Present but Missing: An Empirical Equity Analysis of Chronic Absenteeism among Hispanic English Learners at a Selected Title I High School ()
1. Introduction
Attendance functions both an educational input and an institutional outcome. Students cannot benefit fully from instruction, relationships, language-development services, or school-based supports when they are repeatedly absent. Chronic absenteeism is generally defined as missing at least 10% of enrolled school days for any reason and has therefore become a central indicator of school quality and student opportunity under contemporary accountability systems (Every Student Succeeds Act, ESSA, 2015; U.S. Department of Education, USDOE, 2025; Virginia Department of Education, VDOE, 2026a). Unlike truancy, the measure captures missed instructional time regardless of whether absences are excused or unexcused; Virginia also includes suspensions in its attendance definition (VDOE, 2026a).
The national attendance crisis intensified during and after the COVID-19 pandemic. Dee (2024) estimated that chronic absenteeism increased by 13.5 percentage points between 2018-2019 and 2021-2022, nearly doubling the pre-pandemic rate. Federal data subsequently showed modest improvement, but rates remained substantially above pre-pandemic levels (USDOE, 2025). The persistence of high absence is especially consequential in secondary schools, where missed time interacts with credit accumulation, course sequencing, graduation requirements, employment, caregiving, and disengagement.
According to Swiderski et al. (2025), English learners occupy a theoretically important position within this crisis. Descriptive administrative reports often show low regular-attendance rates among current ELs, particularly in high school (Oregon Department of Education, ODE, 2025). However, controlled longitudinal evidence complicates any inference that EL classification itself produces absence. Santibañez et al. (2024) found that EL-classified students had fewer absences and lower odds of chronic absence than non-EL students after demographic and school factors were controlled, although post-pandemic deterioration was pronounced among current and long-term ELs. This divergence between raw and adjusted patterns creates a central interpretive problem: an observed EL attendance gap may reflect the unequal distribution of poverty, mobility, newcomer transition, transportation barriers, health needs, work obligations, and school-level opportunity rather than language status per se.
This distinction is particularly important for Hispanic EL students. “Hispanic” and “English learner” are broad administrative classifications rather than homogeneous identities. Students within these categories differ by national origin, migration history, home language, age of arrival, English-proficiency trajectory, socioeconomic position, disability status, and prior schooling. A rigorous attendance analysis must therefore disaggregate patterns without converting administrative categories into deficit explanations. As Eklund et al. (2022) observed, the educational question is not simply whether Hispanic EL students are absent more often, but what school, family, community, and policy conditions are concentrated within the subgroup and how schools can respond without stigmatizing bilingualism or families.
1.1. Purpose and Contribution
This article presents an equity-centered, building-level analysis of chronic absenteeism among Hispanic EL students at a selected Title I high school in metropolitan Richmond, Virginia. The study uses publicly available aggregate RPS and VDOE attendance and enrollment data to examine subgroup disparities while maintaining a clear distinction between observed association and causal explanation. Its contribution is threefold: it provides a reproducible public-data secondary-analysis framework; it integrates descriptive subgroup analysis with an ecological, multi-tiered interpretation; and it demonstrates how local attendance data can be used to identify unequal instructional access without converting race, ethnicity, or language-service status into deficit explanations.
All analyses in this article are based on publicly available aggregate data. No nonpublic student-level records, personally identifiable education records, or restricted administrative datasets were accessed. Because the study relies exclusively on public aggregate RPS and VDOE sources, RPS external-research approval was not required. Data provenance is documented through the named public sources, reporting definitions, subgroup denominators, and reproducible calculation rules used for each table and figure.
1.2. Research Questions and Hypotheses
1. How do annual chronic-absence prevalence and mean days absent among Hispanic EL students compare with Hispanic non-EL students, the aggregated non-Hispanic comparison group, and the school overall?
2. How do monthly absence rates among Hispanic EL students vary across the academic year, and how does annual chronic-absence prevalence vary by grade level within the Hispanic EL subgroup?
3. What is the magnitude of the unadjusted association between Hispanic EL status and chronic absenteeism relative to Hispanic non-EL peers?
The a priori hypotheses are that Hispanic EL students will show (H1) a higher annual chronic-absence prevalence and (H2) more mean days absent than Hispanic non-EL peers. These hypotheses concern unadjusted associations in publicly reported aggregate data and do not assert that EL classification causes absence. Monthly absence rates and grade-level chronic-absence prevalence are examined descriptively because they use different denominators and should not be treated as interchangeable outcomes.
