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Generalizing sem in Stata

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  • Jeff Pitblado

    (StataCorp LP)

Abstract

Introducing generalized SEM: (1) SEM with generalized linear response variables, and (2) SEM with multilevel mixed effects, whether linear or generalized linear. Generalized linear response variables mean you can now fit probit, logit, Poisson, multinomial logistic, ordered logit, ordered probit, and other models. They also mean measurements can be continuous, binary, count, categorical, and ordered. Multilevel mixed effects mean you can place latent variables at different levels of the data. You can fit models with fixed or random intercepts and fixed or random slopes. I will present examples using both command syntax and the SEM Builder.

Suggested Citation

  • Jeff Pitblado, 2013. "Generalizing sem in Stata," 2013 Stata Conference 22, Stata Users Group.
    • Handle: RePEc:boc:norl13:22
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    File URL: http://repec.org/norl13/pitblado.pdf
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    Cited by:

    1. Kalahasthi, Lokesh Kumar & Sánchez-Díaz, Iván & Pablo Castrellon, Juan & Gil, Jorge & Browne, Michael & Hayes, Simon & Sentís Ros, Carles, 2022. "Joint modeling of arrivals and parking durations for freight loading zones: Potential applications to improving urban logistics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 307-329.

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