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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 ? Any help/clarification would be much appreciated. 


The 0, 1 values are for the continuous normal x that is then dichotomized at the mean using the CUTPOINTS = x(0) statement at the top of the input. The resulting dichotomous variable is 0.5, 0.25. 


Hello, I'm working on a Monte Carlo analysis for an ordered categorical variable with 6 thresholds (8 waves). I'm proposing development is linear. In the model population, I add variances with start values that increase over time. However, I'm unsure what to use as the scale start values. The coding from one of your examples includes the comment: "this sets the scale factors at the inverted SDs for the u* variables, so that the estimates are in the metric of the Delta parametrizations" How do I do this in my study? 


Act as if the u*s are continuous observed outcomes that have been categorized. Often the variance of such an observed continuous outcome increases over time. This implies a decreasing delta value (inverted SD). 


Ok, thanks. Yes I understand that. I was wondering if the inverted SD meant taking the square root of the variance estimate and then dividing it into 1 (1/x). But that doesn't work out exactly as the values in the example. Am I wrong? 


Yes, inverted SD is taking the square root of the variance and then computing 1 divided by that. Note that the variance is the total variance of the u*, not just the residual variance. 


Ok, now I got it. Thanks for your help DR 

Hanna posted on Sunday, November 25, 2007  2:32 pm



Hello, I am a graduate student using the 4.1 demo version to determine the sample size necessary for my dissertation research. I am using LGM to identify trajectories of a continuous outcome variable measured at 3 timepoints. I have 2 covariates (1 dichotomous, 1 continuous), and I also want to regress the slope growth factor on a continuous variable. I've gotten a bit stuck using Monte Carlo syntax from Muthen and Muthen (2002), as well as from Chapter 11 of the MPlus User's guide. Could you point me in the right direction given that I'm using the demo version at the moment? Thanks! 


Most of the user's guide examples come with input for the Monte Carlo counterpart used to generate the data for the example. I would find an example in the user's guide close to what I want and start with the Monte Carlo counterpart for that example. 

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