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Meta-analytic structural equation modeling

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Meta-analytic structural equation modeling

A technique to synthesizing correlation/covariance matrices for the purpose of fitting structural equation models.

Members: 49
Latest Activity: on Tuesday

Discussion Forum

MASEM for individual-level survey data? 5 Replies

Dear group members,I am rather unexperienced using meta-analytical techniques and thus hope to find some advice here.The setting: Using Mplus, I estimated a simple mediation model with observed…Continue

Started by Empi. Last reply by Mike Cheung May 10.

Problem in "tssem1"

Hi Mike and all,I'm a new learner of meta-analysis and just try to use metaSEM.My dataset comprises 90 sets of 19*19 correlation matrix and every one contains some missing values. I'm trying to…Continue

Started by Eleanor Zhai Feb 20.

testing factor structure: meta-SEM vs CFA followed by EFA vs Rasch vs IRT 7 Replies

Dear Mike and all,I want to examine the factor structure of two scales respectively (scale A: 14 items; scale B: 26 items). We are trying several methods: CFA followed by EFA; Rasch analysis and IRT…Continue

Started by Kevria. Last reply by huiyong fan Feb 7.

MASEM: "Cov" is not positive definite 2 Replies

Dear Mike and Others,I am trying to estimate a random effects tssem for my dissertation.I have read your book and related papers. I am following the wonderful resources provided by you and your team.…Continue

Tags: MASEM

Started by Srikanth Parameswaran. Last reply by Mike Cheung May 9, 2017.

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Comment by Empi on May 18, 2018 at 12:00am

Thank you for your reply - I'll definitely learn R,

best

Empi

Comment by Mike Cheung on May 13, 2018 at 10:21pm

Dear Empi,

There is no grouping variable because the data are summary statistics (correlation matrices). test.cov may include the stacked correlation matrices, e.g.,

1

.2 1

.3 .4 1

1

.3 1

.4 .5 1

The sample sizes are specified by NOBSERVATIONS = 100 200;

By the way, I wonder why you would like to use Mplus to do it. Mplus can only fit the fixed-effects model. Moreover, you have to manually setup the constraints.

In R, you can easily do a random-effects model with one line, e.g.,

random1 <- tssem1(data, n, method="REM", RE.type="Diag")

Regards,
Mike

Comment by Empi on May 13, 2018 at 6:59am

Dear Mike et al.,

 

this post http://www.statmodel.com/discussion/messages/11/3993.html

shows how to use Mplus for pooling correlation matrices.

 

However, I wonder why there is no need to indicate the sample/group the original data refer to.

I would have expected something like the “grouping is” command – but this is not needed? For example, if there were different numbers of observations (NOBSERVATIONS) per group/sample (NGROUP), how would the software know which sample size refers to which group?

Many thanks for your response,

Empi

 

***

Mplus can be used to pool correlation matrices. The following is the sample Mplus code:
TITLE: Pooling correlation matrices

DATA: FILE = test.cov;
NGROUPS=2;
TYPE IS COVARIANCE; ! Pretend correlation matrices as covariance matrices
! See Cheung and Chan (2005)
NOBSERVATIONS = 100 100;
VARIABLE: NAMES ARE x1-x3;
USEVAR ARE ALL;
MODEL:
latent1 BY x1*; ! Estimated standard deviations
latent2 BY x2*;
latent3 BY x3*;
latent1@1;
latent2@1;
latent3@1;
x1@0; ! No measurement errors
x2@0;
x3@0;
latent1 WITH latent2* (1);
latent1 WITH latent3* (2);
latent2 WITH latent3* (3);
MODEL g2:
latent1 BY x1*; ! Estimated standard deviations
latent2 BY x2*;
latent3 BY x3*;
latent1 WITH latent2* (1); ! Constrain correlation matrices
latent1 WITH latent3* (2);
latent2 WITH latent3* (3);
OUTPUT: SAMPSTAT;

 

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