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 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, 2020.
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, 2020.
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.
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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I am happy being here, as a member. We will do it together as events unfold.
Thank You Mike
Dear Vipin Saini,
As indicated in the error message, you need to install the semPlot package first.
Best,
Mike
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....
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
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
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
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?
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
Dear Mike,
How can I test inverse u-shaped variable relationships (quadratic terms) in TSSEM?
Sergio
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