School absenteeism is a major barrier to learning (Gottfried and Kirksey 2017; Kirksey 2019). Pre-pandemic, when 15% of students were chronically absent, or absent on 10% or more days of the year, education leaders warned of an absenteeism “crisis” (U.S. Department of Education 2016). The pandemic underscored the importance of school attendance; economists have estimated that the drop in future workforce productivity attributable to missed schooling could cost the United States $31 trillion, about six times the cost of the 2008 recession (Hanushek and Strauss 2025). Upon the return to in-person learning in 2021-2022, the rate of chronic absenteeism nearly doubled to 29% and has remained elevated at 24% on average, with even higher rates (30%) in low-income districts (Diliberti et al. 2025; Malkus 2025).
But the specific factors driving stubbornly elevated levels of school absenteeism in schools today are not well-understood (Diliberti et al. 2024; Walton Family Foundation 2025). Extant district or state attendance records validate that an absenteeism problem exists across nearly all student groups (Polikoff and Pardo 2025), but the coarse categorizations tracked by most school systems offer less guidance as to why it exists. (Gee et al. 2025 does examine reasons for school absenteeism in one state.)
There could be many underlying reasons for elevated absence, ranging from illness to housing instability and transportation barriers. Policy-relevant work on school absenteeism needs to shift away from describing that absenteeism has increased and toward focusing on reasons why students are absent, including which reasons, if any, systematically predict current absence patterns. While local research can inform a single school system’s unique challenges, more work can be done on a national scale to inform policy more broadly.
Existing state attendance data systems typically lack detailed information on underlying causes of school absenteeism. Furthermore, methods such as interviews or surveys with families, which could provide insights into these underlying factors, are generally impractical for school districts to implement on their own. In this context, a probability-based, nationally representative survey capable of generating population-level estimates can be particularly useful for understanding this persistent issue and informing more effective policy responses. In addition, given the critical roles that both parents and students play in school attendance, research and policy efforts to address absenteeism should incorporate perspectives from both students and their parents wherever feasible.
We leverage the Understanding America Study (UAS 2025), a probability-based, nationally representative panel of households, and the teens within those households, to further understand the causes underlying persistently high absenteeism. We combined in-depth adult interviews with surveys of both adults and teens to offer a national-level analysis of students’ and families’ reasons for school absenteeism in the post-Covid era. To describe the value of this mixed methods approach and what it afforded us, we first describe the data resources available to us, explain how we used those resources, and end by discussing how this approach was critical for generating novel policy-relevant, national-level insights on the current school absenteeism crisis. For greater discussion of our substantive results, see Rapaport et al. 2025 and Rapaport et al. 2026.
Data
The Understanding America Study is a probability-based, nationally representative survey panel of approximately 14,000 US households, administered since 2014 by the Center for Economic and Social Research at the University of Southern California. Households are invited into the panel via address-based sampling, with internet and tablets provided in cases where technology would otherwise be a barrier to participation. A two-step weighting procedure (USC Dornsife Center for Economic and Social Research 2025) is applied to data from each survey administration to achieve national representativeness. UAS data include a wealth of demographic, economic, physical health, mental health, and other social information for all panel members, including parent reports of teen absences and teen mental health beginning in 2023.
Beginning in 2025, the UAS team began recruiting teens aged 13 and above from UAS households where an existing UAS panelist is the parent or guardian of one or more teenage children. At the time of the study, the teen panel included 450 teens. Teen panel survey data are weighted for national representativeness using a similar process to that for adults. All teen panel members share a household identifier with their UAS panel member parent or guardian, which can be linked to historical household-level data and adults’ previous survey responses.
Methods
For the qualitative portion of the study, we conducted 40 in-depth interviews with parents of K-12 children in the UAS panel. We identified UAS participants whose prior responses about a randomly selected child in their household (if they had more than one K-12 child) met specific criteria that we theorized would offer a range of diverse perspectives, including that their student had particularly high (16 or more) or low (15 or fewer) absences in 2023-2024, and those whose reports on a common mental health screener flagged the child in the “abnormal” range or not. During interviews, we asked parents to focus on the experiences of this particular child. We also stratified by age range to ensure we had parents of elementary and secondary-aged children at each level of absence categorization and mental health flag (Table 1). These criteria have been shown to relate to absences (Rapaport et al. 2024). We then randomly selected panelists from within these categories for interviews.
