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. 2023 Feb;83(1):73-92.
doi: 10.1177/00131644211073121. Epub 2022 Jan 31.

Using Simulated Annealing to Investigate Sensitivity of SEM to External Model Misspecification

Charles L Fisk  1 , Jeffrey R Harring  1 , Zuchao Shen  2 , Walter Leite  2 , King Yiu Suen  3 , Katerina M Marcoulides  3
Affiliations

Using Simulated Annealing to Investigate Sensitivity of SEM to External Model Misspecification

Charles L Fisk et al. Educ Psychol Meas. 2023 Feb.

Abstract

Sensitivity analyses encompass a broad set of post-analytic techniques that are characterized as measuring the potential impact of any factor that has an effect on some output variables of a model. This research focuses on the utility of the simulated annealing algorithm to automatically identify path configurations and parameter values of omitted confounders in structural equation modeling (SEM). An empirical example based on a past published study is used to illustrate how strongly related an omitted variable must be to model variables for the conclusions of an analysis to change. The algorithm is outlined in detail and the results stemming from the sensitivity analysis are discussed.

Keywords: metaheuristic algorithms; optimization; sensitivity analysis; simulated annealing; structural equation modeling.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
A Schematic of the Simulated Annealing Algorithm.
Figure 2.
Figure 2.
A Flowchart Outlining the Steps and Decision Points Internal to the Simulated Annealing Algorithm.
Figure 3.
Figure 3.
Latent Variable Path Model From the Spear et al. (2018) Study. Note. Latent emotional support regressed on latent self-efficacy, instructional orientation and knowledge. ECSE = early childhood special educator; BA = Bachelor of Arts; RMSEA = root mean square error of approximation; CFI = comparative fit index; SRMR = standardized root mean square.
Figure 4.
Figure 4.
Phantom Variable Appended to Model Used in Spear et al. (2018). Note. ECSE = early childhood special educator.
Figure 5.
Figure 5.
3 Dimensional Bubble Plots of Combinations of Sensitivity Parameters (x, y) and the Paths in the Model That Would Be Directly Confounded (Bubbles). Note. ECSE = early childhood special educator.
See this image and copyright information in PMC

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