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metaSEM (version 0.9.6)

Meta-Analysis using Structural Equation Modeling

Description

A collection of functions for conducting meta-analysis using a structural equation modeling (SEM) approach via the 'OpenMx' package. It also implements the two-stage SEM approach to conduct meta-analytic structural equation modeling on correlation and covariance matrices.

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Install

install.packages('metaSEM')

Monthly Downloads

820

Version

0.9.6

License

GPL (>= 2)

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Repository

https://github.com/mikewlcheung/metasem

Maintainer

Mike Cheung

Last Published

October 31st, 2015

Functions in metaSEM (0.9.6)

Effects of Open Education Reported by Hedges and Olkin (1985)

Forty-four Studies from Cheung (2013)

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

A Dataset from Bornmann et al. (2007)

Summary Method for tssem1, wls, meta and meta3X Objects

Create a Block Diagonal Matrix by Repeating the Input

Test the Homogeneity of Effect Sizes

Eight studies from Mak et al. (2009)

Multivariate effect sizes between classroom management self-efficacy (CMSE) and other variables reported by Aloe et al. (2014)

Plot method for meta objects

Create an F matrix to select observed variables

Six Studies of Correlation Matrices reported by Becker (1992; 1995)

Convert a Matrix into a Block Diagonal Matrix

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

Data Set from Cheung and Chan (2005; 2009)

Five Studies of Ten Correlation Matrices reported by Becker and Schram (1994)

Display the Accumulative Sample Sizes for the Covariance Matrix

Print Methods for tssem1FEM, tssem1FEM.cluster, tssem1REM, wls, meta, meta3X and reml Objects

First Stage of the Two-Stage Structural Equation Modeling (TSSEM)

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Factor Correlation Matrices of Big Five Model from Digman (1997)

metaSEM-package

Meta-Analysis using Structural Equation Modeling

Matrix Diagonals

Read External Correlation/Covariance Matrices

Convert metaSEM objects into semPlotModel objects for plotting

Estimate Variance Components in Three-Level Univariate Meta-Analysis with Restricted (Residual) Maximum Likelihood Estimation

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

Five Published Trails from Berkey et al. (1998)

Ten Studies of Correlation Matrices used by Becker (2009)

Display the Pattern of Missing Data of a List of Square Matrices

Extract Covariance Matrix Parameter Estimates from Various Objects

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Fifty Studies of Correlation Matrices used in Cheung and Chan (2000)

Studies on the Hospital Anxiety and Depression Scale Reported by Norton et al. (2013)

create.mxMatrix

Create a Vector into MxMatrix-class

Create a Block Diagonal Matrix

Compare Nested Models with Likelihood Ratio Statistic

Convert a Vector into a Symmetric Matrix

Extract Parameter Estimates from tssem1FEM, tssem1FEM.cluster, tssem1REM, wls, wls.cluster, meta, meta3X, reml and MxRAMModel Objects

Data Set from ISSP (2005)

Estimate the asymptotic covariance matrix of standardized or unstandardized indirect and direct effects

Test Positive Definiteness of a List of Square Matrices

Rerun models via mxTryHard()

Conduct a Correlation/Covariance Structure Analysis with WLS

Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis

Selected effect sizes from Cooper et al. (2003)

Studies on Students' School Engagement and Achievement Reported by Roorda et al. (2011)

Convert a List of Symmetric Matrices into a Stacked Matrix

Convert a Matrix into MxMatrix-class

Forty-four Covariance Matrices on Life Satisfaction, Job Satisfaction, and Job Autonomy

Dataset from Jaramillo, Mulki & Marshall (2005)

Studies on Sex Differences in Conformity Reported by Becker (1983)

Simulated Effect Sizes Reported by Hox (2002)

A Data Set from TSSEM User's Guide Version 1.11 by Cheung (2009)