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.
we want to analyse multilevel-data (indiviudals nested in teams) with a level 2 outcome (e.g. leaders' satisfaction), and a level 2 moderator (e.g. a leaders' trait). The independent variable is on level 1. This is our syntax. We are not sure if this is correct. Any corrections or hints are welcome! Does the (cross-level) interaction have to be defined as a between variable?
usevar = Leader_A Member_A Leader_J IactA; CLUSTER IS TEAM_3; BETWEEN ARE Leader_A Leader_J; DEFINE: IactA=Leader_A*Member_A; center Leader_A (grandmean) Member_A (groupmean); ANALYSIS: TYPE IS TWOLEVEL RANDOM; MODEL: %BETWEEN% Leader_J on Leader_A Member_A IactA;
I am interested in analyzing data consisting of repeated measures in clusters (schools) but with different individuals (students) at each time point. The objective is to analyze whether certain intervention had effect on the smoking prevalence in these schools, at two time points after the baseline. Everything is measured at the individual-level, but I'm using some of the measures as aggregated means on school-level, to serve as indicators of the school tobacco control policies. For me, measuring change over time is important, so could you advice how to analyze that in Mplus with this kind of data? I would prefer using binary outcome variable (daily smoker/other).
So are you saying that you want a binary growth model for 3 time points where the repeated outcome is an aggregate over students in the schools? Is the unit of analysis school? How many schools do you have?
Yes, that is my basic objective and the unit of analysis is school. However, I'm also interested whether it is possible to use individual outcome here.
I have altogether 339 schools with data from all three time points. There are altogether 108599 students in the data, but as I mentioned, each student has data only from one time point.
The variables of interest are gender, age, parental smoking, general attitudes towards smoking (these I would like to keep on individual level), school type and four variables related to school tobacco control policies (aggregated to school-level). The studied intervention relates to legislation so there is no specific intervention variable in the data, the time perspective is important for that. Then is the outcome for current student smoking, which could be used on individual level or aggregated to school mean.
If I wanted to study possible moderation effects (e.g. of some school-level policy), what would be a suitable model to test that in this setting?
I very much appreciate your help!
Nina Wirtz posted on Wednesday, March 11, 2015 - 3:57 am
Dear Bengt, I am currently trying to model a cross-level interaction with a level 1 predictor (x), a level 2 moderator (z) and a level 2 outcome (y). k is a level 2 control variable. (See Syntax below).
1. By not defining x as WITHIN variable, I am looking at the latent between-level part of x on level 2. However, as I am only using x on level 2 , I am actually forced to do so. If I define x as WITHIN variable I get an error msg. Is there any way around this or is the latent approach in this case (automatically) the preferable one?
2. Is the interaction term defined correctly? I've also tried the XWITH command, but that did not work.
3. Is the interaction term created with the grand mean-centered variables or with the raw scores?
Thank you very much for your help, I greatly appreciate it! Nina
usevar = x z k y Iact; MISSING = All(-999); CLUSTER IS team; BETWEEN ARE y z k;
DEFINE: center x z k (grandmean); Iact= x*z; ANALYSIS: TYPE IS TWOLEVEL; ESTIMATOR = ML;
1. If you are not interested in level-1 relationships, why don't you simply create a cluster-level version of x using Cluster_mean? Thereby you can do a single-level analysis.
2. The interaction definition is fine, but apply it to the cluster mean of x.
3. Grand-mean centering is done first.
Nina Wirtz posted on Thursday, March 12, 2015 - 2:17 am
Thank you for the helpful response Bengt!
Regarding 1: I have a formative construct on the within level (team members' health). I thought that I would avoid loss of information and get a more accurate estimation by using MLM (in reference to your 2008 paper with Lüdtke et al. on the MLC approach and some recent work by Croon, van Veldhoven, Peccei, & Wood on bathtub models with L2 outcomes). This way, the variance on the within variable as well as the dependence of observations among teams is taken into account, isn't it? In your opinion, does the multilevel structure make sense in my case? I highly appreciate your feedback. Thanks. Nina