I would like to estimate a model to predict a group-level outcome that is measured at three times. The main predictor is "team climate" which is measured at the individual level, but is aggregated to a group level variable. Furthermore, I have some control variables at the individual level (e.g., sex, age). The basic idea is to create a multilevel model that accounts for (1) individual variance both in the measurement of the team climate variable and in the prediction of the team-level outcome, and (2) the variability of the outcome across time. How can I specify such a model in Mplus? Your help is much appreciated.
(1) Here is one way to think about it. You may compare your case with the UG ex 9.1 figure on page 239. For the Within level (individuals) it sounds like you have individuals' team climate ratings as y, and control variables as x's. For Between (group) you have the y circle as a random intercept which varies across groups. That is your aggregate team climate, expressed as a latent variable. On Between it sounds like you don't have any w or xm variables, so you can just say
(2) Here the question is if you want to study growth or if time is just a nuisance and you simply want to take into account correlation across time. Multilevel growth models are shown in UG ex 9.12 and on.
Murphy T. posted on Wednesday, March 02, 2011 - 1:50 am
Thanks for your answer. I have some follow up questions. To specify:
(1) I have team performance as the dependent variable (measured at the team level only) and I want to regress it on team climate (team level) and control variables (individual level). Can I specify team performance (measured at team level) as the dependent variable on both within and between? Or do I have to specify team performance on between only and some other dependent variable on within?
(2) I just want to take it into account and not study growth. How can I specify this?
1. If you have individual-level control variables x, then using the individual level team climate in the way shown seems best.
2. Then you can handle that simply by saying
%Between% teamperf1-teamperf3 on y;
That is, you have 3 between-level team performance variables as 3 columns in your data.
Murphy T. posted on Wednesday, September 21, 2011 - 12:58 am
Thank you! I have now specified the model and it works (I decided to use only one measurement point for theoretical reasons, however).
Now I tried to specify an interaction between two latent variables at the between level. Both are individual-level variables that reflect team-level constructs. I used the XWITH command but got the error message:
"The XWITH option is not available for observed variable interactions. Use the DEFINE command to create an interaction variable. Problem with: ZSOCC_CS | ZSOCCYN XWITH ZCS"
My input was:
CLUSTER = tid; BETWEEN = Zaewg_1; CENTERING = GRANDMEAN (ZCS ZAR ZEM ZMP Zsex Zage Zsoccyn Zaewg_1); Analysis: Type = twolevel RANDOM; ALGORITHM = INTEGRATION; MODEL: %WITHIN% ZCS ZAR ZEM ZMP Zsoccyn on Zsex Zage; %BETWEEN% Zaewg_1 on ZCS ZAR ZEM ZMP Zsoccyn; Zsocc_CS | Zsoccyn XWITH ZCS; Zaewg_1 on Zsocc_CS;
Where "ZCS", "ZAR", "ZEM", "ZMP", "Zsoccyn" are the team climate variables; "Zsex" and "Zage" are individual-level controls and "Zaewg_1" is the team-level outcome.
It would be great if you could help me. Thank you very much.
I assume that your day-level variables have variation across level-2 units. If so, their between-level parts, their random intercepts, can be related to the control variable. That's how variables can relate across levels.
I have a dataset of individuals nested in teams. Some individuals, however, are members of several teams (e.g. 5 teams). Furthermore, my outcome variable is measured at the team level, while all predictors are measured at the individual level.
How would I construct a model incorporating the fact that the outcome variable is measured on the group level and the predictors on the individual level, while also taking into account that some individuals are members of multiple teams?
I've not seen an example in the literature on the combination of these two issues.