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A Review of Herbivore Effects on Seaweed Invasions
Published in S. J. Hawkins, A. J. Evans, A. C. Dale, L. B. Firth, D. J. Hughes, I. P. Smith, Oceanography and Marine Biology, 2017
Enge Swantje, Sagerman Josefin, Sofia A. WikströM, Pavia Henrik
The meta-analysis on consumer preference for non-native versus native seaweeds was conducted using the metafor-package in R (Viechtbauer 2010) and the OpenMee software (Dietz et al. 2016). The weighted overall mean effect of herbivore preference for non-native or native seaweeds was calculated by a random-effects model using the restricted maximum-likelihood estimator for residual heterogeneity. Bootstrapped 95% confidence intervals were calculated for the overall mean effect size generated from 4999 iterations. To check the robustness of the meta-analysis outcome, we calculated the fail-safe number with the weighted method of Rosenberg (2005), which represents the number of additional studies with no effect needed to change the result of the meta-analysis from significant to non-significant. Publication bias was further examined with a funnel-plot and the rank correlation test for funnel plot asymmetry (Begg & Mazumdar 1994). The influence of outliers on the overall mean effects size was tested by evaluating the change of the overall effect when one study at a time was left out of the analysis. Since hypothesis-driven research tends to favour large effect sizes in support of the hypothesis in earlier publications, we examined temporal trends in the data with a cumulative meta-analysis sorted by publication year (Jennions & Møller 2002).
Advance Methods
Published in Atsushi Kawaguchi, Multivariate Analysis for Neuroimaging Data, 2021
Additionally, when writing a paper, generally only good results tend to be published. Because meta-analysis only uses published research, there may be publication bias in the results. In general meta-analysis, there are methods of evaluation (Funnel Plot) and correction (Trim-fill method). When a systematic review is required, the results of sensitivity analysis should be shown as evidence. Sensitivity analysis is a study of how much the meta-analysis results change depending on whether or not a particular study is included. These methods are also necessary for brain image meta-analysis.
Impact of prolonged sitting interruption strategies on shear rate, flow-mediated dilation and blood flow in adults: A systematic review and meta-analysis of randomized cross-over trials
Published in Journal of Sports Sciences, 2022
Francisco Javier Soto-Rodríguez, Eva Isidoro Cabañas, José Manuel Pérez-Mármol
A statistical analysis was performed using the Review Manager 5.4 software (RevMan; The Cochrane Collaboration, 2020). For the meta-analysis, the comparison of mean difference (MD) and a 95% confidence interval were used. Forest plots were generated using RevMan to compare each of the proposed outcome measures at post-treatment in the intervention versus the control conditions in the included studies. Studies not having these data available were excluded from the meta-analysis. A random effects model was used to provide the overall mean difference with a 95% CI. Heterogeneity was assessed using the I2 statistic and was considered high when the value was > 75% (Higgins, 2003). A funnel plot of the included studies was used to assess the presence of publication bias. The plots were assessed visually to indicate the presence of a significant publication bias.
Insights into Older Adults’ Technology Acceptance through Meta-Analysis
Published in International Journal of Human–Computer Interaction, 2021
Qi Ma, Alan H. S. Chan, Pei-Lee Teh
Table 5 summarizes the estimated overall effect size of the size-weighted zero-order correlations of PU–BI (0.509, 95% CI 0.430–0.579, p < .001), PEOU–BI (0.449, 95% CI 0.366–0.525, p < .001), and SI–BI (0.379, 95% CI 0.278–0.472, p < .001) using random effect models. The mean and 95% CI values of each effect size indicated that all three correlations were positive and positively associated with older adults’ intention to use information technologies. Given that random effects models do not provide a quantitative measure of heterogeneity, through the visual inspection of the funnel plots of the effect size of the three pairs of relationships (Egger et al., 1997; Groot et al., 2016), such as the funnel plot of SI–BI in Figure 2(a), we concluded that heterogeneity existed across the primary studies. A funnel plot is a scatterplot of treatment effects against a measure of study precision. It is used primarily as a visual aid for detecting bias or systematic heterogeneity (Egger et al., 1997). In the absence of publication bias, a funnel plot assumes that studies with high precision are plotted near the average, and studies with low precision are spread evenly on both sides of the average, thereby creating a roughly funnel-shaped distribution. In Figure 2(a), deviation from this shape indicates publication bias.
Low-level occupational exposure to BTEX and dyschromatopsia: a systematic review and meta-analysis
Published in International Journal of Occupational Safety and Ergonomics, 2023
Younes Sohrabi, Fatemeh Rahimian, Esmaeel Soleimani, Soheil Hassanipour
Between-study heterogeneity was assessed by Cochran’s Q test (with a significance level of less than 0.1). Assuming heterogeneity, a random-effects model was utilized and in the absence of heterogeneity, a fixed-effects model was applied to assess the effect of BTEX exposure on the CCI. Outcomes were pooled using the inverse variance method. A funnel plot was created to assess publication bias. Also, we used Egger’s linear regression test of funnel plot asymmetry, modified to accommodate between-study heterogeneity. Effect estimates and 95% confidence intervals (CIs) for all studies were depicted on a forest plot. All analyses were performed using Comprehensive Meta-Analysis version 2.0. A p value of less than 0.05 was considered statistically significant.