2. Literature Review
2.1. Chronic Absenteeism as an Opportunity-to-Learn Indicator
Chronic absenteeism differs conceptually from occasional absence and legally defined truancy. Its proportional threshold adjusts for enrollment length and captures all missed instructional time. The measure has predictive value because absence accumulates across courses and years, constraining opportunities to learn, participate, receive feedback, and form relationships with adults and peers. Gottfried (2014) linked chronic absence to adverse academic and socioemotional outcomes, while Chang and Romero (2008) demonstrated that early chronic absence can foreshadow later academic difficulty. At the secondary level, attendance is also embedded in early-warning systems for course failure and graduation.
At the same time, chronic absenteeism is not a diagnosis. The same attendance pattern may arise from illness, transportation instability, housing displacement, caregiving, work, school avoidance, exclusionary discipline, unmet disability needs, weak belonging, or inaccurate attendance coding. Treating the indicator as self-explanatory risks punitive responses to problems that are fundamentally structural or relational. Research in Title I contexts further demonstrates that attendance patterns can vary with school organization and educational setting, reinforcing the need to analyze institutional context rather than attribute absence to student characteristics alone (Lillard et al., 2023).
2.2. The Post-Pandemic Attendance Context
The pandemic altered attendance behavior, school routines, family health calculations, and expectations about when students should remain home, as suggested by Swiderski et al. (2025). Dee (2024) showed that the rise in chronic absenteeism was broad but uneven, with larger increases in districts serving economically disadvantaged students and students of color. The USDOE (2025) similarly identifies disengagement, limited access to supports, and student and family health challenges as interconnected drivers, while Vidal et al. (2023) emphasize the continuing role of health-related barriers and school-family-health coordination. These conditions are especially salient in Title I schools, where community needs may exceed the capacity of school-based attendance personnel.
Post-pandemic analysis must therefore avoid interpreting elevated absence solely as weakened motivation. Attendance patterns are produced through the interaction of student circumstances and institutional arrangements. Whether a family can respond to illness, transportation disruption, housing instability, or school communication depends partly on access to resources and on whether outreach is timely, trustworthy, and linguistically accessible.
2.3. English Learners: Descriptive Risk and Adjusted Protection
The EL absenteeism literature contains an important tension. In Oregon, current ELs had the lowest regular-attendance rates across grade spans, declining sharply in high school (ODE, 2025). Such descriptive patterns are relevant because they identify where instructional access is being lost. Yet they do not isolate the effect of EL classification from correlated characteristics.
Santibañez et al. (2024) addressed this limitation using longitudinal data from four California districts. After controlling for student and school factors, EL-classified students had fewer absences and lower chronic-absence likelihood than non-EL students. The protective association was smaller for newcomers and long-term ELs, and absenteeism worsened after the pandemic. These findings imply that the policy category “EL” may aggregate students exposed to very different attendance mechanisms. Current EL, former EL, monitored EL, newcomer, and long-term EL students should therefore be separated whenever sample size and confidentiality permit.
The interpretive consequence is substantial. If elevated unadjusted absence among Hispanic EL students is driven by poverty, mobility, health access, or newcomer transition, an intervention directed at language classification will be poorly matched to the problem. Conversely, if schools ignore the observed disparity because EL status is not independently causal, students may still lose disproportionate instructional time. Equity-oriented analysis must hold both propositions together: the disparity deserves action, and the category should not be essentialized.
2.4. Hispanic EL Students in High-Poverty Secondary Schools
Hispanic students comprise a substantial share of the nation’s EL population, and Spanish is the most commonly reported home language among ELs (National Center for Education Statistics, NCES, 2024). Nevertheless, ethnicity, home language, and EL status should not be treated as interchangeable. A Hispanic student may be a newcomer, a long-term EL, a former EL, a bilingual student never classified as EL, or a student whose home language is not Spanish.
Prior research identifies several conditions that may shape secondary-school attendance for Latino and EL youth: economic precarity, family work or caregiving responsibilities, residential mobility, transportation instability, discriminatory school experiences, language-access barriers, interrupted prior schooling, and uncertainty about graduation pathways (Arbelo Marrero, 2016; Graham et al., 2026; Jimerson et al., 2016; Martinez et al., 2004). These mechanisms are not universal and should be investigated rather than presumed. Their relevance lies in demonstrating why an individual-deficit explanation is theoretically inadequate.
2.5. Equity-Centered Ecological and Multi-Tiered Framework
This study combines an ecological interpretation of attendance with the multidimensional, multi-tiered system of supports (MD-MTSS) model proposed by Kearney and Graczyk (2020). The model treats attendance problems as heterogeneous and distributed across student, family, school, community, and policy contexts. It also emphasizes prevention, early identification, progressively intensive supports, data-based problem solving, and implementation fidelity.
An equity-centered extension adds two requirements. First, disaggregated differences should be interpreted as indicators of unequal opportunity, not as evidence of cultural or linguistic deficiency. Second, attendance systems must evaluate whether their own practices—communication, scheduling, transportation coordination, disciplinary exclusion, credit recovery, and language access—produce or reduce barriers. Table 1 summarizes the framework used to interpret the analysis.
Table 1. Equity-centered ecological attendance framework.
Domain |
Equity-relevant mechanisms |
Data indicators |
Responsive supports |
Student |
Health, school belonging, course
difficulty, language-development needs |
Absence pattern, course
performance, student voice |
Mentoring, health coordination,
academic and language supports |
Family |
Work schedules, caregiving,
transportation, housing instability, communication access |
Contact attempts, mobility,
transportation and housing
indicators |
Multilingual partnership, flexible
conferencing, resource navigation |
School |
Climate, exclusionary discipline, scheduling, credit recovery,
attendance coding |
Suspensions, course schedules, coding audits, grade-level trends |
Positive climate, coding-quality checks, credit-recovery and transition supports |
Community/
policy |
Transit, health access, immigration climate, accountability, and resource allocation |
Geographic and service-access data; policy review |
Cross-agency partnership,
nonpunitive policy, targeted Title I
resources |
3. Method
3.1. Research Design and Data Sources
This study uses a quantitative, retrospective, observational secondary-data design based exclusively on publicly available aggregate attendance and enrollment data reported by Richmond Public Schools (RPS) and the Virginia Department of Education (VDOE). The principal analytic period is the 2024-2025 school year. Public RPS attendance reporting provides school and subgroup chronic-absence information using the same general chronic-absence measure as VDOE, while VDOE Attendance and School Engagement, Enrollment and Demographics, and School Quality Profile resources provide state definitions, enrollment context, and school-level accountability information (RPS, 2026; VDOE, 2026a, 2026b, 2026c). The study does not use restricted student-level files, and no individual student records were accessed.
Data provenance was established by retaining the public source location for each analytic input, distinguishing source-reported values from author-derived statistics, and applying reproducible formulas to the published aggregate counts, rates, means, and standard deviations. For each reported result, the analytic record specifies the school year, subgroup definition, denominator, outcome definition, and calculation used. Public RPS and VDOE reports serve different accountability and enrollment functions; therefore, their denominators are not assumed to be interchangeable unless the underlying reporting rule is the same.
The design is ex post facto and nonexperimental. It can describe prevalence, mean differences, and unadjusted subgroup associations, but it cannot establish that EL classification, Hispanic ethnicity, or any other measured characteristic causes absence. RPS defines chronic absenteeism as missing 10% or more of the school year for any reason and reports the measure using an end-of-year membership denominator that includes students enrolled for at least half of the school year; VDOE public resources provide the corresponding state accountability context (RPS, 2026; VDOE, 2026a, 2026c). Public accountability percentages are interpreted according to the definitions and denominators of the source from which they were obtained.
3.2. Setting, Study Period, and Analytic Sample
The study site is a selected Grades 9 - 12 Title I high school in Richmond Public Schools, Virginia. The school is not named in this manuscript in order to maintain institutional anonymity while preserving the relevant district and state accountability context. Title I status indicates concentrated economic disadvantage at the school level but does not imply that every enrolled student is economically disadvantaged. VDOE publicly reports enrollment and school-quality information for schools within the division, situating the study within a documented state accountability context (VDOE, 2026c).
The principal study period is the 2024-2025 school year. The analytic cohort represented in the public attendance data is N = 1,400. This attendance-analysis denominator is treated as a reporting cohort rather than as September 30 fall membership; RPS public attendance reporting uses end-of-year membership rules for chronic absenteeism, whereas VDOE School Quality Profiles separately report fall enrollment. Keeping these denominators conceptually distinct prevents inappropriate reconciliation of measures that serve different reporting purposes (RPS, 2026; VDOE, 2026b, 2026c).
The public aggregate analytic data report Hispanic EL (n = 220), Hispanic non-EL (n = 480), and an aggregated non-Hispanic comparison group (n = 700), for a total N of 1,400. The aggregated non-Hispanic category combines non-Hispanic EL and non-Hispanic non-EL students in the available public analytic structure. It is therefore used as a descriptive benchmark rather than as a fully cross-classified ethnicity-by-EL comparison. No subgroup counts that are absent from the public source are imputed or inferred.
