been criticized because they rely on respondent self-reports (Heckman and LaFontaine, 2008, 2010), and some have questioned the degree to which longitudinal data accurately track disadvantaged populations (see National Research Council, 2010). Rates estimated from aggregated counts in administrative data systems have been questioned when adjustments are not made to control for repeating ninth graders or to account for transfer students (Warren, 2005). The ways that states and local school districts classify students as dropouts, graduates, or completers can significantly affect the rates that are calculated.

Whatever the data source, there are also major questions in defining both an appropriate numerator and a denominator in calculating these rates. For example, should it include private school enrollees? Recent émigrés enrolled in U.S. schools but who spent most of their education outside the U.S. education system? GED recipients? Special education students? “On-time” graduates only? Obviously, these choices should be driven by the policy questions being addressed as well as the availability of the desired data. However, until recently, no standard conventions for data inclusion or exclusion have been widely accepted in the education research and policy community. Efforts by the National Governors Association represent some progress toward standardizing methods for estimating graduation rates (National Governors Association Task Force on State High School Graduation Data, 2005). Nevertheless, there remains a lack of understanding about which calculation methods and which data are most appropriate for different policy questions, and often the best data sources may not be available for the calculations.


The Committee for Improved Measurement of High School Dropout and Completion Rates was formed to convene a workshop and to make recommendations about these issues. Specifically, the steering committee was asked to address the following questions:

  1. What are the available measures of dropout and completion rates, how are they determined, and what are their strengths and limitations?

  2. To what extent do current and proposed measures attain the necessary levels of accuracy, given the types of policy decision that they inform?

  3. What is the state of the art with respect to constructing longitudinal student accounting systems for measuring dropout and completion rates? What is the feasibility and desirability of moving to such systems? What are some of the issues that need to be considered when designing these data systems?

  4. In what ways can the analysis of data from current and proposed systems for measuring dropout and completion rates be used to help understand changes in the rates?

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