Tools for Those Who Summarize the Evidence Base
Resources and networking for those who conduct or interpret meta-analyses related to any phenomenon that is gauged in multiple studies.
Dear All,I am learning to do a meta-analysis and I have a question on studying interaction effects with meta-analysis. If my primary studies have 3 variables A, B and C and its correlations with…Continue
Started by saneesh edacherian. Last reply by saneesh edacherian Jun 4.
A message from Nina Lasek to all members of Meta-analytic structural equation modeling on Meta-Analysis Resources! Dear meta-analysers (hope it is correct to post my question here?), I replicated a…Continue
Started by Nina Lasek. Last reply by Mike Cheung Jun 4.
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.
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.
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Dear Nina,
If the data (scales) are comparable across studies, I would suggest using either a multiple-group SEM (fixed-effects model) or a multilevel SEM (random-effects model) in Mplus.
MASEM or using the moderated mediation as the effect size is also fine. But it would be better to maximize the information by using the raw data.
Best,
Mike
A message from Nina Lasek to all members of Meta-analytic structural equation modeling on Meta-Analysis Resources!
Dear meta-analysers,
I replicated a moderated mediation path model across k=11 samples. Indicators and sample sizes (individual data are available, n from approx. 1000 to approx. 2000) differ. But all associations including the moderation of the mediation are in the expected direction.
Currently, the moderation is calculated as a product term using Mplus. I could also do a multigroup analysis with the moderator as grouping variable. Then i would have e.g. two or three groups per sample.
Question is whether such models - either a path model with moderated indirect effects or a multigroup path model with an indirecte effect - might be meta-analysed as MASEM?
Or, perhaps alternatively, I was also thinking about using the regression weights indicating the mediated respectivey the moderated mediated effects as univariate information for a 'conventional' meta-analysis. What do you think? I would be very grateful for tips with regard to applied examples.
Many thanks in advance,
N.
Thank you for your reply - I'll definitely learn R,
best
Empi
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
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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