Interrupted time series

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Short description: Method of analysis involving tracking a long-term period around an intervention

Interrupted time series analysis (ITS), sometimes known as quasi-experimental time series analysis, is a method of statistical analysis involving tracking a long-term period before and after a point of intervention to assess the intervention's effects. The time series refers to the data over the period, while the interruption is the intervention, which is a controlled external influence or set of influences.[1][2] Effects of the intervention are evaluated by changes in the level and slope of the time series and statistical significance of the intervention parameters.[3] Interrupted time series design is the design of experiments based on the interrupted time series approach.

The method is used in various areas of research, such as:

  • political science: impact of changes in laws on the behavior of people;[2] (e.g., Effectiveness of sex offender registration policies in the United States)
  • economics: impact of changes in credit controls on borrowing behavior;[2]
  • sociology: impact of experiments in income maintenance on the behavior of participants in welfare programs;[2]
  • history: impact of major historical events on the behavior of those affected by the events;[2]
  • psychology: impact of expressing emotional experiences on online content;[4]
  • medicine: in medical research, medical treatment is an intervention whose effect are to be studied;
  • marketing research: to analyze the effect of "designed market interventions" (e.g., advertising) on sales.[5]
  • environmental sciences: impacts of human activities on environmental quality and ecosystem dynamics (e.g., forest logging on local climate).[6][7]

See also

  • Quasi-experimental design

References

  1. ↑ Ferron, John; Rendina‐Gobioff, Gianna (2005) (in en), Interrupted Time Series Design, American Cancer Society, doi:10.1002/0470013192.bsa312, ISBN 978-0-470-01319-9, https://onlinelibrary.wiley.com/doi/abs/10.1002/0470013192.bsa312, retrieved 2020-03-09 
  2. ↑ 2.0 2.1 2.2 2.3 2.4 McDowall, David; McCleary, Richard; McCleary, Professor of Criminology Law & Society and Planning Policy & Design Richard; Meidinger, Errol; Jr, Richard A. Hay (August 1980) (in en). Interrupted Time Series Analysis. SAGE. pp. 5–6. ISBN 978-0-8039-1493-3. https://books.google.com/books?id=oAIuJ2JQIngC&pg=PA11. 
  3. ↑ Handbook of Psychology, Research Methods in Psychology, p. 582
  4. ↑ Bollen (2019). "The minute-scale dynamics of online emotions reveal the effects of affect labeling". Nature Human Behaviour 3 (1): 92–100. doi:10.1038/s41562-018-0490-5. PMID 30932057. https://www.nature.com/articles/s41562-018-0490-5. 
  5. ↑ Brodersen (2015). "Inferring causal impact using Bayesian structural time-series models". Annals of Applied Statistics 9: 247–274. doi:10.1214/14-AOAS788. https://ai.google/research/pubs/pub41854. Retrieved 21 March 2019. 
  6. ↑ Li, Yang; Liu, Yanlan; Bohrer, Gil; Cai, Yongyang; Wilson, Aaron; Hu, Tongxi; Wang, Zhihao; Zhao, Kaiguang (2022). "Impacts of forest loss on local climate across the conterminous United States: Evidence from satellite time-series observation". Science of the Total Environment 802: 149651. doi:10.1016/j.scitotenv.2021.149651. PMID 34525747. Bibcode: 2022ScTEn.802n9651L. https://u.osu.edu/agroecosystemresilience/files/2021/09/Li_etal.pdf. 
  7. ↑ Li, Yang; Zhao, Kaiguang; Hu, Tongxi; Zhang, Xuesong. "BEAST: A Bayesian Ensemble Algorithm for Change-Point Detection and Time Series Decomposition". https://github.com/zhaokg/Rbeast. 



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