The Ensemble and Model Comparison Approaches for Big Data Analytics in Social Sciences

Authors

C. H. Yu
H. S. Lee
E. Lara
S. Gan

DOI

https://doi.org/10.7275/hq8a-f262

Abstract

Big data analytics are prevalent in fields like business, engineering, public health, and the physical sciences, but social scientists are slower than their peers in other fields in adopting this new methodology. One major reason for this is that traditional statistical procedures are typically not suitable for the analysis of large and complex data sets. Although data mining techniques could alleviate this problem, it is often unclear to social science researchers which option is the most suitable one to a particular research problem. The main objective of this paper is to illustrate how the model comparison of two popular ensemble methods, namely, boosting and bagging, could yield an improved explanatory model. Accessed 993 times on https://pareonline.net from November 21, 2018 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.

Comments

https://doi.org/10.7275/hq8a-f262

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Recommended Citation

Yu, C. H.; Lee, H. S.; Lara, E.; and Gan, S. (2019) "The Ensemble and Model Comparison Approaches for Big Data Analytics in Social Sciences," Practical Assessment, Research, and Evaluation: Vol. 23, Article 17.
DOI: https://doi.org/10.7275/hq8a-f262
Available at: https://scholarworks.umass.edu/pare/vol23/iss1/17