Dear Professor Cheung,

I am writing this post to seek your help regarding the OSMASEM. Building a model with no moderator works just fine, but adding a moderator to the model does not work properly. I am currently learning the OSMASEM approach by replicating one of the published articles that applied the approach, entitled “Simple View of Reading in Chinese: A One-Stage Meta-Analytic Structural Equation Modeling” written by Peng and his colleagues (https://journals.sagepub.com/doi/abs/10.3102/0034654320964198). Thanks to their open data (Appendix C), I was able to construct a dataset and to replicate their first model with no moderator by computing the same coefficients and p-values in accordance with the figure in the article. However, when I add a moderator, the results are unstable—seemingly inaccurate (very large) standard errors. It would be appreciated if you could kindly review my codes.

Thank you in advance.

Rerunning the model again may help. As you can see, Tau1_2 vecTau1 and Tau1_2vecTau7 are very negative meaning that Tau2_2 and Tau2_7 are very close to 0. Thus, their SEs are large.

Dear Professor Cheung,

I truly appreciate your feedback. It works just fine! I am very delighted to be able to move forward to next learning steps and want to ask one more favor with the following last question: How do I label the computed 36 Tau2s to understand the results of R2 for moderation (osmasemR2)? Given that my RAM$A0 is a 12

12 matrix, I first guessed there should be 66 Taus. Actually, there are 36 Tau2s coming from the osmasemR2 command and this made me confused. Do I have a 99 matrix? When I read MASEM on Nohe et al. (2015) data, it seems that your 6 Tau2s exactly correspond to the lower diagonal elements of a 4*4 matrix with w1,s1,w2,s2 in accordance with the structure of RAM$A0. My best understanding is that this pattern does not apply to my case where both observed and latent variables are involved. When it comes to coefficients, I have their names; however, I am clueless with nameless Tau2s. I look forward to hearing from you, sir.Thank you very much.

Please disregard this message and see the following comment. Thank you.

Dear Professor Cheung,

I truly appreciate your feedback. It works just fine! I am very delighted to be able to move forward to next learning steps and want to ask one more favor with the following last question: How do I label the computed 36 Tau2s to understand the results of R2 for moderation (osmasemR2)? Given that my A0 is a 12X12 matrix, I first guessed there should be 66 Taus (i.e., 66 lower diagonal elements). Actually, there are 36 Tau2s from the osmasemR2 command and this made me confused. Do I have a 9X9 matrix? When I read MASEM on Nohe et al. (2015) data, it seems that your 6 Tau2s exactly correspond to the lower diagonal elements of a 4X4 matrix with w1,s1,w2,s2 in accordance with the structure of A0. My best understanding is that this pattern does not apply to my case where both observed and latent variables are involved. When it comes to coefficients, I have their names; however, I am clueless with Tau2s with no labels. I look forward to hearing from you, sir.

Thank you very much.

As indicated in Equation (1) in Jak and Cheung (2020), the random effects Tau^2 are on the correlation coefficients, not on the SEM parameters. In your data, there are 9 variables. Thus, 9*8/2=36 Tau^2 on the correlation coefficients. It may not be easy to interpret the R^2 in such a model.