title: Non-linear structural equation models with correlated continuous and discrete data.
abstract: Structural equation models (SEMs) have been widely applied to examine interrelationships among latent and observed variables in social and psychological research. Motivated by the fact that correlated discrete variables are frequently encountered in practical applications, a non-linear SEM that accommodates covariates, and mixed continuous, ordered, and unordered categorical variables is proposed. Maximum likelihood methods for estimation and model comparison are discussed. One real-life data set about cardiovascular disease is used to illustrate the methodologies.
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. sylee@sta.cuhk.edu.hk
authors.forename: Sik-Yum
authors.initials: SY
authors.lastname: Lee
authors.forename: Xin-Yuan
authors.initials: XY
authors.lastname: Song
authors.forename: Jing-Heng
authors.initials: JH
authors.lastname: Cai
authors.forename: Wing-Yee
authors.initials: WY
authors.lastname: So
authors.forename: Ching-Wang
authors.initials: CW
authors.lastname: Ma
authors.forename: Chung-Ngor Juliana
authors.initials: CN
authors.lastname: Chan
mesh_terms: D000465:Algorithms; D000483:Alleles; D000704:Analysis of Variance; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000235:genetics; D003627:Data Interpretation, Statistical; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology / Q000235:genetics; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D005838:Genotype; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011584:Psychology / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012307:Risk Factors
chemical_list: D005819:Genetic Markers