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 Holger Muster posted on Friday, November 25, 2011 - 2:36 am
Hi all,

herewith a question concerning standardization in path analysis:

I have a set of observed variables, one is binary. The binary variable is the dependent variable in the first sub-model and one of the covariates in the second sub-model.

Applying "OUTPUT:STANDARDIZED;" I only get STDXY and STD standardization, not STDX. However, from my understanding for both sub-models I only should use STD-coefficients as standardized path weights. Right?
 Linda K. Muthen posted on Friday, November 25, 2011 - 9:48 am
For a binary x, use StdY. You will need to compute it from what you get.
 Holger Muster posted on Monday, November 28, 2011 - 8:28 am
Linda,

thanks for your comment.

If I use StdY for all my covariates just because one of my covariates is binary, do you think the standardized coefficients (=path weights) are comparable? To guarantee that, would not STD be better?

A second question: Which standardization is recommended given a binary dependant variable in path analysis? From my current understanding I believe STD is recommended because for StdXY and StdY I would use standard deviation of a binary variable which does not make sense.

BR Holger
 Linda K. Muthen posted on Monday, November 28, 2011 - 11:33 am
You should use StdY for binary covariates and StdYX for all others. The choice of standardization is based on the covariate not the dependent variable.
 Holger Muster posted on Tuesday, November 29, 2011 - 2:21 am
sorry, but if the dependent variable is binary and all covariates would be continuous, use of StdYX does not make sense from my perspective because StdYX means the resulting "standardized coefficient bStdYX is interpreted as the change in y in y standard deviation units for a standard deviation change in x" (user guide v6, page 642) where y is the dependant binary variable and hence change in y standard deviation units does not make sense. Would you agree?

Thus if I'm right what would be the correct standardization given a binary dependent variable? Would that be STD?
 Linda K. Muthen posted on Tuesday, November 29, 2011 - 11:39 am
It would be StdYX where y is y* the latent response variable underlying the observed categorical variable y.
 dvl posted on Monday, April 23, 2012 - 8:05 am
Dear,

I am doing a path analysis and my outcome variable is dichotomous. I have a binary independent variable (regression 1) next to a continuous independent variable (regression 2). I am figuring out which standardized coefficients I have to use. My output only represents STDYX and STD. My STD coefficients are the same as my original non-standardized coefficients.

I have understood that in case that the independent variable is dichotomous I should use STDY (regression 1). In case that the independent variable is continuous I should use STDYX (regression 2). Is this true?

In order to compare all standardized coefficients from both regressions do I have to use OR stdy OR stdyx? In other words, do I have to choose one standardization method or can I compare STDY and STDYX across the different regressions?

Could please tell me how I calculate stdy from the information I get? Then I am sure that my calculation is right.
 Linda K. Muthen posted on Monday, April 23, 2012 - 12:39 pm
You can combine different standardized coefficients.

If you have StdYX, you divide it by the standard deviation of x to get StdY.
 dvl posted on Saturday, May 12, 2012 - 3:40 am
Dear,

I am running a path analysis (no latent variables). I have a dichotomous independent variable, a continuous mediating variable and a binary outcome variable.

When I study the standardized effect of the dichotomous variable I will use the stdy value (I calculate this value myself). The standardized effect of the continuous mediating variable on the binary outcome variable is a STDYX value.

Now, what do I have to do when I want to study the indirect effect of the dichotomous independent variable on the binary outcome variable. Can I just look at the STDYX value for the indirect effect and divide it by the standard deviation of x?
 Linda K. Muthen posted on Sunday, May 13, 2012 - 10:39 am
You would use StdY for the indirect effect. That is how you would obtain it.
 Minnik Findik posted on Monday, May 21, 2012 - 7:50 am
Dear Linda,
I am running a path analysis. All my variables are categorical (binary) except for 1. Am I correct at assuming I should use stdY estimate? And my next question is mplus do not automatically give me that estimate. How can I compute it? And will it have the same p value as the model results??
Thanks
Best wishes
 Linda K. Muthen posted on Monday, May 21, 2012 - 11:29 am
The choice of StdY versus StdYX is based on whether a covariate is binary or continuous. Use StdY for a binary covariate and StdYX for a continuous covariate. See the STANDARDIZED option in the user's guide for the formula.
 Minnik Findik posted on Monday, May 21, 2012 - 1:18 pm
Dear Linda,
what if the covariate is an ordinal (0,1,2)independent variable hence, can not be defined as categorical. Does that mean I will be using StdYX?
Kind regards.
 Linda K. Muthen posted on Monday, May 21, 2012 - 1:51 pm
An ordinal covariate is treated as continuous. Use StdYX.
 Marco Bardus posted on Sunday, July 29, 2012 - 2:38 am
Dear Drs. Muthén,

I am running a path analysis with a count outcome (or with a binary outcome - not in the example below) censored below variable.

I managed to calculate indirect effects using MODEL CONSTRAINT. But How do I get the standardized estimates of the effects?
Should it be beta = B*SDx/SDy? Where do I get SDx and SDy of the total effects? There might be something I am overseeing.

CENSORED = bpad (bi);
ANALYSIS: ESTIMATOR = MLR;
INTEGRATION = MONTECARLO;

MODEL:
intmean ON attmean (a);
intmean ON snmean (b);
intmean ON pbcmean (c);
bpad ON intmean (d);
attmean WITH snmean pbcmean;
snmean WITH pbcmean;


MODEL CONSTRAINT:
new(ad bd cd);
ad = a*d;
bd = b*d;
cd = c*d;
 Linda K. Muthen posted on Sunday, July 29, 2012 - 9:47 am
This is correct for censored but would not be correct for count. See the following paper which is available on the website:

Muthén, B. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus.

The standardization is correct. You get the x variances from TYPE=BASIC and the y variances from the RESIDUAL output.
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