A Semiparametric Approach for Structural Equation Modeling with Ordinal Data
Bibliographic Data
| ID | 21641683 |
|---|---|
| Authors | Johan Vegelius (0000-0002-0497-0659, Uppsala University, corresponding author), Shaobo Jin (0000-0001-6538-3477, Uppsala University) |
| Year | 2021 |
| Volume | 28 |
| Issue | 4 |
| Pages | 497-505 |
| Publication date | 2021-07-04 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2020.1848431 |
| OpenAlex | W3133187976 |
| Language | EN |
| References cited | 32 |
There is currently a lack of methods for non-linear structural equation modeling (NSEM) for non-parametric relationships between latent variables when data are ordinal. To this end, a semiparametric approach for flexible NSEMs without parametric forms is developed for ordinal data. An indirect application of a finite mixture of structural equation models (SEMM) is employed for modeling the conditional expected mean of endogenous latent variables. In this context, the latent classes are not to be interpreted as groups of observations belonging to those classes, rather they serve as means to model flexible non-linear functions as locally linear functions which together approximate a globally non-linear function. The proposed method is based on a hybrid of direct maximization and expectation-maximization algorithms. Two simulation studies are performed which show that parameter estimates are associated with low bias and a non-linear functional form is satisfactorily estimated using the proposed approach
Econometrics · Expectation–maximization algorithm · Generalized linear model · Latent variable · Latent variable model · Linear model · Mathematical optimization · Maximum likelihood · Nonparametric statistics · Ordinal data · Ordinal regression · Parametric model · Parametric statistics · Semiparametric model · Semiparametric regression · Statistics · Structural equation modeling · Advanced Statistical Modeling Techniques · Bayesian Modeling and Causal Inference · Mathematics · Technology and Data Analysis · Applied Mathematics
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Maximum Likelihood from Incomplete Data Via the EM Algorithm
The theory of planned behavior
An Empirical Evaluation of Alternative Methods of Estimation for Confirmatory Factor Analysis With Ordinal Data.
A Marginal Maximum Likelihood Approach for Extended Quadratic Structural Equation Modeling with Ordinal Data
Quasi-Maximum Likelihood Estimation of Structural Equation Models With Multiple Interaction and Quadratic Effects
Structural Equation Models That are Nonlinear in Latent Variables
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Estimating the nonlinear and interactive effects of latent variables
Measurement error in the analysis of interaction effects between continuous predictors using multiple regression
| Highly cited | No |
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