

THETA parameterization and Allison's ... 

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Hello, It is fairly wellknown that true differences in residual variation across groups confounds crossgroup comparisons in logit and probit regression (e.g., Allison, 1999). This is because the residual variance of y* is fixed at 1 (probit) or pisquared/3 (logit) for identification purposes. Thus one needs to control in some way for true differences in residual variation when doing crossgroup comparisons, or all bets are off. Therefore, I assume that the THETA parameterization is precisely one way of doing this (although I do not think the connection has been made explicitly in the literature). Under PARAMETERIZATION=THETA the residual variances of the y*'s are fixed at 1.0 in one group and free in the other. Does this strategy effectively "control" for possible differences in residual variance and allow you to say that any significant differences in coefficients (e.g., tested with equality constraints or just manual ztests of the differences) are likely to be "true", as opposed to artifacts of differences in residual variation? (I know this would only currently deal with the probit case in Mplus). Thank you for any clarification/confirmation, Cam Allison, P.D. (1999). Comparing logit and probit coefficients across groups. Sociological Methods & Research 28(2), 186208. 


Yes, I think you are looking at this correctly. I saw a related question by you on SEMNET and was going to respond, but ran out of time. The residual variance can be thought of as "existing", but not always being separately identified but confounded with the other curve parameters (slope and intercept/threshold)  the larger the residual variance, the flatter the curve, i.e. the more attenuated the relationship is, which makes sense. Both the Theta and the Delta parameterizations accomodate the possibly different residual variances. A delta parameter is a function of lambda, psi and theta. So if lambda and psi are groupinvariant, delta can still be different due to groupvarying thetas. 


Dear Professor Muthen, Does this mean that allowing two groups to differ both on the slope parameter and on the residual variance makes the model underidentified? I guess this also applies to multigroup IRT? Best, Guillaume 


Yes on both. 


How (if at all) does this related to the discussion of "The Special Case of One Factor" in Muthen & Christoffersson (1981) on page 411? Aren't you and Allison talking about the same problem? Doesn't the MIMIC model implicitly constrain the residual variances (using the theta parameterization) to be equal across groups, making this model, at best, overly restrictive, and at worst, potentially misleading with respect to group differences in factor means? 


Q1. Yes, I think we are talking about the same thing. Q2. I think you mean a MIMIC where a covariate is a grouping variable. In that case, yes I think that there is a certain risk of a distortion. MIMIC can be a useful first step to see which groupings appear most important, followed by the more flexible multiplegroup approach. 

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