Cloudy with a Chance of Enrollment: The effects of weather on student enrollment behavior
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Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
CLOUDY WITH A CHANCE OF ENROLLMENT:
The effects of weather on student enrollment behavior
Kate RalstonDarin Wohlgemuth
Daryl HerzmannCorey Hagruth
Iowa State University
November 12, 2013AACRAO - SEM
Chicago, IL
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
What is Weather?
The state of the atmosphere at a given moment in time at a given location.
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
What is Weather?Measures include the amount of
– sunshine – clouds– rainfall– humidity– wind– temperature
fluctuating on a daily basis.
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Weather vs. Climate
Not the same as climate
may include a combination of the same measures considers them in 30-year increments
Climate Normals
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Why do we care?
Weather affects an array of human behaviors and decisions, often – without us knowing.
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
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
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
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
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Then, the question is…
Does weather during campus visit affect a student’s probability of enrollment?
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Other questions to consider?
Does any particular weather element matter more?What about element combinations?Seasons?Gender?Choice of major college?
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
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
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
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
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
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
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
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?
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Perception of WeatherCombination of objective climate normals and subjective comfort.
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
What is Good Weather?
How do you define and measure good
weather?
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Creating a Weather Index
Weather Property “GOOD” “BAD”Temperature Above 0 Below 1
Wind Chill No 0 Yes 1
Heat Index No 0 Yes 1
Snow No 0 Yes 1
Rain No 0 Yes 1
Clouds No 0 Yes 1
Can’t have wind chill and heat indexCan’t have snow and rain
Index range: 0 to 4, the higher the worse
Bad weather indicator:
Weather index >2
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Recognizing seasons
Cold season: November – MarchWarm season: June-AugustDemi-season: April/May, September/October
Create seasons variables:
Dummy codes: cold, warm, demi = 0/1Effect codes: cold, warm, demi = -1/1Effect switch: cold -1, warm 1, demi 0
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Winter vs. SummerWeather Property GOOD BADTemperature Above 0 Below 1
Wind Chill No 0 Yes 1
Heat Index No 0 Yes 1
Snow No 0 Yes 1
Rain No 0 Yes 1
Clouds No 0 Yes 1
Weather Property GOOD BADTemperature Above 0 Below 1
Wind Chill No 0 Yes 1
Heat Index No 0 Yes 1
Snow No 0 Yes 1
Rain No 0 Yes 1
Clouds No 0 Yes 1
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Understanding climate normals
• Understanding the norm helps adjusting expectations
benchmarking weather is important for informing initial campus visit strategies
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
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
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Benchmarking the weather
Visits
# %
January 1,618 4.64February 3,374 9.67March 4,552 13.04April 4,452 12.75May 852 2.44June 1,219 3.49July 3,766 10.79August 3,244 9.29September 1,416 4.06October 5,573 15.96November 3,644 10.44December 1,198 3.43Total 34,908 100
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Benchmarking the elements
Visits Temperature Snow Rain Heat Wind chill Clouds
# % Above Below No Yes No Yes No Yes No Yes No Yes
January 1,618 4.64 974 644 1,244 374 1,513 105 1,618 0 604 1,014 739 879
February 3,374 9.67 1,935 1,439 2,459 915 3,180 194 3,374 0 1,229 2,145 1,353 2,021
March 4,552 13.04 3,363 1,189 3,782 770 3,546 1,006 4,552 0 3,459 1,093 2,224 2,328
April 4,452 12.75 3,142 1,310 4,389 63 2,518 1,934 4,452 0 4,324 128 2,796 1,656
May 852 2.44 591 261 827 25 500 352 836 16 852 0 566 286
June 1,219 3.49 976 243 1,219 0 722 497 870 349 1,219 0 818 401
July 3,766 10.79 3,095 671 3,766 0 2,595 1,171 1,801 1,965 3,766 0 2,725 1,041
August 3,244 9.29 2,613 631 3,244 0 1,910 1,334 2,210 1,034 3,244 0 2,173 1,071
September 1,416 4.06 1,123 293 1,416 0 1,007 409 1,354 62 1,416 0 1,030 386
October 5,573 15.96 3,388 2,185 5,547 26 3,484 2,089 5,573 0 5,530 43 3,407 2,166
November 3,644 10.44 3,305 339 3,573 71 3,228 416 3,644 0 3,350 294 2,987 657
December 1,198 3.43 662 536 963 235 1,132 66 1,198 0 541 657 579 619
Total 34,908 100 25,167 9,741 32,429 2,479 25,335 9,573 31,482 3,426 29,534 5,374 21,397 13,511
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Bad weather visits
Bad weather visitWeather
Decent BadJanuary 1,146 472February 2,161 1,213March 3,805 747April 3,567 885May 671 181June 1,041 178July 3,146 620August 2,709 535September 1,302 114October 4,448 1,125November 3,559 85December 806 392Total 28,361 6547
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
So…?