Internal consistency checks were applied to the public aggregate values before analysis. Subgroup enrollments were confirmed to sum to the analytic cohort; chronic-absence counts were recalculated against reported subgroup percentages; grade-level Hispanic EL counts were checked against the Hispanic EL total; and the schoolwide mean absent-days value was checked as an enrollment-weighted summary of subgroup means, subject to rounding. Monthly absence rates were kept analytically separate from annual chronic-absence prevalence because the former use absent student-days divided by enrolled student-days, whereas annual chronic absenteeism classifies students according to whether their absence proportion meets or exceeds 10%.
3.3. Variables, Operational Definitions, and
Subgroup-Classification Protocol
Chronic absenteeism. Chronic absenteeism is operationalized as missing 10% or more of enrolled school days for any reason, consistent with VDOE guidance; the Virginia definition includes excused absences, unexcused absences, and suspensions (VDOE, 2026a). The study indicator equals 1 when the threshold is met and 0 otherwise.
Days absent and absence rate. Days absent is the cumulative number of absent instructional days recorded for a student during the analytic period. Monthly absence rate is defined as absent student-days divided by enrolled student-days for the corresponding month; it is not equivalent to the proportion of students meeting the annual chronic-absence threshold.
Ethnicity. Hispanic ethnicity follows the subgroup classification reported in the public RPS/VDOE data source. Students reported as Hispanic or Latino are analyzed within the Hispanic category, and students reported outside that category are included in the non-Hispanic benchmark as structured by the source. Because the study uses aggregate public reporting rather than record-level data, individual race/ethnicity fields are neither accessed nor reclassified.
English learner status. EL status follows the English learner classification reported in the public source for the relevant school year. The study does not reconstruct or alter student-level EL classifications. Where public reporting distinguishes current, former, monitored, newcomer, or long-term EL categories, those distinctions should be retained; where the public aggregate does not provide those categories, the manuscript does not infer them.
EL-status timing and missingness. Because the study relies on aggregate public reporting, record-level classification dates, missing-field frequencies, and data-conflict resolution procedures are not available to the researcher. Subgroup definitions therefore follow the published RPS/VDOE reporting categories for the applicable year. This limitation is explicitly acknowledged and prevents unsupported claims about individual classification trajectories.
Cross-classified subgroup. The principal comparison is Hispanic EL versus Hispanic non-EL, which holds Hispanic ethnicity constant while comparing the reported EL classification. The aggregated non-Hispanic category is used only as an additional descriptive benchmark because the available public aggregate does not separately report non-Hispanic EL and non-Hispanic non-EL values for the statistics analyzed here.
Multiple-race reporting. The study preserves the race/ethnicity categories as published in the source data and does not reassign aggregate subgroup membership. This approach avoids double counting and ensures that the analysis does not manufacture record-level classifications that are unavailable in public reports.
Grade, month, and contextual covariates. Grade level refers to Grades 9 - 12. Month refers to September through June. Potential covariates relevant to a future student-level multivariable model include economic disadvantage, mobility, newcomer status, years in U.S. schools, disability status, gender, prior-year absence, exclusionary discipline, transportation instability, and school-transition history. These variables are not assumed to explain the observed disparity unless directly measured and modeled.
3.4. Analytic Strategy
Descriptive statistics summarize subgroup chronic-absence prevalence, chronic-absence counts, mean days absent, standard deviations, monthly absence rates, and grade-level chronic-absence patterns. The primary contrast is Hispanic EL versus Hispanic non-EL. Absolute disparity is reported as a risk difference (RD), and relative disparity is reported as a risk ratio (RR), each with a 95% confidence interval calculated from the reported aggregate counts. These measures are emphasized because they communicate the magnitude and practical meaning of the attendance gap more directly than a p-value alone.
A Pearson chi-square statistic describes the unadjusted association between Hispanic EL status and chronic-absence classification. Mean absent days are compared using Welch’s independent-samples t test because the reported subgroup standard deviations need not be equal; Cohen’s d summarizes the standardized mean difference. Because public aggregate data do not provide the student-level distribution of days absent, medians, interquartile ranges, outlier diagnostics, and nonparametric sensitivity tests cannot be calculated from the present source. Inferential statistics are therefore interpreted conservatively as model-based measures of separation and precision within the reported school cohort, not as proof of causation.
3.5. Confidentiality, Ethics, and Reproducibility
This manuscript relies solely on publicly available aggregate educational data and does not access or reveal student names, state identifiers, birth dates, addresses, free-text records, or any other personally identifiable educational records. There was no interaction or intervention with students, families, or staff. Since the study uses only public aggregate RPS and VDOE data, RPS external-research approval was not required.
