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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: 57
Latest Activity: Jun 26

Discussion Forum

Univariate random eff ects model using meta()

Dear Mike,I conducted Univariate random effects model using meta() function but I am not sure if the result is Ok as it contrasts with rma() function.ri= correlation coefficientni= sample…Continue

Started by Vipin Saini Jan 17.

MASEM R code error message 2 Replies

Dear Professor Cheung,I am trying to run a MASEM analysis, more specific a meta-analytic path analysis using the R code provided by Suzanne Jak (…Continue

Started by Balazsi Robert. Last reply by Balazsi Robert Jan 7.

Second order correlation from first order 2 Replies

Hi Dr. Cheung ,While conducting a meta-analysis, I am facing difficulty about how to deal with correlations of first-order variables while converting them to second order.For example:Review…Continue

Started by Vipin Saini. Last reply by Vipin Saini Dec 27, 2019.

Using MASEM as a data integration tool? 1 Reply

Dear Group Members,Thanks in advance for any help or advice that you can give!I am currently working with 10 cross-sectional datasets and would like to use this data to explore the relationships…Continue

Started by Anne Zhou. Last reply by Mike Cheung May 30, 2019.

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Comment by Joshua Chiroma Gandi on June 26, 2020 at 2:56am

I am happy being here, as a member. We will do it together as events unfold.

Comment by Vipin Saini on November 18, 2019 at 8:11pm

Thank You Mike

Comment by Mike Cheung on November 18, 2019 at 3:47am

Dear Vipin Saini,

As indicated in the error message, you need to install the semPlot package first.

Best,
Mike

Comment by Vipin Saini on November 18, 2019 at 2:17am

I am conducting meta-analysis based on Two stage SEM method, while doing example exercise of digman97 data I used library(semPlot) which shows error 

" Error in plot.wls(random2, color = "green") :
"semPlot" package is required for this function.

Example references from below link:

https://cran.r-project.org/web/packages/metaSEM/vignettes/Examples....

Comment by Mike Cheung on November 30, 2018 at 10:24pm

Hi Sergio,

I have two clarifications.

The approach by Wilson, Polanin & Lipsey (2016) is not the same as the multi-level multivariate model in Chapter 6 of my 2015 book.


My tssem2() or wls() were developed for fitting SEM with correlation/covariance matrices. I don't know whether it is appropriate to use them to fit multi-endpoint studies.

Regards,
Mike

Comment by Sergio Canavati on November 29, 2018 at 6:42pm

Hi Mike,

Good point. I'm using the approach by Wilson, Polanin & Lipsey (2016) which you cite in Chapter 6 of your 2015 book in the discussion of multi-level multivariate models. You also wrote an introduction to the special issue in which it was published in RSM. Basically, in this approach I use the metafor rma.mv function (indicting random effects at the effect size and study levels [that way I can use multi-endpoint studies]) to estimate no-intercept estimates that I can use to populate my correlation table and generate the asymptotic covariance matrix. 

Then, in the second stage I enter the aCov from metafor into the wls function in metaSEM. My n = sum(sample sizes of all studies). 

Is there another way I can perform MASEM using multi-endpoint studies without having to average effect sizes from multi-endpoint studies?

Thanks,

Sergio

Comment by Mike Cheung on November 29, 2018 at 4:52am

Hi Sergio,

I am confused. I thought that you were fitting SEM with the TSSEM. But you have just mentioned multi-endpoint studies which are not related to the TSSEM.

Regards,
Mike

Comment by Sergio Canavati on November 28, 2018 at 12:51pm

Hi Mike,

I tried OSMASEM for a study I'm conducting but the results changed because it required that I average effect sizes in studies that provide more than one effect size for a specific construct relationship. Since I have an average of 2 effect sizes for each construct relationship in each sample study, I was using this method (https://doi.org/10.1002/jrsm.1199) to estimate the aCov and the used the WLS function to fit the SEM in the second stage. In your recent PsychFrontiers article, you show that the estimates in multi-endpoint studies are biased in multi-level multivariate models when we assume homogeneity of variance. The solution you propose there also doesn't help me because the studies are using provide multiple end-points for the same construct relationships. How do you suggest I proceed?

Comment by Mike Cheung on November 28, 2018 at 2:09am

Dear Sergio,

If you were referring to the regression coefficients or factor loadings are U shape, I don't think that it can be easily done given that this is a meta-analysis. If you were referring to the relationship between a moderator and the regression coefficients is U shape, it might be possible under the One-Stage MASEM. https://psyarxiv.com/ce85j But it won't be an easy task.

Best,
Mike

Comment by Sergio Canavati on November 26, 2018 at 3:24pm

Dear Mike,

How can I test inverse u-shaped variable relationships (quadratic terms) in TSSEM?

Sergio

 

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