Goal – satisfactory experience:
Planning more visits during more favorable weather times
Allocating necessary staff time and resources
Creating plan B for adverse conditions
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Does weather truly matter?
Going beyond frequencies:
correlation t-test/chi-square multiple regression
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AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
CorrelationsDichotomous
variables can be correlated using Pearson’s phi or
mean square contingency coefficient
apply 0.466 0.009 0.028 -0.050 0.053 0.019 -0.020 0.061 -0.077 0.005 0.013
0.466 enroll -0.001 0.031 -0.066 0.054 0.006 -0.029 0.082 -0.121 0.022 -0.002
0.009 -0.001 temp_ind 0.274 -0.205 0.282 0.359 0.139 0.017 -0.113 0.083 0.669
0.028 0.031 0.274 snow_ind -0.091 0.400 0.250 -0.170 0.304 -0.154 -0.177 0.456
-0.050 -0.066 -0.205 -0.091 heat_ind -0.141 -0.175 0.019 -0.276 0.576 -0.227 0.046
0.053 0.054 0.282 0.400 -0.141 windchill_ind 0.205 -0.234 0.482 -0.237 -0.286 0.456
0.019 0.006 0.359 0.250 -0.175 0.205 cloud_ind 0.417 0.112 -0.093 -0.033 0.772
-0.020 -0.029 0.139 -0.170 0.019 -0.234 0.417 rain_ind -0.282 0.113 0.190 0.494
0.061 0.082 0.017 0.304 -0.276 0.482 0.112 -0.282 cold_season -0.465 -0.617 0.089
-0.077 -0.121 -0.113 -0.154 0.576 -0.237 -0.093 0.113 -0.465 warm_season -0.409 0.001
0.005 0.022 0.083 -0.177 -0.227 -0.286 -0.033 0.190 -0.617 -0.409 demi_season -0.093
0.013 -0.002 0.669 0.456 0.046 0.456 0.772 0.494 0.089 0.001 -0.093 weather_index
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
T-Test vs. χ2
T-Test
χ2
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
FindingsEnrollment by weather elements
OverallObs Mean St.D. p-level
Temp Above 25167 0.464 0.499 n.s.Below 9741 0.463 0.499
Snow No 32429 0.460 0.498 .000**Yes 2479 0.520 0.500
Rain No 25335 0.473 0.499 .000**Yes 9573 0.441 0.497
Heat No 31482 0.475 0.499 .000**Yes 3426 0.365 0.481
Windchill No 29534 0.453 0.498 .000**Yes 5374 0.527 0.499
Clouds No 21397 0.462 0.499 n.s.Yes 13511 0.468 0.499
Bad Weather No 28361 0.466 0.499
0.085Yes 6547 0.457 0.498
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Geography matters
-65 -45 -25 -5 15 35
-25
-20
-15
-10
-5
0
5
10
15
20
25
Visitor Density
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
FindingsEnrollment by weather elements
By Region Iowa Contiguous States Non-Contiguous StatesObs Mean St.D. p-level Obs Mean St.D. p-level Obs Mean St.D. p-level
Temp Above 10015 0.718 0.450 0.0521 9160 0.442 0.497 n.s. 988 0.454 0.498 n.s.Below 3826 0.704 0.457 3657 0.445 0.497 396 0.485 0.500
Snow No 12813 0.713 0.452 n.s. 11877 0.439 0.496 .0012** 1259 0.445 0.497 .000***Yes 1028 0.728 0.445 940 0.490 0.500 125 0.648 0.480
Rain No 10331 0.714 0.452 n.s. 9164 0.452 0.498 .000*** 996 0.471 0.499 n.s.Yes 3510 0.715 0.451 3653 0.421 0.494 388 0.443 0.497
Heat No 12868 0.716 0.451 0.0174 11403 0.452 0.498 .000*** 1231 0.478 0.500 .000***Yes 973 0.684 0.465 1414 0.376 0.484 153 0.340 0.475
Windchill No 11472 0.713 0.452 n.s. 10834 0.432 0.495 .000*** 1154 0.441 0.497 .000***Yes 2369 0.718 0.450 1983 0.504 0.500 230 0.574 0.496
Clouds No 8558 0.714 0.452 n.s. 7691 0.444 0.497 n.s. 810 0.442 0.497 .030*Yes 5283 0.714 0.452 5126 0.443 0.497 574 0.493 0.500
Bad Weather No 11393 0.716 0.451 n.s. 10261 0.445 0.497 n.s. 1096 0.450 0.498 .026*Yes 2448 0.704 0.456 2556 0.437 0.496 288 0.514 0.501
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
More?