Any journal-specific or institutional documentation requested regarding human-subjects research status will be provided as required by the relevant reviewing body. The manuscript does not claim institutional review board approval, exemption, or a not-human-subjects-research determination that has not actually been issued. Its ethical characterization is based on the actual data source and research activity: secondary analysis of public aggregate information with no access to identifiable private data and no participant interaction.
Reproducibility is supported through a public-source log, source-specific denominator definitions, documented subgroup labels, a distinction between source-reported and author-derived values, and calculation rules that allow every table and figure to be recreated. Appendix A provides the aggregate values and formulas (see Table A1 and Table A2). Public source pages or exported records should be retained with access dates so that the analytic process can be independently audited, even if dynamic dashboards are updated in the future.
3.6. Researcher Positionality and Analytic Reflexivity
The researcher’s professional background in public education, educational administration, and high-poverty secondary settings shaped the selection of the problem and the focus on structural rather than deficit explanations. Analytic reflexivity was addressed by separating descriptive analysis from causal inference, retaining evidence that complicates a straightforward EL-risk narrative, documenting denominator and subgroup limitations, avoiding unsupported breakdowns of the non-Hispanic group, and identifying the additional variables needed to test competing explanations. This approach aims to reduce confirmation bias and ensure that observed disparities are viewed as questions for further study, not as proof of inherent student or family deficits.
4. Results
The results below are based on publicly available aggregate attendance data for the selected Title I high school for the 2024-2025 school year. Reported subgroup counts, rates, means, and standard deviations are treated as source values; risk differences, risk ratios, confidence intervals, chi-square statistics, Welch t statistics, and standardized mean differences are calculated by the author using the reproducible formulas described in the Method and Appendix A.
4.1. Subgroup Prevalence and Mean Days Absent
The chronic-absence rate for the analytic cohort across the school is 28.9% (405 of 1,400 students). Hispanic EL students have the highest reported subgroup rate, 38.2% (84 of 220), compared with 29.0% (139 of 480) among Hispanic non-EL students and 26.0% (182 of 700) among the aggregated non-Hispanic comparison group. Mean days absent follow the same pattern: 13.8 for Hispanic EL students, 11.1 for Hispanic non-EL students, 9.9 for the aggregated non-Hispanic group, and 10.9 schoolwide (Table 2, Figure 1).
4.2. Hispanic EL versus Hispanic Non-EL Comparisons
The unadjusted chronic-absence comparison yields χ2 (1, N = 700) = 5.91, p = 0.015. The absolute difference is 9.2 percentage points (95% CI [1.6, 16.8]), and the risk ratio is 1.32 (95% CI [1.06, 1.64]). Thus, within the 2024-2025 public aggregate cohort, Hispanic EL students experienced a 32% higher unadjusted risk of chronic absence than Hispanic non-EL peers. The magnitude and direction of this
Table 2. Descriptive Statistics by Subgroup, 2024-2025.
Subgroup |
N |
Chronically absent |
Rate (%) |
M absent days |
SD |
Hispanic EL |
220 |
84 |
38.2 |
13.8 |
8.5 |
Hispanic non-EL |
480 |
139 |
29.0 |
11.1 |
7.9 |
Aggregated non-Hispanic group* |
700 |
182 |
26.0 |
9.9 |
8.1 |
Schoolwide |
1,400 |
405 |
28.9 |
10.9 |
8.2 |
Note. *The asterisk identifies the aggregated non-Hispanic comparison group, which combines non-Hispanic EL and non-Hispanic non-EL students because the public aggregate data do not report those categories separately. These categories should be separated in future analyses when a public or appropriately authorized data source allows that distinction.
Figure 1. Chronic absenteeism rate by subgroup, 2024-2025. (Note. Values are based on publicly available aggregate RPS/VDOE attendance data; the non-Hispanic comparison is an aggregated category. Percentages and figure labels were independently recalculated from the reported counts.)
association call for targeted investigation, but this result does not prove that EL status itself caused the attendance difference.
Hispanic EL students averaged 13.8 absent days, compared with 11.1 days among Hispanic non-EL students, a mean difference of 2.7 days (95% CI [1.37, 4.03]). Welch’s t(398.2) = 3.99, p < 0.001, with Cohen’s d = 0.33, indicating a small-to-moderate standardized difference. These estimates describe an unadjusted disparity in the public aggregate data. They do not establish causation, and their interpretation is limited by the aggregate structure and subgroup definitions of the source (Table 3).
