# 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](/source/statistical_analysis) involving tracking a long-term period before and after a point of intervention to assess the intervention's effects. The [time series](/source/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.<ref>{{Citation|last1=Ferron|first1=John|title=Interrupted Time Series Design|date=2005|url=https://onlinelibrary.wiley.com/doi/abs/10.1002/0470013192.bsa312|encyclopedia=Encyclopedia of Statistics in Behavioral Science|publisher=American Cancer Society|language=en|doi=10.1002/0470013192.bsa312|isbn=978-0-470-01319-9|access-date=2020-03-09|last2=Rendina‐Gobioff|first2=Gianna|url-access=subscription}}</ref><ref name=its1>{{Cite book|last1=McDowall|first1=David|url=https://books.google.com/books?id=oAIuJ2JQIngC&pg=PA11|title=Interrupted Time Series Analysis|last2=McCleary|first2=Richard|last3=Meidinger|first3=Errol|last4=Hay|first4=Richard A. Jr.|date=August 1980|publisher=SAGE|isbn=978-0-8039-1493-3|pages=5–6|language=en}}</ref> Effects of the intervention are evaluated by changes in the level and slope of the time series and [statistical significance](/source/statistical_significance) of the intervention parameters.<ref>Handbook of Psychology, Research Methods in Psychology, [https://books.google.com/books?id=MnOyiy5dtSsC&pg=PA582 p. 582]</ref> '''Interrupted time series design''' is the [design of experiments](/source/design_of_experiments) based on the interrupted time series approach.

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

*[political science](/source/political_science): impact of changes in laws on the behavior of people;<ref name=its1/> (e.g., [Effectiveness of sex offender registration policies in the United States](/source/Effectiveness_of_sex_offender_registration_policies_in_the_United_States))
*[economics](/source/economics): impact of changes in credit controls on borrowing behavior;<ref name=its1/>
*[sociology](/source/sociology): impact of experiments in income maintenance on the behavior of participants in [welfare program](/source/welfare_program)s;<ref name=its1/>
*[history](/source/history): impact of major historical events on the behavior of those affected by the events;<ref name=its1/>
*[psychology](/source/psychology): impact of expressing emotional experiences on online content;<ref>{{cite journal|last1=Bollen|display-authors=et al|title=The minute-scale dynamics of online emotions reveal the effects of affect labeling|journal=Nature Human Behaviour|date=2019|volume=3|issue=1 |pages=92–100|url=https://www.nature.com/articles/s41562-018-0490-5|doi=10.1038/s41562-018-0490-5|pmid=30932057 |s2cid=56399577 |url-access=subscription}}</ref>
*[medicine](/source/medicine): in medical research, [medical treatment](/source/medical_treatment) is an intervention whose effect are to be studied;
*[marketing research](/source/marketing_research): to analyze the effect of "designed market interventions" (e.g., [advertising](/source/advertising)) on sales.<ref>{{cite journal |last1=Brodersen |display-authors=et al |title=Inferring causal impact using Bayesian structural time-series models |journal=Annals of Applied Statistics |date=2015 |volume=9 |pages=247–274 |doi=10.1214/14-AOAS788 |arxiv=1506.00356 |s2cid=2879370 |url=https://ai.google/research/pubs/pub41854 |access-date=21 March 2019}}</ref>
*[environmental sciences](/source/environmental_sciences): impacts of human activities on environmental quality and ecosystem dynamics (e.g., forest logging on local climate).<ref>{{cite journal |last1=Li |first1=Yang |last2=Liu |first2=Yanlan |last3=Bohrer |first3=Gil |last4=Cai |first4=Yongyang |last5=Wilson |first5=Aaron |last6=Hu |first6=Tongxi |last7=Wang |first7=Zhihao |last8=Zhao |first8=Kaiguang |title=Impacts of forest loss on local climate across the conterminous United States: Evidence from satellite time-series observation |journal=Science of the Total Environment |date=2022 |volume=802 |article-number=149651 |doi=10.1016/j.scitotenv.2021.149651 |pmid=34525747 |bibcode=2022ScTEn.802n9651L |url=https://u.osu.edu/agroecosystemresilience/files/2021/09/Li_etal.pdf}}</ref><ref>{{cite web |last1=Li |first1=Yang |last2=Zhao |first2=Kaiguang |last3=Hu |first3=Tongxi |last4=Zhang |first4=Xuesong |title=BEAST: A Bayesian Ensemble Algorithm for Change-Point Detection and Time Series Decomposition |website=[GitHub](/source/GitHub) |url=https://github.com/zhaokg/Rbeast}}</ref> 

==See also==
*[Quasi-experimental design](/source/Quasi-experimental_design)

==References==
{{reflist}}

Category:Time series
Category:Quantitative research
Category:Design of experiments

{{statistics-stub}}

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Adapted from the Wikipedia article [Interrupted time series](https://en.wikipedia.org/wiki/Interrupted_time_series) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/Interrupted_time_series?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.