structural:scholar_edition
Longitudinal studies

Longitudinal studies take measure of the same random sample at multiple time points. Unlike with a successive independent samples design, this design measures the differences in individual participants' responses over time. This means that a researcher can potentially assess the reasons for response changes by assessing the differences in respondents' experiences. Longitudinal studies are the easiest way to assess the effect of a naturally occurring event, such as divorce that cannot be tested experimentally.

However, longitudinal studies are both expensive and difficult to do. It is harder to find a sample that will commit to a months- or years-long study than a 15-minute interview, and participants frequently leave the study before the final assessment. In addition, such studies sometimes require data collection to be confidential or anonymous, which creates additional difficulty in linking participants' responses over time. One potential solution is the use of a self-generated identification code (SGIC). These codes usually are created from elements like 'month of birth' and 'first letter of the mother's middle name.' Some recent anonymous SGIC approaches have also attempted to minimize use of personalized data even further, instead using questions like 'name of your first pet. Depending on the approach used, the ability to match some portion of the sample can be lost.

In addition, the overall attrition of participants is not random, so samples can become less representative with successive assessments. To account for this, a researcher can compare the respondents who left the survey to those that did not, to see if they are statistically different populations. Respondents may also try to be self-consistent in spite of changes to survey answers.