Using Principal Components Analysis (PCA) to Analyze Latino Stress by Agricultural Season and Occupation

by Apr 18, 2020Machine Learning, R Programming0 comments

Principal Components Analysis (PCA) is a commonly used unsupervised machine learning technique. In this presentation, I describe the PCA method with a general description and geometric interpretation using simulated two and three-dimensional data. The description of the PCA methodology is followed by an application of PCA to analyze Latino stress by agricultural season and occupation in a majority-minority agricultural area of eastern Washington State. The results show that Latino farmworkers are generally more stressed than non-farmworker Latinos in the Thinning season. Post-hoc analyses indicate that increased stress among the farmworkers is caused by how hard they have to work, distance from family, lack of childcare options, inability to speak English, and lack of communication with youth in the community.

To download all of the files used to create this presentation, you can clone the associated Git repo git clone https://git.waderstats.com/pca_with_application/.

This presentation should optimally be viewed in full screen.

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Created by Wade K. Copeland | Privacy Policy | This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.