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] Effects of the intervention are evaluated by changes in the level and slope of the time series and statistical significance of the intervention parameters.[2] 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:
impact of changes in laws on the behavior of people;[1] (e.g., Effectiveness of sex offender registration policies in the United States)
impact of changes in credit controls on borrowing behavior;[1]
impact of experiments in income maintenance on the behavior of participants in welfare programs;[1]
impact of major historical events on the behavior of those affected by the events;[1]
impact of expressing emotional experiences on online content;[3]
in medical research, medical treatment is an intervention whose effect are to be studied;
to analyze the effect of "designed market interventions" (e.g., advertising) on sales.[4]
impacts of human activities on environmental quality and ecosystem dynamics (e.g., forest logging on local climate).[5] [6]