|
1
|
Bollen KA, Fisher Z, Lilly A, Brehm C, Luo L, Martinez A, Ye A. Fifty years of structural equation modeling: A history of generalization, unification, and diffusion. SOCIAL SCIENCE RESEARCH 2022; 107:102769. [PMID: 36058611 PMCID: PMC10029695 DOI: 10.1016/j.ssresearch.2022.102769] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 06/09/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Kenneth A Bollen
- Carolina Population Center, Department of Sociology, Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, USA.
| | | | - Adam Lilly
- Carolina Population Center, Department of Sociology, University of North Carolina, Chapel Hill, USA
| | - Christopher Brehm
- Carolina Population Center, Department of Sociology, University of North Carolina, Chapel Hill, USA
| | - Lan Luo
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, USA
| | - Alejandro Martinez
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, USA
| | - Ai Ye
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, USA
| |
Collapse
|
|
2
|
Sanford BT, Ciarrochi J, Hofmann SG, Chin F, Gates KM, Hayes SC. Toward empirical process-based case conceptualization: An idionomic network examination of the process-based assessment tool. JOURNAL OF CONTEXTUAL BEHAVIORAL SCIENCE 2022. [DOI: 10.1016/j.jcbs.2022.05.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
|
3
|
Abstract
Sparse estimation through regularization is gaining popularity in psychological research. Such techniques penalize the complexity of the model and could perform variable/path selection in an automatic way, and thus are particularly useful in models that have small parameter-to-sample-size ratios. This paper gives a detailed tutorial of the R package regsem, which implements regularization for structural equation models. Example R code is also provided to highlight the key arguments of implementing regularized structural equation models in this package. The tutorial ends by discussing remedies of some known drawbacks of a popular type of regularization, computational methods supported by the package that can improve the selection result, and some other practical issues such as dealing with missing data and categorical variables.
Collapse
|