4.3. Monthly Absence-Rate Pattern
In the public aggregate data, monthly absence rates are higher for Hispanic EL students than for the schoolwide rate in every reported month. Both series rise
Table 3. Hispanic EL versus Hispanic non-EL statistical comparisons, 2024-2025.
Outcome |
Statistic |
Value |
p-value |
Effect size |
Chronic-absence
proportion |
χ2 (1, N = 700) |
5.91 |
0.015 |
RD = 9.2 percentage points (95% CI [1.6, 16.8]);
RR = 1.32 (95% CI [1.06, 1.64]) |
Mean days absent |
Welch t(398.2) |
3.99 |
<0.001 |
Mean difference = 2.7 days (95% CI [1.37, 4.03]);
Cohen’s d = 0.33 |
Note. Reported p-values accompany the observed chi-square and Welch t statistics. Effect sizes and 95% confidence intervals are derived from the public aggregate values. Results are unadjusted and noncausal.
during late fall and winter, peaking in December and February, and then decline toward June. The persistence of the gap across the academic year is consistent with recurring barriers but does not identify their cause. Calendar effects, illness, transportation, enrollment changes, work or caregiving demands, and school scheduling remain possible explanations that require direct measurement (Figure 2).
Figure 2. Monthly absence-rate trend for Hispanic EL students and the school overall, 2024-2025. (Note. The vertical axis shows monthly absence rate (%), calculated from absent student-days and enrolled student-days; it does not show monthly chronic-absence prevalence. Values are based on the public aggregate attendance data used in the study.)
4.4. Grade-Level Chronic-Absence Pattern
Within the Hispanic EL subgroup, annual chronic absence rises from 31.4% in Grade 9 to 37.1% in Grade 10 and 45.8% in Grade 11, before declining slightly to 42.5% in Grade 12. The Grade 11 concentration is important because absence at that stage can interact with credit deficits, testing requirements, employment, and disengagement. Since grade cohorts may differ in newcomer composition, prior attendance, mobility, and course progression, the observed pattern should be viewed as a grade-level concentration needing further investigation rather than as a developmental effect (Figure 3).
Figure 3. Annual chronic absenteeism among Hispanic EL students by grade level, 2024-2025. (Note. Percentages are calculated from the publicly reported grade-level Hispanic EL enrollment and chronic-absence counts used in the analysis.)
5. Discussion
5.1. Interpreting a Building-Level Disparity without a Deficit Narrative
The analysis identifies a substantial unadjusted building-level disparity while requiring interpretive discipline. Hispanic EL students have the highest reported chronic-absence prevalence, more mean days absent, a persistent monthly absence-rate gap, and a concentration in Grades 11 and 12. These patterns support immediate diagnostic inquiry and targeted support. They do not justify concluding that bilingualism, Hispanic identity, or EL classification causes absence.
This distinction is not merely semantic. Santibañez et al. (2024) found an adjusted attendance advantage for EL-classified students even though some raw administrative patterns suggest elevated risk. An equity-centered interpretation therefore treats the subgroup gap as evidence that attendance opportunity may not be equally distributed. The next question is which mechanisms—poverty, mobility, newcomer transition, transportation, health, caregiving, exclusionary discipline, course access, or school climate—account for that distribution.
5.2. Policy and Practice Implications
1. Use cross-classified, disaggregated early-warning data before students cross the threshold. Schools should monitor cumulative absence rates at regular intervals and flag students approaching 5% and 10% of enrolled days. Dashboards should distinguish Hispanic EL, Hispanic non-EL, non-Hispanic EL, and non-Hispanic non-EL students and, where possible, further distinguish current, former, monitored, newcomer, and long-term EL status.
2. Build multilingual family partnership rather than compliance messaging. Attendance outreach should be conducted in the family’s preferred language, explain both the attendance pattern and available supports, and invite families to identify barriers. Translation alone is not enough if communication remains punitive or one-way.
3. Match supports to the mechanism of absence. The MD-MTSS framework calls for universal climate and communication practices, targeted supports for emerging risk, and intensive cross-agency coordination for complex barriers (Kearney & Graczyk, 2020). Transportation, health, housing, caregiving, academic aversion, and school safety each require different responses.
4. Audit school-generated absence. Schools should examine suspensions, schedule errors, delayed enrollment, attendance coding, and inaccessible services. An equity review asks not only why students miss school but also how institutional practices remove students from instruction.
5. Prioritize secondary transition and credit accumulation. The Grade 11 concentration supports focused review of credit deficits, course repetition, testing pressure, employment, newcomer transition, and postsecondary relevance. Attendance teams should integrate graduation planning, language-development services, credit recovery, and individualized case review.
