Categorical indep var in cross-lagged... PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
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 ResNL posted on Sunday, March 13, 2011 - 11:10 am
Dear Drs Muthen,

I specified a cross-lagged panel analysis in Mplus with four variables (x1 and y1 measured at T1 and x2 and y2 measured at T2) in the following way:

x2 on x1 y1;
y2 on x1 y1;
x1 with y1;
x2 with y2;

When doing so I got a warning stating that my independent variables x1 and y1 were probably categorical but specified as continuous. My independent variables are indeed categorical but I cannot find how I can specify this in my model. Could you indicate how I can do that?

Thank you for your help,

Kind regards
 ResNL posted on Monday, March 14, 2011 - 6:13 am
Dear Drs. Muthen,

In my previous message I forgot to add that all variables are observed,

Thanks for your help,

Kind regards
 Linda K. Muthen posted on Monday, March 14, 2011 - 7:05 am
The scale of independent variables do not need to be specified. Also, means, variances, and covariances of independent variables should not be specified in the MODEL command as these parameters are not part of a regression model. When you specify them, the variables are treated as dependent variables and distributional assumptions are made about them.
 rongqin posted on Thursday, July 12, 2012 - 11:41 am
Dear Linda,

I am trying to do a cross-lagged model between a continuous variable and a categorical variable. I wonder whether I should keep the within waves correlation in the cross-lagged model. In addition, i don't understand why the model with the within waves in have bigger sample size than models without. Here is my syntax:
usevariables are VvdelaaV WwdelaaW vaip waip; CATEGORICAL ARE waip;
MISSING ARE ALL (99999);
analysis:
parameterization = theta;
model:
WwdelaaW on VvdelaaV;
XxdelaaX on WwdelaaW:
waip on vaip;
xaip on waip;
vaip with VvdelaaV;
waip with WwdelaaW;
WwdelaaW on vaip;
XxdelaaX on waip;
waip on VvdelaaV;
xaip on WwdelaaW;

Thanks a lot for your help!
Yongking
 rongqin posted on Friday, July 13, 2012 - 2:35 am
follow up my question yesterday. What if I need to estimate the correlation between vaip and VvdelaaV, how can i do that? Thanks you in advance!
 Linda K. Muthen posted on Friday, July 13, 2012 - 10:48 am
Yes, should keep the within waves correlation in the cross-lagged model. See the TECH4 or RESIDUAL output for the correlations.
 rongqin posted on Friday, July 13, 2012 - 11:27 am
Dear Linda,

Thanks a lot for your response!

Sorry, I am a bit confused: because in some cases you recommended that in cross-lagged path model when there is one categorical variable: "means, variance, and COVARIANCE of independent variables should not be specified in the MODEL command as these parameters are not part of a regression model. " However, for my model, you said within-wave correlation should be included (which also include the correlation between two independent variables?)

I tried the model with within-wave correlations in anyway, the mean/intercepts/thresholds for vaip (.085) was much smaller than that of waip (1.309). And the estimated covariance between vaip and VvdelaaV (.072) was much smaller than correlations between waip and WWDELAAW (.319).Could that be because I did not and could not specify independent variable vaip as categorical. Therefore, when I keep within correlations: vaip with VvdelaaV; in the model, Mplus treat vaip as continuous data?
Many thanks for your advice!
Yongking
 Linda K. Muthen posted on Friday, July 13, 2012 - 5:07 pm
You should make a distinction between exogenous variables and endogenous variables. Exogenous variables are correlated. Their correlations are, however, not model parameters. With endogenous variables, you are estimating a residual covariance not a correlation.
 rongqin posted on Monday, July 16, 2012 - 12:01 pm
Dear Linda,
Thanks a lot for the clarification. As I understood from you. I don't have to put the within-correlation between independent variables. I adjusted my syntax to the following:
usevariables are VvdelaaV WwdelaaW vaip waip; CATEGORICAL ARE waip;
MISSING ARE ALL (99999);
analysis:
parameterization = theta;
model:
WwdelaaW on VvdelaaV;
XxdelaaX on WwdelaaW:
waip on vaip;
xaip on waip;

waip with WwdelaaW;

WwdelaaW on vaip;
XxdelaaX on waip;
waip on VvdelaaV;
xaip on WwdelaaW;
My questions are: 1)Is the syntax correct? Is it ok to use Theta estimation in this situation? 2)what if i need to control for gender? then my independent variables become dependent variables? In that case, I should include correlation between them in the cross-lagged model?

Thanks in advance!
 Linda K. Muthen posted on Monday, July 16, 2012 - 4:23 pm
1. Running an analysis is the best syntax check. See if you get what you want. I would use Delta unless Theta is required. If you run with Delta and Theta is required, you will be told.

2. Adding gender as a control covariate does not make it a dependent variable.
 Jon Elhai posted on Tuesday, May 14, 2013 - 9:13 am
Linda. Do you have any videos online for cross-lagged analyses?
 Linda K. Muthen posted on Tuesday, May 14, 2013 - 2:02 pm
I don't think we do. It's just a set of ON statements so shouldn't be hard to set up.
 Bo Fu posted on Friday, June 07, 2013 - 11:12 am
Dear Drs Muthen,

I am doing a cross-lagged analysis on a longitudinal data with 2 continuous outcomes and 5 repeated measurements for each outcome.

For the first step, I would like to model only for those two outcomes, with no independent factor involved.

I got non convergence issue (NO CONVERGENCE. NUMBER OF ITERATIONS EXCEEDED).
Your suggestion is highly appreciated.

