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Showing Up: Disparities in Chronic Absenteeism Between Students With and Without Disabilities in Traditional Public Schools


by Michael A. Gottfried, Leanna Stiefel, Amy Ellen Schwartz & Bryant Hopkins - 2019

Background: While chronic absenteeism hurts all students, one particularly vulnerable group, students with disabilities (SWDs), has received little attention in research or policy. Particularly troubling is the dearth of research into the patterns of absences for SWDs and GENs who attend school together in urban school systems, given relatively higher absenteeism when compared to suburban and rural districts.

Research Questions: First, how do rates of chronic absenteeism compare between SWDs and students without disabilities (GENs) attending the same schools (hereafter traditional schools)? Second, are there differences between SWDs who are educated in “GEN-majority” classrooms and those educated in “SWD-majority” classrooms? Finally, do these patterns differ for students with different disabilities?

Subjects: Our study consists of GENs and SWDs in grades 1–6 who attended a traditional NYC public school between 2006 and 2012. Our sample includes 653,736 students across 37,867 classrooms, and 1,148 public elementary schools. Measures include race/ethnicity, gender, age, foreign-born status, limited English proficiency, free/reduced price lunch eligibility, grade level, classroom ID, school ID, the number of days each student was absent, and the total number of school days each student was registered in the district. For SWDs, the data include indicators for the thirteen disability classifications defined under IDEA and a primary assigned special education setting.

Research Design: We begin with a baseline model, where being chronically absent (i.e., missing 10% or more of the school year) is regressed on an indicator for being a SWD, controlling for grade and year. We build on this model by first including demographic control variables, then school fixed effects, and finally classroom fixed effects. We then explore this model for differences by type of classroom setting as well as by type of disability.

Findings: Chronic absenteeism is considerably higher for SWDs than GENs in traditional schools, and there is important heterogeneity by disability classifications. Specifically, students with emotional disturbances exhibit extremely high rates of chronic absenteeism and the largest group of SWDs, students with learning disabilities, have quite high rates as well. Further, SWDs in GEN-majority classrooms are less likely to be chronically absent than those in SWD-majority classrooms, again with variation by disability.

Conclusions: As the nationwide trend of providing SWDs with more education in GEN-majority classrooms continues to press forward, our study shows that increasingly GEN-majority settings are associated with fewer absences for SWDs. And while school attendance, among other non-achievement outcomes, are not the primary focus of IDEA, our findings point to how some school settings might be beneficial to some but put others at risk.



School absenteeism has moved into the spotlight as a national concern. Recent estimates suggest that 5–7.5 million students miss at least one cumulative month of school each year (Balfanz & Byrnes, 2012), representing 10%–15% percent of all K–12 students in the United States and approximately 150–225 million days of lost instruction. Missing school cripples students’ own achievement and social development (Gershenson, Jacknowitz, & Brannegan, 2016; Goodman, 2014; Gottfried, 2014; Gottfried & Kirksey, 2017) and also produces negative spillover effects, hurting classmates’ test scores (Gottfried, 2011). Moreover absenteeism affects financial resources of schools and districts in states using attendance to allocate financial resources. For example, in the 2014–2015 academic year, California school districts experienced $1 billion in forgone dollars as a result of daily absences (Harris, 2016).


Chronic absenteeism, an extreme form of absenteeism that is conventionally defined as missing 10% or more of the school year, is alarmingly high. According to Chang and Davis (2015), approximately 10% of early elementary school students miss more than a month of school. Using data from the Early Childhood Longitudinal Study-Kindergarten Class of 1998–1999, Romero and Lee (2007) estimate that more than 11% of kindergartners are chronically absent, with the rate falling slowly through elementary grades. A larger group is regarded as “at-risk” for chronic absenteeism, missing the 10% cutoff for chronic absenteeism by one to six days. The at-risk group includes roughly 14% of kindergartners, 12% of first graders, 11% of third graders, and 10% of fifth graders (Romero & Lee, 2007.)


Obscured in the aggregate data is the extremely high absenteeism among students with disabilities (SWDs) Cortiella and Horowitz (2014), for example, report that high school freshman with disabilities accrued 50% more absences, on average, than students not receiving special education services (GENs). Among younger SWDs, the patterns are similar. Elementary-age SWDs are more than 50% more likely to be chronically absent than GENs (U.S. Department of Education, 2016). While absenteeism starts early for all students, it is particularly high among young SWDs.


We have little empirical research probing the SWD-GEN disparity in chronic absenteeism. Particularly troubling is the dearth of research into the patterns of absences for SWDs and GENs who attend school together in urban school systems, given relatively higher absenteeism when compared to suburban and rural districts (Nauer, Mader, Robinson, & Jacobs, 2014). This study begins to close that gap, providing a rich descriptive exploration of chronic absenteeism in New York City (NYC), the nation’s largest school district. We begin by addressing the following question:


Research Question 1: Is chronic absenteeism higher among SWDs than GENs in NYC, a large urban district? Are these patterns robust to controlling for differences in student characteristics and disabilities?


The answers will inform policymakers as they consider how to develop and target absence-reduction programs. Specifically, understanding the SWD-GEN chronic absenteeism disparity situates the magnitude of the problem, and since SWDs are a diverse group of students, disaggregating them to identify whether—and which—SWDs are at greater risk may be particularly helpful.


Our second step is to investigate potential correlations with observed disparities, focusing on the classroom setting. While many claim SWDs are absent more than GENs strictly due to health reasons, Chang and Davis (2015) challenge this claim, arguing that school context and school structures also matter. Thus, we look towards possible school-based patterns in absenteeism.1 That is, we explore whether—and to what extent—schooling factors explain the size of the observed SWD-GEN chronic absenteeism gap.


