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Cloudy with a Chance of Enrollment: The effects of weather on student enrollment behavior

Kate Ralston Darin Wohlgemuth Daryl Herzmann Corey Hagruth Iowa State University November 12, 2013 AACRAO - SEM Chicago, IL. Cloudy with a Chance of Enrollment: The effects of weather on student enrollment behavior. What is Weather?.

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Cloudy with a Chance of Enrollment: The effects of weather on student enrollment behavior

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  1. Kate Ralston Darin Wohlgemuth Daryl Herzmann Corey Hagruth Iowa State University November 12, 2013 AACRAO - SEM Chicago, IL Cloudy with a Chance of Enrollment: The effects of weather on student enrollment behavior

  2. What is Weather? The state of the atmosphere at a  given moment in time at a given location.

  3. What is Weather? Measures include the amount of • sunshine • clouds • rainfall • humidity • wind • temperature fluctuating on a daily basis.

  4. Weather vs. Climate Not the same as climate may include a combination of the same measures considers them in 30-year increments Climate Normals

  5. Why do we care?  Weather affects an array of human behaviors and decisions, often – without us knowing.

  6. Effects of weather on behavior • Hirshleifer, D. and Shumway, T. (2003): Sunny days  higher stock returns • Rind, B. (1996): Cloudy skies  less tips to servers • Anderson, C. (1989): Hotter weather  higher levels of aggression

  7. Weather in Higher Ed? • Baxter (2009): Rainy days  Less med school applicants accepted • Simonsohn (2007, 2009): Cloudy skies  Academic rigor of applicants matters more than extra-curricular achievements to admissions and applicants

  8. Then, the question is… Does weather during campus visit affect a student’s probability of enrollment?

  9. Other questions to consider? Does any particular weather element matter more? • What about element combinations? • Seasons? • Gender? • Choice of major college?

  10. Getting Data Enrollment: individual level, students Meteorology: - daily 8-hour average, town - climatology, regional Joint File: Visits, enrollment, high-school status, daily weather variables, climate averages

  11. Preparing the data Office of Admissions:  Daily visit data, 5 years  High school status – Senior/Junior  Application and admission status  Enrollment information from census  Other relevant information

  12. Preparing the data Iowa State Meteorology Department:  Daily readings from Ames  Hourly readings, averaged across regular business hours  Daily climate normal for IA  Daily climate normal, regional

  13. Preparing the data Identifying influential weather variables: • Temperature: Above/Below average • Wind-chill: Yes/No • Heat Index: Yes/No • Snow: Yes/No • Clouds: Yes/No • Rain: Yes/No What else matters?

  14. Perception of Weather Combination of objective climate normals and subjective comfort.

  15. What is Good Weather? How do you define and measure good weather?

  16. Creating a Weather Index • Can’t have wind chill and heat index • Can’t have snow and rain Index range: 0 to 4, the higher the worse Bad weather indicator: Weather index >2

  17. Recognizing seasons Cold season: November – March Warm season: June-August Demi-season: April/May, September/October Create seasons variables: Dummy codes: cold, warm, demi = 0/1 Effect codes: cold, warm, demi = -1/1 Effect switch: cold -1, warm 1, demi 0

  18. Winter vs. Summer

  19. Understanding climate normals • Understanding the norm helps adjusting expectations benchmarking weather is important for informing initial campus visit strategies

  20. Iowa weather is… • Best in early spring and fall • Sunniest: September-October • Best temperature: March/April • Extreme heat: ~ 2 months a year • Extreme cold: ~ 2 months a year • Rain: ~30% of the year

  21. Benchmarking the weather

  22. Benchmarking the elements

  23. Bad weather visits

  24. So…? Goal – satisfactory experience: Planning more visits during more favorable weather times Allocating necessary staff time and resources Creating plan B for adverse conditions

  25. Does weather truly matter? Going beyond frequencies:  correlation  t-test/chi-square  multiple regression

  26. Correlations Dichotomous variables can be correlated using Pearson’s phi or mean square contingency coefficient

  27. T-Test vs. χ2 T-Test χ2

  28. Findings

  29. Geography matters

  30. Findings

  31. More? Sensitive to elements, tend to enroll less during “element” days

  32. Regressions: Weather only

  33. Regressions: Weather/ HS Status

  34. Weather, demographic, academic

  35. Expectations matter: IA case

  36. Implications • Elements do make a difference • Not all differences are intuitive • Not all elements impact enrollment • Care matters (windchill findings) • So does timing (visiting as senior vs. junior)

  37. Comments & Questions Contact Information kroha@iastate.edu darinw@iastate.edu Thank you for attending this morning!

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