Message/Author 


Hello, I have *three waves* of data for individual children: (1) Time 0 = Kindergarten (readiness for school measure) (2) Time 1 = Grade 4 (academic achievement measure) (3) Time 2 = Grade 7 (academic achievement measure) My ultimate interest is seeing the way in which children's developmental trajectories (from Time 0 to Time 1 to Time 2) vary across residential neighbourhoods. Here is the rub: Because the Time 0 measure is 'nonequatable' with the Time 1&2 measures (they measure different constructs), then I *cannot* include the measures in a threewave growth model, or else the results would simply be uninterpretable (as per Singer & Willett, 2003). Therefore, can I instead run a regular twolevel HLM model (kids within neighbouhoods) within the SEM framework, in which the Time 2 measure is my DV and the Time 0&1 measures simply serve as Level1 predictors? If so, can I use SEM to explore both (a) the effect of particular Level1 mediators and (b) the role of particular Level2 predictors? Thanks for any guidance! 


It is hard to do growth modeling even with three repeated measures. I would stick with a simple model of either: y2 ON y1; y1 ON y0; or y2 ON y1 y0; y1 ON y0; How many neighborhoods do you have and how many children per neighborhood? 


Dear Dr. Muthen, Thanks so much for your reply. I have 30 neighbourhoods, and approximately 40100 children per neighbourhood. I figured that a simple twolevel model was going to be the way to go, rather than a growth model. Therefore, I am leaning towards [y2 ON y1 y0]. Thanks again! 


Dear Professors, In a study with repeated measures, there are ten waves. The research question is whether an observed continuous UV influences an observed continuous AV. One is not interested in whether there is a trend in time. One would like to know, instead, whether the UV at t influences the AV at t+1. My questions: Q1: Is it possible to model this case as a twolevel model in Mplus (with the individuals at level 2 and the measures of the waves at level 1?) Q2: Given that one is not interested in the trends, is it possible to ignore the sequence of the waves (except the fact that the UV at t is assigned to the AV at t+1)? Best regards, Daniel 


Q1: Check our Time series page for ways to do this: http://www.statmodel.com/TimeSeries.shtml The UG has many such examples in Chapter 9. Also, we have Short Course Topics 12 and 13 with videos and handouts on this. 10 times points is a bit small for this, but your model seems simple. Q2: Don't know what you mean by "ignore the sequence" but I think the above sources will answer this. 


Many thanks. If I may specify my question a bit: If I am not interested in the effect of time or any trends, but only interested in the effect of a measured continuous UV, could I simply specify a multilevel model (with individuals at level 2, and time points at level 1)? Is there a problem with this twolevel design? 


Q1: Regular multilevel modeling does not easily allow for UV at t influencing the AV at t+1 (the time points need to be the same). But the time series approach I referred to does allow this in a very straightforward way. And it also allows for autoregression. Q2: See above. 


Thank you! The short courses, the examples, and your comments were very helpful. If I may doublecheck whether I specify my model correctly: The aim of following model is to test whether x has an effect on y. (For the sake of simplicity, there is only an immediate effect of x on y modelled in this example.) %Within% sy y on y&1; sx y on x; %Between% y on xm; y sy sx with y sy sx; 


(The background for that preceding post: I was wondering whether one could specify "s  y on y&1 x;" but since I have not found this line in an example, I guess that this is not advisable.) 


With only 10 time points, you need to keep the model simple and not use random slopes, only random intercepts. So say: %Within% y on y&1; y on x&1; x on x&1; %Between% y on x; Regarding number of time points and N, see Schultzberg, M. & Muthén, B. (2018). Number of subjects and time points needed for multilevel time series analysis: A simulation study of dynamic structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 25:4, 495515, DOI:10.1080/10705511.2017.1392862. (Supplementary material). 

Back to top 