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Asking people a question about performing a target behavior influences future performance of that behavior. While contextually robust and methodologically simple, this “question–behavior effect” reveals theoretical complexity as evidenced by the large number of proposed explanations for the effect. Furthermore, considerable heterogeneity exists regarding the “question” used to elicit the effect and the variety of different types of target “behaviors” for which the effect has manifested. A meta-analytic synthesis of 104 question-behavior studies across 51 published and unpublished papers is presented. Grouping proposed theories for the effect into four overarching categories, we derive and test systematic predictions regarding potential moderators of conceptual and practical significance. Our findings provide varying degrees of support for four different theoretical mechanisms (attitudes, consistency, fluency and motivations) proposed to underlie the effect. Insights into these mechanisms are presented and outstanding gaps in our understanding are identified as opportunities for future research. [ABSTRACT FROM AUTHOR]

Meta-analysis synthesizes a body of research investigating a common research question. Outcomes from meta-analyses provide a more objective and transparent summary of a research area than traditional narrative reviews. Moreover, they are often used to support research grant applications, guide clinical practice, and direct health policy. The aim of this article is to provide a practical and non-technical guide for psychological scientists that outlines the steps involved in planning and performing a meta-analysis of correlational datasets. I provide a supplementary R script to demonstrate each analytical step described in the paper, which is readily adaptable for researchers to use for their analyses. While the worked example is the analysis of a correlational dataset, the general meta-analytic process described in this paper is applicable for all types of effect sizes. I also emphasize the importance of meta-analysis protocols and pre-registration to improve transparency and help avoid unintended duplication. An improved understanding this tool will not only help scientists to conduct their own meta-analyses but also improve their evaluation of published meta-analyses. [ABSTRACT FROM AUTHOR]

Research synthesis and meta-analysis provide a pathway to bring together findings in a given domain with greater systematicity, objectivity, and transparency than traditional reviews. The same techniques and corresponding benefits can be and have been applied to examine methodological practices in second language (L2) research (e.g., Plonsky, ). In the first half of this paper, we integrate findings, trends, and critiques from a number of syntheses to both illustrate the potential of this approach and to promote more methodologically informed research practices. Our emphasis here is on study designs and sampling practices. In the second half, we provide an example of a methodological synthesis that reviews the use of one particular statistical technique as applied in L2 research: exploratory factor analysis (EFA). Here and throughout the chapter, we provide specific recommendations for primary research as well as for future efforts to synthesize methodological practices in the field. [ABSTRACT FROM AUTHOR]

Graphs are an essential part of scientific communication. Complex datasets, of which meta-analyses are textbook examples, benefit the most from visualization. Although a number of graph options for meta-analyses exist, the extent to which these are used was hitherto unclear. A systematic review on graph use in meta-analyses in three disciplines (medicine, psychology, and business) and nine journals was conducted. Interdisciplinary differences, which are mirrored in the respective journals, were revealed, that is, graph use correlates with external factors rather than methodological considerations. There was only limited variation in graph types (with forest plots as the most important representatives), and diagnostic plots were very rare. Although an increase in graph use over time could be observed, it is unlikely that this phenomenon is specific to meta-analyses. There is a gaping discrepancy between available graphic methods and their application in meta-analyses. This may be rooted in a number of factors, namely, (i) insufficient dissemination of new developments, (ii) unsatisfactory implementation in software packages, and (iii) minor attention on graphics in meta-analysis reporting guidelines. Using visualization methods to their full capacity is a further step in using meta-analysis to its full potential. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Thus far, most systematic reviews commissioned to inform international development policy have focused on questions of ‘what works’, drawing on experimental and quasi-experimental studies of the effects of interventions. This article argues that systematic review methods can be applied to answer a range of different questions for international development and pays particular attention to methods of synthesising qualitative evidence that apply the key principles of systematic reviewing of being comprehensive, systematic and transparent. The article introduces readers to the types of questions for which reviews of qualitative evidence might be appropriate, the types of evidence such reviews might include and the range of methods available for their synthesis. [ABSTRACT FROM PUBLISHER]

The authors discuss aspects of journal publications that have the capacity to publish and accurately cite quantitative descriptions of complex datasets. They explain that ecological meta-analyses will increasingly use the datasets directly alongside primary researches to ensure that the most effective syntheses are viable. The authors believe that creative synthesis of analysis and knowledge standards must be enforced to promote the collective effects of data, theory, and synthesis.