A Practical Guide to Variable Selection in Structural Equation Models with Regularized MIMIC Models.

ABSTRACT: Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real world behavioral data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This poses a difficult modeling scenario that entails large models with a comparably limited number of observations given the number of parameters. We here describe a particular strategy to overcoming this challenge, called regularization. Regularization, a method to penalize model complexity during estimation, has proven a viable option for estimating parameters in this small n, large p setting, but has so far mostly been used in linear regression

SUBMITTER: Jacobucci R 

PROVIDER: S-EPMC6713564 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature