What Are Longitudinal Data?
A dataset is longitudinal if it tracks the same type of information on the same subjects at multiple points in time. For example, part of a longitudinal dataset could contain specific students and their standardized test scores in six successive years.
The primary advantage of longitudinal databases is that they can measure change. So we can estimate, for example, the effect of various factors on improvement in student achievement. We can also estimate the overall effectiveness of individual teachers by examining the performance of successive classes of students they teach, as well as examine the extent to which teacher effectiveness changes with experience or the composition of their class.
The longitudinal data extend into the past as well as the present. So we can evaluate the effect of a specific policy by looking at student performance or teacher turnover before as well as after the policy was introduced. Longitudinal data also allow us to use sophisticated analytic strategies to measure the impact of various policies with reasonable precision.
CALDER is capitalizing on the richest source of information about schools, teachers, and students in the United States—state administrative longitudinal databases. Several features of CALDER’s data enable the Center’s experts to conduct first-rate research:
Detailed demographic information on students enables CALDER to distinguish patterns in racial, social, and achievement segregation, as well as patterns in teacher quality at the district, school, and classroom levels