In particular, for students who attend schools educating SWDs and GENs (i.e., traditional schools), we believe it is important to explore if there are differences by the classroom setting in which SWDs are educated. We thus explore whether the SWD-GEN absenteeism gap is larger when SWDs are educated in “GEN-majority” versus “SWD-majority” classrooms (i.e., in classrooms where the majority of students are GENs or SWDs, respectively).


The classroom environment is selected based on the educational goals and services prescribed within a SWD’s Individualized Education Program (IEP), as described further on. Multiple considerations are weighed when determining if a SWD should be educated in a SWD-majority classroom, including the feasibility of providing services within a GEN-majority classroom, the potential for classroom disruption, influence on a teacher’s ability to adequately meet the needs of other students in the classroom, as well as the necessity of curriculum modifications.


Given these considerations, SWDs in SWD-majority classrooms likely differ from SWDs in GEN-majority classrooms in ways that are not captured in our data by our variables on disability classification or demographic characteristics.  It is also possible that these unobserved factors that correlate with provision of services in SWD-majority classrooms also correlate with greater chronic absenteeism. Limitations in our data make it difficult to rule out the possibility that observed disparities in chronic absenteeism are due to this selection process and we do not claim to do so in this research. As a result, these descriptive analyses will not tell us what causes gaps, but they will nonetheless be informative about where to focus attention as trends in chronic absenteeism for SWDs across different classroom settings have yet to be documented.


We are especially motivated to examine the classroom context because of the repeated emphasis in reauthorizations of the Individuals with Disabilities Education Act (IDEA) requiring the inclusion of SWDs in the general education environment to the maximum extent possible. Furthermore, recent federal legislation, such as Endrew F. v. Douglas County School District from 2017, has moved the conversation forward not simply with regards to placement but also academic progress. In light of these momentous policy moves within the special education realm, national-level statistics confirms that indeed changes are occurring, namely that SWDs are educated alongside GENs in traditional schools more now than ever in the nation’s history (U.S. Department of Education, Office of Special Education and Rehabilitative Services, 2007, 2015). The pattern in NYC reflects the national trend. For instance, between 2007 and 2011, the percentage of SWDs educated in SWD-majority classrooms declined nine percentage points while the percentage educated in GEN-majority classrooms increased correspondingly (Stiefel, Shiferaw, Schwartz, & Gottfried, 2018; New York City Student-Level Administrative Data, n.d.). This increasing integration of SWDs and GENs suggests attention should be paid to measuring the association between GEN- and SWD-majority classrooms and absenteeism. Thus, our second research question is:


Research Question 2: Does the size of the SWD-GEN absenteeism gap differ for SWDs who are educated in GEN-majority or SWD-majority classrooms? How do these patterns differ by disability?


In sum, little to no research to date has documented the extent, or explored associations to, school factors that might be linked to gaps in chronic absenteeism between SWDs and GENs in traditional schools. More so, NYC provides an excellent setting for the study. NYC educates more SWDs in more schools than any other U.S. district, permitting in-depth study for a variety of fine-grained SWD classifications and assigned services. Many school districts struggle with how to educate SWDs to the highest standards. Results of this study will be valuable to practitioners and policymakers in school districts and to researchers seeking to understand whether disparities are large and to focus attention where gaps may be particularly significant.


BACKGROUND


WHY EXAMINE ABSENTEEISM?2


Students with more school absences have lower test scores and grades (Gottfried, 2009; Dryfoos, 1990; Finn, 1993; Gershenson et al., 2016; Goodman, 2014), greater chances of dropping out of high school (Rumberger, 1995), and higher odds of future unemployment (Alexander, Entwisle, & Horsey, 1997; Broadhurst, Paton, & May-Chahal, 2005; Kane, 2006). Absenteeism has also been associated with higher propensity to use tobacco, alcohol, and other drugs (Hallfors et al., 2002), and more behavioral problems, including social disengagement and alienation (Gottfried, 2014; Johnson, 2005). Absences are also predictive of future absenteeism (Connolly & Olson, 2012). Finally, students with more absences hurt their classmates’ testing performance (Gottfried, 2011), indicating a negative spillover of absenteeism.


WHY ANALYZE ASSOCIATION WITH GEN- VERSUS SWD-MAJORITY CLASSROOMS?


In NYC—as in most districts—SWDs have been educated historically in either GEN-majority or SWD-majority classrooms (Rogers, 1993). GEN-majority classrooms are those where SWDs are taught alongside a significant proportion of GEN peers either with a single general education teacher or in conjunction with a special education teacher. In SWD-majority classrooms, SWDs are taught primarily with other SWDs, with a special education teacher, although to be clear, there are GEN students in these classrooms as well (they are simply not the majority). Current federal law (i.e., IDEA) stipulates that SWDs are to be educated in SWD-majority classrooms only when the school determines that services provided in a GEN-majority classroom cannot address their needs (U.S. Department of Education, Office of Special Education and Rehabilitative Services, 2007, 2015).


Without any prior empirical or theoretical basis to link SWDs’ classroom setting and absenteeism, we speculate that GEN-majority classrooms could hypothetically have a positive or a negative influence on absences. On the one hand, absences may be lower when SWDs share more educational experiences and spend more time with their GEN classmates. Spending more time with GENs may be associated with feelings of belonging among SWDs (Fitch, 1999; Freeman & Alkin, 2000; Schnorr, 1990), and feelings of belonging have ultimately been linked to fewer absences (Balfanz & Byrnes, 2012; Gottfried, 2014). These feelings also have been linked to higher levels of self-efficacy, engagement, and educational attainment (Anderman, 2002; Finn, 1989; Uwah, McMahon & Furlow, 2008), which in turn may reduce absenteeism.


