Reviewing National Trends in Chronic Absenteeism

NEED Series, Data Blog [2]

Students walk down the hallway of their high school, the Omaha Street School. This photo was captured by Bridget Fogarty.

There has been a sharp national increase in student absences since the re-introduction of students into in-person classrooms after COVID lockdowns. Coupled with this is the looming threat of learning disruptions and losses from the sudden—yet necessary—pivot towards online learning formats in March of 2019. It will likely take years, if not decades, of research to fully understand how the national pandemic impacted schooling.

A recent pre-print specifically delved into student truancy, and warned that the extent of enrollment loss in public school students may be greater than it appears. Using the data they collected, I provide additional examination into the cohabitation of growth in chronic absenteeism, COVID-19 infection rates, and state-mandated masking policies in public schools.

A student who is chronically absent is one who misses 10% or about 15 days (though this number is inconsistent across states, districts) of school. It is up to local discretion when deciding what constitutes a student “absence”. Absent students not only miss out on instruction, but also meals, socialization, healthcare, and counseling. As Dee (2023) mentions in their pre-print, since the introduction of the Every Student Succeeds Act (ESSA) in 2015, schools often collect and report chronic absenteeism as a form of school accountability to reflect school quality and student success. Because ESSA was established before the pandemic, we can track how chronic absenteeism has changed between pre- and post-COVID19 lockdown school years.

Using the interactive map in Figure 1 below, you can examine change in chronic absenteeism across the United States. Change in chronic absenteeism is defined as the difference in percent of chronic absenteeism during the 2018-2019 (i.e., pre-lockdown) school year and the 2021-2022 (i.e., post-lockdown) school year. For instance, the public school students in the state of Nebraska showed an increase of 9.20% for chronic absenteeism. Change is not detectable in states colored in white due to missing data in at least one time point. Make your own comparisons by hovering over the map below!

Figure 1

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The authors also wished to declare that the observed differences in increases of chronic absenteeism across states was not attributable to state-level mask mandate policies in public schools during the 2021-2022 academic year. The three categories of mandates included: no policy, mask mandate, and mask mandate ban. While this classification is not exactly a precise measure of mask mandate implementation, it is the data I have available to me. For instance, Nebraska is one of 25 states that did not have a statewide, blanket mask mandate policy, though many individual districts and schools did enact policies themselves.

Weekly COVID-19 infection case counts derived from the Center for Disease Control while the state populations from the U.S. Census Bureau. See the pre-print’s supporting information for more on these data sources. Table 1 below outlines the differences in average state-level infection and chronic absenteeism rate among the three masking policy categories.

Table 1

	Mean Statistic
Mask Mandate	Growth in Chronic Absenteeism (SD)	COVID Rate (SD)	Number of States
No policy	11.64% (4.35%)	15% (3%)	25
Mask mandate banned	12.27% (5.68%)	15% (2%)	8
Mask mandate	14.04% (3.94%)	15% (3%)	18

Just as Dee (2023) performed, the average percent of chronic absenteeism was compared across the three categories of student masking policies using an analysis of variance (“ANOVA”) null hypothesis statistical test. Given that the model’s p-value is > .05 (i.e., our alpha rate), as reflected in the ANOVA table (see Table 2), there was no significant differences in chronic absenteeism among the three mask mandate policy groups. In other words, there is no reason to believe that differences in chronic absenteeism is directly explained by whether a student was in a state that enacted a mask mandate policy, mask mandate ban, or no policy at all.

Table 2

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Predictor	SS	df	MS	F	p	Partial eta-squared	90% CI partial eta-squared
(Intercept)	3151.70	1	3151.70	162.77	.000
Type of Mask Mandate	49.91	2	24.95	1.29	.288	.07	[.00, .19]
Error	716.43	37	19.36

Warning

The ANOVA model seen above is likely an underpowered model given small sample size (e.g., the number of states with a mask mandate ban n = 8) per mandate condition. Although, there are a finite number of states in America..

Since we know that mask mandate alone does not explain differences in student absences, what about infection rates? After all, I would expect students to be out of school if they are sick from COVID-19.. Accompanying the above ANOVA model, a correlation coefficient can summarize the relationship between two variables. A correlation can assist in understanding if the percent of student chronic absenteeism and state COVID-19 infection rates change in a similar fashion.

The correlation between the percent change in chronic absenteeism and state COVID-19 infection rate is r = 0.18 (p = 0.26). The magnitude of this correlation is low and non-significant, which means there is no noticeable association between student chronic absenteeism and COVID-19 infection. Alongside this statistic is a scatterplot (Figure 2), which is a common way to visualize a correlation between two variables. The scatterplot does not appear to depict a truly discernible pattern of data, i.e., one variable does not increase or decrease with the other. In other words, as infection rate increases, percent of chronic absenteeism appears to be unrelated. This agrees with the above correlation coefficient that was non-significant.

Figure 2

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Mask policies and COVID-19 infection rates are not the singular causes of the pronounced growth in chronic absenteeism since pre-lockdown schooling. So…

What is really going on?

Childs & Lofton (2021) explains chronic absenteeism as a “wicked problem”. That is, an intersectional problem that cuts across multiple, overlapping causes that are difficult to nail down into one model. Explanations of chronic absenteeism can stem from school-related and non-school-related factors. Before the 2018-2019 academic year, common explanations of chronic absenteeisms included:

• Social, Economic, and Environmental Factors
  • Food insecurity
  • Student mobility and lack of housing
  • Inaccessible health care
  • Transportation challenges
  • School climate and safety
  • Punitive discipline and zero tolerance standards
• Health Conditions
  • Mental health
  • Learning disabilities
  • Trauma (family, community)
  • Personal safety, violence, bullying
  • Substance use

But since 2019, something about chronic absenteeism has changed. So what other factors may increase post-lockdown schooling? Here’s my short list:

• Absence culture
• Devaluation of in-person class after the shift from remote classrooms
• Student disengagement, lack of motivation, lost relationships and connection

How do we combat the growing chronic absenteeism in our students? In a recent NPR article, Hedy Chang, founder of Attendance Works, reports she feels that:

“[Students have] lost connections to peers, they’ve lost connections to adults, and it has certainly been exacerbated by very challenging staffing issues in schools. But that means we need to be even more intentional about relationship building, connecting to kids.”