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.

Student Name

Grade 1
(2001)
Raw Score

Grade 2
(2002)
Raw Score

Grade 3
(2003)
Raw Score

Grade 4
(2004)
 Raw Score

Grade 5
(2005)
Raw Score

Grade 6
(2006)
Raw Score

Mike

339

350

361

366

381

390

Jasmine

332

343

350

351

351

355

Thomas

360

380

400

420

430

438

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, say, 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:

  1. The state databases at the core of our work are census files, which means that the Center has access to ALL districts, schools, teachers, and students in a state. Census files do not restrict CALDER’s research to the imperfect assumptions and margins of error in sampling a population.
  2. Having access to multiple state databases allows the Center to replicate studies across states to study policies in comparative perspective and test the robustness of its findings.
  3. Data span multiple years of observation, enabling CALDER’s experts to study both contemporary and past policies relative to a baseline.

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