On the other hand, in GEN-majority classrooms, there is the potential that SWDs may be more absent. First, because SWDs instruction alongside GEN classmates, SWDs may feel more singled out within the classroom as they compare themselves to GENs who typically have higher academic performance levels and/or fewer behavioral issues (Freeman & Alkin, 2000). In making these comparisons, SWDs may not feel as though they belong in the classroom, thereby decreasing classroom engagement and ultimately increasing absences. Relatedly, given that lower school belonging may not be solely associated with declining classroom engagement but also with increased school refusal (Kearney, Pursell, & Alvarez, 2002). Increased school refusal has been linked to negative school-going attitudes (Tangney, Baumeister & Boone, 2004), and these negative attitudes have been associated with school avoidanc—i.e., absenteeism (Ladd & Price, 1987). In sum, lower school belonging may increase disengagement or may increase school refusal behaviors, both of which appear to be associated with higher levels of absenteeism.


Second, because some SWDs may sometimes have weaker social adjustment compared to GENs (Biordi & Nicholson, 2012; Murray & Greenberg, 2001; Pearl et al., 1998; Turner & Noh, 1988; Van Gundy & Schieman, 2001), there is the possibility that time spent in classrooms with more GENs may exacerbate these feelings. Indeed, Frostad and Pijl (2007) found that in GEN-majority classrooms, SWDs were less accepted and had fewer friendships. Under these circumstances, belonging declines and absences may be higher—potentially through educational disengagement or school refusal, as described above.


WHY ANALYZE DIFFERENCES BY DISABILITY?


SWDs are a heterogeneous group of students and the school factors hypothesized so far are general and may differ by disability—hence our motivation for examining not only the overall SWD sample of students but also disability subgroups. For instance, students with emotional or behavioral disabilities may feel less included among GENs, given potential disruptions (Coie, Dodge, & Kupersmidt, 1990; Siperstein & Leffert, 1997). Feeling less part of the classroom may be exacerbated when spending the entire school day included in all classroom activities rather than in SWD-majority classrooms (Kozleski & Jackson, 1993; Freeman & Alkin, 2000), hence lowering belonging and lowering educational engagement or increasing school refusal, as described above, and thereby increasing the potential for absenteeism. Hence, for many groups of SWDs, behavior plays a large role in the disability. It is possible that social and behavioral skills could be a factor of absenteeism, as moderated by SWD-majority or GEN-majority classroom setting.


To the contrary, students with learning disabilities have been shown to have much stronger academic performance when educated in GEN-majority classrooms, conditional on having appropriate educational supports within that classroom (Dexter, Park, & Hughes, 2011; Therrien, Taylor, Hosp, Kaldenberg, & Gorsh, 2011). This may increase feelings of engagement and belonging, as students with learning disabilities begin to thrive alongside GEN classmates and absences potentially decline. These are two of many examples of how the importance of the classroom setting may differ by specific disability, and thus the intersection of where classroom and disability type becomes especially important in this analysis.


METHODS


DATA SOURCES


We use longitudinal, student-level administrative data from the New York City Department of Education (NYCDOE) to analyze descriptively SWD-GEN chronic absenteeism gaps. The data include measures of race/ethnicity, gender, age, foreign-born status, limited English proficiency (LEP), free/reduced price lunch (FRPL) eligibility, grade level, classroom ID, and school ID. The dataset also includes the number of days each student was absent as well as the total number of school days each student was registered in the district. For SWDs, the data include indicators for the 13 disability classifications defined under IDEA and a primary assigned special education service.


Our analytic sample consists of GENs and SWDs in grades 1–6 or ungraded3 who attended a traditional NYC public school for at least two consecutive academic years between 2006–2007 (2007) and 2011–2012 (2012). We exclude students attending charter or alternative schools, SWDs without a disability classification or who are educated in a non-NYCDOE facility, as well as schools with fewer than five SWDs, enrollment below 25 students, or schools serving only SWDs. On average, schools educating only SWDs (called District 75 schools in NYC) educate 13.4% of SWDs between 2007 and 2012, but do not educate any GENs, and thus are excluded given that our analyses compare SWDs and GENs sharing schools or classrooms.  Table A1 in the Appendix shows the rates of chronic absenteeism and numbers of students for District 75. Our final analytic sample includes 653,736 unique students across 37,867 classrooms, and 1,148 public elementary schools. Across all years, this amounts to 2,312,219 student-year observations.


MEASURES


Outcome


Our key dependent variable is a binary measure that takes a value of one if the student meets the criteria for chronic absenteeism, defined as missing at least 10% of the instructional days in a single academic year (Balfanz & Byrnes, 2012). To be specific, we measure the absenteeism rate as one minus the attendance rate, calculated by dividing the total number of days a student was present in the school year by the number of days he or she was registered to be in school (i.e., this accounts for students starting the year late or leaving the district before the end of the school year). Note that in our sensitivity analyses, we estimate models using several different cutoffs as well as a continuous measure of absenteeism.  


SWDs


In our data, SWDs are identified as having an official Individualized Education Program (IEP) on record with the NYCDOE.4 In the analyses to follow, a student with an IEP on file was coded as binary (1, being yes; 0, being no) in each academic year.


