Logistic Discriminant Analysis and Structural Equation Modeling Both Identify Effects in Random Data
Document Type
Article
Publication Date
5-16-2019
Publication Title
Optimal Data Analysis
Volume
8
Pages
97-102
Publisher Name
Optimal Data Analysis LLC
Abstract
Recent research compared the ability of various classification algorithms [logistic regression (LR), random forests (RF), support vector machines (SVM), boosted regression (BR), multi-layer perceptron neural net model (MLP), and classification tree analysis (CTA)] to correctly fail to identify a relationship between a binary class (dependent) variable and ten randomly generated attributes (covariates): only CTA failed to find a model. We use the same ten-variable N=1,000 dataset to assess training classification accuracy of models developed by logistic discriminant analysis (LDA), generalized structural equation modelling (GSEM), and robust diagonally-weighted least-squares (DWLS) SEM for binary outcomes. Except for CTA, all machine-learning algorithms assessed thus far have identified training effects in random data.
Identifier
2155-0182
Recommended Citation
Linden, Ariel; Bryant, Fred B.; and Yarnold, Paul R.. Logistic Discriminant Analysis and Structural Equation Modeling Both Identify Effects in Random Data. Optimal Data Analysis, 8, : 97-102, 2019. Retrieved from Loyola eCommons, Psychology: Faculty Publications and Other Works,
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Copyright Statement
© Optimal Data Analysis LLC, 2019.
Comments
Author Posting © Optimal Data Analysis LLC, 2019. This article is posted here by permission of Optimal Data Analysis LLC for personal use, not for redistribution. The article was published in Optimal Data Analysis, Volume 8, May 2019, https://odajournal.files.wordpress.com/2019/05/v8a21.pdf