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]
• environmental sciences: impacts of human activities on environmental quality and ecosystem dynamics (e.g., forest logging on local climate).^[6][7]

See also

• Quasi-experimental design