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qualitative analysis, data analysis, programming, social science


Key Points:

  • Assess the relationship between computers, data, and society from equity and social justice perspectives.

  • Analyze and present findings on a dataset of a student’s choice.


Detailed Description:

Technology and data influence how society operates and innovates. Individuals who work closely with data hold important power over how their findings impact the day-to-day experiences of individuals and communities. FIRE Computing and Society gives computing majors the opportunity to assess how existing datasets have been analyzed by others, analyze a dataset of their choice, and share their research process and findings with local and national communities. 


Key Points:

  • Computing research can have negative and positive consequences on society.

  • Researchers must be able to see how computing research is also social science research.


Detailed Description:

Developers and researchers who collect and analyze data do not always understand the impact that their findings and research can have on people and communities. For example, multiple scholars of color have repeatedly shown how data has, intentionally or unintentionally, exacerbated racial, gender, and socioeconomic injustice in society. By becoming a community of critically conscious researchers, we can dismantle inequities that arise in the research process by informing the design of future computing research efforts.


Key Points:

  • The entire research process and how to analyze existing research.

  • Social science and data analysis research methodologies.

  • Statistical analyses.

  • Programming in R.

  • The formal publication process and venues of publication.


Detailed Description:

This stream is an opportunity for students to understand the research process from start to finish: 1) design a research question that they are passionate about, 2) analyze a dataset to answer their research question using practices from both social science and data science fields, and 3) write and publish their findings to share with the broader research community. All students will learn how to use R to clean and analyze datasets. They will learn how to perform and interpret multiple regression and factor analyses. If interested, students can also learn how to use NVivo, a qualitative analysis software, to analyze interview data.

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