Background: Student absenteeism is a growing concern in U.S. schools. In 2015-16, states' chronic absenteeism rates -- defined as rates of students missing 10% or more of a school year -- ranged from 9.5% to 26.9% of students (U.S. Department of Education, n.d.). Although abundant research exists on absenteeism and its effects on student outcomes, little examines those effects based on the timing of absences or types of class missed. Authors (2017) found that missing school in the spring -- when schools administer state assessments -- is associated with lower achievement on those assessments. Olson(2014) found that Baltimore students who missed two to four days in September were five times more likely to be chronically absent that same year. No extant literature details the effects of absenteeism based on day of the week or time of day missed. Studies on the differential effects on academic outcomes based on the type of class missed represent a clear gap in the literature. One reason for this is, while most studies look at day-level attendance, very few look at period-level. Notable exceptions are Gottfried et al. (2022), who use period-level data to determine whether students are less likely to miss class with a same-race teacher, and Whitney & Liu (2017), who inform much of our understanding on which times of day and types of classes students miss most often. Purpose: Excessive absenteeism exerts negative effects on students' test scores (Gottfried, 2015; Ilter, 2021; Rhoad-Drogalis & Justice, 2018), class grades (Ansari & Pianta, 2019; Author, 2019b), graduation rates (Author, 2019b), and social-emotional skills (Gottfried, 2014; Santibañez & Guarino, 2021). What is less clear is whether different types of absences have differential effects on these outcomes. Students miss class on different periods of the day and days of the week. They also miss different types of classes. Yet no known research examines whether these differences matter for students' academic success. Knowing these differences could do much to inform policy decisions. For example, it may be possible that students miss the first or last period of the day, or the period after lunch, more often. Students may miss class more often on Monday or Friday, or academic more often than non-academic classes. These differences may point to the times of day, days of the week, of types of class in which students are least engaged, or when the least engaged students are most likely to miss school. Lack of school engagement directly links to absenteeism (Bealing, 1990; Broadhurst et al., 2005): (1) Which days of the week and periods of the day do students most often experience unexcused absences?; (2) Do these associations differ based on course type or student-level characteristics? Research Design: Our sample consists of the 1,684 students who were in the 9 grade in the 2013-14 school year who were enrolled on the first day of school. For our analysis, the sample was expanded to the student-by-period-by-day level (i.e., every possible class period the student could have attended; N = 5,680,840) and the student-byday level (i.e., every possible day the student could have attended; N = 1,077,476). In the first two research questions, the key variables of interest are different measures of timing. These include the class period of the day and day of the week. To explore which periods of the day students were most likely to have an unexcused absence, we employed a linear probability model with multiple fixed effects, including date fixed effects (e.g., October 3, 2013). We also added fixed effect indicators for course type (e.g., Algebra I). Next, to determine whether students were more likely to miss classes on a particular day of the week, we substitute a week fixed effect for the date fixed effect in Model 1 to allow variation in absence between days in the dataset. To examine whether associations for research question 1 differ by course type and student-level characteristics, we employed the same models above as fully interacted regressions by restricting the sample to each course and student characteristic described above. Results: Table 2 presents estimates from Model 2 to explore whether students or more or less likely to have an unexcused based on the period of the day. The weighted mean for all unexcused absences was used for reference in order to not leave out one of the periods. Coefficients represent the percentage point changes in the likelihood of an unexcused absence compared to the weighted mean of the likelihood of an unexcused absence. While there was some slight variation during the school day around the time of the lunch break for several subgroups, Column 1 shows that sixth hour, the last hour of the day, produced a statistically significant lower likelihood in unexcused absenteeism by 0.5 percentage point. Table 3 presents estimates from Model 1 to explore whether students are more or less likely to have an unexcused absence based on day of the week. Looking at the first column of Table 3, two differences in likelihood of absence by day of the week emerged. First, Tuesdays were associated with a statistically significant decrease in unexcused absenteeism. The entire cohort was less likely to be absent on Tuesdays compared to the weighted mean by 0.9 percentage points. Second, Fridays were associated with a statistically significant increase in unexcused absenteeism. The entire cohort was more likely to be absent on Fridays compared to the average weighted mean by 1.8 percentage points. Table 3 also illustrates some differences by student subgroup. We notice is that students who receive services are consistently less likely to have an unexcused absence on a Friday than their peers who are not receiving services. As seen in Tables 5-6, in general, students were less likely to miss a required or academic class than a class that was not required or non-academic. The most significant differences in increased absenteeism, seen on Fridays does seem to vary slightly by class type. While there was some variation between academic and non-academic classes, the largest differences were between required and not required classes.