Hello,
I am conducting a MASEM study using TSSEM (random-effects) approach. There are in total six variables involved (i.e., PS, PA, MA, Voc, WR, and RC) in the hypothesized model. In the tested model, PS is the predictor and RA serves as the latent variable, which can be loaded on WR and RC. And the PA, MA, Voc would serve as the mediators. I do have several questions listed below:
- Whether the inequality of the sample size would lead to a biased estimation of the parameters? Or, what is the ideal number of studies/participants to fit the proposed model?
Below, please kindly find the detailed information about my study
1) There are in total 68 studies included. The average sample size for each study is 86.
2) Number of studies:
PS WR RC PA MA Voc
PS 68 67 23 55 15 36
WR 67 67 23 52 16 32
RC 23 23 25 18 3 14
PA 55 52 18 58 15 32
MA 15 16 3 15 17 11
Voc 36 32 14 32 11 38
3) Number of participants:
PS WR RC PA MA Voc
PS 5511 5396 1852 4572 1607 2847
WR 5396 5396 1820 4623 1899 2723
RC 1852 1820 1999 1391 550 832
PA 4572 4623 1391 4873 1831 2661
MA 1607 1899 550 1831 2002 1207
Voc 2847 2723 832 2661 1207 3021
4) Sample size for each study
[1] 126 98 103 110 16 133 6 49 33 61 103 93 70
[14] 85 86 70 104 25 25 20 23 45 80 18 33 115
[27] 44 85 62 75 93 50 114 70 81 370 30 77 80
[40] 80 63 54 76 211 48 60 76 66 69 56 130 199
[53] 123 44 25 24 110 35 78 29 18 84 64 31 161
[66] 180 54 302
- It is obvious that there are 67 studies reporting the correlation between the PS and WR. However, the number of studies for other pairs of correlation decreases dramatically. One of the reasons is probably because our inclusion criteria clearly stated that all the studies included in the meta-analysis must report the correlation between PS and WR (because it would address the first research question and control the scope of the study). I noticed that some MASEM studies would include all the correlations (i.e., at least report one pair of correlations of interest)
In short, whether this inclusion criterion limits the estimation for the model (which is actually not the full picture). Or are there any potential methodological problems?
- In generating the list of the correlation matrix, I do encounter some warnings. How to interpret and fix the warnings "In mat[lower.tri(mat, diag = FALSE)] <- x :a number of items to replace is not a multiple of replacement length"?
R code:
cormatrices <- readstack(dataSEM04, no.var = 6, var.names = varnames, diag = FALSE)
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I am wondering if there are any approaches to generate the pooled correlation matrix in One-stage MASEM?
-
To address the missing data, I am wondering if there are any functions in metaSEM to evaluate the missing data rate? And to implement the FIML, is it necessary to run a code separately? Or, FIML has been integrated into the function of "tssem"?
It would be highly appreciated if you are willing to help!! Thanks again for your contribution to the open science community! :)
Best regards,
Eva