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Greetings  For a six time point model where the data demonstrate nonlinearity, I've specified a freed loading growth model where time 1 is centered at 0 and time 6 is centered at 5. My understanding is that the slope will reflect the mean change from time 1 to time 6 while the freed estimate will provide an indication of general acceleration or deceleration across the other time points. My raw means for time 1 and 6 are 9.9 and 13.9, respectively, and the predicted mean for the growth curve at time 1 is 8.9 with a slope of 1.15. Is accurate to state that the mean change from t1 to t6 is 1.15 but to get the fitted mean it is t1+(1.15*5)? 


The mean change from t1 to t6 is 1.15. The estimated mean at time t (t=16) is [i]+ lambda_t*[s] where [i] is the intercept growth factor mean, lambda_t is the estimated time score for time t (lambda_1=0), and [s] is the slope growth factor. If you want the SE of the expression above, you define it as a New parameter in Model Constraint. 

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