Such detailed district data also provide IDEA-designated disability classifications. We group the 13 IDEA disability classifications into the five largest groups: Emotional Disturbance (ED, 4.8% of SWDs in the analytic sample), Learning Disability (LD, 39.9%), Speech Impairment (SI, 41.7%), Other Health Impairment (OH, 8.3%), and Low Incidence disabilities (LI, 5.3%). The NYCDOE describes the disability classifications as follows (2016): An ED is a disability with one or more of the following characteristics: an inability to learn that cannot be explained by intellectual, sensory, or health factors; an inability to build satisfactory interpersonal relationships; inappropriate types of behaviors under normal circumstances; and/or pervasive mood of unhappiness or depression. An LD is a disability in one or more psychological processes involved in understanding or using language, which results in an imperfect ability to speak, think, write, spell, or do math calculations. An SI is a speech impediment or communication disorder. An OH includes limited strength or alertness to environmental stimuli that results in a limited alertness to the educational environment. Lastly, the LI category includes the remaining nine disabilities5 as a single classification due to their relatively low incidence compared to the other groups.


ASSIGNED SERVICE AND CLASSROOM SETTING


Each SWD’s IEP includes a primary assigned service, which is the service provided for the largest proportion of time during the school day. NYCDOE classifies all services in a continuum from “least to most restrictive” (NYCDOE, n.d.). The least restrictive, Related Services (RS, 13.4% of SWDs in the analytic sample), includes support services such as counseling, hearing services, physical therapy, and speech/language therapy.6 Special Education Teacher Support Services (SETSS, 26.4%) encompass supplemental instruction that helps SWDs remain in the general education classroom.7 Integrated Co-Teaching (ICT, 25.9%) is a service through which GENs and SWDs are educated by both a general and special education teacher in a classroom in which at most 40% of students are SWDs. Self-Contained (SC, 34.3%) services are provided to SWDs with similar needs in SWD-majority classrooms.


SWDs receiving one of the three less restrictive services (RS, SETSS, or ICT) were coded as 1 for being educated in a GEN-majority classroom. SWDs receiving SC were coded as 0, meaning that they were in a SWD-majority classroom. Note that GENs are taught alongside SWDs receiving all four types of services; a higher percentage of GENs were taught alongside SWDs receiving RS, SETTS, and ICT, and a higher percentage of SWDs were taught alongside SWDs receiving SC.


Full-time Versus Part-time Classroom Setting


It is also possible to further divide GEN-majority classrooms into full-time (ICT services provided) or part-time (RS and SETSS services provided). The full- and part-time designations are determined by the district as the proportion of a school day a SWD spends with a certified special education teacher (the remaining time is spent with a general education teacher). For example, SWDs whose primary assigned service is ICT are always in the classroom as a special education teacher, hence are receiving full-time services in GEN-majority classrooms. But as noted before, ICT classrooms contain both SWDs and GENs who are always co-taught by general and special education teachers. Conversely, SWDs assigned RS and SETSS are services in GEN-majority classrooms that are not always in contact with a special education teacher. SWDs in these classrooms receive either in-class or pull-out services, thus only part-time instruction from a special education teacher. SWDs with SCs are receiving full-time services.


ANALYTIC STRATEGY  


Our baseline model is as follows:


[39_22755.htm_g/00002.jpg]


where Yigst is a dichotomous variable that takes a value of 1 if student i is chronically absent in grade g, school s, and year t, and 0 otherwise; SWDigst takes a value of 1 if student i has an IEP in grade g, school s, and year t, and 0 otherwise; θg and τt are indicators for grade and school year, respectively, and are included to control for grade and year specific absenteeism differences. εigst is an error term and standard errors are clustered at the school level in order to correct for potential correlations in the errors among students attending the same school. Our key coefficient, δ, provides an estimate of the difference in probability of exhibiting chronic absenteeism between SWDs and GENs.   


While equation (1) provides an unadjusted, baseline estimate of the difference in probability of exhibiting chronic absenteeism, we next explore a model that includes demographic controls:


[39_22755.htm_g/00004.jpg]


Here X’igst represents a set of student characteristics (gender, race/ethnicity, age, native born, LEP, and FRPL).


Finally, because our sample includes students across many schools and classrooms, we include school or classroom fixed effects in our models, with the final model as follows:


[39_22755.htm_g/00006.jpg]


[39_22755.htm_g/00008.jpg] is either the school or classroom effect, and all other variables are the same as in equation (2).8 Including school fixed effects helps control for school-to-school differences. For example, our estimate of δ might be low, for instance, if a school leader is highly invested in ensuring SWDs receive appropriate supports and services as well as committed to addressing chronic absenteeism.


A similar story might be told within schools and between classrooms. That is, some teachers might be more effective than others in addressing the needs of SWDs as well as addressing chronic absenteeism. Alternatively, a principal may have placed all highly-attending students together in a classroom. Therefore, rather than comparing SWDs and GENs within an entire grade in a school, classroom effects allow us to compare students within the same classrooms. Because ordinary least squares (OLS) results are easy to interpret, we present models estimated with OLS, although qualitatively similar results are obtained using logit (available from authors).


Heterogeneity by Disability Classification


We next explore whether there is heterogeneity in chronic absenteeism by SWD classifications described above:


[39_22755.htm_g/00010.jpg]


In this model, EDigst takes on a value of 1 if a student has been classified as an ED in grade g, school s, and year t, and 0 otherwise. LDigst, SIigst, OHigst, and LIigst are defined similarly and all other variables are as previously defined. The coefficients on each disability group (δ1 through δ5) capture differences between these groups and GENs.