The interviews deepened our understanding of the factors that affect school absenteeism, their interactions, and parents’ perspectives on school- and district-level strategies to combat absenteeism. Insights from these interviews informed survey design and analysis, as discussed in the next section.
We then fielded surveys to 2500 UAS panel members with K-12 students in the household and to all UAS teen panel members active at the time of the study (N=450). Surveys were designed to generate population estimates of students’ and families’ reasons for school absenteeism, the frequency of those absences, and the kinds of solutions they found most promising.
Importantly, we administered parallel surveys to both UAS adult and teen panel members. To our knowledge, no other study has compared teen and parent reports of absenteeism frequency and reasons. This method allowed examination of how results differed based on who was reporting, and whether those differences have important policy implications. In sum, our results provided policy-relevant results applicable on a broad scale and can inform current conversations among educators, policymakers, and district leaders about how to tackle the chronic absenteeism problem (Rapaport et al. 2026). In this paper, we focus on how a mixed methods approach to instrument development enhanced the quality of data collected and informed interpretation of results.
Results
Interview Results
Existing UAS data allowed us to pre-select interviewees with known and varied backgrounds, including those flagged on a mental health screener and those with excessive absence patterns. This selection process ensured inclusion in our interviews of individuals with children with low incidence, but critically important experiences who might have been missed in a fully random selection process. It was important to interview households with both “typical” and “atypical” backgrounds to ensure our surveys captured a comprehensive range of experiences.
This benefit was exemplified when interviewees revealed that the reasons underlying high absenteeism were often qualitatively different from the reasons underlying lower rates of absenteeism. Though plausible that students who miss a lot of school do so for similar reasons as students who miss less school, this was often not what we heard. For example, of 18 interviews with parents of often-absent students, 10 reported low-incidence types of circumstances driving those high absences. These included loss of a parent, wildfires closing schools, severe mental health issues requiring hospitalization, chronic diseases or illnesses (e.g., cystic fibrosis, traumatic brain injury), and extended trips abroad. Just two of our 22 low-absence interviewees reported experiencing these types of circumstances.
Parents also expressed uncertainty or imprecise recall about their child’s absences, particularly in households with multiple school-aged children, underscoring the importance of surveying and comparing responses from both adults and teens themselves.
How Interview Results Informed Survey Design
Survey design would have suffered without the richness of our interview data. Interviewees mentioned absence reasons we had not considered, adding to the items and response options we provided to adult and teen survey respondents. For example, based on interviews, we split what had been an “illness” category into “potentially contagious illness” and “mild illness symptoms” in our fielded survey, as many parents explained that schools strongly encouraged parents to keep students home with any illness symptom or for 24-48 hours following an illness. We also added a new category of “menstrual cramps or other period-related issues,” which came up in multiple interviews.
Respondents made use of these differentiated reason options: 74% of teens reported missing 1 or more days due to a potentially contagious illness, 59% for mild illness symptoms, and 50% of girls due to period-related issues. These nuances would not have been captured with a single “illness” category, and further analyses using these discrete categories can meaningfully inform attendance interventions. For example, conservative “return-to-school-after-illness” policies designed during the pandemic years have lingered, potentially keeping students home longer than necessary. Our approach enables projections of how many more school days students might attend if rigid illness policies were eased: we expect that many mild illness absences, but fewer of those due to contagious illness, might disappear under less rigid policies.
In addition, adults spoke at length about the extent to which schools are no longer meeting students’ needs – we heard that students are bored, learning is not engaging, and being present doesn’t feel necessary. Based on these conversations, we included “just didn’t want to go” in our list of absence reasons. A full 16% of teens used this category (6% of adults), and those who did select it reported missing many more days for this reason than for more common reasons like illness: 9 days versus 4 days, on average among parent reports; 6 days versus 3 days among teens.
How Interview Results Informed Analysis of Survey Results
The interview finding that reasons for absence differed for highly absent versus less-absent students prompted us to investigate whether a similar pattern might emerge in our nationwide sample, which would have important policy implications. To do so, we examined quantitative absence data in our final survey data in two different ways: we derived (1) a simple average number of reported days absent for each reason across all students and (2) an average number of reported days absent for each reason only among those students who ever missed for that reason.
