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| Hans Berten posted on Thursday, September 07, 2006 - 8:23 am
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Dear dr. Muthén,
My question is the following: is it possible to handle a non-recursive path model in M-Plus with a categorical and a continuous endogenous variable (no latent variables)?
For example:
VARIABLE: USEVARIABLES ARE y1 y2 x1 x2 x3 x4 x5 x6;
CATEGORICAL IS y2;
MISSING ARE ALL (999);
ANALYZE : ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL: y1 ON y2 x1 x2 x3 x4 x5;
y2 ON y1 x1 x2 x3 x4 x6;
Is it possible to analyse this model using the ML estimator? It seems to work only when using WLSMV (and with theta parameterization). When I try to analyze this non-recursive model choosing ML estimator I get the fatal error message: reciprocal interaction problem.
Do I need to constrain the model by fixing the covariance between the residuals y1 and y2 to zero? If yes, how does it work?
The same problem arises when I try this model in a multilevel analysis.
Thanks in advance! |
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Here is a post from SEMNET some time ago -the last paragraph is relevant to your question.
I want to expand on Cameron McIntosh's answer to Ricardo on Friday
regarding a recursive model with one continuous and one dichotomous
DV. Cameron's said that categorical-variable SEM methods can handle the
situation if the observed dichotomous DV is (i) a "categorized version of a
continuous variable", but not when it is (ii) "truly categorical".
Consider Model A,
(1) y1 = beta_1 * y2* + gamma_1 * x1 + e1,
(2) y2* = gamma_2 * x2 + e2,
where y1 is a continuous observed variable and y2* is a continuous latent
response variable underlying the dichotomous observed variable y2. Here,
y2 =0/1 depends on y2* exceeding a threshold or not. This is the standard
categorical-variable SEM model, where the case (i) specification is made in
(1). The model where y1 influences y2* instead of being influenced by y2*
also falls into this standard case.
In contrast, Model B
(3) y1 = beta_1 * y2 + gamma_1 * x1 + e1,
(4) y2* = gamma_2 * x2 + e2,
lets the observed dichotomous y2 influence y1 as seen in (3) and therefore,
I think, represents the "truly categorical" case (ii). Note that in (4)
the y2* construction is not necessary because (4) is simply a standard
probit/logit regression model. In Mplus, Model A can be estimated via WLS
using probit and Model B can be estimated via ML using logit.
Similar modeling topics have been discussed in the 1983 article by Winship
& Mare. Note also that a corresponding non-recursive, i.e. reciprocal
interaction, version of Model B is internally inconsistent unless beta_1 or
beta_2 is zero - see Maddala's 1983 book, pp. 117-118 (Model 1), leading
back to the recursive case.
Bengt Muthen |
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Dear Drs. Muthen,
I have been working with a path model with a mixture of continuous, ordinal, and binary endogenous variables. The model uses the ML estimator and montecarlo integration. It was recently suggested that we evaluate the possibility of a bidirectional relationship between two of the variables. Adding the reverse direction to the existing model statements yielded a message of FATAL ERROR: RECIPROCAL INTERACTION PROBLEM.
Can bidirectional relationships be fit using ML and montecarlo? Are there any specific criteria that must be met?
Thank you for your time,
Becky |
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| Please send the output and your license number to support@statmodel.com. |
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| Tom Bailey posted on Wednesday, April 09, 2014 - 6:16 pm
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Dear Dr Muthen
I was hoping you may be so kind as to advise on an issue I am having when I run a bi-directional path in one of my models. I get much larger standardised path estimates for both variables in this relationship, and much bigger standard errors to boot. It also no longer calculates an interpretable r squared for either variable.
I have run the model (below) with a variety of estimators and the results are similar, if you could advise me on what I should or shouldn't be doing in this context I would be very grateful
Regards
Tom
MODEL: SRJP BY NPARCJ1 NPARCJ2 NPARCJ3 ;
ADV BY A1 A4 A5 ;
SELE BY F1 F4 F5 ;
SRJP ON ADV SELE;
ELE ON SRJP ;
ANALYSIS: ESTIMATOR = WLSMV ;
OUTPUT: standardized; modindices; TECH4 ; |
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Do you mean
SRJP ON ADV SELE;
SELE ON SRJP ;
I believe you must have a covariate in sele ON srjp that is unique like you have in the other regression.
You might want to see what Ken Bollen says in his SEM book. |
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