Opinion
Student Well-Being Opinion

Not All School Attendance Data Are Created Equal

By Russell W. Rumberger & Michael Gottfried — June 07, 2016 3 min read
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More than 7 million school children in America are absent more than 10 percent of the school year. The problem has garnered increased attention from local, state, and federal officials, including President Barack Obama. In February, the Obama administration launched an initiative to raise awareness and to help solve the problem with the use of student mentors.

One reason for the increased focus on this issue is the growing body of research documenting the detrimental impact of chronic absenteeism. Students who are more frequently absent from school have weaker performance on state exams, higher odds of grade retention and dropout, reduced psychological development, increased problem behaviors, higher chances of alcohol and drug use, and lower employment prospects. These outcomes not only have an impact on students themselves; they also generate substantial costs to taxpayers in reduced tax payments, higher health-care costs, and an increased toll of crime.

More effectively addressing the problem requires good data. With the right data, school districts can identify those students who are chronically absent and intervene before they fall behind. But which data are good data?

BRIC ARCHIVE

At first glance, it may appear that simply knowing the number of days that a student misses school is sufficient. However, not all absences are created equal. Research reveals that excused versus unexcused absences have completely different implications for how well students fare at school.

Two students who miss the same amount of school will experience different outcomes if one is sick and one is cutting class. Neither is great for student performance, but the latter has more significant consequences. Tracking the reasons for missing school also might lead to different systems of support. A reliable data-collection system must take into account the type of absence. Any data collection must also take into account truancy—the act of missing school for unexcused reasons. Truancy includes not only unexcused absences but unexcused tardies as well.

If we are to understand all reasons why a student might be missing school, it can be critical to examine tardies. With truant tardies, students are missing increments of school. While it may not seem as dramatic as missing an entire day, missing small portions of in-school time adds up, over time, to large amounts of valuable instructional minutes.

Not all absences are created equal."

Hence, a focus strictly on absences may obscure the full portrait of who is missing school and how much school they’re actually missing and why. To identify students who are frequently absent as well as frequently tardy, the reach of attendance programs and practices must be far broader in order to have a greater impact.

Another element of missing school involves days students are not enrolled. Some students enter school late, after the first day of class. One study found that 19 percent of middle school students entered school after the first day of class. Other students transfer schools midyear and miss school because they fail to enroll in their new school immediately after leaving their old school.

Enrollment data need to be coupled with absenteeism data to get a complete picture of how many school days students are enrolled in and attending school over the course of the year.

Finally, it is important to consider rates of absences. Recently, there has been a focus on “chronic” absenteeism—students missing 10 percent or more of the school year, or about 18 days. But we shouldn’t get too fixated on that rate. A study in Chicago found that missing even five days of school in a semester reduced graduation rates by 24 percentage points.

The bottom line? Good data are critical for addressing America’s school attendance crisis.

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A version of this article appeared in the June 08, 2016 edition of Education Week as Improving School Attendance Requires Good Data

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