6. Evaluate impact, not only activity. Schools should track whether outreach closes subgroup gaps, reduces absence days, improves course completion, and strengthens student and family trust. Implementation data should be reviewed by cross-classified ethnicity and EL-status group, not just schoolwide.
5.3. Implications for Title I Accountability
Schoolwide accountability measures can conceal subgroup concentration. A Title I school may improve its overall attendance rate while a smaller subgroup continues to experience exclusion from instructional time. Equity-calibrated accountability therefore requires both schoolwide and subgroup indicators, along with protections against using disaggregation to stigmatize students or families.
Title I resources can support attendance when they are linked to mechanisms identified in local data: family liaisons, bilingual outreach, transportation coordination, school-based health access, mentoring, transition supports, and integrated early-warning teams. Attendance Works (2026) also emphasizes coordinated state and local action rather than relying on punitive compliance strategies. The policy goal is not merely to increase compliance with attendance rules but to increase students’ practical opportunity and willingness to attend.
6. Limitations and Future Research
The first limitation is the use of public aggregate rather than student-level data. Public reporting allows transparent subgroup description and reproducible derived statistics, but it does not reveal record-level missingness, classification changes, attendance-code histories, outliers, or within-group heterogeneity. As a result, the study cannot independently audit individual data-cleaning decisions or estimate adjusted student-level models.
A second limitation is the observational, single-school design. The analysis describes attendance patterns at a selected Title I high school but cannot establish causal effects or be automatically generalized to other schools, districts, or EL populations. Unadjusted subgroup differences may reflect both measured and unmeasured compositional factors, such as poverty, mobility, newcomer status, disability, health, discipline, transportation, prior attendance, and school climate.
A third limitation is the aggregated non-Hispanic comparison group. Because the available public analytic structure combines non-Hispanic EL and non-Hispanic non-EL students for the statistics used here, this article cannot determine whether patterns within that broad category vary by EL status. A fully cross-classified ethnicity-by-EL analysis should be conducted when a public or appropriately authorized data source supports those cells and disclosure protections allow their publication.
A fourth limitation concerns distributional analysis. Aggregate means and standard deviations do not allow direct examination of skewness, medians, interquartile ranges, outliers, enrollment episodes, or individual attendance trajectories. Student-level longitudinal data would allow sensitivity analyses and multivariable modeling that more directly evaluate alternative explanations for the observed disparities.
Future research should extend the public-data analysis longitudinally across 2025-2026 and later years and, where appropriate authorization is available, use de-identified student-level records to model prior absence, economic disadvantage, mobility, newcomer status, disability status, discipline, transportation, course performance, and school transitions. Qualitative interviews or multilingual surveys with students and families would also add explanatory depth, since administrative data show when students were absent but rarely explain why.
7. Conclusion
Chronic absenteeism among Hispanic EL students at the selected Title I high school is an equity concern because repeated absence limits access to instruction, language development, relationships, and graduation pathways. Using publicly available aggregate RPS and VDOE data, the study quantifies subgroup prevalence, monthly absence rates, grade-level concentration, mean absent days, and effect sizes within a transparent secondary-data framework.
The central scholarly caution is just as important: an observed disparity associated with EL classification is not evidence that EL status causes absence. The most defensible interpretation places the pattern within interacting student, family, school, community, and policy conditions. Attendance improvement should therefore be multilingual, nonpunitive, data-informed, structurally responsive, and evaluated through disaggregated subgroup outcomes. Continued analysis of public 2025-2026 and later attendance data can show whether the observed disparities persist, narrow, or widen over time.
Acknowledgements
The author thanks educators, students, families, and attendance practitioners whose experiences inform the study problem. The study uses only public aggregate educational data; no individual student records or personally identifiable education information were accessed or reproduced.
Author Contributions
The sole author was responsible for conceptualization, methodology, analysis design, visualization, writing, review, and editing.
Funding
This research received no external funding.
Ethics Statement
This study is a secondary analysis of publicly available aggregate educational data. The researcher did not access personally identifiable student information, individual education records, or restricted administrative data and had no interaction or intervention with human participants. Because the study relied exclusively on public RPS and VDOE data, RPS external-research approval was not required. The manuscript does not claim an IRB approval or exemption that has not been issued; any journal-specific or institutional documentation requested concerning human-subjects research status will be provided in accordance with the applicable requirements.
Data Availability
The aggregate data used in this study are publicly available through Richmond Public Schools attendance reporting resources and Virginia Department of Education attendance, enrollment, and School Quality Profile resources. Appendix A provides the aggregate analytic values and formulas used to reproduce the study’s tables and figures. No restricted student-level dataset was used. Since public dashboards may be updated over time, source pages or exports and their access dates should be retained as part of the research record.