Enrollment, from the North
By RegionIowa Cont NonC
p-level p-level p-levelTemp n.s. n.s. n.s.Snow n.s. .0023** n.s.Rain n.s. .000*** n.s.Heat n.s. .000*** n.s.Windchill n.s. .000*** n.s.Clouds n.s. n.s. n.s.Bad Weather n.s. n.s. n.s.
No Yes
Sensitive to elements, tend to enroll less during “element” days
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Regressions: Weather onlyRegression using weather variables, excluding athletes
enroll
overall Iowa Contiguous States Non-Contiguous States
N F( 14, 30625) R² N F( 14, 13521) R² N F( 14, 9882) R² N F( 14, 1212) R²
30640 52.29 0.0229 13563 5.06 0.0042 9897 9.13 0.0114 1227 3.29 0.0255
Coef. t P>t Coef. t P>t Coef. t P>t Coef. t P>t
isu_di -0.0001915 -14.4 0.000 0.000031 0.79 0.432 0.000304 3.46 0.001 1.13E-05 0.4 0.689temp_ind -0.0261321 -2.63 0.008 -0.00888 -0.65 0.515 -0.01836 -1.05 0.292 -0.03401 -0.68 0.496snow_ind 0.0167511 1.22 0.222 0.02207 1.19 0.235 0.022175 0.93 0.353 0.144117 2.08 0.038heat_ind 0.010846 0.82 0.410 0.051107 2.51 0.012 0.013466 0.6 0.546 -0.02907 -0.48 0.630windchill_ind 0.0198601 1.85 0.064 -0.00811 -0.57 0.567 0.016881 0.89 0.374 0.082091 1.46 0.144cloud_ind -0.0012187 -0.14 0.891 0.015102 1.24 0.214 -0.01531 -0.96 0.335 0.066767 1.53 0.126av_temp -0.0008563 -2.14 0.032 -0.00041 -0.75 0.454 -0.00123 -1.71 0.087 0.001506 0.77 0.442av_hum 0.000103 0.39 0.697 -0.00094 -2.62 0.009 0.000147 0.31 0.757 -0.00163 -1.17 0.240rain_ind -0.0004061 -0.05 0.963 0.028398 2.36 0.018 -0.00745 -0.49 0.624 0.03198 0.73 0.468switch -0.0000732 -0.04 0.971 -0.00524 -1.77 0.076 -0.00154 -0.42 0.671 0.008977 1.26 0.210cold_season 0.0027814 0.27 0.785 0.012435 0.86 0.392 -0.00656 -0.37 0.713 0.047321 0.99 0.323warm_season -0.1071873 -9.16 0.000 -0.06541 -3.93 0.000 -0.07816 -3.68 0.000 -0.11974 -2.07 0.039north -0.0310308 -5.38 0.000 0.012608 1.56 0.120 0.018349 1.75 0.081 0.02434 0.53 0.594bad_weather -0.0151523 -1.25 0.209 -0.01141 -0.69 0.493 -0.01425 -0.68 0.498 -0.038 -0.61 0.541
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Regressions: Weather/ HS StatusRegression using weather variables, excluding athletes
enroll
overall Iowa Contiguous States Non-Contiguous States
N F( 15, 30624) R² N F( 15, 13520) R² N F( 15, 9881) R² N F( 15, 1211) R²
30640 73.57 0.0343 13563 5.69 0.0052 9897 10.92 0.0148 1227 3.46 0.0293
Coef. t P>t Coef. t P>t Coef. t P>t Coef. t P>t
