Interdisciplinary Anthropology and Data Science Master’s Thesis: A Quick and Dirty Project Summary

This is a quick and dirty summary of my master’s practicum research project with Indicia Consulting over the summer of 2018. For anyone interested in more detail, here is a more detailed report, and here is the final report with Indicia. 

Background

My practicum was the sixth stage of a several year-long research project. The California Energy Commission commissioned this larger project to understand the potential relationship between individual energy consumption and technology usage. In stages one through five, we isolated certain clusters of behavior and attitudes around new technology adoption – which Indicia called cybersensitivity – and demonstrated that cybersensitivity tended to associate with a willingness to adopt energy-saving technology like smart meters.

This led to a key question: How can one identify cybersensivity among a broader population such as a community, county, or state? Answering this question was the main goal of my practicum project.

In the past stages of the research project, the team used ethnographic research to establish criteria for whether someone was a cybersensitive based on several hours of interviews and observations about their technology usage. These interviews and observations certainly helped the research team analyze behavioral and attitudinal patterns, determine what patterns were significant, and develop those into the concept of cybersensitivity, but they are too time- and resource-intensive to perform with an entire population. One generally does not have the ability to interview everyone in a community, county, or state. I sought to address this directly in my project.

Task	Timeline	Task Name	Research Technique	Description
Task 1	June 2015-Sept 2018	General Project Tasks	Administrative (N/A)	Developed project scope and timeline, adjusting as the project unfolds
Task 2	July 2015 – July 2016	Documenting and analyzing emerging attitudes, emotions, experiences, habits, and practices around technology adoption	Survey	Conducted survey research to observe patterns of attitudes and behaviors among cybersensitives/awares.
Task 3	Sept 2016 – Dec 2016	Identifying the attributes and characteristics and psychological drivers of cybersensitives	Interviews and Participant-Observation	Conducted in-depth interviews and observations coding for psych factor, energy consumption attitudes and behaviors, and technological device purchasing/usage.
Task 4*	Sept 2016 – July 2017	Assessing cybersensitives’ valence with technology	Statistical Analysis	Tested for statistically significant differences in demographics, behaviors, and beliefs/attitudes between cyber status groups
Task 5	Aug 2017 – Dec 2018	Developing critical insights for supporting residential engagement in energy efficient behaviors	Statistical Analysis	Analyzed utility data patterns of study participants, comparing it with the general population.
Task 6	March 2018 – Aug 2018	Recommending an alternative energy efficiency potential model	Decision Tree Modeling	Constructed decision tree models to classify an individual’s cyber status

Project Goal

The overall goal for the project was to produce a scalable method to assess whether someone exhibits cybersensitivity based on data measurable across an entire population. In doing this, the project also helped address the following research needs:

1. Created a method to further to scale across a larger population, assessing whether cybersensitives were more willing to adopt energy saving technologies across a community, county, or state
2. Provided the infrastructure to determine how much promoting energy-saving campaigns targeting cybersensitives specifically would reduce energy consumption in California
3. Helped the California Energy Commission determine the best means to reach cybersensitives for specific energy-saving campaigns

The Project

I used machine learning modeling to create a decision-making flow to isolate cybersensitives in a population. Random forests and decision trees produced the best models for Indicia’s needs: random forests in accuracy and robustness and decision trees in human decipherability. Through them, I created a programmable yet human-comprehensible framework to determine whether an individual is cybersensitive based on behaviors and other characteristics that an organization could be easily assess within a whole population. Thus, any energy organization could easily understand, replicate, and further develop the model since it was both easy for humans to read and encodable computationally. This way organizations could both use and refine it for their purposes.

Conclusion

This is a quick overview of my master’s practicum project. For more details on what modeling I did, how I did it, what results it produced, and how it fit within the wider needs of the multi-year research project, please see my full report.

I really appreciated the opportunity it posed to get my hands dirty integrating ethnography and data science to help address a real-world problem. This summary only scratches the surface of what Indicia did with the Californian Energy Commission to encourage sustainable energy usage societally. Hopefully, though, it will inspire you to integrate ethnography and data science to address whatever complex questions you face. It certainly did for me.

Thank you to Susan Mazur-Stommen and Haley Gilbert for your help in organizing and completing the project. I would like to thank my professorial committee at the University of Memphis – Dr. Keri Brondo, Dr. Ted Maclin, Dr. Deepak Venugopal, and Dr. Katherine Hicks – for their academic support as well.