Averages under the first derivation simply illustrated that contagious illness is the most common underlying reason for absence—2.1 days missed on average according to adults, 2.6 according to teens—since almost everyone misses at least 1 day for this reason. But the average number of days missed was small across all other reasons, ranging from 0-2, given a high number of zeroes factored into the means. The second derivation was especially useful for showcasing the importance of low-incidence reasons for school absenteeism among those students impacted by those underlying reasons. When computing average days absent among the smaller subset of students who missed school for each reason, the average number of days missed revealed remarkably high averages for some uncommon reasons. For example, adults reporting on students who ever missed because they “just didn’t want to go” or had “serious mental health symptoms” reported those students missed 10 and 7 days, respectively, compared to just 3 days missed for “mild illness symptoms” and 4 for “potentially contagious illness.” The high number of absences for students experiencing “uncommon” circumstances among interviewed parents attuned us to the possibility that examining absence this way might be enlightening.
Most of what is currently known about school absenteeism is from parent report or district reporting systems (Ansari 2025; Kirksey 2025; Malkus 2024). It is understudied whether takeaways would differ if the reporting person were the teen him/herself. With 88% of teen respondents having a parent who responded to the adult survey, we were able to make this comparison. We believe a comparison like this is particularly important in the context of school absenteeism since it may be a topic for which omitting details to parents, or missing school without parent awareness, could be common.
Triangulating adult and teen perspectives did yield novel insights: overall, teens reported more absences than their parents did (an average of 32 days according to teens compared to an average of 11 days according to parents). Teens were not simply exaggerating or over-reporting in one or two categories (e.g., skipping class for fun); rather, they reported a higher number of days missed in almost every category for which they had a reported absence at all (Table 2). There were some exceptions: parents reported teens missed more days due to general disengagement than teens did, and teens and adults reported similar numbers of days missed for physical illnesses. Our data cannot explain what drives these discrepancies; in some cases teens may intentionally obscure their real reasons for wanting to stay home, resulting in different reason-reporting by adults and teens for the same absence. For example, a teen may tell a parent they just don’t want to go to school, which a parent might report as disengagement, while the real reason is that they feel unsafe. In other instances, social desirability motives may differentially impact the two generations, such that teens might report staying home because they “just didn’t want to go”, while their parent might report the same absence for a more socially acceptable reason, like illness. We note that the fact that UAS panel members’ responses are confidential mitigates this concern. It is also possible that the attribution made for a given absence may simply be different—a parent might point to a teen’s anxiety while a teen might point to school being the problem.
Discussion
As elevated school absenteeism continues to be a major barrier to students’ academic progress, policies designed to encourage school attendance must be responsive to the reasons students miss school. By relying on previously collected UAS survey data on student mental health and absence rates to select interviewees, then using qualitative interview data to inform survey design and analysis, we were able to reveal policy-relevant absence patterns from both parents’ and students’ perspectives. Our findings (Rapaport et al. 2026) provide important considerations for education leaders designing interventions to boost school attendance.
The inclusion of initial in-depth interviews was critical for identifying key drivers of school absenteeism that might have otherwise been excluded from the survey. In addition, these interviews allowed for a more nuanced analysis of the frequency of low-incidence reasons for absenteeism, helping to uncover important contributors to chronic absenteeism that might otherwise have been masked. Given that one major limitation of our approach was our reliance on self-report data, a fruitful next step for this line of research is to validate survey reports. We are currently assessing the extent to which monthly reports of student absence yield better estimates than year-end reports. In addition, comparing survey to administrative data would allow validation of the total number of days absent, if not reasons for absence.
Further, our findings suggested that, at least in some cases, teens and parents have different understandings of the reasons driving school absenteeism. Our project did not focus on teen interviews, and an important next step might be to dig deeply into these differences via further interview work to determine why such differences exist and whether survey results from either teens or adults reflect reality more reliably, on average. Although there is much still to do in this area, our mixed methods study offers an important step forward in understanding elevated school absenteeism on a national level.
Corresponding author contact information
Daniel Silver, 653 Downey Way, VPD, Los Angeles, CA 90089. Email: dsilver@usc.edu