Formative (causal) measurement model... PreviousNext
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 Christian Klode posted on Friday, December 01, 2006 - 6:11 am
hi linda and
hi bengt!

i have a question concerning fixed parameters within formative (or causal) measurement models (FMM) like SES. my aim is to perform a SEM with exogenous FMM and endogen ordinal reflective measurement model (RMM). the model fit of WRMR = .907 looks appropriate, and comparable parameter estimates structure were obtained by a PLS-model.
my problem for now is the following: apparantly the FMM regression weights are very sensitive due to the fixing value. the following model provide a value of .01 as a nice approximation.

is there a rule-of-thumb for the fixing value within FMMs?
here is the corresponding model:

categorical: pride1 pride2 pride3;
model: repu by;
repu on repu1@0.01 repu2 repu3;
pride by pride1 pride 2 pride3;
pride on repu;

thanks in advance!
 Linda K. Muthen posted on Friday, December 01, 2006 - 9:53 am
If you set the metric using .01 instead of 1, then the FMM regression weights will be changed. Their relative size should remain constant.
 Christian Klode posted on Friday, December 08, 2006 - 6:44 am
linda, thank you for the immediate reply.
i have an additional question concerning the 'statistical fit' of formative indicators with a potential regard to scale purification. let's assume one has no access to PLS (within a theory 'building'-approach), then there would be no guidance for setting the metric via a discrete indicator-weight (in order to obtain trustful t-values).

is there an alternative to the PLS-pre-'testing' respectively would you denote this procedure as appropriate?

 Bengt O. Muthen posted on Friday, December 08, 2006 - 6:23 pm
The choice of constant or variable for setting the metric does not affect the model fit - the same restrictions on the covariances between the FMM indicators and the DVs are obtained.
 Richard E. Zinbarg posted on Thursday, March 15, 2012 - 8:15 am
Hi Linda and/or Bengt,
A student and I are trying to fit our first causal indicator measurement model. From the syntax on slide 246 of the Mplus Short Courses Topic 1 Handout, it looks to us as if there is no disturbance term for f - your formative construct. Are we interpreting that correctly? If so, this would seem to us to be more akin to what Bollen would call a composite indicator model than a causal indicator model and we would be interested in guidance regarding syntax we should use to give the construct a disturbance term?
Thanks very much!
 Linda K. Muthen posted on Thursday, March 15, 2012 - 9:41 am
I am not sure what Bollen means by a causal indicator model. Can you send the paper to
 Bengt O. Muthen posted on Saturday, March 17, 2012 - 1:28 pm
Yes, the formative model we give the specification for is what Bollen-Bauldry (2011) Psych Meth call composite indicators. What they refer to as causal indicators can simply be specified as a MIMIC-type model; no special syntax needed. See their Figure 4, where the causal indicators behave like regular covariates - to me, it is more of a conceptual distinction.
 Randall MacIntosh posted on Thursday, December 27, 2012 - 10:02 am
What alterations would I have to make to the model in slide 246 (topic 1) to obtain the model shown in Fig. 4a of the Bollen-Baudary paper?

Is it possible?

 Bengt O. Muthen posted on Thursday, December 27, 2012 - 2:05 pm
What's the reference? You don't mean the Bollen-Bauldry (2010) SM&R paper, right?
 Randall MacIntosh posted on Thursday, December 27, 2012 - 3:51 pm
Bollen & Bauldry (2011) Three Cs in Measurement Models:Causal Indicators, Composite Indicators, and Covariates. Psychological Methods 2011, Vol. 16, No. 3, 265284

Figure 4a is on page 276.
 Bengt O. Muthen posted on Friday, December 28, 2012 - 5:37 pm
So I assume you want the parameterization of eq. (13) on page 11. So say:

eta1 BY y1@1; [y1@0];
eta1 ON x1-x3; [eta1];
 Randall MacIntosh posted on Saturday, January 05, 2013 - 12:27 pm
Bollen and Bauldry suggest on p.279 (or p.14) looking at the indicator's unique validity variance, which they define as the difference between the r-square for eta with all causal indicators and r-square for eta, less causal variable x_i.

Mplus provides r-square for the latent variable. How can I get (or compute) the second value to obtain the unique validity variance?

Thank you.
 Bengt O. Muthen posted on Saturday, January 05, 2013 - 4:17 pm
Are you referring to eqn (21)?
 Randall MacIntosh posted on Sunday, January 06, 2013 - 9:48 am
Eqn. 24
 Bengt O. Muthen posted on Sunday, January 06, 2013 - 5:23 pm
I think you would have to do 2 different runs and get the 2 R-square values.
 Lois Downey posted on Thursday, June 30, 2016 - 1:20 pm
I used a suggestion from Bollen and Bauldry's article to build the following syntax for estimating a model with causal indicators:

USEVARIABLES = y1 y2 x1-x4;
Factor by;
Factor on x1@1 x2-x4;
y1 y2 on Factor;
y1 with y2@0;

Although that is a different method for achieving model identification than the one you suggest in your course handout, it avoids having to use a composite variable between the indicators and the latent variable of interest, and the model ran as expected.

I then wanted to test the model for between-group invariance of the factor indicators. To do that, I added a grouping variable and made changes to the MODEL statement as follows:

GROUPING = country (0=US 1=Canada);

Factor by;
Factor on x1@1
x2 (1)
x3 (2)
x4 (3);
y1 y2 on Factor;
y1 with y2@0;

MODEL Canada:

However, this model resulted in an error message, indicating that the model may not be identified and pointing to the factor intercept in the Canada group as the problematic component.

What additional constraint(s) does the model need for statistical identification?

 Bengt O. Muthen posted on Friday, July 01, 2016 - 11:39 am
Your single-group model seems to have a free residual variance for the factor - is that really identified?

Your two-group model is not identified because the two intercepts of y1, y2 in their regressions on the factor cannot be identified together with the factor intercepts. It would be identified if you hold those y intercepts equal across the groups.
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