Using Structural Equation Modeling to Examine the Influence of Social, Behavioral, and Nutritional Variables on Health Outcomes Based on NHANES Data: Addressing Complex Design, Nonnormally Distributed Variables, and Missing Information
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Keywords:
Structural equation modeling
multiple imputation
complex survey design
quasi-maximum likelihood
NHANES
Abbreviations used:
CFI
comparative fit index
EM
expectation maximization algorithm
FIML
full information maximum likelihood
MAR
missing at random
MCAR
missing completely at random
MI
multiple imputation
ML
maximum likelihood
NMAR
not missing at random
PHQ
Patient Health Questionnaire
NHANES
(depression screener questionnaire variable prefix: DPQ)
PSU
primary sampling unit
QML
quasimaximum likelihood with Satorra-Bentler correction
RMSEA
root mean square error of approximation
SEM
structural equation modeling
SRMR
standardized root mean square residual
TLI
Tucker-Lewis Index.
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Research reported in this paper was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number P20GM109097. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Author disclosures: the authors declare no conflicts of interest.
Copyright © 2019 American Society for Nutrition. Published by Elsevier Inc.