isu_di -0.0001929 -14.59 0.000 3.03E-05 0.77 0.443 0.00028 3.19 0.001 1.79E-06 0.06 0.950temp_ind -0.0252887 -2.56 0.010 -0.00871 -0.64 0.523 -0.0169 -0.97 0.332 -0.03301 -0.66 0.508snow_ind 0.0268426 1.97 0.049 0.024391 1.31 0.189 0.02899 1.22 0.224 0.140268 2.03 0.043heat_ind 0.0278332 2.12 0.034 0.054743 2.68 0.007 0.0247 1.11 0.269 -0.02668 -0.44 0.658windchill_ind 0.0116948 1.1 0.272 -0.00925 -0.65 0.513 0.010339 0.54 0.586 0.080711 1.44 0.150cloud_ind 0.0039237 0.44 0.657 0.015828 1.3 0.192 -0.01171 -0.74 0.460 0.066789 1.53 0.125av_temp -0.0006504 -1.63 0.102 -0.00033 -0.6 0.545 -0.00109 -1.52 0.129 0.001747 0.89 0.372av_hum -0.0001959 -0.74 0.457 -0.00101 -2.8 0.005 -4.1E-05 -0.09 0.931 -0.00173 -1.24 0.214rain_ind 0.0052827 0.61 0.539 0.030242 2.52 0.012 -0.00456 -0.3 0.764 0.031013 0.7 0.481switch -0.0017792 -0.9 0.370 -0.00546 -1.85 0.065 -0.00274 -0.76 0.450 0.007807 1.09 0.275cold_season 0.0043397 0.43 0.669 0.011939 0.82 0.410 -0.00391 -0.22 0.826 0.046624 0.98 0.329warm_season -0.0772267 -6.58 0.000 -0.05443 -3.22 0.001 -0.06244 -2.92 0.004 -0.08557 -1.44 0.150north -0.0309105 -5.39 0.000 0.01261 1.56 0.120 0.018176 1.73 0.083 0.020269 0.44 0.657bad_weather -0.0076171 -0.63 0.526 -0.00894 -0.54 0.591 -0.01035 -0.49 0.622 -0.03433 -0.55 0.580senior 0.1189781 19.05 0.000 0.03505 3.8 0.000 0.067095 5.96 0.000 0.085587 2.39 0.017
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Weather, demographic, academicRegression using weather, demographic andacademic variables, excluding athletes
enroll
overall Iowa Contiguous States Non-Contiguous StatesN F(28, 16011) R² N F(28, 9172) R² N F(28, 4418) R² N F( 28, 406) R²
16040 189.39 0.2475 9201 29.6 0.0801 4447 15.15 0.0818 435 3.3 0.1294Coef. t P>t Coef. t P>t Coef. t P>t Coef. t P>t
isu_di -0.00025 -10.510 0.000 0.000 -1.090 0.276 0.001 4.200 0.000 0.000 0.880 0.381temp_ind -0.014 -1.130 0.256 -0.010 -0.620 0.535 -0.034 -1.330 0.183 -0.012 -0.150 0.884snow_ind 0.021 1.280 0.202 0.023 1.070 0.283 -0.003 -0.080 0.940 0.282 2.260 0.025heat_ind 0.041 2.580 0.010 0.078 3.400 0.001 0.044 1.400 0.162 0.057 0.610 0.541windchill_ind 0.005 0.380 0.705 0.005 0.280 0.778 -0.007 -0.230 0.819 -0.044 -0.440 0.664cloud_ind -0.001 -0.090 0.927 0.007 0.520 0.603 -0.034 -1.460 0.144 0.031 0.390 0.695av_temp -0.001 -1.180 0.238 0.000 -0.420 0.674 -0.002 -2.180 0.029 -0.003 -1.040 0.297av_hum -0.001 -2.230 0.026 -0.001 -1.950 0.051 -0.001 -1.170 0.242 -0.001 -0.240 0.809rain_ind 0.022 2.080 0.037 0.025 1.820 0.068 0.023 1.080 0.281 0.011 0.150 0.877switch -0.002 -0.870 0.387 -0.005 -1.610 0.108 -0.003 -0.630 0.527 -0.003 -0.270 0.785cold_season 0.003 0.240 0.808 0.003 0.180 0.856 0.000 -0.020 0.986 -0.085 -1.070 0.286warm_season -0.045 -3.180 0.001 -0.062 -3.220 0.001 -0.011 -0.370 0.711 -0.065 -0.700 0.486senior 0.080 10.490 0.000 