Classroom Setting


Finally, we analyze the association between classroom setting and chronic absenteeism. We estimate two versions of this model, first distinguishing GEN-majority classrooms (with SWDs receiving RS, ICT, or SETSS) from SWD-majority classrooms (with SWDs receiving SC), and further subdividing GEN-majority classrooms into full-time (ICT) and part-time (RS and SETSS). The two models are as follows:


[39_22755.htm_g/00012.jpg]

[39_22755.htm_g/00014.jpg]


Zigst represents either SWDigst or the complete set of disability classifications described above, depending on if we are analyzing the SWD group aggregately or by disability category. GENmajor, GENmajorFT, and GENmajorPT equal 1 for SWDs educated in GEN-majority classrooms, full-time, and part-time, respectively, and 0 otherwise. All other variables are as above. Even after all these controls and fixed effects, however, there is likely some selection into classroom types. Nonetheless, having minimized this to the extent possible, if we find large disparities that remain, they are likely to be ones that policy, practice, and future research should address.


Sensitivity Analyses


We explore the sensitivity of our results in three dimensions.


Defining chronic absenteeism


The definition of chronic absenteeism is set at 10%. While analysts and federal policy makers have adopted this cutoff, there has been no discussion of whether deviations from this cutoff, or even a measure of continuous absenteeism, would yield different results. Thus, we re-estimate our models using several different cutoffs for chronic absenteeism, as well as a continuous absence rate as the dependent variable.


Excluding Sixth Grade


Prior research has consistently shown that the incidence of chronic absenteeism is highest among younger elementary students, declines as students progress through elementary school, and spikes as children move to new middle schools (Balfanz & Byrnes 2012). Thus, to ensure that the resurgence of chronic absenteeism for sixth grade students is not driving results for the entire analysis, we exclude sixth graders from the analytic sample and re-estimate the models.


Higher- Versus Lower-poverty Schools


Prior literature provides evidence that the incidence of absenteeism is higher in urban, high-poverty school districts (Nauer et al., 2014). Furthermore, absenteeism is unevenly distributed across students in cities, with at-risk students exhibiting higher rates (Balfanz & Byrnes, 2012). To investigate if the rate of chronic absenteeism in higher-poverty schools is driving our results, we re-estimate our models after limiting the analytic sample to include only the “lowest-poverty” or “highest-poverty” quintile of schools in the school district. These schools were identified based on the percentage of students within a school who are eligible for free or reduced-price lunches. For each of these analyses, we show results for our specification including grade and year effects, student controls, and classroom fixed effects.


RESULTS


DESCRIPTIVE STATISTICS


Table 1 Panel A shows descriptive statistics for all students and by chronic absenteeism status. SWDs represent 16.5% of the analytic sample, reflecting a just-slightly higher percentage than national rates in 2013–2014, where approximately 14% of all public school students received special education services (National Center for Education Statistics, 2016).9 As shown in Columns 2 and 3, students who are chronically absent differ from those who are not. Those who are chronically absent are disproportionately SWD, male, Hispanic, Black, native born, and FRPL, and younger.



Table 1. Descriptive Statistics: Analytic Sample of GENs and SWDs, grades 1-6, NYC Traditional* Public Schools, 2007–2012

  

All

Chronic

Not Chronic

Panel A (column percents)

    

ALL STUDENTS13

GEN

83.53

75.59

85.50

 

SWD

16.47

24.63

14.50

 

Female

48.92

47.11

49.36

 

Hispanic

40.66

48.25

38.82

 

Black

28.41

36.74

26.40

 

White

15.28

9.76

16.61

 

Asian/Other

15.65

5.25

18.17

 

Native Born

88.97

94.15

87.72

 

FRPL

83.00

90.39

81.21

 

LEP

15.23

12.99

15.78

 

Age (years)

9.26

9.23

9.26

ALL SWDs13

RS

13.35

7.74

15.65

 

SETSS

26.39

22.24

28.10

 

ICT

25.94

23.02

27.15

 

SC

34.32

47.01

29.10

 

GEN-Majority

65.68

53.00

70.90

 

SWD-Majority

34.32

47.01

29.10

 

ED

4.82

7.78

3.60

 

LD

39.94

43.23

38.59

 

SI

41.72

34.78

44.58

 

OH

8.26

7.93

8.39

 

LI

5.26

6.28

4.84

Panel B (row percents)

    

ALL STUDENTS13

ALL

 

19.51

80.49

 

GEN

 

17.60

82.40

 

SWD

 

29.16

70.84

ALL SWDs13

RS

 

16.91

83.09

 

SETSS

 

24.57

75.43

 

ICT

 

25.87

74.13

 

SC

 

39.94

60.06

 

GEN-Majority

 

23.53

76.47

 

SWD-Majority

 

39.94

60.06

 

ED

 

47.07

52.93

 

LD

 

31.56

68.44

 

SI

 

24.31

75.69

 

OH

 

28.02

71.98

 

LI

 

34.81

65.19

GRADE13

1

 

23.38

76.62

 

2

 

19.35

80.65

 

3

 

17.02

82.98

 

4

 

16.08

83.92

 

5

 

16.08

83.92

 

6

 

18.63

81.37

 

95

 

39.72

60.28

 

S-Y Obs.

2,321,344

452,774

1,868,570

 

N-students

653,737

214,426

594,432

 

N-schools

1,273

1,143

1,270

 

N-classrooms

37,992

32,628

32,495




In Panel B, comparing across rows reveals that the incidence of chronic absenteeism is more than 11 percentage points higher for SWDs than for GENs—with more than 29% of SWDs and almost 18% of GENs chronically absent. In the rows describing services, chronic absenteeism is lowest among students receiving RS (16.9%), and highest among SC students (39.9%). In rows showing classroom settings, rates are lower for SWDs educated in GEN-majority (23.5%) compared to SWD-majority classrooms (39.9%).10 As for disability types, students with EDs (47.1%) are the most and students with SIs (24.3%) are the least likely to be chronically absent. Finally, the incidence of chronic absenteeism is highest in earlier grades, with a peak of 23.4% in first grade. The rate of chronic absenteeism steadily declines through grade five, rising slightly in sixth grade.11


It is possible that students in GEN-majority classrooms could differ from students in SWD-majority classrooms, even within specific disability categories. Such differences would make descriptive results less useful than if there were fewer differences. To explore this, for SWDs in each disability classification as well as for GENs, we examined partial correlations between being in GEN-majority classrooms and observable characteristics displayed in the first column of Table 2. Partial correlations were purposefully chosen, as they take into account the joint influence of all other variables. As seen in Table 2, there are only small correlation coefficients between being in GEN-majority classrooms and the set of characteristics. The sizes of the correlation coefficients in Table 2 suggest that there is little evidence pointing to a systematic pattern of SWDs being educated in GEN-majority classrooms based on our observable characteristics.


