scholarly journals Student Employment and Education: A Meta-Analysis

2021 ◽  
Author(s):  
Katerina Kroupova ◽  
Tomas Havranek ◽  
Zuzana Irsova
Keyword(s):  
Young People ◽  
Publication Bias ◽  
Educational Outcomes ◽  
High Intensity ◽  
Meta Analysis ◽  
Model Averaging ◽  
Student Employment ◽  
Frequentist Model Averaging

Educational outcomes have many determinants, but one that most young people can readily control is choosing whether to work while in school. Sixty-nine studies have estimated the effect, but results vary from large negative to positive estimates. We show that the results are systematically driven by context, publication bias, and treatment of endogeneity. Studies ignoring endogeneity suffer from an upward bias, which is almost fully compensated by publication selection in favor of negative estimates. Net of the biases, the literature suggests a negative but economically inconsequential mean effect. The effect is more negative for high-intensity employment and educational outcomes measured as decisions to dropout, but it is positive in Germany. To derive these results we collect 861 previously reported estimates together with 32 variables reflecting estimation context, use recently developed nonlinear techniques to correct for publication bias, and employ Bayesian and frequentist model averaging to assign a pattern to the heterogeneity in the literature.

scholarly journals Publication and Identification Biases in Measuring the Intertemporal Substitution of Labor Supply

2021 ◽  
Author(s):  
Tomas Havranek ◽  
Roman Horvath ◽  
Ali Elminejad
Keyword(s):  
Labor Supply ◽  
Publication Bias ◽  
Model Uncertainty ◽  
Model Averaging ◽  
Extensive Margin ◽  
Real Business Cycle ◽  
Intertemporal Substitution ◽  
The Mean ◽  
Frisch Elasticity ◽  
Frequentist Model Averaging

The intertemporal substitution (Frisch) elasticity of labor supply governs the predictions of real business cycle models and models of taxation. We show that, for the extensive margin elasticity, two biases conspire to systematically produce large positive estimates when the elasticity is in fact zero. Among 723 estimates in 36 studies, the mean reported elasticity is 0.5. One half of that number is due to publication bias: larger estimates are reported preferentially. The other half is due to identification bias: studies with less exogenous time variation in wages report larger elasticities. Net of the biases, the literature implies a zero mean elasticity and, with 95% confidence, is inconsistent with calibrations above 0.25. To derive these results we collect 23 variables that reflect the context in which the elasticity was obtained, use nonlinear techniques to correct for publication bias, and employ Bayesian and frequentist model averaging to address model uncertainty.

scholarly journals Robust Bayesian Meta-Analysis: Addressing Publication Bias with Model-Averaging

2020 ◽  
Author(s):  
Maximilian Maier ◽  
František Bartoš ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Publication Bias ◽  
Meta Analysis ◽  
Model Misspecification ◽  
Model Averaging ◽  
False Positives ◽  
Selection Models ◽  
Quantitative Tool

Meta-analysis is an important quantitative tool for cumulative science, but its application is frustrated by publication bias. In order to test and adjust for publication bias, we extend model-averaged Bayesian meta-analysis with selection models. The resulting Robust Bayesian Meta-analysis (RoBMA) methodology does not require all-or-none decisions about the presence of publication bias, can quantify evidence in favor of the absence of publication bias, and performs well under high heterogeneity. By model-averaging over a set of 12 models, RoBMA is relatively robust to model misspecification and simulations show that it outperforms existing methods. We demonstrate that RoBMA finds evidence for the absence of publication bias in Registered Replication Reports and reliably avoids false positives. We provide an implementation in R and JASP so that researchers can easily apply the new methodology to their data.

scholarly journals No Need to Choose: Robust Bayesian Meta-Analysis with Competing Publication Bias Adjustment Methods

2021 ◽  
Author(s):  
František Bartoš ◽  
Maximilian Maier ◽  
Eric-Jan Wagenmakers ◽  
Hristos Doucouliagos ◽  
T D Stanley
Keyword(s):  
Publication Bias ◽  
Bayesian Model Averaging ◽  
Meta Analysis ◽  
Scientific Evidence ◽  
Model Averaging ◽  
Bias Adjustment ◽  
Model Average ◽  
Wide Range ◽  
Data Generating Process ◽  
The Impact

Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods’ performance to depend on the true data generating process – no method consistently outperforms the others across a wide range of conditions. To avoid the condition-dependent, all-or-none choice between competing methods we extend robust Bayesian meta-analysis and model-average across two prominent approaches of adjusting for publication bias: (1) selection models of p-values and (2) models of the relationship between effect sizes and their standard errors. The resulting estimator weights the models with the support they receive from the existing research record. Applications, simulations, and comparisons to preregistered, multi-lab replications demonstrate the benefits of Bayesian model-averaging of competing publication bias adjustment methods.

