Matrix Decomposition Approach for Structural Equation Modeling as an Alternative to Covariance Structure Analysis and Its Theoretical Properties
Bibliographic Data
| ID | 21641778 |
|---|---|
| Authors | Naoto Yamashita (0000-0002-8819-4262, Kansai University, Suita, Osaka, Japan, corresponding author) |
| Year | 2024 |
| Volume | 31 |
| Issue | 5 |
| Pages | 817-834 |
| Publication date | 2024-09-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| 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 | 10.1080/10705511.2024.2342381 |
| OpenAlex | W4399203427 |
| Language | EN |
| Citations received | 1 |
| References cited | 43 |
Matrix decomposition structural equation modeling (MDSEM) is introduced as a novel approach in structural equation modeling, contrasting with traditional structural equation modeling (SEM). MDSEM approximates the data matrix using a model generated by the hypothetical model and addresses limitations faced by conventional SEM procedures by emphasizing factor analysis with L2 penalization. Key advantages of MDSEM include preventing improper solutions, the ability to compute observation-wise residuals without post-hoc factor score estimation and ease in identifying equivalent models. These benefits are attributed to its matrix decomposition techniques, allowing for direct model fitting to the data matrix, unlike the covariance structure fitting in CS-SEM. An iterative algorithm for parameter estimation is proposed, guaranteeing a monotonically decreasing function value. Theoretical properties of MDSEM are examined, revealing its shared characteristics with existing factor analysis and SEM. Numerical simulations and real data examples validate that MDSEM produces results comparable to existing methods when adequately calibrated
Covariance · Covariance matrix · Decomposition · Econometrics · Mathematical optimization · Physics · Statistical physics · Statistics · Structural equation modeling · Advanced Statistical Modeling Techniques · Chemistry · Computer Science · Materials Science · Mathematics · Mental Health Research Topics · Psychometric Methodologies and Testing · Applied Mathematics
Modern factor analysis
Structural Equation Modeling
Latent Variable Path Modeling with Partial Least Squares
Use of structural equation modeling in operations management research
PLS path modeling
Consistent Partial Least Squares Path Modeling1
Confirmatory Factor Analyses of Multitrait-Multimethod Data
Applications of Structural Equation Modeling in Psychological Research
The Effect of Sampling Error on Convergence, Improper Solutions, and Goodness-of-Fit Indices for Maximum Likelihood Confirmatory Factor Analysis
Multivariate Analysis with Latent Variables
Structured Factor Analysis
The Effects of Sampling Error and Model Characteristics on Parameter Estimation for Maximum Likelihood Confirmatory Factor Analysis
Metaphor Taken as Math
An Overview of Analytic Rotation in Exploratory Factor Analysis
Changing a Causal Hypothesis without Changing the Fit
Coming Full Circle in the History of Factor Indeterminancy
A Simple Rule for Generating Equivalent Models in Covariance Structure Modeling
Foundations of Factor Analysis
An empirical application of confirmatory factor analysis to the multitrait-multimethod matrix
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |