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Cheung, Mike W.-L.; Cheung, Shu Fai – Research Synthesis Methods, 2016
Meta-analytic structural equation modeling (MASEM) combines the techniques of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Both fixed-effects and random-effects models can be defined in MASEM.…
Descriptors: Statistical Analysis, Models, Meta Analysis, Structural Equation Models
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Oort, Frans J.; Jak, Suzanne – Research Synthesis Methods, 2016
Meta-analytic structural equation modeling (MASEM) involves fitting models to a common population correlation matrix that is estimated on the basis of correlation coefficients that are reported by a number of independent studies. MASEM typically consist of two stages. The method that has been found to perform best in terms of statistical…
Descriptors: Maximum Likelihood Statistics, Meta Analysis, Structural Equation Models, Correlation
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Gnambs, Timo; Staufenbiel, Thomas – Research Synthesis Methods, 2016
Two new methods for the meta-analysis of factor loadings are introduced and evaluated by Monte Carlo simulations. The direct method pools each factor loading individually, whereas the indirect method synthesizes correlation matrices reproduced from factor loadings. The results of the two simulations demonstrated that the accuracy of…
Descriptors: Accuracy, Meta Analysis, Factor Structure, Monte Carlo Methods
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Sheng, Zitong; Kong, Wenmo; Cortina, Jose M.; Hou, Shuofei – Research Synthesis Methods, 2016
Researchers have become increasingly interested in conducting analyses on meta-analytic correlation matrices. Methodologists have provided guidance and recommended practices for the application of this technique. The purpose of this article is to review current practices regarding analyzing meta-analytic correlation matrices, to identify the gaps…
Descriptors: Meta Analysis, Correlation, Research Methodology, Best Practices
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Hedges, Larry V. – Research Synthesis Methods, 2016
Structural equation models play an important role in the social sciences. Consequently, there is an increasing use of meta-analytic methods to combine evidence from studies that estimate the parameters of structural equation models. Two approaches are used to combine evidence from structural equation models: A direct approach that combines…
Descriptors: Meta Analysis, Structural Equation Models, Social Science Research, Research Methodology
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Hong, Hwanhee; Chu, Haitao; Zhang, Jing; Carlin, Bradley P. – Research Synthesis Methods, 2016
Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular because of their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (although richer than standard meta-analysis, comparing only two…
Descriptors: Bayesian Statistics, Meta Analysis, Outcomes of Treatment, Comparative Analysis
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Levay, Paul; Ainsworth, Nicola; Kettle, Rachel; Morgan, Antony – Research Synthesis Methods, 2016
Aim: To examine how effectively forwards citation searching with Web of Science (WOS) or Google Scholar (GS) identified evidence to support public health guidance published by the National Institute for Health and Care Excellence. Method: Forwards citation searching was performed using GS on a base set of 46 publications and replicated using WOS.…
Descriptors: Evidence, Public Health, Publications, Comparative Analysis
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Corbett, Mark Stephen; Moe-Byrne, Thirimon; Oddie, Sam; McGuire, William – Research Synthesis Methods, 2016
Background: Quasi-randomization might expedite recruitment into trials in emergency care settings but may also introduce selection bias. Methods: We searched the Cochrane Library and other databases for systematic reviews of interventions in emergency medicine or urgent care settings. We assessed selection bias (baseline imbalances) in prognostic…
Descriptors: Quasiexperimental Design, Randomized Controlled Trials, Medical Services, Medicine
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Veroniki, Areti Angeliki; Jackson, Dan; Viechtbauer, Wolfgang; Bender, Ralf; Bowden, Jack; Knapp, Guido; Kuss, Oliver; Higgins, Julian P. T.; Langan, Dean; Salanti, Georgia – Research Synthesis Methods, 2016
Meta-analyses are typically used to estimate the overall/mean of an outcome of interest. However, inference about between-study variability, which is typically modelled using a between-study variance parameter, is usually an additional aim. The DerSimonian and Laird method, currently widely used by default to estimate the between-study variance,…
Descriptors: Meta Analysis, Methods, Computation, Simulation
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van Valkenhoef, Gert; Dias, Sofia; Ades, A. E.; Welton, Nicky J. – Research Synthesis Methods, 2016
Network meta-analysis enables the simultaneous synthesis of a network of clinical trials comparing any number of treatments. Potential inconsistencies between estimates of relative treatment effects are an important concern, and several methods to detect inconsistency have been proposed. This paper is concerned with the node-splitting approach,…
Descriptors: Networks, Meta Analysis, Automation, Models
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Wilson, Sandra Jo; Polanin, Joshua R.; Lipsey, Mark W. – Research Synthesis Methods, 2016
A modification of the first stage of the standard procedure for two-stage meta-analytic structural equation modeling for use with large complex datasets is presented. This modification addresses two common problems that arise in such meta-analyses: (a) primary studies that provide multiple measures of the same construct and (b) the correlation…
Descriptors: Meta Analysis, Structural Equation Models, Correlation, Research Methodology
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Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G. – Research Synthesis Methods, 2015
In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall…
Descriptors: Bayesian Statistics, Meta Analysis, Prediction, Nonparametric Statistics
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Mengersen, Kerrie; MacNeil, M. Aaron; Caley, M. Julian – Research Synthesis Methods, 2015
Meta-analysis and decision analysis are underpinned by well-developed methods that are commonly applied to a variety of problems and disciplines. While these two fields have been closely linked in some disciplines such as medicine, comparatively little attention has been paid to the potential benefits of linking them in ecology, despite reasonable…
Descriptors: Meta Analysis, Ecology, Decision Making, Statistical Analysis
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Bayliss, Helen R.; Beyer, Fiona R. – Research Synthesis Methods, 2015
Research syntheses are increasingly being conducted within the fields of ecology and environmental management. Information retrieval is crucial in any synthesis in identifying data for inclusion whilst potentially reducing biases in the dataset gathered, yet the nature of ecological information provides several challenges when compared with…
Descriptors: Ecology, Conservation (Environment), Information Retrieval, Research Methodology
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Carroll, Christopher; Booth, Andrew – Research Synthesis Methods, 2015
The critical appraisal and quality assessment of primary research are key stages in systematic review and evidence synthesis. These processes are driven by the need to determine how far the primary research evidence, singly and collectively, should inform findings and, potentially, practice recommendations. Quality assessment of primary…
Descriptors: Educational Assessment, Educational Quality, Statistical Analysis, Evidence
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