'center' it around "one common mean" (David Kenny; Betsy Coach), and the covariate has been measured at say x3 occasions (e.g.a1, a2, a3), then what statement would be used to generate this GMC time-varying covariate using the one-common" grand mean of "all" person-time ...
I've tried the syntax below provided in the Mplus guide, and it works well. The output/findings are quite similar to SPSS, which makes sense. As you can see, I've removed the "between" part of the model to focus on the within-person effects. DATA: FILE = Datab2.dat; VARIABLE:...
However, if I just leave my indicators in the within option part without being group-mean centred (otherwise I get an error as they are indicators of a within latent variable) would this between person variability would be controlled for? I am not sure how else would I be able to control...
(2000). Integrating person-centered and variable-centered analysis: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891. (#85) Muthén, B. & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm....
I conducted a diary study and thus have variables measured on person level (L2) as well as variables measured day level (L1). Basically I am interested in testing cross-level and three-way interactions: (a) A chronic stressor W (measured on L2) should moderate the relationship between a ...
With data in the wide format, multivariate modeling takes care of the fact that several variables are measured for each person. The 8 conditions not measured should be represented as missing data. There is no need for multilevel modeling. Andrea Vocino posted on Saturday, February 13, 2010 -...
u1 = 1 if person died then IF (t>4) THEN u2=0; IF ((t>2) .AND. (t<4)) THEN u2=1-c; IF (t<2) THEN u2=_missing; ! u2 is missing either because u1 = 1 or because ! u1 = 0 and c = 1 163 Translating Continuous-Time Survival Data To Discrete-Time Survival Data (...
Piecewise LGM v1@1v2@1v3@1v4@1v5@1; At this point I have tried many models and have large standardized residuals with each model, except one that may not be identified. The best-fitting model is a 3-piece piecewise:
a) if it were significant this would probably mean that a person with a high intercept also has a high upturn towards the end. b) when the initial decline is steeper, the ending upturn is higher. anonymous posted on Tuesday, February 17, 2009 - 3:20 pm Hello, I conducted a conditi...
Instead can I just use a definition variable approach fixing the slope loadings to each person's age at the given wave, but center the definition variables in such a way that across groups the intercept would be modeled at the same age (the knot)? That way the time scores are more ...