scholarly journals The Impact of Age Difference on the Efficacy and Safety of COVID-19 Vaccines: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiting Wang ◽  
Yue Tong ◽  
Duo Li ◽  
Jun Li ◽  
Yaling Li
Keyword(s):  
Older Adults ◽  
Young Adults ◽  
Young People ◽  
Publication Bias ◽  
Meta Analysis ◽  
Geometric Mean ◽  
The Elderly ◽  
Age Difference ◽  
Efficacy And Safety ◽  
The Impact

ObjectiveThis meta-analysis compared the efficacy and safety of five kinds of COVID-19 vaccines in different age groups (young adults and older adults), aiming to analyze the difference of adverse events (AEs) rate and virus geometric mean titer (GMT) values between young and older people, in order to find a specific trend, and explore the causes of this trend through meta-analysis.MethodMeta-analysis was used to analyze the five eligible articles. The modified Jadad scoring scale was used to evaluate the quality of eligible literature with a scoring system of 1 to 7. The primary endpoint of the effectiveness index was GMT. The primary endpoints of the safety index were the incidence of local AEs and systemic AEs. Stata 12.0 software was used for meta-analysis. Revman 5.0 software was used to map the risk of publication bias, and Egger’s test was used to analyze publication bias.ResultsThe GMT values of young adults were higher than older adults (SMD = 1.40, 95% CI (0.79, 2.02), P<0.01). There was a higher incidence of local and systemic AEs in young people than in the elderly (OR = 1.10, 95% CI (1.08, 1.12), P<0.01; OR = 1.18, 95% CI (1.14, 1.22), P<0.01).ConclusionThe immune effect of young people after being vaccinated with COVID-19 vaccines was better than that of the elderly, but the safety was worse than that of old people, the most common AEs were fever, rash, and local muscle pain, which were tolerable for young people. As the AEs of the elderly were lower, they can also be vaccinated safely; the reason for the low level of GMT in the elderly was related to Immunosenescence. The vaccine tolerance of people of different ages needs to be studied continuously.

scholarly journals The Elasticity of Substitution between Skilled and Unskilled Labor: A Meta-Analysis

2020 ◽  
Author(s):  
Tomas Havranek ◽  
Zuzana Irsova ◽  
Lubica Laslopova ◽  
Olesia Zeynalova
Keyword(s):  
Wage Inequality ◽  
Meta Analysis ◽  
Model Averaging ◽  
Developed Countries ◽  
Elasticity Of Substitution ◽  
Unskilled Labor ◽  
Common View ◽  
The Us ◽  
Skilled And Unskilled Labor ◽  
Frequentist Model Averaging

A key parameter in the analysis of wage inequality is the elasticity of substitution between skilled and unskilled labor. We question the common view that the elasticity exceeds 1. Two biases, publication and attenuation, conspire to pull the mean elasticity reported in the literature to 1.9. After correcting for the biases, the literature is consistent with the elasticity in the US of 0.6--0.9. Our analysis relies on 729 estimates of the elasticity collected from 76 studies as well as 37 controls that reflect the context in which the estimates were obtained. We use recently developed nonlinear techniques to correct for publication bias and employ Bayesian and frequentist model averaging to address model uncertainty. Our results suggest that, first, insignificant estimates of the elasticity are underreported. Second, because researchers typically estimate the elasticity's inverse, measurement error exaggerates the elasticity, and we show the exaggeration is substantial. Third, elasticities are systematically larger for developed countries, translog estimation, and methods that ignore endogeneity.

scholarly journals Death to the Cobb-Douglas Production Function? A Meta-Analysis of the Capital-Labor Substitution Elasticity

2019 ◽  
Author(s):  
Tomas Havranek ◽  
Zuzana Irsova ◽  
Sebastian Gechert ◽  
Dominika Kolcunova
Keyword(s):  
Publication Bias ◽  
Production Function ◽  
Meta Analysis ◽  
Model Averaging ◽  
Weight Of Evidence ◽  
Empirical Literature ◽  
Order Condition ◽  
Aggregated Data ◽  
First Order ◽  
The Mean

