Papers by Polina Dimitruk
Should all nonlinear effects in structural equation models be always analyzed simultaneously?
Methodology, 2007
Challenges in evaluating nonlinear effects in multiple regression analyses include reliability, v... more Challenges in evaluating nonlinear effects in multiple regression analyses include reliability, validity, multicollinearity, and dichotomization of continuous variables. While reliability and validity issues are solved by employing nonlinear structural equation modeling, multicollinearity remains a problem which may even be aggravated when using latent variable approaches. Further challenges of nonlinear latent analyses comprise the distribution of latent product terms, a problem especially relevant for approaches using maximum likelihood estimation methods based on multivariate normally distributed variables, and unbiased estimates of nonlinear effects under multicollinearity. The only methods that explicitly take the nonnormality of nonlinear latent models into account are latent moderated structural equations (LMS) and quasi-maximum likelihood (QML). In a small simulation study both methods yielded unbiased parameter estimates and correct estimates of standard errors for inferential statistics. The advantages and limitations of nonlinear structural equation modeling are discussed.
Analyse von nichtlinearen Effekten in Strukturgleichungsmodellen: Methodische Probleme und Lösungsansätze
... Analyse von nichtlinearen Effekten in Strukturgleichungsmodellen : Methodische Probleme und L... more ... Analyse von nichtlinearen Effekten in Strukturgleichungsmodellen : Methodische Probleme und Lösungsansätze. Polina Dimitruk. ... Beide Methoden wiesen unter den realisierten Bedingungen bezüglich der Güte der Parameterschätzungen keine bedeutsamen Unterschiede auf. ...
Multicollinearity and Missing Constraints: A Comparison of Three Approaches for the Analysis of Latent Nonlinear Effects
Methodology European Journal of Research Methods For the Behavioral and Social Sciences, 2008
Multicollinearity complicates the simultaneous estimation of interaction and quadratic effects in... more Multicollinearity complicates the simultaneous estimation of interaction and quadratic effects in structural equation modeling (SEM). So far, approaches developed within the Kenny-Judd (1984 ) tradition have failed to specify additional and necessary constraints on the measurement error covariances of the nonlinear indicators. Given that the constraints comprise, in part, latent linear predictor correlations, multicollinearity poses a problem for such approaches. Klein and Moosbrugger’s (2000 ) latent moderated structural equations approach (LMS) approach does not utilize nonlinear indicators and should therefore not be affected by this problem. In the context of a simulation study, we varied predictor correlation and the number of nonlinear effects in order to compare the performance of three approaches developed for the estimation of simultaneous nonlinear effects: Ping’s (1996 ) two-step approach, a correctly extended Jöreskog-Yang (1996 ) approach, and LMS. Results show that in contrast to the Jöreskog-Yang approach and LMS, the two-step approach produces biased parameter estimates and false inferences under heightened multicollinearity. Ping’s approach resulted in overestimated interaction effects and underestimated quadratic effects.
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Papers by Polina Dimitruk