The videos and resources below, presented by Dr. Michael Young, Professor of Psychological Sciences and 2021-2022 DAIR Faculty Fellow, offer guidance in using Power BI effectively to answer key questions. Contact us for personalized training or further assistance.
I. Data Mastery (Video | PDF)
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- Definitions: Know what you are looking at
- Sensitivity to Sample Size: Small samples may require aggregation, be wary of percentages without knowing the sample size
- Focus on Long-Term Trends: Short-term changes are insufficient to support good inferences about the future
- Context: Data without context is not actionable
II. Classroom-level Data Part I (Video | PDF) & Part II (Video | PDF)
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- Obtaining class enrollment data for any semester
- Grade distributions, withdrawal/incompletes, DFW rates, and how student grades in one class relate to grades in another
- Visualizing class enrollment data across time and as a function of student characteristics (major, etc.)
III: Demographics (Video | PDF)
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- Student characteristics at any level – sex, race, ethnicity, first gen, county, state, and country of origin
- Headcounts, degrees, retention, and graduation numbers by major, race/ethnicity, first gen, etc., across semesters
IV: Dynamic Data Part I (Video | PDF) & Part II (Video | PDF)
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- Part I: Admissions data including admit rates, yield rates, examining data for the current date, and comparing programs by admissions metrics
- Part II: Daily enrollments including headcounts and SCH as of the current date, cumulative headcounts and SCH to reveal the dynamic of enrollment over long periods of time, and anticipating the upcoming term's enrollment