Measurement Error in Research on Financial Literacy: How Much Error is There and How Does it Influence Effect Size Estimates?

2021 ◽  
Author(s):  
Gilles E. Gignac ◽  
Elizabeth Ooi
Keyword(s):  
Measurement Error ◽  
Effect Size ◽  
Financial Literacy ◽  
Influence Effect ◽  
Size Estimates

scholarly journals The Relationship Between a Lifetime History of Sexual Victimization and Perinatal Depression: A Systematic Review and Meta-Analysis

2021 ◽  
pp. 152483802110216
Author(s):  
Brooke N. Lombardi ◽  
Todd M. Jensen ◽  
Anna B. Parisi ◽  
Melissa Jenkins ◽  
Sarah E. Bledsoe
Keyword(s):  
Systematic Review ◽  
Effect Size ◽  
Sexual Victimization ◽  
Perinatal Depression ◽  
Meta Analysis ◽  
Effect Size Estimate ◽  
Lifetime History ◽  
Size Estimate ◽  
History Of ◽  
Size Estimates

Background: The association between a lifetime history of sexual victimization and the well-being of women during the perinatal period has received increasing attention. However, research investigating this relationship has yet to be systematically reviewed or quantitatively synthesized. Aim: This systematic review and meta-analysis aims to calculate the pooled effect size estimate of the statistical association between a lifetime history of sexual victimization and perinatal depression (PND). Method: Four bibliographic databases were systematically searched, and reference harvesting was conducted to identify peer-reviewed articles that empirically examined associations between a lifetime history of sexual victimization and PND. A random effects model was used to ascertain an overall pooled effect size estimate in the form of an odds ratio and corresponding 95% confidence intervals (CIs). Subgroup analyses were also conducted to assess whether particular study features and sample characteristic (e.g., race and ethnicity) influenced the magnitude of effect size estimates. Results: This review included 36 studies, with 45 effect size estimates available for meta-analysis. Women with a lifetime history of sexual victimization had 51% greater odds of experiencing PND relative to women with no history of sexual victimization ( OR = 1.51, 95% CI [1.35, 1.67]). Effect size estimates varied considerably according to the PND instrument used in each study and the racial/ethnic composition of each sample. Conclusion: Findings provide compelling evidence for an association between a lifetime history of sexual victimization and PND. Future research should focus on screening practices and interventions that identify and support survivors of sexual victimization perinatally.

scholarly journals A Cautionary Note on Estimating Effect Size

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592199203
Author(s):  
Don van den Bergh ◽  
Julia M. Haaf ◽  
Alexander Ly ◽  
Jeffrey N. Rouder ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Statistical Inference ◽  
Effect Size ◽  
Null Hypothesis ◽  
Current Approach ◽  
Slab Model ◽  
Cautionary Note ◽  
Popular Approach ◽  
Estimation Process ◽  
Size Estimates

An increasingly popular approach to statistical inference is to focus on the estimation of effect size. Yet this approach is implicitly based on the assumption that there is an effect while ignoring the null hypothesis that the effect is absent. We demonstrate how this common null-hypothesis neglect may result in effect size estimates that are overly optimistic. As an alternative to the current approach, a spike-and-slab model explicitly incorporates the plausibility of the null hypothesis into the estimation process. We illustrate the implications of this approach and provide an empirical example.

scholarly journals Sex-specific differences in effect size estimates at established complex trait loci

2012 ◽  
Vol 41 (5) ◽  
pp. 1376-1382 ◽  
Author(s):  
Gisela Orozco ◽  
John PA Ioannidis ◽  
Andrew Morris ◽  
Eleftheria Zeggini ◽  
Keyword(s):  
Effect Size ◽  
Complex Trait ◽  
Trait Loci ◽  
Size Estimates

Factors That Influence Effect Size in15O PET Studies: A Meta-analytic Review

NeuroImage ◽  
1997 ◽  
Vol 5 (4) ◽  
pp. 280-291 ◽  
Author(s):  
Sherri Gold ◽  
Stephan Arndt ◽  
Debra Johnson ◽  
Daniel S. O'Leary ◽  
Nancy C. Andreasen
Keyword(s):  
Effect Size ◽  
Influence Effect ◽  
Analytic Review

Bias Corrections for Standardized Effect Size Estimates Used With Single-Subject Experimental Designs

2013 ◽  
Vol 82 (3) ◽  
pp. 358-374 ◽  
Author(s):  
Maaike Ugille ◽  
Mariola Moeyaert ◽  
S. Natasha Beretvas ◽  
John M. Ferron ◽  
Wim Van den Noortgate
Keyword(s):  
Effect Size ◽  
Experimental Designs ◽  
Single Subject ◽  
Size Estimates ◽  
Bias Corrections

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 Seven Steps Toward Transparency and Replicability in Psychological Science

2020 ◽  
Author(s):  
D. Stephen Lindsay
Keyword(s):  
Measurement Error ◽  
Publication Bias ◽  
Effect Size ◽  
Statistical Tests ◽  
Research Practices ◽  
Psychological Science ◽  
Statistical Knowledge

Psychological scientists strive to advance understanding of how and why we animals do and think and feel as we do. This is difficult, in part because flukes of chance and measurement error obscure researchers’ perceptions. Many psychologists use inferential statistical tests to peer through the murk of chance and discern relationships between variables. Those tests are powerful tools, but they must be wielded with skill. Moreover, research reports must convey to readers a detailed and accurate understanding of how the data were obtained and analyzed. Research psychologists often fall short in those regards. This paper attempts to motivate and explain ways to enhance the transparency and replicability of psychological science. Specifically, I speak to how publication bias and p hacking contribute to effect-size exaggeration in the published literature, and how effect-size exaggeration contributes, in turn, to replication failures. Then I present seven steps toward addressing these problems: Telling the truth; upgrading statistical knowledge; standardizing aspects of research practices; documenting lab procedures in a lab manual; making materials, data, and analysis scripts transparent; addressing constraints on generality; and collaborating.

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