Even as applied to simple mean differences, NHSTs can be rhetorically useful in defending research against criticisms that random factors adequately explain the results, or that the direction of mean difference was not demonstrated convincingly Principled argument and counterargument produce the lore, or communal understanding, in a field, which in turn helps guide new research Alternative procedures–confidence intervals, effect sizes, and meta-analysis–are discussed Although these alternatives are not totally free from criticism either, they deserve more frequent use, without an unwise ban on NHSTs

scholarly journals Pre-post effect sizes should be avoided in meta-analyses

2016 ◽  
Vol 26 (4) ◽  
pp. 364-368 ◽  
Author(s):  
P. Cuijpers ◽  
E. Weitz ◽  
I. A. Cristea ◽  
J. Twisk
Keyword(s):  
Comparison Group ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Behaviour Therapy ◽  
Natural Processes ◽  
Cognitive Behaviour ◽  
Post Test ◽  
The Difference ◽  
Meta Analyses ◽  
Highly Cited

AimsThe standardised mean difference (SMD) is one of the most used effect sizes to indicate the effects of treatments. It indicates the difference between a treatment and comparison group after treatment has ended, in terms of standard deviations. Some meta-analyses, including several highly cited and influential ones, use the pre-post SMD, indicating the difference between baseline and post-test within one (treatment group).MethodsIn this paper, we argue that these pre-post SMDs should be avoided in meta-analyses and we describe the arguments why pre-post SMDs can result in biased outcomes.ResultsOne important reason why pre-post SMDs should be avoided is that the scores on baseline and post-test are not independent of each other. The value for the correlation should be used in the calculation of the SMD, while this value is typically not known. We used data from an ‘individual patient data’ meta-analysis of trials comparing cognitive behaviour therapy and anti-depressive medication, to show that this problem can lead to considerable errors in the estimation of the SMDs. Another even more important reason why pre-post SMDs should be avoided in meta-analyses is that they are influenced by natural processes and characteristics of the patients and settings, and these cannot be discerned from the effects of the intervention. Between-group SMDs are much better because they control for such variables and these variables only affect the between group SMD when they are related to the effects of the intervention.ConclusionsWe conclude that pre-post SMDs should be avoided in meta-analyses as using them probably results in biased outcomes.

scholarly journals A Statistical Method for Synthesizing Meta-Analyses

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Liansheng Larry Tang ◽  
Michael Caudy ◽  
Faye Taxman
Keyword(s):  
Statistical Method ◽  
Effect Size ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Weighted Mean ◽  
Summary Effect ◽  
Different Types ◽  
Meta Analyses ◽  
Size Estimates ◽  
Similar Search

Multiple meta-analyses may use similar search criteria and focus on the same topic of interest, but they may yield different or sometimes discordant results. The lack of statistical methods for synthesizing these findings makes it challenging to properly interpret the results from multiple meta-analyses, especially when their results are conflicting. In this paper, we first introduce a method to synthesize the meta-analytic results when multiple meta-analyses use the same type of summary effect estimates. When meta-analyses use different types of effect sizes, the meta-analysis results cannot be directly combined. We propose a two-step frequentist procedure to first convert the effect size estimates to the same metric and then summarize them with a weighted mean estimate. Our proposed method offers several advantages over existing methods by Hemming et al. (2012). First, different types of summary effect sizes are considered. Second, our method provides the same overall effect size as conducting a meta-analysis on all individual studies from multiple meta-analyses. We illustrate the application of the proposed methods in two examples and discuss their implications for the field of meta-analysis.

scholarly journals Changes of Ankle Dorsiflexion Using Compression Tissue Flossing: A Systematic Review and Meta-Analysis

2020 ◽  
pp. 1-9
Author(s):  
Devin S. Kielur ◽  
Cameron J. Powden
Keyword(s):  
Confidence Interval ◽  
Range Of Motion ◽  
Effect Size ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Lower Extremity Injury ◽  
Level Of Evidence ◽  
Physically Active ◽  
Evidence Database

