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Using bounded estimation to avoid nonconvergence in small sample structural equation modeling

Julie De Jonckere (UGent) and Yves Rosseel (UGent)
Author
Organization
Abstract
The most frustrating outcome of an SEM analysis is nonconvergence. Nonconvergence typically happens when the sample size is small (N < 100) or very small (N < 50). To minimize the frequency of nonconvergence, this paper proposes a solution called bounded estimation. The idea is to use data-driven lower and upper bounds for a subset of the model parameters during estimation. In this paper, we provide a rationale to compute these bounds, and we study the effect of different approaches to employ these bounds on the frequency of nonconvergence. A simulation study shows that bounded estimation dramatically decreases the frequency of nonconvergence in both correctly and misspecified models, without any (negative) effects on the quality of the point estimates for the unbounded parameters.
Keywords
General Economics, Econometrics and Finance, Sociology and Political Science, Modeling and Simulation, General Decision Sciences, Small samples, estimation, nonconvergence, IMPROPER SOLUTIONS, CONSEQUENCES, COMMUNALITY, STATISTICS

Citation

Please use this url to cite or link to this publication:

MLA
De Jonckere, Julie, and Yves Rosseel. “Using Bounded Estimation to Avoid Nonconvergence in Small Sample Structural Equation Modeling.” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 2022, pp. 1–16, doi:10.1080/10705511.2021.1982716.
APA
De Jonckere, J., & Rosseel, Y. (2022). Using bounded estimation to avoid nonconvergence in small sample structural equation modeling. STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 1–16. https://doi.org/10.1080/10705511.2021.1982716
Chicago author-date
De Jonckere, Julie, and Yves Rosseel. 2022. “Using Bounded Estimation to Avoid Nonconvergence in Small Sample Structural Equation Modeling.” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 1–16. https://doi.org/10.1080/10705511.2021.1982716.
Chicago author-date (all authors)
De Jonckere, Julie, and Yves Rosseel. 2022. “Using Bounded Estimation to Avoid Nonconvergence in Small Sample Structural Equation Modeling.” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL: 1–16. doi:10.1080/10705511.2021.1982716.
Vancouver
1.
De Jonckere J, Rosseel Y. Using bounded estimation to avoid nonconvergence in small sample structural equation modeling. STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL. 2022;1–16.
IEEE
[1]
J. De Jonckere and Y. Rosseel, “Using bounded estimation to avoid nonconvergence in small sample structural equation modeling,” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, pp. 1–16, 2022.
@article{8739770,
  abstract     = {{The most frustrating outcome of an SEM analysis is nonconvergence. Nonconvergence typically happens when the sample size is small (N < 100) or very small (N < 50). To minimize the frequency of nonconvergence, this paper proposes a solution called bounded estimation. The idea is to use data-driven lower and upper bounds for a subset of the model parameters during estimation. In this paper, we provide a rationale to compute these bounds, and we study the effect of different approaches to employ these bounds on the frequency of nonconvergence. A simulation study shows that bounded estimation dramatically decreases the frequency of nonconvergence in both correctly and misspecified models, without any (negative) effects on the quality of the point estimates for the unbounded parameters.}},
  author       = {{De Jonckere, Julie and Rosseel, Yves}},
  issn         = {{1070-5511}},
  journal      = {{STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL}},
  keywords     = {{General Economics,Econometrics and Finance,Sociology and Political Science,Modeling and Simulation,General Decision Sciences,Small samples,estimation,nonconvergence,IMPROPER SOLUTIONS,CONSEQUENCES,COMMUNALITY,STATISTICS}},
  language     = {{eng}},
  pages        = {{1--16}},
  title        = {{Using bounded estimation to avoid nonconvergence in small sample structural equation modeling}},
  url          = {{http://dx.doi.org/10.1080/10705511.2021.1982716}},
  year         = {{2022}},
}

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