Importantly, this table includes previous year’s reading and math scores as well as chronic absenteeism. Even for these performance variables, the partial correlation coefficients with being in GEN-majority classrooms remain fairly weak. Thus, while this study is designed to provide rich descriptive evidence on patterns of SWD chronic absenteeism, finding that there are not large relationships between classroom types and observable characteristics makes results more informative.



Table 2. Partial Correlation Coefficients between Students in GEN-Majority Classrooms and Other Characteristics, grades 1–6, NYC Traditional Public Schools, 2007–2012


 

ED

LD

SI

OH

LI

GEN

Female

0.01

0.03

-0.01

0.00

0.02

0.10

Hispanic

-0.13

-0.05

-0.03

-0.07

-0.01

0.04

Black

-0.15

-0.12

-0.09

-0.15

-0.09

0.05

Asian/Other

-0.01

0.01

0.02

0.00

0.02

0.05

Age

0.05

0.05

0.03

0.03

0.04

-0.06

Native Born

0.02

0.02

0.00

0.01

-0.02

-0.07

FRPL

-0.03

-0.03

-0.01

-0.05

0.00

0.02

LEP

-0.07

-0.12

-0.13

-0.12

-0.16

-0.06

Lag Z-Math

-0.15

-0.15

-0.18

-0.11

-0.06

0.08

Lag Z-Read

0.03

0.05

0.04

0.04

0.18

0.13

Chronic

-0.18

-0.11

-0.13

-0.12

-0.13

-0.04

Notes: Each column represents the partial correlation between the indicator for a student being in a GEN-majority classroom for the sample listed at the top of the column and a given row characteristic, controlling for all other row characteristics. The Lag Z-Read and Lag Z-Math variables represent a student’s prior year’s standardized reading and math scores.




Recall also that GENs share classrooms with SWDs even in SWD-majority classrooms. One might be concerned that these GENs are really “SWDs-to-be,” just not yet identified as such by their schools. To address this, we examine our sample of GENs and regress an indicator of being classified as SWD in year t + 1 on being in a SWD-majority classroom in year t (as well as observable individual characteristics and school, grade, and year controls). The coefficients in this model are small, with probabilities less than 1% (results available from authors). In fact, overall, the probability of a GEN being classified as a SWD in year t+1, conditional on being in a SWD-majority classroom, is only 0.5%. Thus, our analysis does not support that GENs in SWD-majority classrooms are simply not yet identified as SWD.


BASELINE RESULTS


Table 3 presents estimates of the relationship between chronic absenteeism for SWDs compared to GENs in traditional schools. In Panel A, the first column presents estimates for the baseline model: SWDs are 5.7 percentage points more likely to be chronically absent compared to their GEN peers. Estimates for Equations 2 and 3 are provided in Columns 2–4, beginning with inclusion of student controls and then adding school or classroom fixed effects in progressive columns. Controlling for student characteristics tempers the results: SWDs are more likely to be chronically absent, but the magnitude of the disparity is somewhat lower. School-to-school differences do not explain much of the variation in the rates of chronic absenteeism between SWDs and GENs, as shown in Column 3 when school effects are added, but variation across classrooms is more significant, as seen in Column 4, where SWDs are now only 3.3 percentage points more likely to be chronically absent. Regardless of the specification, SWDs are consistently more likely to be chronically absent compared to GENs, and all estimates are statistically significant at the 1% level.  



Table 3. Chronic Absenteeism, SWD and SWD classification


DV: Chronic

(1)

(2)

(3)

(4)

     

Panel A

    
     

SWD

0.057***

0.044***

0.045***

0.033***

 

(0.002)

(0.002)

(0.001)

(0.001)

Age

0.040***

0.034***

0.029***

0.043***

 

(0.001)

(0.001)

(0.001)

(0.001)

Female

 

-0.004***

-0.003***

-0.001

  

(0.001)

(0.001)

(0.001)

Black

 

0.088***

0.015***

0.007*

  

(0.004)

(0.003)

(0.003)

Hispanic

 

0.078***

0.031***

0.027***

  

(0.004)

(0.003)

(0.003)

Asian/Other

 

-0.058***

-0.053***

-0.053***

  

(0.003)

(0.003)

(0.003)

Native Born

 

0.060***

0.058***

0.060***

  

(0.003)

(0.003)

(0.003)

FRPL

 

0.074***

0.048***

0.045***

  

(0.002)

(0.002)

(0.002)

LEP

 

-0.047***

-0.046***

-0.044***

  

(0.003)

(0.002)

(0.002)

constant

-0.258***

-0.337***

-0.219***

-0.300***

 

(0.012)

(0.012)

(0.011)

(0.023)

     

Student Controls

N

Y

Y

Y

School FE

N

N

Y

N

Classroom FE

N

N

N

Y

     

N

2,321,219

2,321,219

2,321,219

2,321,219

     

