Principal reports flag serious student absenteeism in 43% of Moroccan schools

A survey of principals in Morocco found that student absenteeism was considered a serious problem in an estimated 43.1% of Grade 8 schools participating in TIMSS 2023, although the design-based uncertainty was substantial. The estimate, derived from 243 schools with valid responses, had a 95% confidence interval of 29.2% to 57.0%. This percentage describes schools whose principals rated absenteeism as serious, not the share of individual students absent or meeting a clinical threshold for chronic absence. In the complete-case analysis of 232 schools, broad indicators including economic disadvantage, rural location, internet access, resource adequacy and school emphasis on academic success contributed little to distinguishing schools reporting severe attendance problems. Adding a closely related assessment of teacher lateness and absenteeism changed the picture dramatically, raising internal out-of-sample discrimination from near chance to moderately strong levels.

The study used the official Moroccan Grade 8 school-context file from the 2023 Trends in International Mathematics and Science Study, known as TIMSS. Schools, rather than students or teachers, were the unit of analysis because both the outcome and the explanatory variables came from principal questionnaires. The principal-rated outcome asked whether student absenteeism was not a problem, a minor problem, a moderate problem or a serious problem. For the primary statistical comparison, the researchers contrasted serious problems with the other three categories, while also examining the full four-level response as a sensitivity analysis. The common complete-case sample covered 232 of the 250 schools in the file and included 100 serious cases and 132 lower-severity cases.

Because TIMSS uses unequal school weights and a complex sampling design, population estimates incorporated the school weight, while uncertainty was calculated with 150 full jackknife repeated-replication estimates based on the survey’s 75 jackknife zones. The weighted models were binary logistic regressions. Their first specification contained five preselected contextual indicators: the share of pupils from economically disadvantaged homes, rural location, availability of high-speed internet, adequacy of mathematics and science resources, and the school’s emphasis on academic success. These variables represented social composition, geography, infrastructure, resources and academic organization, rather than a data-driven search across the full TIMSS questionnaire. The study was not preregistered, so the final predictor set was theory-guided rather than confirmatory in a preregistration sense.

None of the five contextual indicators remained statistically significant after false-discovery-rate correction within the contextual model. Resource adequacy had a nominal positive association with a serious principal rating, with an odds ratio of 1.81 per weighted standard deviation and a 95% confidence interval of 1.06 to 3.09, but its adjusted p-value was 0.145. Economic disadvantage had an odds ratio of 1.64, with a confidence interval of 0.98 to 2.72, while the point estimates for rural location and internet access were positive and that for academic emphasis was negative. None of these estimates provided a multiplicity-robust independent association in the available data. The authors emphasized that this does not show that contextual conditions are unimportant; the broad indicators may simply have been too indirect or imprecisely measured in a small design-weighted sample.

The analysis then added a two-item indicator combining principals’ reports of teacher lateness and teacher absenteeism. A one-standard-deviation increase in reported teacher-attendance problems was associated with 8.24 times the odds that student absenteeism would be rated serious, with a 95% confidence interval of 3.60 to 18.83 and an adjusted p-value below 0.001. Economic disadvantage also remained associated in this expanded model, with an odds ratio of 2.91 and a 95% confidence interval of 1.25 to 6.79. The association between economic disadvantage became stronger after teacher attendance was included, but the researchers said this might reflect suppression, changed adjustment or instability caused by unequal weights and should not be interpreted as evidence of a causal pathway mediated by teacher attendance.

For an assessment of discrimination, the researchers used regularized logistic regression with Elastic Net penalties and repeated nested cross-validation. The procedure comprised five outer folds repeated 10 times, with four-fold inner validation for choosing regularization strength and the balance between Ridge and L1 penalties. Folds were stratified by the binary outcome and grouped by jackknife zone, reducing the chance that design-linked schools would appear in both training and validation sets. All preprocessing was performed within training folds. Metrics included balanced accuracy, the area under the receiver operating characteristic curve, average precision, Brier score, log loss and calibration.

Broad context alone performed almost as poorly as random classification. Its mean balanced accuracy was 0.511 and its ROC area under the curve was 0.499, with average precision of 0.437. Sensitivity was 0.216 and specificity was 0.807, showing that the procedure identified relatively few of the schools whose principals called absenteeism serious. The mean Brier score was 0.258, log loss was 0.715, and the calibration slope was approximately zero. Once the teacher-attendance indicator was included, mean balanced accuracy rose to 0.728, ROC area rose to 0.774 and average precision rose to 0.695. Sensitivity increased from 0.216 to 0.688, while specificity changed only from 0.807 to 0.767. Probability error also fell, with a Brier score of 0.196 and log loss of 0.596.

The performance gain is striking but has a crucial measurement qualification. The principal who rated student absenteeism also rated teacher lateness and teacher absenteeism, using similar problem-severity categories. The added variable therefore shared both the respondent and the attendance domain with the outcome. Its strong association could reflect genuine shared organizational conditions, such as transport disruption, timetable instability, weak supervision or broader strains affecting pupils and staff. It could also partly reflect common-source measurement covariance: a principal with a generally severe assessment style might rate both staff and student attendance problems highly. Independent records would be required to separate those substantive and methodological explanations. Consequently, the results demonstrate incremental information within the questionnaire, not that teacher absenteeism causes student absenteeism.

Several further limitations constrain what the study can establish. It is cross-sectional, and the teacher and student attendance judgements were contemporaneous, so the validation results describe internal discrimination rather than prospective forecasting. The outcome was a principal’s perception of severity, not an administrative rate of recorded absence; it did not reveal the number of missed days, the identity of absent pupils, whether absences were excused or their causes. The raw sample of 232 schools also had a much lower Kish effective sample size of 60.5 because of unequal weighting. Complete-case analysis excluded 18 schools, including 11 with observed outcomes among which the weighted prevalence of serious reports was higher than in the retained sample, although this small, selectively observed group could not support a reliable estimate of bias. A four-category sensitivity analysis broadly supported the teacher-attendance result, but the lowest category contained only 13 schools and the academic-success variable violated the proportional-odds assumption.

Overall, the research identifies a sharp contrast between broad school context and information that is close to the outcome in both content and reporting source. The findings support further attendance research in Morocco using administrative student and teacher records, family information and reports from multiple informants. Such data could test whether the association between staff and student attendance persists when common-principal measurement effects are removed. Repeated or longitudinal observations would also be needed to establish temporal ordering, while larger samples and more specific measures of transport, health, caregiving, seasonal mobility and school attendance practices could clarify why economically disadvantaged schools appear more vulnerable. The current study does not validate the TIMSS item, establish causal effects or provide a forecasting tool, but it shows that how an absence problem is measured—and who reports it—can dramatically alter what appears knowable about school attendance.

Subject of Research: School-level associations with principal-reported student absenteeism in Moroccan Grade 8 schools using TIMSS 2023 data.

Article Title: Principal-reported school absenteeism in Morocco: contextual correlates and same-respondent attendance information in TIMSS 2023

Article References: Elouafi, A., Tammouch, I., Eddarouich, S., & laassar, I. (2026). Principal-reported school absenteeism in Morocco: contextual correlates and same-respondent attendance information in TIMSS 2023. Large-scale Assessments in Education, 14(1), Article 55. https://doi.org/10.1186/s40536-026-00327-5

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00327-5

Keywords: TIMSS 2023, Morocco, school absenteeism, teacher attendance, economic disadvantage, principal reports, large-scale assessment, survey weighting, out-of-sample validation, common-method bias, Principal-reported, school