[gov3009-l] 11/8 Applied Stats: Kenneth Bollen

Dana Higgins danahiggins at fas.harvard.edu
Mon Nov 6 16:13:51 EST 2017


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Kenneth
Bollen*, Professor of Psychology and Neuroscience at the University of
North Carolina at Chapel Hill. He will be presenting work entitled *Model
Implied Instrumental Variables*.  Please find the abstract below and on the
Applied Stats website here
<https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>.



As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be
provided.  See you all there!

-- Dana Higgins




*Title:* *Model Implied Instrumental Variables: An Alternative Orientation
to Structural Equation Models*


*Abstract:*  It is hardly controversial to say that our models are
approximations to reality. Yet when it comes to estimating structural
equation models (SEMs), we use estimators that assume true models (e.g.,
ML) and that can spread bias through estimated parameters when the model is
approximate. This talk presents the Model Implied Instrumental Variable
(MIIV) approach to SEMs originally proposed in Bollen (1996). The MIIV
estimator using Two Stage Least Squares (2SLS) or MIIV-2SLS has greater
robustness to structural misspecifications and the conditions for
robustness are better understood than other estimators. In addition, the
MIIV-2SLS estimator is asymptotically distribution free. Furthermore,
MIIV-2SLS has equation based overidentification tests that can help
pinpoint errors in specification. Beyond these features, the MIIV approach
has other desirable qualities (e.g., a new test of dimensionality). MIIV
methods apply to higher order factor analyses, categorical measures, growth
curve models, dynamic factor analysis, and nonlinear latent variables.
Finally, it permits researchers to estimate and test only the
latent variable model or any other subset of equations. Despite these
promising features, research is needed to better understand its performance
under a variety of conditions that represent real world empirical examples.
In addition, other MIIV estimators beyond 2SLS are available. This
presentation will provide an overview of this new orientation to SEMs and
illustrate MIIVsem, an R package that implements it.
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