Panel B

    
     

ED

0.191***

0.158***

0.144***

0.132***

 

(0.006)

(0.005)

(0.005)

(0.005)

LD

0.081***

0.068***

0.070***

0.060***

 

(0.002)

(0.002)

(0.002)

(0.002)

SI

0.020***

0.006***

0.007***

-0.008***

 

(0.002)

(0.002)

(0.002)

(0.002)

OH

0.067***

0.065***

0.067***

0.054***

 

(0.005)

(0.004)

(0.004)

(0.003)

LI

0.080***

0.083***

0.086***

0.058***

 

(0.007)

(0.006)

(0.007)

(0.006)

constant

-0.231***

-0.311***

-0.194***

-0.282***

 

(0.012)

(0.012)

(0.011)

(0.023)

     

Student Controls

N

Y

Y

Y

School FE

N

N

Y

N

Classroom FE

N

N

N

Y

     

N

2,321,219

2,321,219

2,321,219

2,321,219

Standard errors are in parentheses and clustered at the school level. * p < 0.10, ** p < 0.05, *** p < 0.01

Notes: All models include grade and year effects. Student controls for gender, race, age, foreign-born status, LEP, and FRPL, are included in Models 2, 3, and 4.




HETEROGENEITY BY DISABILITY CLASSIFICATION


Table 3 Panel B presents the models with the SWD variable disaggregated into the five largest disability groups. The baseline estimates show that students with EDs are more than 19 percentage points more likely to be chronically absent compared to GENs, and students with LDs and SIs are 8.1 and 2.0 percentage points more likely, respectively. Given the large population of students with LDs (39.9% of all SWDs) and with SIs (41.7%) in the analytic sample, the results in panel A are driven by their sheer numbers and do not represent the heterogeneity among SWDs. The magnitude of each estimate is tempered as we add student controls and school and classroom effects to the specification. EDs continue to have the highest likelihood of exhibiting chronic absenteeism, with probabilities 13.2 percentage points above GENs in the most complete model with classroom effects, and with LDs, the largest group of SWDs, 6.0 percentage points above GENs. Overall, except for the SI result in the final column (0.8 percentage points lower than GENs), SWDs continue to have higher probabilities of exhibiting chronic absenteeism compared to GENs.


CLASSROOM SETTING


The results in Table 3 contribute to our understanding of the incidence of chronic absenteeism, showing that it varies among SWDs and is particularly high among the ED group. Simply confirming a disparity, however, is less useful for policymakers who wish to understand if there are associations between existing school settings and gaps in chronic absenteeism. In order to delve more deeply, we analyze the association between being educated in GEN-majority and SWD-majority classrooms and chronic absenteeism. Table 4 reports these associations for SWDs in GEN-majority and SWD-majority classrooms (Column 1), as well as by full-time and part-time GEN-majority classrooms (Columns 2), compared to GENs. We present within-classroom results only, as they are similar to within-school results.



Table 4. Chronic Absenteeism and Assigned Services


DV: Chronic

(1)

(2)

   

SWD

0.140***

0.141***

 

(0.006)

(0.006)

SWD GENmajor

-0.109***

 
 

(0.006)

 

SWD GENmajor Full Time

 

-0.088***

  

(0.006)

SWD GENmajor Part Time

 

-0.123***

  

(0.006)

constant

-0.300***

0.150***

 

(0.023)

(0.024)

   

Student Controls

Y

Y

School FE

N

N

Classroom FE

Y

Y

   

N

2,321,219

2,321,219

Standard errors are in parentheses and clustered at the school level.

* p < 0.10, ** p < 0.05, *** p < 0.01

Notes: All models include grade and year effects, as well as student controls for gender, race, age, foreign-born status, LEP, and FRPL.




Starting with Columns 1, note that the SWD-GEN gap in chronic absenteeism for SWDs in SWD-majority classrooms is captured by the coefficient in the first row (on SWD), and the SWD-GEN gap in chronic absenteeism for SWDs in GEN-majority classrooms is captured by the sum of the coefficient in the first row (on SWD) plus the coefficient in the second row (on SWD GENmajor). Column 1 shows that SWDs in SWD-majority classrooms are 14.0 percentage points more likely to be chronically absent than GENs, while SWDs in GEN-majority classrooms are only 3.1 percentage points more likely to be chronically absent compared to GENs (14.0 – 10.9 = 3.1). Overall, GENs are the least likely to be chronically absent regardless of their classroom setting, followed by SWDs in GEN-majority classrooms, and finally SWDs in SWD-majority classrooms are the most absent.


We further disaggregate the indicator for GEN-majority classrooms into full- and part-time services in Column 2. In this column, we continue to see that SWDs in SWD-majority classrooms exhibit the highest rates of chronic absenteeism (14.1 percentage points in Column 2). This is followed by SWDs receiving full-time services in GEN-majority classrooms (5.3 percentage points, 14.1–8.8), and that is followed by SWDs receiving part-time services in GEN-majority classrooms (1.8 percentage points, 14.1–12.3). GENs continues to be the least likely to be chronically absent.


The results in Table 5 are presented exactly as those in Table 4, except that SWDs are disaggregated into disability groups. The coefficients on variables ED, LD, SI, OH, and LI (first five rows) represent each of the different types of SWDs in SWD-majority classrooms. The first row of the table shows that EDs remain the most at-risk group of all SWDs: in SWD-majority classrooms, EDs are the most likely to be chronically absent of any type of student (over 23 percentage points). In SWD-majority classrooms, EDs are followed, in the order of their likelihood of being chronically absent, by the groups LIs, OHs, LDs, and SIs. All groups show high rates, never less than nine percentage points above GENs.



