A practical guide to variable selection in structural equation modeling by using regularized multiple-indicators, multiple-causes models
R Jacobucci, AM Brandmaier… - Advances in methods …, 2019 - journals.sagepub.com
Methodological innovations have allowed researchers to consider increasingly
sophisticated statistical models that are better in line with the complexities of real-world …
sophisticated statistical models that are better in line with the complexities of real-world …
Modeling data with measurement errors but without predefined metrics: Fact versus fallacy
Data in social and behavioral sciences typically contain measurement errors and also do not
have predefined metrics. Structural equation modeling (SEM) is commonly used to analyze …
have predefined metrics. Structural equation modeling (SEM) is commonly used to analyze …
Bias and efficiency in structural equation modeling: Maximum likelihood versus robust methods
X Zhong, KH Yuan - Multivariate Behavioral Research, 2011 - Taylor & Francis
In the structural equation modeling literature, the normal-distribution-based maximum
likelihood (ML) method is most widely used, partly because the resulting estimator is …
likelihood (ML) method is most widely used, partly because the resulting estimator is …
Limited information parameter estimates for latent or mixed manifest and latent variable models
CE Lance, JM Cornwell, SA Mulaik - Multivariate Behavioral …, 1988 - Taylor & Francis
We argue for separate analyses of the measurement and structural portions of latent or
mixed manifest and latent variable models. We present limited information (single equation) …
mixed manifest and latent variable models. We present limited information (single equation) …
Small sample methods for multilevel modeling: A colloquial elucidation of REML and the Kenward-Roger correction
D McNeish - Multivariate behavioral research, 2017 - Taylor & Francis
Studies on small sample properties of multilevel models have become increasingly
prominent in the methodological literature in response to the frequency with which small …
prominent in the methodological literature in response to the frequency with which small …
Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial
YA Wang, M Rhemtulla - Advances in Methods and Practices …, 2021 - journals.sagepub.com
Despite the widespread and rising popularity of structural equation modeling (SEM) in
psychology, there is still much confusion surrounding how to choose an appropriate sample …
psychology, there is still much confusion surrounding how to choose an appropriate sample …
On the interpretation of parameters in multivariate multilevel models across different combinations of model specification and estimation
L Hoffman - Advances in Methods and Practices in …, 2019 - journals.sagepub.com
The increasing availability of software with which to estimate multivariate multilevel models
(also called multilevel structural equation models) makes it easier than ever before to …
(also called multilevel structural equation models) makes it easier than ever before to …
Unconstrained structural equation models of latent interactions: Contrasting residual-and mean-centered approaches
Little, Bovaird and Widaman (2006) proposed an unconstrained approach with residual
centering for estimating latent interaction effects as an alternative to the mean-centered …
centering for estimating latent interaction effects as an alternative to the mean-centered …
Estimation of MIMIC model parameters with multilevel data
WH Finch, BF French - Structural Equation Modeling, 2011 - Taylor & Francis
The purpose of this simulation study was to assess the performance of latent variable
models that take into account the complex sampling mechanism that often underlies data …
models that take into account the complex sampling mechanism that often underlies data …
Comparison of frequentist and Bayesian regularization in structural equation modeling
R Jacobucci, KJ Grimm - Structural Equation Modeling: A …, 2018 - Taylor & Francis
Research in regularization, as applied to structural equation modeling (SEM), remains in its
infancy. Specifically, very little work has compared regularization approaches across both …
infancy. Specifically, very little work has compared regularization approaches across both …