Effects of Open Education Reported by Hedges and Olkin (1985)
A Dataset from TSSEM User's Guide Version 1.11 by Cheung (2009)
Two Datasets from Gleser and Olkin (1994)
Matrix Diagonals
Factor Correlation Matrices of Big Five Model from Digman (1997)
Convert correlation or covariance matrices into a dataframe of correlations or
covariances with their sampling covariance matrices
Correlation Matrices from Gnambs, Scharl, and Schroeders (2018)
Selected effect sizes from Cooper et al. (2003)
Simulated Effect Sizes Reported by Hox (2002)
Correlation Matrices from Cooke et al. (2016)
Multivariate effect sizes reported by Kalaian and Raudenbush (1996)
Fourteen Studies of Correlation Matrices reported by Hunter (1983)
Correlation Matrices from Scalco et al. (2017)
Studies on Students' School Engagement and Achievement Reported
by Roorda et al. (2011)
Correlation Matrices from Nohe et al. (2015)
Dataset on the Environmental Tobacco Smoke (ETS) on children's health
Eight studies from Mak et al. (2009)
Dataset from Jaramillo, Mulki & Marshall (2005)
Correlation Matrices from Mathieu et al. (2015)
Studies on the Hospital Anxiety and Depression Scale Reported by Norton et al. (2013)
Convert a Character Matrix into MxAlgebra-class
Correlation coefficients reported by Tenenbaum and Leaper (2002)
Correlations from Stadler et al. (2015)
Extract Variance-Covariance Matrix of the Random Effects
Convert a Character Matrix with Starting Values to a Character Matrix
without Starting Values
Convert a Matrix into MxMatrix-class
Compare Nested Models with Likelihood Ratio Statistic
Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix
Extract Parameter Estimates from various classes.
Create an F matrix to select observed variables
Check the correctness of the RAM formulation
Create a Block Diagonal Matrix by Repeating the Input
Calculate Effect Sizes using lavaan Models
Create a Block Diagonal Matrix
Create a V-known matrix
Create a variance component of the heterogeneity of the random effects
Create a Vector into MxMatrix-class
A Dataset from Cheung and Chan (2005; 2009)
Estimate the asymptotic covariance matrix of standardized or unstandardized indirect and direct effects
Create an mxModel
Fit Models on the bootstrapped correlation matrices
Parametric bootstrap on the univariate R (uniR) object
Univariate and Multivariate Meta-Analysis with Maximum
Likelihood Estimation
Create or Generate the Model Implied Correlation or Covariance Matrices
Create a model implied correlation matrix with implicit diagonal constraints
Convert a List of Symmetric Matrices into a Stacked Matrix
Create a moderator matrix used in OSMASEM
Convert lavaan
models to RAM models
Display the Accumulative Sample Sizes for the Covariance Matrix
A Dataset from ISSP (2005)
Test the Homogeneity of Effect Sizes
Test Positive Definiteness of a List of Square Matrices
Convert a Matrix into a Block Diagonal Matrix
Display the Pattern of Missing Data of a List of Square Matrices
Meta-Analysis using Structural Equation Modeling
Calculate the SRMR in OSMASEM and OSMASEM3L
Calculate the R2 in OSMASEM and OSMASEM3L
Rerun models via mxTryHard()
One-stage meta-analytic structural equation modeling
Plot methods for various objects
Read External Correlation/Covariance Matrices
Print Methods for various Objects
Generate (Nested) Sample/Population Correlation/Covariance Matrices
Compute Effect Sizes for Multiple Treatment Studies
Summary Method for tssem1, wls, meta, and meta3LFIML Objects
Convert metaSEM
objects into semPlotModel
objects for plotting
First Stage of the Two-Stage Structural Equation Modeling (TSSEM)
Estimate Variance Components in Three-Level Univariate
Meta-Analysis with Restricted (Residual) Maximum
Likelihood Estimation
Estimate Variance Components with Restricted (Residual) Maximum
Likelihood Estimation
Extract Covariance Matrix Parameter Estimates from Objects of
Various Classes
Estimate the heterogeneity (SD) of the parameter estimates of the
TSSEM object
Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation
Dataset on the effectiveness of multidimensional family therapy in treating
adolescents with multiple behavior problems
Second Stage analysis of the univariate R (uniR) approach
First Stage analysis of the univariate R (uniR) approach
Compute Effect Sizes for Multiple End-point Studies
Conduct a Correlation/Covariance Structure Analysis with WLS
Convert a Vector into a Symmetric Matrix
Forty-four Studies from Cheung (2013)
Forty-four Covariance Matrices on Life Satisfaction, Job Satisfaction, and Job Autonomy
Studies on Sex Differences in Conformity Reported by Becker (1983)
Multivariate effect sizes between classroom management
self-efficacy (CMSE) and other variables reported by Aloe et al. (2014)
Fifty Studies of Correlation Matrices used in Cheung and Chan (2000)
Five Studies of Ten Correlation Matrices reported by Becker and
Schram (1994)
Ten Studies of Correlation Matrices used by Becker (2009)
Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis
Correlation Matrices from Boer et al. (2016)
A Dataset from Bornmann et al. (2007)
Five Published Trails from Berkey et al. (1998)
Six Studies of Correlation Matrices reported by Becker (1992; 1995)