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A comparison between logistic regression and neural networks in a constructed response item study
Journal of the Korean Data & Information Science Society 2019;30:1161-75
Published online September 30, 2019;  https://doi.org/10.7465/jkdi.2019.30.5.1161
© 2019 Korean Data and Information Science Society.

Minho Kwak1 · Chelwoo Park2

1Dankook University
2Department of Statistics, University of Georgia
Correspondence to: Research professor, Dankook University, 152 Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do, Korea. E-mail: 12191930@dankook.ac.kr

This article is based on the first author's master's thesis of the same title (University of Georgia, 2019).
Received August 6, 2019; Revised September 18, 2019; Accepted September 20, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The purpose of the study is to demonstrate the prediction quality of logistic regres- sion and artificial neural networks. The main results of the study are the comparisons of the accuracy of both methods. The response variable of the model is a comment assignment by a human rater, and the four predictors are topic proportions estimated from latent Dirichlet allocation. The constructed models for both analyses are mainly concerned with predicting the comment assignment by using the topic proportions as the predictors. The results show that the accuracy of the test data set is generally higher than the accuracy of the cross-validation quality of the logistic regression, and these results are well matched with previous empirical studies. Also, although the use of this accuracy for practical purposes remains still questionable, the results reveal the potential utility the neural network if larger sample size is available in the future.
Keywords : Artificial neural networks, latent Dirichlet allocation, logistic regression.