Ordinary least squares regression-based difference-in-differences estimation approach for evaluating specialized nuclear emergency preparedness program

Cha, Seokki

doi:10.61186/ijrr.22.2.405

[Home ] [Archive]

International Journal of Radiation Research

Main Menu

Home

IJRR Information

For Authors

For Reviewers

Subscription

News & Events

Web Mail

Search in website

Receive site information

ISSN

Hard Copy 2322-3243

Online 2345-4229

Online Submission

Now you can send your articles to IJRR office using the article submission system.

Volume 22, Issue 2 (4-2024)

Int J Radiat Res 2024, 22(2): 405-411

Back to browse issues page

Ordinary least squares regression-based difference-in-differences estimation approach for evaluating specialized nuclear emergency preparedness program

Seokki Cha

Senior Researcher in R&D Strategy Center of Korea Institute of Science and Technology Information (KISTI), Seoul, South Korea , sc04@kisti.re.kr

Abstract: (210 Views)

Background: This investigation elucidates the significance of radiation emergency medicine (REM) within South Korea, while addressing the multifaceted challenges linked to the education of medical personnel in the field of radiation emergency responses. The efficacy of REM training initiatives has undergone scrupulous evaluation through a variety of techniques, including, but not limited to, the application of the DISASTER Paradigm and engagement in simulation-based training exercises. Materials and Methods: The present research is structured to evaluate the incremental utility of REM training programs by applying the Difference-in-Difference (DID) estimation using OLS regression methodologies. Simultaneously, it aims to suggest potential improvements to existing training modules. Central to the methodology is the estimation of the DID model via the 'sm.ols' function in the Python programming environment. In the equation 'outcome ~ T_d + P_t + T_d * P_t', 'outcome' denotes the dependent variable under review, 'T_d' signifies the treatment dummy variable, and 'P_t' represents the period dummy variable. The interaction term 'T_d * P_t' elucidates the average effect of the treatment post-intervention, taking into account the temporal trend. Results: The conclusions drawn from this scholarly investigation have manifested negative net utilities across the three pivotal DISASTER Paradigm indicators (T, E, and R). Through the adept implementation of a Python-infused computational methodology, this study has yielded results characterized by precision and veracity. These insights furnish empirical evidence, indicating that the intervention in question may not have yielded an enhancement in the net utility for the designated target cohort. Conclusion: This scholarly inquiry underscores the efficacy and meticulous precision of OLS-DiD estimations executed via a Python-centric computational approach. The empirical findings emanating from this research serve to fortify a robust foundation for the strategic navigation of unique challenges within the intersecting realms of nuclear science and medical studies, with particular emphasis on advancing the field of radiation emergency medicine (REM) education.

Keywords: Radiation emergency medicine, training programs, difference-in-difference analysis, computational social science.

Full-Text [PDF 579 kb] (45 Downloads)

Type of Study: Original Research | Subject: Radiation Biology

References

1. 1. Cha S, Tsujiguchi T, Kim ST, et al. (2020) Radiation emergency medicine strategies based on the classification and analysis of nuclear and radiological emergencies. Radiation Environment and Medicine, 9(2): 47-55.

2. Cha S, Kim ST, Song S, et al. (2020) Examining the effectiveness of an initial response training program for nuclear emergency preparedness. Int J Radiat Res, 18(4): 715-721. [DOI:10.52547/ijrr.18.4.715]

3. Korea Institute of Radiological & Medical Sciences (KIRAMS) (2016) Radiation emergency medical safety regulation infrastructure construction project: Government report.

4. Hamada N and Fujimichi Y (2014) Classification of radiation effects for dose limitation purposes: history, current situation and future prospects. J Radiat Res, 55(4): 629-640. [DOI:10.1093/jrr/rru019]

5. Stawkowski ME (2016) I am a radioactive mutant: Emergent biological subjectivities at Kazakhstan's Semipalatinsk Nuclear Test Site. American Ethnologist, 43(1): 144-157. [DOI:10.1111/amet.12269]

6. Raymond E, Italo S, David S (2012) Basic disaster life support 3.0 course manual (1st ed.). Chicago, IL: American Medical Association.

