Interrupted time series
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. Effects of the intervention are evaluated by changes in the level and slope of the time series and statistical significance of the intervention parameters. 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; 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
- sociology: impact of experiments in income maintenance on the behavior of participants in welfare programs
- history: impact of major historical events on the behavior of those affected by the events
- 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.
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.
- Ferron, John; Rendina‐Gobioff, Gianna (2005), "Interrupted Time Series Design", Encyclopedia of Statistics in Behavioral Science, American Cancer Society, doi:10.1002/0470013192.bsa312, ISBN 978-0-470-01319-9, retrieved 2020-03-09
- McDowall, David; McCleary, Richard; McCleary, Professor of Criminology Law & Society and Planning Policy & Design Richard; Meidinger, Errol; Jr, Richard A. Hay (August 1980). Interrupted Time Series Analysis. SAGE. pp. 5–6. ISBN 978-0-8039-1493-3.
- Handbook of Psychology, Research Methods in Psychology, p. 582
- Brodersen; et al. (2015). "Inferring causal impact using Bayesian structural time-series models". Annals of Applied Statistics. 9: 247–274. Retrieved 21 March 2019.
- The Design and Analysis of Research Studies, p. 168
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