0.061 5.850 0.000 0.086 5.490 0.000 0.125 2.330 0.020north -0.002 -0.220 0.825 -0.003 -0.360 0.720 0.050 3.360 0.001 0.088 1.120 0.265bad_weather -0.011 -0.740 0.461 -0.009 -0.480 0.629 -0.002 -0.080 0.939 -0.074 -0.730 0.464agri 0.099 8.910 0.000 0.160 11.210 0.000 0.056 2.250 0.024 -0.035 -0.450 0.650design 0.088 6.530 0.000 0.125 7.030 0.000 0.119 4.190 0.000 -0.146 -1.640 0.102engin 0.110 11.200 0.000 0.141 10.750 0.000 0.185 9.000 0.000 0.021 0.310 0.754humsci 0.105 8.540 0.000 0.125 8.260 0.000 0.134 4.470 0.000 -0.148 -1.690 0.092biz 0.040 2.920 0.003 0.064 3.680 0.000 0.057 1.820 0.068 -0.135 -1.040 0.301minority 0.030 2.530 0.011 0.046 2.960 0.003 0.013 0.510 0.607 0.096 1.370 0.170female -0.021 -2.690 0.007 -0.017 -1.720 0.086 -0.009 -0.550 0.583 -0.066 -1.230 0.218legacy 0.082 8.920 0.000 0.038 3.580 0.000 0.089 3.930 0.000 0.097 1.830 0.069
Academic Prep
-0.095 -26.150 0.000 -0.096 -16.730 0.000 -0.077 -11.540 0.000 -0.111 -5.500 0.000-0.012 -12.170 0.000 -0.007 -4.170 0.000 -0.010 -3.640 0.000 -0.015 -1.840 0.0660.013 2.450 0.014 0.139 6.200 0.000 0.211 6.660 0.000 0.288 3.210 0.001-0.001 -2.380 0.017 -0.003 -5.280 0.000 -0.002 -2.070 0.039 -0.002 -0.510 0.6070.002 30.500 0.000 0.001 4.370 0.000 -0.001 -2.140 0.032 -0.002 -1.090 0.278
_cons 0.546 12.440 0.000 0.265 3.880 0.000 0.503 4.300 0.000 0.904 2.460 0.014
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Expectations matter: IA caseIowans only
enroll
Female Male
N F(15, 6938) R² N F(15,6566) R²
6954 5.03 0.0086 6582 2.11 0.0025
Coef. t P>t Coef. t P>t
isu_di 0.0000133 0.24 0.809 0.0000516 0.91 0.364
temp_ind 0.0043364 0.23 0.821 -0.0204216 -1.05 0.292
snow_ind 0.052 1.950 0.051 -0.004 -0.170 0.866
heat_ind 0.073 2.560 0.011 0.040 1.360 0.174
windchill_ind -0.002 -0.110 0.911 -0.017 -0.880 0.381
cloud_ind 0.036 2.100 0.036 -0.003 -0.160 0.875
av_temp 0.000 0.530 0.599 -0.001 -1.350 0.177
av_hum -0.001 -2.560 0.011 -0.001 -1.540 0.124
rain_ind 0.055 3.230 0.001 0.006 0.330 0.744
switch -0.005 -1.180 0.237 -0.006 -1.520 0.129
cold_season 0.044 2.140 0.032 -0.021 -1.030 0.305
warm_season -0.078 -3.370 0.001 -0.030 -1.220 0.223
senior 0.037 2.830 0.005 0.035 2.730 0.006
north 0.013 1.110 0.268 0.011 1.000 0.319
bad_weather -0.058 -2.480 0.013 0.041 1.720 0.085
_cons 0.692 11.790 0.000 0.811 13.570 0.000
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
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)
Enrollment Research Team
AACRAO-SEMNov. 2013Chicago Ralston, Wohlgemuth, Herzmann, Hagruth,
2013
Comments & Questions
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