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I am planning to run a latent growth curve model involving U.S. States. I have 17 years of repeated measures for all 50 states plus DC. So, essentially my study involves the entire population of U.S. states. So, I could easily just report descriptive stats, but I want to analyze growth overtime in the outcome variable as well as examine the factors that predict the growth rate. Since I am analyzing the population, should I worry about the fit indices? My thinking is that the implied covariance matrix will be the same as the population covariance matrix, which should make the chisquare test statistic and others unnecessary. Please weigh in on this and let me know what you think. 


Even if you don't do inference, you still want to know if the means, variance and covariances of your estimated model comes close to those in your data/population. 

Lazarus Adua posted on Wednesday, October 18, 2017  10:41 am



Thank you This is very helpful. With your suggestion I can say something about the modeldata fit in my reporting. 

Lazarus Adua posted on Wednesday, October 25, 2017  10:59 am



I hope this is not a silly question. I am running a latent growth curve model for a sample of 50 and 24 time points (19902014). I also have 3 timeinvariant and 4 timevarying predictors. Is it technically feasible to model all 24 time points? Or will you rather recommend I do every other year, which will give rise to 12 time points? My preference will be to use all 24 time points, but I've in past experienced some difficulty estimating a model with so many time points. Thank you. 


24 time points should work for N=50. If not with ML in wide format, then using long format and perhaps Bayes. 

Lazarus Adua posted on Wednesday, October 25, 2017  11:08 am



Thank you. 

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