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]
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]

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.^[6]

References[]

^ 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
^ ^a ^b ^c ^d ^e 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
^ Bollen; et al. (2019). "The minute-scale dynamics of online emotions reveal the effects of affect labeling". Nature Human Behaviour. 3: 92–100. doi:10.1038/s41562-018-0490-5.
^ Brodersen; et al. (2015). "Inferring causal impact using Bayesian structural time-series models". Annals of Applied Statistics. 9: 247–274. arXiv:1506.00356. doi:10.1214/14-AOAS788. S2CID 2879370. Retrieved 21 March 2019.
^ The Design and Analysis of Research Studies, p. 168

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[1] 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

[its1-2] 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.

[3] Handbook of Psychology, Research Methods in Psychology, p. 582

[4] Bollen; et al. (2019). "The minute-scale dynamics of online emotions reveal the effects of affect labeling". Nature Human Behaviour. 3: 92–100. doi:10.1038/s41562-018-0490-5.

[5] Brodersen; et al. (2015). "Inferring causal impact using Bayesian structural time-series models". Annals of Applied Statistics. 9: 247–274. arXiv:1506.00356. doi:10.1214/14-AOAS788. S2CID 2879370. Retrieved 21 March 2019.

[6] The Design and Analysis of Research Studies, p. 168

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Interrupted time series

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