Respected Prof. Muthen, I am trying to use BSEM to test multigroup (3 groups) invariance - measurement & structural. 1.) I am using Muthén & Asparouhov(2013). BSEM measurement invariance analysis, as the reference to conduct Measurement invariance.However I am not sure how to proceed with structural invariance within BSEM. Kindly guide me on the article for conducting structural invariance in BSEM framework. 2.) I found the following two articles while trying to conduct structural invariance. These articles elaborate on "testing informative hypoheses"; is it the same as testing structural invariance? Also these article refer to R-script? Is it necessary to use the script always for conducting structural invariance? Rens van de Schoot, Marjolein Verhoeven & Herbert Hoijtink (2012): Bayesian evaluation of informative hypotheses in SEM using Mplus: A black bear story, European Journal of Developmental Psychology Van de Schoot, R., Hoijtink, H., Hallquist, M. N., & Boelen, P.A. (2012). Bayesian Evaluation of inequality-constrained Hypotheses in SEM Models using Mplus. Structural Equation Modeling, 19, 593-609 My apologies for an elaborate question. Basically, I am slightly confused with the steps to be taken to conduct structural invariance in a BSEM setup. Please guide me on the steps, & Mplus scripts with examples. Thanking you so very much in advance. Sincerely Arun
you would proceed just like with approximate invariance hypotheses for measurement parameters. So for a particular structural parameter you impose equality with zero-mean, small-variance priors and look to see where you find significant differences across groups. You can use Model Constraint to create a new parameter which is the difference between a structural parameter and its average across groups. This is done automatically and printed in the output for measurement parameters, but you can do it also for structural parameters. I don't have any scripts for doing this.
Thank you Prof. Muthen. I will try as per your suggestions.
Tait Medina posted on Thursday, January 23, 2014 - 11:44 am
I am trying to think through the difference between the following two approaches to detecting measurement non-invariance when indicator variables are continuous:
(1) using an ML approach to multiple-group analysis where all measurement parameters are constrained to be invariant across groups and modification indices are used to determine which parameters should be freely estimated.
(2) using a BSEM approach to detecting invariant and non-invariant items as described in Web Note 17.
I am wondering if you know of any work that compares these two approaches and if the same parameters are found to be invariant/non-invariant?
There is hardly any work on this to date. One related article is
van de Schoot, R., Tummers, L., Lugtig, P., Kluytmans, A., Hox, J. & Muthén, B. (2013). Choosing between Scylla and Charybdis? A comparison of scalar, partial and the novel possibility of approximate measurement invariance. Frontiers in Psychology, 4, 1-15. doi: 10.3389/fpsyg.2013.00770.
which is on our website.
Two other approaches suitable for working with many groups are discussed in this paper on our website:
Muthén and Asparouhov (2013). New methods for the study of measurement invariance with many groups. Mplus scripts are available here.
Tait Medina posted on Thursday, January 23, 2014 - 2:24 pm
Tait Medina posted on Thursday, April 03, 2014 - 12:31 pm
I am conducting a Bayesian multiple group model with approximate measurement invariance using 2 groups (for now). I am having difficulty understanding how the second column (headed Std. Dev.) is obtained. The first column appears to represent the average of the estimates across the groups, and the last columns the group-specific deviations from the average which are starred if they are more than 2 times the std. dev (using .01 as the prior variance). But, how is the standard deviation obtained?
The standard deviation reported in this output is the standard deviation for the average parameter. After the model is estimated and the posterior distribution for every parameter is estimated we compute the posterior distribution for the average parameter. From there we get the standard deviation. The significance is also evaluated in Bayes terms. For each group specific parameter we compute the posterior distribution for the difference between the average parameter and the group specific parameter and if 0 is not between the 2.5% and the 97.5% quantiles of that posterior distribution we conclude that the difference is significant.