
Message/Author 

Hans Berten posted on Thursday, September 07, 2006  8:23 am



Dear dr. Muthén, My question is the following: is it possible to handle a nonrecursive path model in MPlus 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 nonrecursive 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! 


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 categoricalvariable 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 categoricalvariable 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 nonrecursive, 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. 117118 (Model 1), leading back to the recursive case. Bengt Muthen 


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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Tom Bailey posted on Wednesday, April 09, 2014  6:16 pm



Dear Dr Muthen I was hoping you may be so kind as to advise on an issue I am having when I run a bidirectional 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 ; 


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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