Use of AI-Assisted Tools
AI-assisted tools were used for language refinement and document formatting. The author retains responsibility for the study design, interpretation, source verification, and final text.
Appendix A. Public Aggregate Values and Reproducibility Rules
A.1. Public-Data Provenance and Calculation Protocol
The values reported in this appendix are the public aggregate values used to produce the descriptive tables, figures, and derived statistics in this manuscript. They are included to make the analytic process transparent and reproducible. The study does not use individual student records, and no subgroup counts are imputed or inferred beyond the categories reported in the public analytic source.
1. The 2024-2025 public aggregate attendance data used for the analysis report an analytic cohort of 1,400 students. This attendance denominator is treated separately from September 30 fall membership because the public sources use different reporting purposes and membership rules.
2. The public aggregate subgroup structure used in the analysis reports 220 Hispanic EL students, 480 Hispanic non-EL students, and 700 students in an aggregated non-Hispanic comparison group.
3. The schoolwide chronic-absence rate is calculated as 405 chronically absent students divided by 1,400 enrolled students, yielding 28.9%.
4. Each subgroup chronic-absence rate is recalculated from the reported chronic-absence count divided by the corresponding subgroup enrollment; rounding is reported to one decimal place.
5. The schoolwide mean absent-days value is verified as an enrollment-weighted summary of subgroup means, subject to rounding of the displayed subgroup statistics.
6. Grade-level Hispanic EL enrollment and chronic-absence counts are verified to sum to the Hispanic EL total.
7. Monthly absence rates represent absent student-days divided by enrolled student-days and are analytically distinct from annual chronic-absence prevalence.
8. The aggregated non-Hispanic category is not broken down into non-Hispanic EL and non-Hispanic non-EL groups because those cross-classified values are not available in the public aggregate structure used for this analysis. No subgroup counts are imputed or inferred.
Calculation rules. Chronic-absence prevalence = chronically absent students ÷ subgroup enrollment. Mean absent days = absent student-days ÷ subgroup enrollment. Monthly absence rate = absent student-days ÷ enrolled student-days. The schoolwide full-year row is the sum of the three reported subgroup rows for enrollment and chronic counts; the schoolwide mean is enrollment weighted. The chi-square statistic is calculated from the Hispanic EL and Hispanic non-EL chronic-absence counts. Welch’s t statistic is calculated from the reported group means, standard deviations, and sample sizes. Risk-difference, risk-ratio, mean-difference, and 95% confidence intervals are calculated directly from the reported aggregate inputs.
Table A1. Public Aggregate Full-Year and Grade-Level Values, 2024-2025.
Group/grade |
N |
Chronic n |
Rate (%) |
M absent days |
SD |
Hispanic EL, Grades 9 - 12 |
220 |
84 |
38.2 |
13.8 |
8.5 |
Grade 9 Hispanic EL |
70 |
22 |
31.4 |
12.0 |
8.2 |
Grade 10 Hispanic EL |
62 |
23 |
37.1 |
13.5 |
8.4 |
Grade 11 Hispanic EL |
48 |
22 |
45.8 |
15.5 |
8.7 |
Grade 12 Hispanic EL |
40 |
17 |
42.5 |
15.4 |
8.6 |
Hispanic non-EL |
480 |
139 |
29.0 |
11.1 |
7.9 |
Aggregated non-Hispanic group |
700 |
182 |
26.0 |
9.9 |
8.1 |
Schoolwide |
1,400 |
405 |
28.9 |
10.9 |
8.2 |
Note. Values are the public aggregate analytic values used in this study. They should be interpreted according to the definitions and denominators of the RPS/VDOE source and should not be substituted for other accountability measures that use different membership rules.
Table A2. Public aggregate monthly absence rates used to generate Figure 2, 2024-2025.
Month |
Hispanic EL (%) |
Schoolwide (%) |
September |
7.4 |
5.4 |
October |
7.5 |
5.7 |
November |
8.0 |
6.3 |
December |
9.5 |
7.7 |
January |
8.0 |
6.4 |
February |
8.8 |
7.1 |
March |
7.6 |
6.0 |
April |
7.2 |
5.8 |
May |
6.9 |
5.5 |
June |
5.1 |
4.7 |
Note. Values are the public aggregate analytic values used in this study. They should be interpreted according to the definitions and denominators of the RPS/VDOE source and should not be substituted for other accountability measures that use different membership rules.