Suppose that I have three individuals. The first one has 3 observations, the second one has 4 observations and the third one has 5 observations as follows:
ID Y1 Y2 Y3 Y4 Y5 t1 t2 t3 t4 t5 1 y11 y21 y31 NA NA t11 t12 t13 NA NA 2 y21 y22 NA y24 y25 t21 t22 t23 . t25 3 y31 y32 y33 y34 y34 t31 t32 t33 t34 t35
where ID identify subjects, Y1-Y5: The five measures (with possibility of missing) made in times t1-t5. So I can use my latent growth model by: VARIABLE: NAMES ARE y1-y5 t1-t5 TSCORES = t1-t5 TYPE = MISSING Model i s | y1-y5 at t1-t5
IS it like that that I should write my program? and how to tell to the program that my missing values are represented by NA's?
When you use the ML estimator, the default missing data option in Mplus is MAR (see e.g. the Enders, 2010, book). So for instance if your CFA outcomes are related to performance, MAR will be a good approach to use.
Wen-Hsu Lin posted on Thursday, January 14, 2016 - 4:54 am
Dear Mplus team: I have one question regarding unbalance design. I have a repeated measure across 7 time points; however, at each time point I have different n (attrition is different).Some individuals come and go. Can I estimate LGM with different n at different time point? If I turn on listwise, I will have too few cases. If I use FIML, the n is based on wave 1. Thank you so much.