Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 3 |
Descriptor
Author
Bengt Muthén | 1 |
DiStefano, Christine | 1 |
Gonzales, Joseph E. | 1 |
Jiang, Zhehan | 1 |
Liu, Ren | 1 |
Shi, Dexin | 1 |
Tihomir Asparouhov | 1 |
Zheng, Xiaying | 1 |
Publication Type
Journal Articles | 3 |
Reports - Descriptive | 1 |
Reports - Evaluative | 1 |
Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Tihomir Asparouhov; Bengt Muthén – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Penalized structural equation models (PSEM) is a new powerful estimation technique that can be used to tackle a variety of difficult structural estimation problems that can not be handled with previously developed methods. In this paper we describe the PSEM framework and illustrate the quality of the method with simulation studies.…
Descriptors: Structural Equation Models, Computation, Factor Analysis, Measurement Techniques
Gonzales, Joseph E. – Measurement: Interdisciplinary Research and Perspectives, 2021
JMP® Pro has introduced a new structural equation modeling (SEM) platform to its suite of multivariate methods of analysis. Utilizing their graphical user interface, JMP Pro has created a SEM platform that is easily navigable for both experienced and novice SEM users. As a new platform, JMP Pro does not have the capacity to implement certain…
Descriptors: Structural Equation Models, Multivariate Analysis, Usability, Factor Analysis
Shi, Dexin; DiStefano, Christine; Zheng, Xiaying; Liu, Ren; Jiang, Zhehan – International Journal of Behavioral Development, 2021
This study investigates the performance of robust maximum likelihood (ML) estimators when fitting and evaluating small sample latent growth models with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., N < 100). Among the robust ML…
Descriptors: Growth Models, Maximum Likelihood Statistics, Factor Analysis, Sample Size