IDEAS home Printed from https://ideas.repec.org/a/eee/epplan/v73y2019icp62-70.html

Evaluation of a drug and alcohol safety education program in aviation using interrupted time series and the Kirkpatrick framework

Author

Listed:
  • Buriak, Susan E.
  • Ayars, Candace L.

Abstract

Compliance with drug and alcohol regulations are required by 14 CFR Part 120/ and 49 CFR Part 40. These regulations affect approximately 7200 aviation-related companies and their associated services. Consequences for noncompliance can include loss of revenue from imposition of civil penalties, suspension, or revocation of the company’s certificate to conduct business. Front End Analysis (FEA) was conducted to determine specific performance problems and provide five tailored educational interventions to address them. Program evaluation was conducted using Interrupted Time Series (ITS) modeling. Results showed significant (p < .05) decreases in nonconformities across all five models with small to moderate effect sizes. Based on the relative effects, values for reductions in civil penalty costs between 16% and 47%, were predicted. Actual sanction reductions from the pre-to-post-intervention periods were confirmed to be 24.21%. The study supported the efficacy of the ITS approach for implementation of level four Kirkpatrick evaluation.

Suggested Citation

  • Buriak, Susan E. & Ayars, Candace L., 2019. "Evaluation of a drug and alcohol safety education program in aviation using interrupted time series and the Kirkpatrick framework," Evaluation and Program Planning, Elsevier, vol. 73(C), pages 62-70.
    • Handle: RePEc:eee:epplan:v:73:y:2019:i:c:p:62-70
    DOI: 10.1016/j.evalprogplan.2018.11.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0149718918300594
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.evalprogplan.2018.11.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kayenaat Bahl & Ravi Kiran & Anupam Sharma, 2024. "Evaluating the effectiveness of training of managerial and non-managerial bank employees using Kirkpatrick’s model for evaluation of training," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    2. Laurence T Kell & Iago Mosqueira & Henning Winker & Rishi Sharma & Toshihide Kitakado & Massimiliano Cardinale, 2024. "Empirical validation of integrated stock assessment models to ensuring risk equivalence: A pathway to resilient fisheries management," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-21, July.
    3. Cabral, Joilson de Assis & Freitas Cabral, Maria Viviana de & Pereira Júnior, Amaro Olímpio, 2020. "Elasticity estimation and forecasting: An analysis of residential electricity demand in Brazil," Utilities Policy, Elsevier, vol. 66(C).
    4. Das, Prashant & Füss, Roland & Hanle, Benjamin & Russ, Isabel Nina, 2020. "The cross-over effect of irrational sentiments in housing, commercial property, and stock markets," Journal of Banking & Finance, Elsevier, vol. 114(C).
    5. Thé, Jesse & Yu, Hesheng, 2017. "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods," Energy, Elsevier, vol. 138(C), pages 257-289.
    6. Ioannis Badounas & Georgios Pitselis, 2020. "Loss Reserving Estimation With Correlated Run-Off Triangles in a Quantile Longitudinal Model," Risks, MDPI, vol. 8(1), pages 1-26, February.
    7. Artur Budzyński & Maria Cieśla, 2025. "Enhancing Road Freight Price Forecasting Using Gradient Boosting Ensemble Supervised Machine Learning Algorithm," Mathematics, MDPI, vol. 13(18), pages 1-24, September.
    8. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    9. Michael Kostmann & Wolfgang K. Härdle, 2019. "Forecasting in Blockchain-Based Local Energy Markets," Energies, MDPI, vol. 12(14), pages 1-27, July.
    10. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    11. Pankaj Kumar, 2021. "Deep Hawkes Process for High-Frequency Market Making," Papers 2109.15110, arXiv.org.
    12. Bloom, David E. & Canning, David & Fink, Gunther & Finlay, Jocelyn E., 2007. "Does age structure forecast economic growth?," International Journal of Forecasting, Elsevier, vol. 23(4), pages 569-585.
    13. Mengyang Wang & Hui Wang & Jiao Wang & Hongwei Liu & Rui Lu & Tongqing Duan & Xiaowen Gong & Siyuan Feng & Yuanyuan Liu & Zhuang Cui & Changping Li & Jun Ma, 2019. "A novel model for malaria prediction based on ensemble algorithms," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-15, December.
    14. Taleb, Nassim Nicholas, 2020. "On the statistical differences between binary forecasts and real-world payoffs," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1228-1240.
    15. Andrikopoulos, Andreas & Merika, Anna & Stoupos, Nikolaos, 2025. "The effect of oil prices on the US shipping stock prices: The mediating role of freight rates and economic indicators," Journal of Commodity Markets, Elsevier, vol. 38(C).
    16. Olivares, Kin G. & Meetei, O. Nganba & Ma, Ruijun & Reddy, Rohan & Cao, Mengfei & Dicker, Lee, 2024. "Probabilistic hierarchical forecasting with deep Poisson mixtures," International Journal of Forecasting, Elsevier, vol. 40(2), pages 470-489.
    17. Marco Zanotti, 2025. "On the stability of global forecasting models," Working Papers 553, University of Milano-Bicocca, Department of Economics.
    18. Marcelo Bourguignon, 2016. "Poisson–geometric INAR(1) process for modeling count time series with overdispersion," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(3), pages 176-192, August.
    19. Dima, Bogdan & Dima, Ştefana Maria & Ioan, Roxana, 2025. "The short-run impact of investor expectations’ past volatility on current predictions: The case of VIX," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 98(C).
    20. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    21. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:epplan:v:73:y:2019:i:c:p:62-70. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/evalprogplan .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.