scholarly journals A meta-analysis on interparental conflict, parenting, and child adjustment in divorced families: Examining mediation using meta-analytic structural equation models

2020 ◽  
Vol 79 ◽  
pp. 101861 ◽  
Author(s):  
Rianne van Dijk ◽  
Inge E. van der Valk ◽  
Maja Deković ◽  
Susan Branje
2001 ◽  
Vol 26 (1) ◽  
pp. 105-132 ◽  
Author(s):  
Douglas A. Powell ◽  
William D. Schafer

The robustness literature for the structural equation model was synthesized following the method of Harwell which employs meta-analysis as developed by Hedges and Vevea. The study focused on the explanation of empirical Type I error rates for six principal classes of estimators: two that assume multivariate normality (maximum likelihood and generalized least squares), elliptical estimators, two distribution-free estimators (asymptotic and others), and latent projection. Generally, the chi-square tests for overall model fit were found to be sensitive to non-normality and the size of the model for all estimators (with the possible exception of the elliptical estimators with respect to model size and the latent projection techniques with respect to non-normality). The asymptotic distribution-free (ADF) and latent projection techniques were also found to be sensitive to sample sizes. Distribution-free methods other than ADF showed, in general, much less sensitivity to all factors considered.


2019 ◽  
Vol 22 (1) ◽  
pp. 147-160 ◽  
Author(s):  
Hend Eltanamly ◽  
Patty Leijten ◽  
Suzanne Jak ◽  
Geertjan Overbeek

This mixed methods systematic review and meta-analysis sheds more light on the role parenting practices play in children’s adjustment after war exposure. Specifically, we quantitatively examined whether parenting behavior explained some of the well-known associations between war exposure and children’s adjustment. In addition, we meta-synthesized qualitative evidence answering when and why parenting practices might change for war-affected families. We searched nine electronic databases and contacted experts in the field for relevant studies published until March 2018, identifying 4,147 unique publications that were further screened by title and abstract, resulting in 158 publications being fully screened. By running a meta-analytic structural equation model with 38 quantitative studies ( N = 54,372, M age = 12.00, SD age = 3.54), we found that more war-exposed parents showed less warmth and more harshness toward their children, which partly mediated the association between war exposure and child adjustment, that is, post-traumatic stress symptoms, depression and anxiety, social problems, externalizing behavior, and lower positive outcomes (e.g., quality of life). War exposure was not associated with parents’ exercise of behavioral control. By meta-synthesizing 10 qualitative studies ( N = 1,042; age range = 0−18), we found that the nature of war-related trauma affected parenting differently. That is, parents showed harshness, hostility, inconsistency, and less warmth in highly dangerous settings and more warmth and overprotection when only living under threat. We conclude that it is both how much and what families have seen that shapes parenting in times of war.


2019 ◽  
Vol 23 (4) ◽  
pp. 620-650
Author(s):  
Christian Dormann ◽  
Christina Guthier ◽  
Jose M. Cortina

Meta-analysis of panel data is uniquely suited to uncovering phenomena that develop over time, but extant approaches are limited. There is no straightforward means of aggregating findings of primary panel studies that use different time lags and different numbers of waves. We introduce continuous time meta-analysis (CoTiMA) as a parameter-based approach to meta-analysis of cross-lagged panel correlation matrices. CoTiMA enables aggregation of studies using two or more waves even if there are varying time lags within and between studies. CoTiMA thus provides meta-analytic estimates of cross-lagged effects for a given time lag regardless of the frequency with which that time lag is used in primary studies. We describe the continuous time underpinnings of CoTiMA, its advantages over discrete-time, correlation-based meta-analysis of structural equation models (MASEM), and how CoTiMA would be applied to meta-analysis of panel studies. An example is then used to illustrate the approach. We also conducted Monte Carlo simulations demonstrating that bias is larger for time category–based MASEM than for CoTiMA under various conditions. Finally, we discuss data requirements, open questions, and possible future extensions.


2021 ◽  
Author(s):  
Mike W.-L. Cheung

Structural equation modeling (SEM) and meta-analysis are two popular techniques in the behavioral, medical, and social sciences. They have their own research communities, terminologies, models, software packages, and even journals. This chapter introduces SEM-based meta-analysis, an approach to conduct meta-analyses using the SEM framework. By conceptualizing studies in a meta-analysis as subjects in a structural equation model, univariate, multivariate, and three-level meta-analyses can be fitted as structural equation models using definition variables. We will review fixed-, random-, and mixed-effects models using the SEM framework. Examples will be used to illustrate the procedures using the metaSEM and OpenMx packages in R. This chapter closes with a discussion of some future directions for research.


Author(s):  
Mike W.-L. Cheung

Meta-analysis and structural equation modeling (SEM) are two popular statistical models in the social, behavioral, and management sciences. Meta-analysis summarizes research findings to provide an estimate of the average effect and its heterogeneity. When there is moderate to high heterogeneity, moderators such as study characteristics may be used to explain the heterogeneity in the data. On the other hand, SEM includes several special cases, including the general linear model, path model, and confirmatory factor analytic model. SEM allows researchers to test hypothetical models with empirical data. Meta-analytic structural equation modeling (MASEM) is a statistical approach combining the advantages of both meta-analysis and SEM for fitting structural equation models on a pool of correlation matrices. There are usually two stages in the analyses. In the first stage of analysis, a pool of correlation matrices is combined to form an average correlation matrix. In the second stage of analysis, proposed structural equation models are tested against the average correlation matrix. MASEM enables researchers to synthesize researching findings using SEM as the research tool in primary studies. There are several popular approaches to conduct MASEM, including the univariate-r, generalized least squares, two-stage SEM (TSSEM), and one-stage MASEM (OSMASEM). MASEM helps to answer the following key research questions: (a) Are the correlation matrices homogeneous? (b) Do the proposed models fit the data? (c) Are there moderators that can be used to explain the heterogeneity of the correlation matrices? The MASEM framework has also been expanded to analyze large datasets or big data with or without the raw data.


2011 ◽  
Author(s):  
Richard Hermida ◽  
Joseph Luchman ◽  
Vias Nicolaides ◽  
Cristina F. Wilcox

2017 ◽  
Vol 81 (3) ◽  
pp. 41-61 ◽  
Author(s):  
Jens Hogreve ◽  
Anja Iseke ◽  
Klaus Derfuss ◽  
Tönnjes Eller

The service–profit chain (SPC) has served as a prominent guidepost for service managers and researchers alike. This meta-analysis provides the first comprehensive test of the SPC, showing that all the proposed links are statistically significant and substantial. However, the effect sizes vary considerably, partly according to the type of service provided. Meta-analytic structural equation models show that internal service quality translates into service performance through various mechanisms beyond employee satisfaction, and they highlight the importance of the service encounter and customer relationship characteristics for customer responses. The findings not only indicate the need to integrate complementary paths in the SPC framework but also challenge the implicit SPC rationale that firms should always maximize employee satisfaction and external service quality to optimize firm performance.


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