Assessing the predictive performance of structural equation model estimators

Joerg Evermann and Mary Tate

Journal of Business Research, 2016, vol. 69, issue 10, 4565-4582

Abstract: Structural equation models are traditionally used for theory testing. With the increasing importance of predictive analytics, and the ability of structural equation models to maintain theoretical plausibility in the context of predictive modeling, identifying how best to predict from structural equation models is important. Recent calls for a refocusing of partial least squares path modeling (PLSPM) on predictive applications further increase the need to assess and compare the predictive power of different estimation methods for structural equation models. This paper presents two simulation studies that evaluate the performance of different modes and variations of PLSPM and covariance analysis on prediction from structural equation models. Study 1 examines all-reflective models using blindfolding and the Q2 statistic. Study 2 examines mixed formative-reflective models using out-of-sample cross-validation and the RMSE statistic. Recommendations to guide researchers in the choice of appropriate prediction method are offered.

Keywords: Prediction; Structural equation models; Partial least squares; Simulation study (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (42)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S014829631630128X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:69:y:2016:i:10:p:4565-4582

DOI: 10.1016/j.jbusres.2016.03.050

Access Statistics for this article

Journal of Business Research is currently edited by A. G. Woodside

More articles in Journal of Business Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().