Context: Impaired dorsiflexion range of motion (DFROM) has been established as a predictor of lower-extremity injury. Compression tissue flossing (CTF) may address tissue restrictions associated with impaired DFROM; however, a consensus is yet to support these effects. Objectives: To summarize the available literature regarding CTF on DFROM in physically active individuals. Evidence Acquisition: PubMed and EBSCOhost (CINAHL, MEDLINE, and SPORTDiscus) were searched from 1965 to July 2019 for related articles using combination terms related to CTF and DRFOM. Articles were included if they measured the immediate effects of CTF on DFROM. Methodological quality was assessed using the Physiotherapy Evidence Database scale. The level of evidence was assessed using the Strength of Recommendation Taxonomy. The magnitude of CTF effects from pre-CTF to post-CTF and compared with a control of range of motion activities only were examined using Hedges g effect sizes and 95% confidence intervals. Randomeffects meta-analysis was performed to synthesize DFROM changes. Evidence Synthesis: A total of 6 studies were included in the analysis. The average Physiotherapy Evidence Database score was 60% (range = 30%–80%) with 4 out of 6 studies considered high quality and 2 as low quality. Meta-analysis indicated no DFROM improvements for CTF compared with range of motion activities only (effect size = 0.124; 95% confidence interval, −0.137 to 0.384; P = .352) and moderate improvements from pre-CTF to post-CTF (effect size = 0.455; 95% confidence interval, 0.022 to 0.889; P = .040). Conclusions: There is grade B evidence to suggest CTF may have no effect on DFROM when compared with a control of range of motion activities only and results in moderate improvements from pre-CTF to post-CTF. This suggests that DFROM improvements were most likely due to exercises completed rather than the band application.

Work Engagement: A meta-Analysis Using the Job Demands-Resources Model

2021 ◽  
pp. 003329412110519
Author(s):  
Greta Mazzetti ◽  
Enrique Robledo ◽  
Michela Vignoli ◽  
Gabriela Topa ◽  
Dina Guglielmi ◽  
...  
Keyword(s):  
Work Engagement ◽  
Effect Size ◽  
Turnover Intention ◽  
Meta Analysis ◽  
Well Being ◽  
Personal Resources ◽  
Research Reporting ◽  
Occupational Role ◽  
Volunteer Workers ◽  
Lower Effect

Although the construct of work engagement has been extensively explored, a systematic meta-analysis based on a consistent categorization of engagement antecedents, outcomes, and well-being correlates is still lacking. The results of prior research reporting 533 correlations from 113 independent samples ( k = 94, n = 119,420) were coded using a meta-analytic approach. The effect size for development resources ( r = .45) and personal resources ( r = .48) was higher than for social resources ( r = .36) and for job resources ( r = .37). Among the outcomes and well-being correlates explored, the effect size was highest for job satisfaction ( r = .60) and commitment ( r = .63). Furthermore, moderation analysis showed that (a) concerning the occupational role, work engagement finds a low association with turnover intention among civil servants, volunteer workers, and educators; (b) collectivist cultural environments reported a greater association of feedback with engagement than individualistic environments; (c) the relationship between personal resources and engagement was stronger among workers with university degrees than workers with high school diplomas. Furthermore, the absorption dimension showed a lower effect with all variables under investigation than vigor and dedication.

Effect Sizes: Conventional Choices and Calculations

2013 ◽  
Author(s):  
Michael S. Rosenberg ◽  
Hannah R. Rothstein ◽  
Jessica Gurevitch
Keyword(s):  
Critical Point ◽  
Effect Size ◽  
Evolutionary Biology ◽  
Meta Analysis ◽  
Statistical Parameter ◽  
Effect Sizes ◽  
Common Effect ◽  
Inference Methods ◽  
Conventional Effect ◽  
Estimate Effect Size

One of the fundamental concepts in meta-analysis is that of the effect size. An effect size is a statistical parameter that can be used to compare, on the same scale, the results of different studies in which a common effect of interest has been measured. This chapter describes the conventional effect sizes most commonly encountered in ecology and evolutionary biology, and the types of data associated with them. While choice of a specific measure of effect size may influence the interpretation of results, it does not influence the actual inference methods of meta-analysis. One critical point to remember is that one cannot combine different measures of effect size in a single meta-analysis: once you have chosen how you are going to estimate effect size, you need to use it for all of the studies to be analyzed.

M. Meta-Analysis

2021 ◽  
pp. 163-165
Author(s):  
Noémie Laurens
Keyword(s):  
Literature Review ◽  
Empirical Research ◽  
Effect Size ◽  
Meta Analysis ◽  
Research Synthesis ◽  
Effect Sizes ◽  
Primary Research ◽  
Unit Of Analysis ◽  
Research Studies

This chapter illustrates meta-analysis, which is a specific type of literature review, and more precisely a type of research synthesis, alongside traditional narrative reviews. Unlike in primary research, the unit of analysis of a meta-analysis is the results of individual studies. And unlike traditional reviews, meta-analysis only applies to: empirical research studies with quantitative findings hat are conceptually comparable and configured in similar statistical forms. What further distinguishes meta-analysis from other research syntheses is the method of synthesizing the results of studies — i.e. the use of statistics and, in particular, of effect sizes. An effect size represents the degree to which the phenomenon under study exists.

Sign in / Sign up

Export Citation Format

Share Document