Enrollment Forecasting Approaches for Open Admission Institutions

1. Enrollment Forecasting Approaches for Open Admission Institutions R. Ty Jones Director of Institutional Research Columbia Basin College PNAIRP Annual Conference Portland, Oregon November 7, 2012

2. Links If you would like to follow along with the data and techniques and presentation, here are the links.http://dl.dropbox.com/u/9234919/2012_3Way.xlsx http://dl.dropbox.com/u/9234919/2012_MP.xlsx http://dl.dropbox.com/u/9234919/SPSSEnrollment.sav http://dl.dropbox.com/u/9234919/SPSSMLR.sav http://dl.dropbox.com/u/9234919/2012_Forecast_Data.xlsx http://dl.dropbox.com/u/9234919/20121106_Forecast_Workshop.pptx

3. Overview • Approximate Timeline • Rational and pragmatic philosophy to enrollment forecasting • (5 Minutes) • Forecasting basics (5 Minutes) • Linear Regression approaches (SLR) (15 minutes) • Fitted Curve approaches (CLR) (10 Minutes) • Multivariate Linear Regression (MLR) (20 minutes) • Autoregressive–moving-average models (ARIMA) (20 minutes) • Data imputation (10 minutes) • Mixed methods (10 minutes) • Other approaches (5 minutes) • Forecast weighting (5 minutes) • Presenting the data (5 minutes) • Conclusion, questions and answers (10 minutes) • That’s 120 minutes plus a break to fit into 90 minutes! So, lets go!!!!

4. Philosophy • Predicting the future is hard! • Forecasting is easy. • There is no such thing as a perfect forecast. • A forecast is only as good as the data that goes into it. • All forecasting methods have strengths. • All forecasting methods have weaknesses. • E pluribus unum! • If it doesn’t make sense, don’t use it. • If you can’t explain it, don’t use it. • Prepare, prepare, prepare!

5. Basics “Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be an estimation of some variable of interest at some specified future date.” - Wikipedia Forecasting requires process and estimation. Anything else is WAG! The processes chosen by institutional research must be founded on statistical and/or mathematical principles. That means data must be at its core to have any validity.

6. Linear Regression Linear regression uses the process of least squares to model the relationship between a dependent variable and an explanatory variable. • Strengths: • Robust • Minimal data requirements • Easily explained • Weaknesses: • Variances make short and long term estimates difficult • Tends to over simplify trends

7. Fitted Curve Fitted curve regression operates on a similar basis as linear regression. Instead, transformations to the data optimize the least square process to fit an equation line dictated by the transformation. • Strengths: • In many cases, curve fitting better fits time series data. • Provides stronger explanation than linear models. • Weaknesses: • Variances can force large margins of error in making estimates. • Some curve fitting may be significant, but not make actual sense.

8. Multivariate Linear Regression • Multivariate linear regression uses the process of least squares to model the relationship between a dependent variable and multiple explanatory variables. • Strengths: • Robust • High explanatory value • Once model is established, allows a lot of different “what if” scenarios to be looked at. • Weaknesses: • Extending the model for significant independent variables into the future can be difficult. • Interactions can make model interpretation difficult. • Resulting models can be very complex.

9. ARIMA • An autoregressive–moving-average model uses a combination of data smoothing and regression in time series data. Unlike true regression approaches, uses only dependent data to estimate future outcomes.Strengths: • Often better reflects cyclical dependent data. • Lack of dependence on explanatory factors allows sbetter long term projections. • Weaknesses: • Getting the correct model can be very difficult • Explaining the model can be difficult. • Sometimes, no model can be generated.

10. Imputation • There are a variety of data imputation techniques. All aim at filling holes or extending estimates. All use various formulae to look for patterns in existing data to estimate missing data. • Strengths: • Not as effected by variances so short term and long term estimates are more consistent. • Mathematically more straight forward. • Weaknesses: • Can miss cyclic patterns. • Using the wrong imputation for the data can result in large out of range errors.

11. Mixed Methods • Mixed method models use a combination of forecasting approaches to arrive at estimations.Strengths: • Mixing methods may provide data smoothing to highly variable data. • May allow access to estimates that a single model approach would not allow. • Weaknesses: • Can result in amplified error and variance of estimates. • Explaining the model can be difficult. • Measuring confidence in the model is difficult

12. Other Issues • Other forecasting methods • Bayesian estimate models • Hot-Decking • Random Wandering Models • Forecasting weighting

13. Presenting The Data

14. Finish Conclusion, questions and maybe some answers… Thank you for participating!