Using Bounded Estimation to Avoid Nonconvergence in Small Sample Structural Equation Modeling

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:

• Small samples
• estimation
• nonconvergence

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ The code that was used for these two studies can be found using the following link: https://osf.io/f7z6j/