Brief Description: This measure consists of school records of student absences (both excused and unexcused).
Rationale: Evaluators and researchers often rely on students to report the number of days they miss school. However, research has shown that there is a weak association between self-reported absenteeism and absences reported in administrative data, with students frequently underestimating days absent, particularly when absences are unexcused.
Cautions: When planning to collect student absences directly from schools or school districts, be sure to set aside adequate time and staff resources to work with school personnel. Interpreting school records requires the ability to collate and analyze data. Programs without on-staff expertise may want to work with an external program evaluator.
Access and permissions: When working with an outside agency (e.g., a school or district) to collect school records, access to their data typically involves strict confidentiality conditions (see FERPA guidelines). You may be required to provide written parent permission with very specific information included (that can vary across schools or districts). Standard permission/consent language can be incorporated into program enrollment forms (see sample). Also, consider budgeting funds to reimburse time for school officials to gather needed data. More extensive guides on federal privacy guidelines and how to establish data sharing partnerships with school districts can be found in the Evaluation Guidance and Resources section of this Toolkit.
What to Collect: Suggestions for variables to request from schools or school districts can be found in a formatted data collection guide, here. If you are collecting report cards from parents or youth, this guide can be used to help structure your database for storage and analysis.
How to Collect:
Sources: One option for collecting school absence records is to get them directly from parents or youth (e.g., copies of the students’ report card). A small incentive for providing this information, when possible, may be helpful. Another option is to get administrative data directly from schools or school district offices, in which case, a formal MOU will typically be required. Schools or districts may agree only to provide “deidentified” data (i.e., data that do not include student names or other identifying information). If so, it is advisable in the data request to attach information to each youth’s name, such as basic demographics (gender or race/ethnicity) or program participation status so that the data once obtained (with this information attached to each line of data, but with the youth’s name removed) will allow you to use this information in analyses. Care must be taken, however, to ensure this type of attached information does not allow a youth to be inadvertently identified; a general rule of thumb is to ensure that the data once obtained do not include subgroups (e.g., male Native American youth) of fewer than 10 youth.
Additional Considerations: If you are interested in assessing changes over time, make sure to collect a "baseline" in the period before the student began program involvement. If mentoring program participation is less than the full school year, be sure to collect a similar time period for comparison to account for seasonal variations in absence rates (e.g., fall semester to fall semester). If possible, you may also want to consider collecting absence data for a comparable group of students not participating in the mentoring program. These data can be used to compare outcomes for program and non-program participants, which is a more robust evaluation design than simply looking at changes over the course of program involvement for program participants.
How to Analyze: It is important to work closely with school or district officials to understand how they record absences/non-attending. For example, there may be variations in whether absences include when students are late or leave school early, how staff determines what counts as an excused absence, and whether suspensions are included in the number of days absent. Attendance records may also vary across grade levels, dual enrollment programs, and virtual learning environments. Additionally, school records may or may not flag chronic absenteeism, defined by the U.S. Department of Education as missing 10% of the academic year (e.g., 18 days in a 180-day school year). If the schools you are working with do not track chronic absenteeism, ask for the total number of days in the academic year and divide the number of days a student has been absent (both excused and unexcused) by the total number of school days. Finally, excessive unexcused absences can trigger legal action under a state or locality’s truancy statute. School records typically indicate whether a student is considered truant, however, the number of allowable absences before the threshold for truancy is met may vary across schools or districts.
How to interpret findings: Programs can report changes in total days missed, with a reduction in this number indicating student improvement. Here, it is important to distinguish between excused and unexcused absences, as the latter is more strongly associated with poor academic outcomes. Changes from chronically absent or truant during the baseline period to no chronic absenteeism or truancy in the follow-up period also indicates improvement in school attendance over time.
Attendance Works. (2014). The Attendance Imperative: How States Can Advance Achievement by Reducing Chronic Absence. Retrieved from http://www.attendanceworks.org/state-policy-brief-attendance-imperative
Hancock, K. J., Gottfried, M. A., & Zubrick, S. R. (2018). Does the reason matter? How student‐reported reasons for school absence contribute to differences in achievement outcomes among 14–15 year olds. British Educational Research Journal, 44 (1), 141-174.
Keppens, G., Spruyt, B., & Dockx, J. (2019). Measuring school absenteeism: Administrative attendance data collected by schools differ from self-reports in systematic ways. Frontiers in Psychology, 10, 2623.
National Forum on Education Statistics. (2018). Forum Guide to Collecting and Using Attendance Data (NFES 2017-007). U.S. Department of Education. Washington, DC: National Center for Education Statistics. https://nces.ed.gov/pubs2017/NFES2017007.pdf
Teye, A. C., & Peaslee, L. (2015, December). Measuring educational outcomes for at-risk children and youth: Issues with the validity of self-reported data. In Child & Youth Care Forum (Vol. 44, No. 6, pp. 853-873). Springer US.