

Individual varying times of observation 

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Hello, I ran a growth curve model with individually varying times of observation and am wondering how to interpret the results in terms of units of time. My time variable is hours from birth and my dependent variable is attention/orientation. Since I am not setting any specific time unit, could I say that hours from birth is my unit such that an hour increase in birth results in a beta change in attention? 


Yes. 

Yaoyue Hu posted on Thursday, December 12, 2013  11:59 am



Hi, I am running a growth curve model of 4 timepoint data with individually varying time of observation. In this case, chisquare cannot be calculated as the covariance matrices could be held constant across individuals. However, given loglikelohood, AIC and BIC, how could I tell which model fits the data better, the linear growth curve model or the nonlinear model (add a quadratic growth factor)? Many thanks! 


See if the quadratic mean is significant. If so, go with the quadratic model. 

Yaoyue Hu posted on Sunday, December 15, 2013  2:43 am



Dear Linda Thank you very much for your reply. Given my data, I have more questions about the way to fit the growth curve model: 1. I have a four timepoint data of cognitive function from participants aged 4570 at the first measurement occasion, as well as individually varying time of followup. As suggested by Mehta & West (2000), I am planing to carry out my analysis centering the age at 59£¨mean age at baseline£©at every measurement occasion. Therefore, the interept factor is the mean cognitive function at 59 years old and the slope factor is the rate of change in cognitive funtion per year. Does the age in my data have a too wide span, which might not be suitable to use the centering age at a specific age technique? 2.When I fitted an unconditional nonlinear growth curve model, the linear slope is not significant but the quadratic slope is. Further adding covariates into the nonlinear model, neither linear slope nor quadratic slope is significant any more. Should I choose the model, the linear or nonlinear, based on unconditional model or conditional one? Again, many thanks for your help! 


1. That centering should be fine. The wide age range has more impact on the growth function  whether it can be said to be linear or e.g. quadratic. 2. When you add covariates, your output shows the intercepts, not the means, of the growth factors. The means are seen in TECH4. 

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