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.[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]
- 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]
- medical treatmentis an intervention whose effect are to be studied;
- marketing research: to analyze the effect of "designed market interventions" (e.g., advertising) on sales.[5]
See also
- Quasi-experimental design
References
- ISBN 978-0-470-01319-9, retrieved 2020-03-09
- ^ ISBN 978-0-8039-1493-3.
- ^ Handbook of Psychology, Research Methods in Psychology, p. 582
- S2CID 56399577.
- S2CID 2879370. Retrieved 21 March 2019.
- PMID 34525747.
- ^ Li, Yang; Zhao, Kaiguang; Hu, Tongxi; Zhang, Xuesong. "BEAST: A Bayesian Ensemble Algorithm for Change-Point Detection and Time Series Decomposition". GitHub.
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