An interrupted time series design is in essence a single-group pretest-posttest model that has more than one pre-and-post-measure. Tests are administered before and after a manipulation of independent variables of natural occurrence. Interrupted time series design is a very economical way to measure and determine the outcome of variables on a large scale.

This design is most effective when the treatment variable is anticipated to have a quick and substantial effect on the group. Additionally, interrupted time series design is more appropriate when the treatment is presented at one time. When the independent variable is modified at one time, the participants are more likely to have cognition of it.

Interrupted Time Series Design Example

An example of this would be if you wanted to measure the effects of a tax increase on alcohol. Prior to the new tax being levied, you could survey your target group in that community about their alcohol consumptions. After the tax is introduced, you can survey the community several times to see how their responses to the tax varied over time.

While this is a very useful analytical technique, it does have some limitations. First, this research design does not control for threats to internal or external validity. When using this methodology, it is advisable to collect qualitative data to provide context to the quantitative data and results.