Multilevel model with group-level out...
Message/Author
 Murphy Tucson posted on Tuesday, March 01, 2011 - 12:42 pm
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?
 Bengt O. Muthen posted on Tuesday, March 01, 2011 - 1:45 pm
(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

y;

(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

(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?

Thanks very much from a new Mplus user.
 Bengt O. Muthen posted on Wednesday, March 02, 2011 - 10:37 am
(1) You say

Between = teamperf;

in the VARIABLE command and in the MODEL command:

%Within
y on x1 x2;
! x1 x2 are control variables and y refers to
!team climate

%Between%
teamperf on y;
! y is between part of team climate (the
! random intercept)

Is only the group-level outcome team performance measured 3 times, or are the other variables also measured 3 times?

To learn quicker, you may want to consider attending our multilevel course that we give end of March at Johns Hopkins.
 Murphy T. posted on Friday, March 04, 2011 - 9:50 am
Thank you very much.

(1) Do I understand it correctly that team climate has to be the individual level team climate variables rather then the (team-level) aggregated scores?

(2) Only the group-level outcome team performance is measured at 3 times; the other variables are measured at one time.
 Bengt O. Muthen posted on Saturday, March 05, 2011 - 2:43 pm
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.
 Linda K. Muthen posted on Wednesday, September 21, 2011 - 9:12 am
You can put a factor behind each of them on between, for example,

f1 BY Zsoccyn;
Zsoccyn@0;

and use the factors in XWITH.
 Johnna Capitano posted on Friday, April 26, 2013 - 1:07 pm
I have a dataset of days clustered within people. My indirect model is all at a within level (all day level variables). I want to control for a between (level 2) variable.

Since the analysis is Type=Twolevel, I have the MODEL: %Within% followed by the model relationships.

How do I specify the controls? It seems that since the outcomes are at L1 and the controls are at L2, it will not allow me to regress on one the other in either a %between% or %within% statement.

Thank you!
 Bengt O. Muthen posted on Friday, April 26, 2013 - 2:08 pm
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.
 J. Botterman posted on Friday, November 22, 2013 - 2:55 am
Hi Bengt/Linda,

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.

 Bengt O. Muthen posted on Sunday, November 24, 2013 - 3:37 pm
You may want to take a look at the multiple membership literature:

http://www.bristol.ac.uk/cmm/team/hg/xcmmrev2.pdf

and perhaps also the cross-classified literature:

Gonzalez, De Boeck, Tuerlinckx (2008) A Double-Structure Structural
Equation Model for Three-Mode Data. Psychological Methods, 337 -
353
 Thomas Rigotti posted on Friday, October 10, 2014 - 7:36 am
Hello,

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?

CLUSTER IS TEAM_3;
DEFINE:
ANALYSIS: TYPE IS TWOLEVEL RANDOM;
MODEL:
%BETWEEN%
 Bengt O. Muthen posted on Friday, October 10, 2014 - 3:24 pm
So you intend "Member_A" to be the latent between-level part of the Member_A variable. Read about that under Part 2 of the UG ex 9.1 on page 262.

You should drop RANDOM in the Analysis command since you have only a random intercept/mean.
 Hanna Ollila posted on Tuesday, October 21, 2014 - 5:31 am
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).
 Bengt O. Muthen posted on Tuesday, October 21, 2014 - 12:23 pm
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?
 Hanna Ollila posted on Tuesday, October 21, 2014 - 11:11 pm
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

Syntax:

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;

MODEL:
%WITHIN%

%BETWEEN%
x with k;
y on k z x Iact;
 Bengt O. Muthen posted on Wednesday, March 11, 2015 - 6:22 pm
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
 Bengt O. Muthen posted on Thursday, March 12, 2015 - 8:31 am
If you have a model for Within, I would include it, but not if it is just one variable - unless you are really keen on getting that latent variable decomposition (desirable with small cluster sizes).
 Allison L.. West posted on Tuesday, August 11, 2015 - 6:45 am
I would like to estimate a model to predict a group level outcome (y). I have a level 1 predictor (x) and several level 2 predictors (z1 z2 z3). HVID is the level 2 cluster variable. Can you please verify that this is the correct syntax?

