Skip to main content
Log in

Using Multiple Methods to Provide Prediction Bands of K-12 Enrollment Projections

  • Research Briefs
  • Published:
Population Research and Policy Review Aims and scope Submit manuscript

Abstract

Often, demographers charged with projecting enrollments for school districts are asked to provide a range of future enrollments, as point estimates are not satisfactory to stakeholders. Three New Jersey school districts in varying populations sizes (small, medium, and large) were used to project enrollments 5 years into the future. Prediction bands were created using empirically-based methods, whereby confidence intervals were constructed, and by model-based methods, which utilizes stochastic forecasting. While stochastic forecasting is typically used in projecting the population of large geographies such as states or countries, it has rarely been used for a small level of geography such as a school district. The results showed that confidence intervals may have limited utility in projecting an enrollment range for larger districts, particularly in the short term (1–2 years). Prediction intervals using stochastic forecasting have limited utility in school district projections, regardless of school district size, as the prediction bands are too wide to allow for any meaningful use of the data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. The authors can be contacted to receive the programming code used for this paper.

References

  • Alho, J. M. (1990). Stochastic methods in population forecasting. International Journal of Forecasting,6(4), 521–530.

    Article  Google Scholar 

  • Andersen, M., Cole, R. Dunn, T., Krupka, D., & Pato, J. (2014). Forecasts for the Lexington Public Schools: FY2015-FY2020 Report of the enrollment working group, Report prepared for District.

  • Keilman, N., Pham, D. Q., & Hetland, A. (2002). Why population forecasts should be probabilistic—illustrated by the case of Norway. Demographic Research,6(15), 409–454.

    Article  Google Scholar 

  • Keyfitz, N. (1972). On future population. Journal of the American Statistical Association,67, 347–362.

    Article  Google Scholar 

  • Keyfitz, N. (1981). The limits of population forecasting. Population and Development Review,7, 579–593.

    Article  Google Scholar 

  • Lapkoff & Gobalet. Demographic Research Inc. Report for San Francisco Unified School District, November 23, 2015. Report prepared for District.

  • Lapkoff, S., & Gobalet, J. Forecasting School Enrollments: Demographic Tools to Help Schools Prepare for Change. Draft, February 2019. lapkoff@demographers.com, Gobalet@demographers.com

  • Pflaumer, P. (1988). Confidence intervals for population projections based on monte carlo methods. International Journal of Forecasting,4(1), 135–142.

    Article  Google Scholar 

  • R Core Team. (2013). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.

    Google Scholar 

  • Rayer, S., Smith, S. K., & Tayman, J. (2009). Empirical prediction intervals for county population forecasts. Population Research and Policy Review,28(6), 773–793.

    Article  Google Scholar 

  • Smith, S. K., Tayman, J., & Swanson, D. A. (2001). State and local population projections: Methodology and analysis. New York: Kluwer Academic/Plenum Publishers.

    Google Scholar 

  • Tayman, J. (2011). Assessing uncertainty in small area forecasts: State of the practice and implementation strategy. Population Research and Policy Review,30(5), 781–800.

    Article  Google Scholar 

  • Tayman, J., Schafer, E., & Carter, L. (1998). The role of population size in the determination and prediction of population forecast errors: An evaluation using confidence intervals for subcounty areas. Population Research and Policy Review,17(1), 1–20.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Richard S. Grip.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Grip, R.S., Grip, M.L. Using Multiple Methods to Provide Prediction Bands of K-12 Enrollment Projections. Popul Res Policy Rev 39, 1–22 (2020). https://doi.org/10.1007/s11113-019-09533-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11113-019-09533-2

Keywords

Navigation