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A faster procedure for estimating CFA models applying Minimum Distance Estimators

Kreiberg, David; Marcoulides, Katerina; Olsson, Ulf H.
Journal article, Peer reviewed
Accepted version
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Kreiberg_et_al_2021.pdf (1.045Mb)
URI
https://hdl.handle.net/11250/2988639
Date
2020
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  • Scientific articles [1667]
Original version
Structural Equation Modeling: A Multidisciplinary Journal, 2021, 28 (5), 725-739   10.1080/10705511.2020.1835484
Abstract
This paper presents a numerically more efficient implementation of the quadratic form minimum distance (MD) estimator with a fixed weight matrix for confirmatory factor analysis (CFA) models. In structural equation modeling (SEM) computer software, such as EQS, lavaan, LISREL and Mplus, various MD estimators are available to the user. Standard procedures for implementing MD estimators involve a one-step approach applying non-linear optimization techniques. Our implementation differs from the standard approach by utilizing a two-step estimation procedure. In the first step, only a subset of the parameters are estimated using non-linear optimization. In the second step, the remaining parameters are obtained using numerically efficient linear least squares (LLS) methods. Through examples, it is demonstrated that the proposed implementation of MD estimators may be considerably faster than what the standard implementation offer. The proposed procedure will be of particular interest in computationally intensive applications such as simulation, bootstrapping, and other procedures involving re-sampling.
Publisher
Taylor and Francis
Journal
Structural Equation Modeling
Copyright
Taylor and Francis

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