Variable:

NAMES ARE = HVID z1 z2 z3 x y;
MISSING = All(-99, -88);
CLUSTER = HVID;
BETWEEN ARE z1 z2 z3 y;

DEFINE:
ANALYSIS: TYPE IS TWOLEVEL;
ESTIMATOR = ML;

MODEL:
%WITHIN%
x;
%BETWEEN%
y on z1 z2 z3 x;

Thanks,
Allison
 Bengt O. Muthen posted on Tuesday, August 11, 2015 - 2:02 pm
That looks right. The x variable on Between is the latent between part of x which is what you want.
 Rick Vogel posted on Wednesday, June 29, 2016 - 2:13 pm
Dear all,

I have exactly the same data structure as in Allison's example above, with the exception that my group level outcome y is categorical.

When running the model, the error message is "Unrestricted x-variables for analysis with TYPE=TWOLEVEL and ALGORITHM=INTEGRATION must be specified as either a WITHIN or BETWEEN variable. The following variable cannot exist on both levels: x".

What are my options for solving this problem? 1) Is it correct to include x on the within level and the cluster mean of x on the between level? 2) How would instead a latent variable approach look like? 3) What else could I do?

Rick
 Bengt O. Muthen posted on Wednesday, June 29, 2016 - 3:11 pm
Q1 Yes.

Q2. Create a factor measured by x on both levels.
 Rick Vogel posted on Thursday, June 30, 2016 - 12:18 am
Thanks for the response. Just a follow-up question with regard to Q2: Is it correct that an equivalent solution would be to keep x on the within level and to create the factor only on the group level, as follows:

MODEL:
%WITHIN%
x;
%BETWEEN%
f by x;
y on z1 z2 z3 f;
 Bengt O. Muthen posted on Friday, July 01, 2016 - 11:46 am
I think so.
 Lisa Legault posted on Thursday, August 25, 2016 - 3:34 pm
I am trying to conduct a multilevel path/mediation analysis with a categorical predictor (a high vs. low feedback intervention), an individual-level mediator (emotion) and a group level outcome (electricity consumption in shared apartments). The outcome is clustered within apartments (77 clusters)

I have the following input (I've tried various others), which is not currently converging and I'm looking for advice:

!level-1 variables
m=Emo2_c; !emotions (disgust and empathy)

!level-2 dv
y=elect; !electricity use
z=Feed; !feedback

VARIABLE: NAMES ARE Mot Feed Feed2 apt_case elect water hotwater
Intrins Emo2 Emo2_c;

USEVARIABLES ARE y m z;
BETWEEN ARE y z;
CLUSTER IS apt_case;

MISSING ARE ALL (-99);

ANALYSIS: TYPE IS TWOLEVEL;
MODEL:
%WITHIN%
m;
%BETWEEN%
y ON m z;
m ON z;

MODEL INDIRECT: y IND m z;
 Linda K. Muthen posted on Thursday, August 25, 2016 - 4:57 pm
 Dayna Walker posted on Wednesday, February 15, 2017 - 12:08 pm
Dear Drs. Muthen,

I have the same model as Allison above. Thank you for confirming this is the right syntax! My questions are about interpretation:

1) Should I interpret model fit statistics (e.g., CFI, TLI, RMSEA, SRMR) before interpreting significance of individual predictors, as with other model estimation techniques?

2) What does it mean if the p value of my level 1 predictor is different in the standardized (STDYX) output than in the non-standardized output (p = .046 vs p = .05)? Also, is STDYX the correct section of standardized output I should be using for interpretation (vs. STDY or STD)? I have both continuous and binary predictors in the model. The latent, level 1 predictor is continuous.

 Bengt O. Muthen posted on Wednesday, February 15, 2017 - 3:07 pm
1) Yes. The model must be a good representation of the data before interpreted.

2) See our FAQ:

Standardized coefficient can have different significance than unstandardized

STDYX is used for continuous predictors and STDY for binary ones.

Both of these questions are discussed in our new book.
Post:
This is a private posting area. Only registered users and moderators may post messages here.