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Coursera

Inclusive Analytic Techniques

University of Toronto via Coursera

Overview

Many policies, products, services or processes that we think of as gender-neutral actually have gendered outcomes. Everything from snow plowing to car safety to investment advising to infrastructure investment has impacts that differ by gender. These outcomes can be even more biased if we look at important intersections with race, indigeneity, differences in ability, ethnicity, sexual orientation, and other identities. The question is, what can you do to change this? And, how can you avoid the risks of bias or create innovative new offerings using gender-based insights?

Inclusive Analytics Techniques will provide you with the tools and analytical techniques to uncover these intersectional insights. The course covers both quantitative and qualitative data collection and analysis, including basic statistical techniques and practical instructions for working with customers, beneficiaries and other stakeholders. You will learn to incorporate multiple sources of rich evidence in order to develop innovative insights into how policies, products, services and processes can be made more equitable or serve unique communities.

This is the second course of the Gender Analytics Specialization offered by the Institute for Gender and the Economy (GATE) at the University of Toronto's Rotman School of Management. It's great on its own, and you will get even more out of it if you take it as part of the Specialization.

Syllabus

  • Ethical and legal considerations in inclusive data collection
    • When collecting and analyzing data from diverse communities, it is important to recognize that this can create vulnerabilities for marginalized individuals and groups. In this module, you will learn about the legal frameworks and ethical requirements related to collecting, storing, analyzing, and disseminating data, paying attention to different country contexts. By the end of the week, you will understand potential risks to research participants and find ways to mitigate such risks and appropriately compensate them for their time and efforts in the data collection and design process. These considerations are important to take into account before you move forward with any data collection and analysis projects.
  • Quantitative data analysis through a gender lens: probability
    • This session will review basic principles of quantitative data analysis, including probability and hypothesis testing, through fun examples and exercises. By the end of the week, you will be able to conduct basic calculations to analyze quantitative data and develop the intuition behind statistical inference and hypothesis testing to understand analytical reports generated by others.
  • Quantitative data analysis through a gender lens: data and interpretation
    • This week, we will shed light on how data is produced and how to uncover gender-based insights from data. By the end of the week, you will understand the data generation process, know where to locate sources of gender-disaggregated data, and analyze relationships to interpret results. You will see how emerging insights from gender-disaggregated data analysis can shape the evolution of the problem statement and identify areas for further data collection.
  • Qualitative data collection: community-based engagement with stakeholders
    • A big part of applying a gender lens to data analysis is obtaining different perspectives, especially from underrepresented groups. One way to do this is through qualitative research in the communities of interest. This week, you will explore the art of meaningful community engagement. By the end of this week, you will have a better understanding of the concept and value of community engagement as a qualitative data source. You will learn the steps to collect and analyze qualitative data to gain insight into people’s emotions, motivations, aspirations, and pain points. You will also learn how engage responsibly with vulnerable or marginalized communities.

Taught by

Sarah Kaplan, Brian Silverman, Chanel Grenaway and Karen Sihra, PhD

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