Interrupted time series

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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] see, e.g., Effectiveness of sex offender registration policies in the United States#Interrupted time series analysis studies.
  • 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]
  • 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.[4]

The ITS design is the base of the comparative time series design, whereby there is a control series and an interrupted series, and the effect of an intervention is confirmed by the control series.[5]

See also

  • Quasi-experimental design

References

  1. Ferron, John; Rendina‐Gobioff, Gianna (2005), "Interrupted Time Series Design" (in en), Encyclopedia of Statistics in Behavioral Science (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.co.uk/books?hl=en&id=oAIuJ2JQIngC&pg=PA11. 
  3. Handbook of Psychology, Research Methods in Psychology, p. 582
  4. Brodersen (2015). "Inferring causal impact using Bayesian structural time-series models". Annals of Applied Statistics 9: 247-274. https://ai.google/research/pubs/pub41854. Retrieved 21 March 2019. 
  5. The Design and Analysis of Research Studies, p. 168