We show that the large elasticity of substitution between capital and labor estimated in the literature on average, 0.9, can be explained by three factors: publication bias, use of aggregated data, and omission of the first-order condition for capital. The mean elasticity conditional on the absence of publication bias, disaggregated data, and inclusion of information from the first-order condition for capital is 0.3. To obtain this result, we collect 3,186 estimates of the elasticity reported in 121 studies, codify 71 variables that reflect the context in which researchers produce their estimates, and address model uncertainty by Bayesian and frequentist model averaging. We employ nonlinear techniques to correct for publication bias, which is responsible for at least half of the overall reduction in the mean elasticity from 0.9 to 0.3. Our findings also suggest that a failure to normalize the production function leads to a substantial upward bias in the estimated elasticity. The weight of evidence accumulated in the empirical literature emphatically rejects the Cobb-Douglas specification.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

A Meta-Analysis of the Facial Feedback Literature: Effects of Facial Feedback on Emotional Experience Are Small and Variable

2017 ◽  
Author(s):  
Nicholas Alvaro Coles ◽  
Jeff T. Larsen ◽  
Heather Lench
Keyword(s):  
Publication Bias ◽  
Emotional Experience ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Detection Methods ◽  
Feedback Effects ◽  
Facial Feedback ◽  
Facial Movements ◽  
Facial Feedback Hypothesis ◽  
Variance Estimates

The facial feedback hypothesis suggests that an individual’s experience of emotion is influenced by feedback from their facial movements. To evaluate the cumulative evidence for this hypothesis, we conducted a meta-analysis on 286 effect sizes derived from 138 studies that manipulated facial feedback and collected emotion self-reports. Using random effects meta-regression with robust variance estimates, we found that the overall effect of facial feedback was significant, but small. Results also indicated that feedback effects are stronger in some circumstances than others. We examined 12 potential moderators, and three were associated with differences in effect sizes. 1. Type of emotional outcome: Facial feedback influenced emotional experience (e.g., reported amusement) and, to a greater degree, affective judgments of a stimulus (e.g., the objective funniness of a cartoon). Three publication bias detection methods did not reveal evidence of publication bias in studies examining the effects of facial feedback on emotional experience, but all three methods revealed evidence of publication bias in studies examining affective judgments. 2. Presence of emotional stimuli: Facial feedback effects on emotional experience were larger in the absence of emotionally evocative stimuli (e.g., cartoons). 3. Type of stimuli: When participants were presented with emotionally evocative stimuli, facial feedback effects were larger in the presence of some types of stimuli (e.g., emotional sentences) than others (e.g., pictures). The available evidence supports the facial feedback hypothesis’ central claim that facial feedback influences emotional experience, although these effects tend to be small and heterogeneous.

scholarly journals Overweight and Obesity Among Adult HIV Infected Peoples Receiving ART in Ethiopia: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 12 ◽  
pp. 215013272199364
Author(s):  
Robel Hussen Kabthymer ◽  
Solomon Nega Techane ◽  
Temesgen Muche ◽  
Helen Ali Ewune ◽  
Semagn Mekonnen Abate ◽  
...  
Keyword(s):  
Publication Bias ◽  
Addis Ababa ◽  
Data Extraction ◽  
Meta Analysis ◽  
Overweight And Obesity ◽  
Obesity And Overweight ◽  
Pooled Prevalence ◽  
Nutrition And Diet ◽  
Prevalence Of Obesity ◽  
Trim And Fill

Background: Over-nutrition and diet-linked non-communicable morbidities are showing increasing trend overtime. Even if there are different factors that affect the change in BMI other than ART, several authors have reported increases in BMI among PLHIV on treatment that are equal to or surpass the general population. This study is aimed to estimate the prevalence of obesity and overweight among adult HIV infected peoples taking ART in Ethiopia. Method: PubMed, CINAHL, Web of science, global health and Google scholar electronic databases were used to perform a systematic literature search. Two authors independently extracted all the necessary data using a structured data extraction format. Data analysis was done using STATA Version 14. The heterogeneity of the studies was assessed by using I2 test. A random-effects model was used to estimate the pooled prevalence. Publication bias was checked using Funnel plot and Egger’s test. Result: Two thousand seven hundred and fifty-one studies were reviewed and 13 studies fulfilling the inclusion criteria were included in the meta-analysis. The meta-analysis of 13 studies, comprising 4994 participants resulted in pooled prevalence of overweight to be 17.85% (95% CI: 12.22-23.47). Whereas, the pooled prevalence of overweight was found to be 3.90 (95% CI: 2.31-5.49) but after adjusting for publication bias using trim and fill analysis it has become 3.58 (95% CI: 2.04-5.13). Magnitude of both overweight and obesity was higher in studies conducted in Addis Ababa, studies done after 2016 and studies having sample size of less than 400, in subgroup analysis. Conclusion: The magnitude of overweight and obesity among HIV infected peoples taking ART in Ethiopia is high. There is a need to have a routine screening to PLWHA on the risk of over-nutrition in order to facilitate early detection.

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