"
USEVARIABLES = S_1–S_5 O_1-O_5;
ANALYSIS:
TYPE = GENERAL;
ITERATIONS = 10000;
MODEL:
S_5 ON S_4 O_4 ;
S_4 ON S_3 O_3 ;
S_3 ON S_2 O_2 ;
S_2 ON S_1 O_1 ;
O_5 ON O_4 S_4 ;
O_4 ON O_3 S_3 ;
O_3 ON O_2 S_2 ;
O_2 ON O_1 S_1 ;

S_1 with O_1;
S_2 with O_2;
S_3 with O_3;
S_4 with O_4;
S_5 with O_5;
OUTPUT: modindices standardized;
"

Thank you so much!
 Linda K. Muthen posted on Friday, June 07, 2013 - 11:43 am
Please send the output and your license number to support@statmodel.com.
 Sointu Leikas posted on Tuesday, August 13, 2013 - 2:24 am
Dear Drs Muthén,

first, I apologize if my question is very basic but I don't seem to be able to find the answer on my own.

I'm testing a cross-lagged model with two time points and with one categorical (s) and one continuous (E) variable, both measured at times 2008 (8) and 2011 (11). The continuous variable is modelled as a latent variable with 3 indicators.

The (important part of) syntax looks like this:

Variable: categorical is s8 s11;

Model:
E8 BY e18 e28 e38;
E11 BY e111 e211 e311;
E11 ON E8 s8;
s11 ON E8 s8;

s8 WITH E8;
s11 WITH E11;

I'm not sure if I should mark s8 as categorical or not. If I don't, the MPlus output says "WARNING: VARIABLE S8 MAY BE DICHOTOMOUS BUT DECLARED AS CONTINUOUS." But I suppose this doesn't matter because s8 is an independent variable and thus its scale shouldn't matter?

However, my actual problem is that I get different results depending on whether I mark s8 as categorical or not. Specifically, in the above model, the path 's11 ON E8' is significant if s8 is marked as categorical, but non-significant if it is not.

Furthermore, if s8 is marked as categorical, MPlus doesn't compute standardized parameter estimates for the regression paths. Do you have any suggestions?

sincerely,

Sointu
 Linda K. Muthen posted on Tuesday, August 13, 2013 - 9:08 am
The variable s8 should not be on the CATEGORICAL list. This list is for dependent variables only. If you are using the default WLSMV, you should related s8 and e8 using the ON option not the WITH option, for example,

s8 ON e8;
 Sointu Leikas posted on Wednesday, August 14, 2013 - 5:55 am
Thank you very much for your help! The model works well now and the results make sense.
 ellen posted on Sunday, October 20, 2013 - 9:53 pm
Hi,
I am running a cross-lagged model based on a 2-wave longitudinal data set. Below is my measurement model.

Variable:
Names are a b c d e f g h i j k L;
Categorical Are a-f;
Missing All (-11);
Model:

XTime1 by a (1);
XTime1 by b (2);
XTime1 by c (3);

XTime2 by d (1);
XTime2 by e (2);
XTime2 by f (3);

YTime1 by g (4);
YTime1 by h (5);
YTime1 by i (6);

YTime2 by j (4);
YTime2 by k (5);
YTime2 by L (6);

a with d;
b with e;
c with f;

g with j;
h with k;
i with L;

In the measurement model output, it indicates that there was NOT a significant bivariate correlation between the two latent constructs of XTime1 and YTime2 (r=.012, p =.735).

However, when I performed the cross-lagged structural model by indicating that,

XTime2 ON XTime1 YTime1;
YTime2 ON YTime1 XTime1;
XTime1 with YTime1;
XTime2 with YTime2;

Somehow the output showed that XTime1 significantly and negatively predicted YTime2 (B= -.08, p = .006), even though the correlation between XTime1 and YTime2 was NOT significant (and slightly positive) in the measurement model (r=.012, p =.735).

Why is this happening and what does it mean?
 Linda K. Muthen posted on Monday, October 21, 2013 - 8:31 am
You are comparing a covariance to a partial regression coefficient.
 Kaigang Li posted on Tuesday, November 19, 2013 - 8:32 am
Hello Drs. Muthen,

The syntax below is to fit a cross-lagged stability model with categorical variables W1-W3. Given that W1 is independent variable, so I only claimed W2 and W3 as categorical.

CATEGORICAL are
W2 W3
;
Model:
W3 on W2;
W2 on W1;

S3 on S2;
S2 on S1;

W1 with S1;

Based on your answer on the very top of this page "means, variances, and covariances of independent variables should not be specified in the MODEL command" it seems that "W1 with S1;" should not be specified.

Am I right?

Thank you!
 Bengt O. Muthen posted on Tuesday, November 19, 2013 - 9:52 am
Right.
 Kaigang Li posted on Tuesday, November 19, 2013 - 10:18 am
Thanks Dr. Muthen,

Then I am little confused.

When answering the question posted at the line "Sointu Leikas posted on Tuesday, August 13, 2013 - 2:24 am" above, Linda suggests using "s8 ON e8;" instead of "s8 WITH E8;" S8 and e8 are independent variables.

So, no matter which statement is used, it means that the correlation between s8 and e8 needs to be specified. However, the outputs are different between including and not including that statement.

Would you please clarify this as I think I may have misunderstood some part?

Thank you!
 Lina Homman posted on Tuesday, February 25, 2014 - 5:49 am
Hi,

I am running a cross lagged panel analysis with 3 time points and two measures. I am just wondering whether I should include residual correlations between measures at time point 2 and 3? I find that if I do not do this I get significant cross lagged paths while if I do include them they are not significant.

Thanks
 Bengt O. Muthen posted on Tuesday, February 25, 2014 - 12:18 pm
I would include them a priori since they represent effects of excluded predictors.
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