7. Kabi A, Dhar M, Arora P, et al. (2021) Effectiveness of a simulation-based training program in improving the preparedness of health care workers involved in the airway management of Covid-19 patients. Cureus, 13(8), e17323. [DOI:10.7759/cureus.17323]

8. Abadie A (2003) Semiparametric difference-in-differences estimators. Report of Harvard University and NBER.

9. Lechner M, Rodriguez-Planas N, Fernández-Kranz D (2016) Difference-in-difference estimation by FE and OLS when there is panel non-response. Journal of Applied Statistics, 43(11): 2033-2040. [DOI:10.1080/02664763.2015.1126240]

10. Butsic , V., Lewis, D. J., Radeloff, V. C., Baumann, M., & Kuemmerle, T. (2017). Quasi-experimental methods enable stronger inferences from observational data in ecology. Basic and Applied Ecology, 19: 1-10. [DOI:10.1016/j.baae.2017.01.005]

11. Nakano Y, Tsusaka TW, Aida T, Pede VO (2018) Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania. World Development, 105: 336-351. [DOI:10.1016/j.worlddev.2017.12.013]

12. Deschacht, N., & Goeman, K. (2015). The effect of blended learning on course persistence and performance of adult learners: A difference-in-differences analysis. Computers & Education, 87: 83-89. [DOI:10.1016/j.compedu.2015.03.020]

13. Field E, Jayachandran S, Pande R, Rigol N (2016) Friendship at work: Can peer effects catalyze female entrepreneurship? American Economic Journal: Economic Policy, 8(2): 125-153. [DOI:10.1257/pol.20140215]

14. Mathôt S, Schreij D, Theeuwes J (2011) OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44: 314-324. [DOI:10.3758/s13428-011-0168-7]

15. Boeing G (2016) Visual analysis of nonlinear dynamical systems: Chaos, fractals, self-similarity and the limits of prediction. Systems, 4(4): 37. [DOI:10.3390/systems4040037]

16. Herodotou H and Babu S (2011) Profiling, what-if analysis, and cost-based optimization of MapReduce programs. Proceedings of the VLDB Endowment, 4(11): 1111-1122. [DOI:10.14778/3402707.3402746]

17. Veenema, T. G., Lavin, R. P., Bender, A., Thornton, C. P., & Schneider-Firestone, S. (2019). National nurse readiness for radiation emergencies and nuclear events: A systematic review of the literature. Nursing outlook, 67(1): 54-88. [DOI:10.1016/j.outlook.2018.10.005]

18. Pohlman, J. T., & Leitner, D. W. (2003). A comparison of ordinary least squares and logistic regression.

19. Campbell DT and Stanley JC (2015) Experimental and quasi-experimental designs for research. Ravenio books.

20. Lechner M, Rodriguez-Planas N, Fernández D (2015) Difference-in-Difference Estimation by FE and OLS when there is panel non-response (IZA DP No. 9490). [DOI:10.2139/ssrn.2690738]

21. Bisong E and Bisong E (2019) Matplotlib and seaborn. Building machine learning and deep learning models on google cloud platform: A Comprehensive Guide for Beginners, 151-165. [DOI:10.1007/978-1-4842-4470-8_12]

22. Benedict LH and Gould RD (1996) Towards better uncertainty estimates for turbulence statistics. Experiments in Fluids, 22(2): 129-136. [DOI:10.1007/s003480050030]

23. Nicholls N (2001) The insignificance of significance testing. Bulletin of the American Meteorological Society, 82(5): 981-986. https://doi.org/10.1175/1520-0477(2001)082<0981:CAATIO>2.3.CO;2 [DOI:10.1175/1520-0477(2001)0822.3.CO;2]

24. Lechner M (2011) The estimation of causal effects by difference-in-difference methods. Foundations and Trends® in Econometrics, 4(3): 165-224. [DOI:10.1561/0800000014]

25. De Chaisemartin C and d'Haultfoeuille X (2020) Two-way fixed effects estimators with heterogeneous treatment effects. American Economic Review, 110(9): 2964-2996. [DOI:10.1257/aer.20181169]

26. De Chaisemartin C and d'Haultfoeuille X (2023) Two-way fixed effects and differences-in-differences estimators with several treatments. Journal of Econometrics, 236(2): 105480. [DOI:10.1016/j.jeconom.2023.105480]

Send email to the article author

Add your comments about this article

‎ 10.61186/ijrr.22.2.405

Mendeley

Zotero

RefWorks

Cha S. Ordinary least squares regression-based difference-in-differences estimation approach for evaluating specialized nuclear emergency preparedness program. Int J Radiat Res 2024; 22 (2) :405-411
URL: http://ijrr.com/article-1-5451-en.html

Rights and permissions
	This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Volume 22, Issue 2 (4-2024)

Back to browse issues page

Persian site map - English site map - Created in 0.05 seconds with 50 queries by YEKTAWEB 4657