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Introduction to Meta-Analysis

About: The article was published on 2009-04-27 and is currently open access. It has received 10518 citations till now.

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Citations
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Journal ArticleDOI
TL;DR: As the global epidemic of obesity fuels metabolic conditions, the clinical and economic burden of NAFLD will become enormous, and random‐effects models were used to provide point estimates of prevalence, incidence, mortality and incidence rate ratios.

6,746 citations

Book
01 Jun 2015
TL;DR: A practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses and a detailed overview of the similarities and differences between within- and between-subjects designs is provided.
Abstract: Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA’s such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.

5,374 citations

Journal ArticleDOI
TL;DR: This paper explains the key assumptions of each model, and outlines the differences between the models, to conclude with a discussion of factors to consider when choosing between the two models.
Abstract: There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd.

3,883 citations

Journal ArticleDOI
TL;DR: A meta-analysis of the built environment-travel literature existing at the end of 2009 is conducted in order to draw generalizable conclusions for practice, and finds that vehicle miles traveled is most strongly related to measures of accessibility to destinations and secondarily to street network design variables.
Abstract: Problem: Localities and states are turning to land planning and urban design for help in reducing automobile use and related social and environmental costs. The effects of such strategies on travel demand have not been generalized in recent years from the multitude of available studies. Purpose: We conducted a meta-analysis of the built environment-travel literature existing at the end of 2009 in order to draw generalizable conclusions for practice. We aimed to quantify effect sizes, update earlier work, include additional outcome measures, and address the methodological issue of self-selection. Methods: We computed elasticities for individual studies and pooled them to produce weighted averages. Results and conclusions: Travel variables are generally inelastic with respect to change in measures of the built environment. Of the environmental variables considered here, none has a weighted average travel elasticity of absolute magnitude greater than 0.39, and most are much less. Still, the combined effect o...

3,551 citations


Cites background or methods from "Introduction to Meta-Analysis"

  • ...…to less precise estimates of effect sizes, the preferred method is to calculate a meta-analysis weight as an inverse variance weight, or the inverse of the squared standard error (Borenstein et al., 2009; Hunter & Schmidt, 2004; Lipsey & Wilson, 2001; Littell et al., 2008; Schulze, 2004)....

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  • ...D ow nl oa de d by [ U ni ve rs ity o f A ri zo na ] at 0 0: 29 2 3 D ec em be r 20 12 Borenstein et al. (2009) argue against another possibility, using significance levels as proxies for effect size, since they depend not only on effect size but also on sample size: “Because we work with the…...

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Journal ArticleDOI
TL;DR: A reciprocal link between depression and obesity was found to increase the risk of depression, most pronounced among Americans and for clinically diagnosed depression, in addition to depression being predictive of developing obesity.
Abstract: Context: Association between obesity and depression has repeatedly been established. For treatment and prevention purposes, it is important to acquire more insight into their longitudinal interaction. Objective: To conduct a systematic review and meta-analysis on the longitudinal relationship between depression, overweight, and obesity and to identify possible influencing factors. Data Sources: Studies were found using PubMed, PsycINFO, and EMBASE databases and selected on several criteria. Study Selection: Studies examining the longitudinal bidirectional relation between depression and overweight (body mass index 25-29.99) or obesity (body mass index >= 30) were selected. Data Extraction: Unadjusted and adjusted odds ratios (ORs) were extracted or provided by the authors. Data Synthesis: Overall, unadjusted ORs were calculated and subgroup analyses were performed for the 15 included studies (N = 58 745) to estimate the effect of possible moderators (sex, age, depression severity). Obesity at baseline increased the risk of onset of depression at follow-up (unadjusted OR, 1.55; 95% confidence interval [CI], 1.22-1.98; P = 60 years) but not among younger persons (aged < 20 years). Baseline depression (symptoms and disorder) was not predictive of overweight over time. However, depression increased the odds for developing obesity (OR, 1.58; 95% CI, 1.33-1.87; P < .001). Subgroup analyses did not reveal specific moderators of the association. Conclusions: This meta-analysis confirms a reciprocal link between depression and obesity. Obesity was found to increase the risk of depression, most pronounced among Americans and for clinically diagnosed depression. In addition, depression was found to be predictive of developing obesity.

3,499 citations

References
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22 Sep 2008
TL;DR: This extract is made available solely for use in the authoring, editing or refereeing of Cochrane reviews, or for training in these processes by representatives of formal entities of The Cochrane Collaboration.
Abstract: This extract is made available solely for use in the authoring, editing or refereeing of Cochrane reviews, or for training in these processes by representatives of formal entities of The Cochrane Collaboration. Other than for the purposes just stated, no part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the copyright holders.

2,558 citations

01 Jan 2010
TL;DR: Hunter—Schmidt元分析范式是应用心理学领域30年来最重要的进展之一,它抽样误差、测量误pre�和全
Abstract: Hunter—Schmidt元分析范式是一种以随机效应模型为基础的比较完备的元分析技术,可以校正效应值的抽样误差、测量误差和全距误差。Hunter—Schmidt元分析范式是应用心理学领域30年来最重要的进展之一,它拓展了心理学的研究方法,开创了校正各种测验误差的方法论研究,激励了心理学家对预测因子的效度概化研究,确认了预测因子和效标之间的水平关系特征和结构关系特征。

1 citations