Resampling Based Bias Correction for Small Sample SEM

Abstract

Structural Equation Models (SEMs) are typically estimated via Maximum Likelihood.Grounded in large sample theory, estimates are prone to finite sample bias. Although Restricted Maximum Likelihood (REML) can alleviate this bias, its applicability is constrained to SEMs that are mathematically equivalent to mixed e ect models. Via Monte Carlo simulations, we explored whether resampling based corrections could serve as viable, more broadly applicable alternatives. Results indicate that Bootstrap and Jackknife corrections e ectively attenuate small sample bias, at the expected expense of an increase in variability. Similar conclusions are drawn with respect to a more recently proposed analytic approach by Ozenne, Fisher, and Budtz-Jørgensen (2020), which was included for comparison. For all corrective methods, caution is advised when dealing with non-normal data and/or low reliability.

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“…𝑒𝑓𝑓(𝛽 ̂𝐽𝐾 , 𝛽 ̂𝐷𝐽𝐾 ) = 𝑉(𝛽 ̂𝐷𝐽𝐾 ) 𝑉(𝛽 ̂𝐽𝐾 ) (17) Description: 𝑒𝑓𝑓(𝛽 ̂𝐽𝐾 , 𝛽 ̂𝐷𝐽𝐾 ) = Efficiency between Jackknife and Double jackknife resampling method estimator 𝑉(𝛽 ̂𝐽𝐾 )…”