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One of the main goals of meta-analysis is to test for and estimate the heterogeneity of effect sizes. We examined the effect of publication bias on the Q test and assessments of heterogeneity as a function of true heterogeneity, publication bias, true effect size, number of studies, and variation of sample sizes. The present study has two main contributions and is relevant to all researchers conducting meta-analysis. First, we show when and how publication bias affects the assessment of heterogeneity. The expected values of heterogeneity measures H² and I² were analytically derived, and the power and Type I error rate of the Q test were examined in a Monte Carlo simulation study. Our results show that the effect of publication bias on the Q test and assessment of heterogeneity is large, complex, and nonlinear. Publication bias can both dramatically decrease and increase heterogeneity in true effect size, particularly if the number of studies is large and population effect size is small. We therefore conclude that the Q test of homogeneity and heterogeneity measures H² and I² are generally not valid when publication bias is present. Our second contribution is that we introduce a web application, Q-sense, which can be used to determine the impact of publication bias on the assessment of heterogeneity within a certain meta-analysis and to assess the robustness of the meta-analytic estimate to publication bias. Furthermore, we apply Q-sense to 2 published meta-analyses, showing how publication bias can result in invalid estimates of effect size and heterogeneity. [ABSTRACT FROM AUTHOR]

In exploratory factor analysis (EFA), most popular methods for dimensionality assessment such as the screeplot, the Kaiser criterion, or--the current gold standard--parallel analysis, are based on eigenvalues of the correlation matrix. To further understanding and development of factor retention methods, results on population and sample eigenvalue distributions are introduced based on random matrix theory and Monte Carlo simulations. These results are used to develop a new factor retention method, the Empirical Kaiser Criterion. The performance of the Empirical Kaiser Criterion and parallel analysis is examined in typical research settings, with multiple scales that are desired to be relatively short, but still reliable. Theoretical and simulation results illustrate that the new Empirical Kaiser Criterion performs as well as parallel analysis in typical research settings with uncorrelated scales, but much better when scales are both correlated and short. We conclude that the Empirical Kaiser Criterion is a powerful and promising factor retention method, because it is based on distribution theory of eigenvalues, shows good performance, is easily visualized and computed, and is useful for power analysis and sample size planning for EFA. [ABSTRACT FROM AUTHOR]

Research has shown that independent groups often differ not only in their means, but also in their variances. Comparing and testing variances is therefore of crucial importance to understand the effect of a grouping variable on an outcome variable. Researchers may have specific expectations concerning the relations between the variances of multiple groups. Such expectations can be translated into hypotheses with inequality and/or equality constraints on the group variances. Currently, however, no methods are available for testing (in)equality constrained hypotheses on variances. This article proposes a novel Bayesian approach to this challenging testing problem. Our approach has the following useful properties: First, it can be used to simultaneously test multiple (non)nested hypotheses with equality as well as inequality constraints on the variances. Second, our approach is fully automatic in the sense that no subjective prior specification is needed. Only the hypotheses need to be provided. Third, a user-friendly software application is included that can be used to perform this Bayesian test in an easy manner. [ABSTRACT FROM AUTHOR]

Publication bias threatens the validity of meta-analytic results and leads to overestimation of the effect size in traditional meta-analysis. This particularly applies to meta-analyses that feature small studies, which are ubiquitous in psychology. Here we develop a new method for meta-analysis that deals with publication bias. This method, p-uniform, enables (a) testing of publication bias, (b) effect size estimation, and (c) testing of the null-hypothesis of no effect. No current method for meta-analysis possesses all 3 qualities. Application of p-uniform is straightforward because no additional data on missing studies are needed and no sophisticated assumptions or choices need to be made before applying it. Simulations show that p-uniform generally outperforms the trim-and-fill method and the test of excess significance (TES; Ioannidis & Trikalinos, 2007b) if publication bias exists and population effect size is homogenous or heterogeneity is slight. For illustration, p-uniform and other publication bias analyses are applied to the meta-analysis of McCall and Carriger (1993) examining the association between infants' habituation to a stimulus and their later cognitive ability (IQ). We conclude that p-uniform is a valuable technique for examining publication bias and estimating population effects in fixed-effect meta-analyses, and as sensitivity analysis to draw inferences about publication bias. [ABSTRACT FROM AUTHOR]

Highlight: [•] Network public goods with asymmetric preference and degree information are analyzed. [•] The social exchange heuristic is formalized and applied to groups and networks. [•] Under degree independence denser groups and networks cooperate (weakly) more. [•] Under degree dependence there is no monotone density-cooperation relation. [•] The ‘closure argument’ always holds for networks with degree independence. [Copyright &y& Elsevier]

Abstract: Exchange theories or their implementations in algorithms have limited utility because they can be applied only to quite small networks. They cannot be applied to larger networks until that size limit is removed. Domain Analysis cuts networks into smaller pieces at the boundaries of strong power domains. Domain Analysis identifies strong power and breaks, and distinguishes domains that function exactly as they would were they free-standing, and components that do not. Support for the finding of breaks and the distinction between domains and components are obtained using both experimental data and simulations based on X-Net. To illustrate the use of Domain Analysis, it is applied to find the incidence of strong power in large exchange networks. The application shows that the incidence of strong power decreases as network density increases, and that strong power occurs only infrequently in dense networks. We conclude by calling for ever more general analytic procedures. [Copyright &y& Elsevier]

Abstract: Economic and sociological exchange theories predict divisions of exchange benefits given an assumed fixed network of exchange relations. Since network structure has been found to have a large impact on actors’ payoffs, actors have strong incentives for network change. We answer the question what happens to both the network structure and actor payoffs when myopic actors change their links in order to maximize their payoffs. We investigate the networks that are stable, the networks that are efficient or egalitarian with varying tie costs, and the occurrence of social dilemmas. Only few networks are stable over a wide range of tie costs, and all of them can be divided into two types: efficient networks consisting of only dyads and at most one isolate, and Pareto efficient and egalitarian cycles with an odd number of actors. Social dilemmas are observed in even-sized networks at low tie costs. [Copyright &y& Elsevier]

The present study evaluates four well-known theories of exchange in networks. In previous research these theories’ predictions were compared for a small set of networks using experimental data. We compared their predictions for all 12,112 networks up to size 8. By comparing these predictions we (i) identified anomalies in theories of network exchange, (ii) investigated to what extent the theories satisfy basic principles of exchange, (iii) identified ‘critical’ networks for which predictions are very different. We conclude that exchange in networks is not yet well understood. [Copyright &y& Elsevier]

Many real-life examples of exchanges with externalities exist. Externalities of exchange are defined as direct consequences of exchanges for the payoff of actors who are not involved in the exchange. This paper focuses on how externalities influence the partner choice in exchange networks. In an experiment two externality conditions are created such that different exchange patterns are predicted in the simplest exchange network with two structurally different complete exchange patterns, the 4-Line. Predictions concerning exchange. patterns and ratios are derived from a generalization of the core from game theory. Hypotheses are derived by comparing the predictions for the experimental conditions and by comparison to data from previous experiments on the 4-Line, without externalities. Hypotheses concerning the changes in exchange patterns were corroborated. [ABSTRACT FROM AUTHOR]