Rich Jones posted on Tuesday, September 26, 2000 - 7:10 pm
In regard the computation of the noncentrality parameter (N-1*FCN) when using the Satorra-Sarris method to power estimation for intervention study using a Muthen and Curran (1997) and technical appendix latent growth model approach:
(1) Is the correct function (FCN) to use in computing the noncentrality parameter the "quasi-Newton" function?
(2) Is the 'N' the number of subjects in both the intervention and control groups?
My second question is motivated by noticing that the minimum of the fitting function varies with NOBSERVATIONS given the same covariance matrix, but yet the SAS programs distributed with the Technical Appendix consider power of as a function of N given a FCN.
bmuthen posted on Wednesday, September 27, 2000 - 5:34 pm
Yes, on both questions.
Anonymous posted on Wednesday, January 26, 2005 - 2:49 pm
I tried to copy Mplus code from Muthen & Muthen (2002) "How to use a monte carlo study to decide on sample size and determine power" into Mplus 3, but got some error messages. Is the syntax chaged a lot? One of the error I got is "*** ERROR in Montecarlo command Unknown option: NCLASSES"
In "How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power", (SEM v9, n4, 2002) a monte carlo analyis for a growth model with a covariate is discussed (p. 604). The mean and variance of the covariate, x, are 0.5 and .25, respectively (p. 604). However, when setting up the monte carlo analysis in MPLUS (p. 614) the mean and variance for the covariate, x, are fixed to 0 and 1 (p.614).
My question is simply why 0 and 1 instead of the values given on p. 604 of the article, i.e., .5 and .25? Does it possibly have to do with centering the covariate so that the intercept growth factor will estimated at the average value of the covariate ?
Emily Blood posted on Friday, November 09, 2007 - 4:04 pm
In your article "How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power" you define the effect size of the treatment variable on the latent intercept as the value of the coefficient of the treatment variable divided by the standard deviation of the latent intercept. I am trying to describe the power of a study where there is a mediating variable between the treatment indicator and the latent intercept, so there is a direct and indirect effect of the treatment on the latent intercept. In this setting, is it appropriate to define a treatment effect as the value of the total effect (a+b*c) divided by the standard deviation of the latent intercept? Is this still true if there are additional covariate predictors in the model? Thanks.
I am trying to calculate an effect size estimate for a single group treatment study using an unconditional growth curve model. I am assuming that I can simple calculate the estimated mean change from pre to post using the growth curve model parameters, but am unsure what SD I should use (estimated pre SD?) or how to actually calculate this using the output. It seems that calculating effect sizes using the observed means may be beneficial given that most studies use this methodology and therefore would make it easier to compare to previous studies. Any suggestions?
I am not exactly sure how to use the command in this manner. Could you provide an example of how this could be used with a growth model to get the mean difference between time 1 (pre) and 2 (post), with the standard error for the means at time 1 and 2? If this is too time consuming for you I understand.
We have a Monte Carlo simulation for treatment effects on intercept and slope in a latent growth model and would like to look at moderation by a binary or continuous covariate. For a binary covariate, we could use multiple groups, but are not sure how to go about this with MPlus 7. Thank you!
I want to run a montecarlo to get power estimates for an intervention study with three treatment arms vs. a control arm. I have modified Ex. 6.10 to omit the time-varying covariates, but am wondering about my three X variables. How do I specify that they are only coded 1 for 25% of the sample? The two X variables in the example look like they could be any proportion of the sample. I expect my X's to have lower power because I have three arms rather than simply a treatment vs. a control.