Table 5. Chronic Absenteeism, Disability Classification and Assigned Services


DV: Chronic

(1)

(2)

ED

0.232***

0.234***

 

(0.008)

(0.008)

LD

0.141***

0.143***

 

(0.006)

(0.006)

SI

0.090***

0.092***

 

(0.006)

(0.006)

OH

0.155***

0.157***

 

(0.009)

(0.009)

LI

0.167***

0.170***

 

(0.009)

(0.009)

ED GENmajor

-0.125***

 
 

(0.010)

 

LD GENmajor

-0.079***

 
 

(0.006)

 

SI GENmajor

-0.101***

 
 

(0.006)

 

OH GENmajor

-0.105***

 
 

(0.009)

 

LI GENmajor

-0.127***

 
 

(0.011)

 

ED GENmajor Full Time

 

-0.099***

  

(0.013)

ED GENmajor Part Time

 

-0.146***

  

(0.012)

LD GENmajor Full Time

 

-0.055***

  

(0.007)

LD GENmajor Part Time

 

-0.094***

  

(0.006)

SI GENmajor Full Time

 

-0.081***

  

(0.007)

SI GENmajor Part Time

 

-0.115***

  

(0.007)

OH GENmajor Full Time

 

-0.087***

  

(0.010)

OH GENmajor Part Time

 

-0.116***

  

(0.010)

LI GENmajor Full Time

 

-0.123***

  

(0.016)

LI GENmajor Part Time

 

-0.131***

  

(0.012)

   

Student Controls

Y

Y

School FE

N

N

Classroom FE

Y

Y

N

2,321,219

2,321,219

Standard errors in parentheses and clustered at the school level. * p < 0.10, ** p < 0.05, *** p < 0.01. Notes: All models include grade and year effects, as well as student controls for gender, race, age, foreign-born status, LEP, and FRPL.




Next, we turn to the SWD-GEN gap for SWDs in GEN-majority classrooms. EDs in those classrooms are 10.7 percentage points (23.2–12.5) more likely to be chronically absent than GENs in those rooms. This stands in stark comparison to the 23.2 percentage point gap for EDs in SWD-majority classrooms, as discussed in the previous paragraph. Next are LDs, OHs, and LIs (in that order), who are all still more likely to be chronically absent compared to their GEN counterparts. All of the students in these groups, however, are less likely to be chronically absent than students in the same disability groups in SWD-majority classrooms. SIs in GEN-majority classrooms are the only subgroup of SWDs, compared to GENs, who are slightly less likely to be chronically absent than GENs (1.1 percentage points less likely).


Disaggregating GEN-majority classrooms into full- and part-time receipt of services (Column 2) reveals even more heterogeneity in chronic absenteeism for SWDs. As before, any SWD group in a GEN-majority classroom is less likely to be chronically absent compared to students in the same disability group in a SWD-majority classroom. In addition, SWDs receiving part-time services in GEN-majority classrooms are less likely to be chronically absent compared to students classified with a similar disability and receiving full-time services in GEN-majority classrooms. For example, compared to GENs, EDs receiving part-time services are only 8.8 percentage points more likely to be chronically absent, 13.5 percentage points for EDs receiving full-time services, through this is much lower than 23.4 for EDs in SWD-majority classrooms. SIs in part-time services in GEN-majority classrooms are the only subgroup of SWDs less likely to be chronically absent compared to GENs (-2.3 percentage points). SIs receiving full-time services in GEN-majority classrooms are still more likely to be chronically absent compared to GENs (1.1 percentage points).


SENSITIVITY ANALYSES


We re-estimate the within-classroom analysis, presented in Table 6 (including grade and year effects, student controls, and classroom fixed effects), for each of the previously mentioned robustness tests. The first three columns of Table 6 include new definitions of chronic absenteeism as the dependent variable: two different chronic absence rate cutoffs and a continuous measure (defined on a 0–1 scale, where 0 implies a student has perfect attendance).12 The final three columns incorporate alterations to the analytic sample: keeping only grades one through five, the lowest-poverty quintile of schools, or the highest-poverty quintile of schools.


Table 6. Sensitivity Checks


DV: Chronic

(9%)

(11%)

(Continuous)

(No 6th)

(Top 20%)

(Bottom 20%)

Panel A

      
       

ED

0.242***

0.226***

0.049***

0.228***

0.233***

0.250***

 

(0.008)

(0.008)

(0.002)

(0.008)

(0.009)

(0.055)

LD

0.151***

0.136***

0.028***

0.140***

0.135***

0.188***

 

(0.006)

(0.006)

(0.001)

(0.007)

(0.007)

(0.037)

SI

0.101***

0.084***

0.018***

0.087***

0.088***

0.119***

 

(0.007)

(0.006)

(0.001)

(0.007)

(0.007)

(0.039)

OH

0.170***

0.146***

0.031***

0.151***

0.159***

0.181***

 

(0.009)

(0.009)

(0.002)

(0.009)

(0.011)

(0.056)

LI

0.184***

0.158***

0.034***

0.165***

0.169***

0.204***

 

(0.010)

(0.009)

(0.002)

(0.010)

(0.011)

(0.061)

ED GENmajor

-0.132***

-0.125***

-0.027***

-0.127***

-0.123***

-0.174***

 

(0.011)

(0.010)

(0.002)

(0.011)

(0.013)

(0.065)

LD GENmajor

-0.082***

-0.081***

-0.017***

-0.073***

-0.075***

-0.135***

 

(0.006)

(0.006)

(0.001)

(0.007)

(0.007)

(0.039)

SI GENmajor

-0.111***

-0.095***

-0.020***

-0.097***

-0.106***

-0.125***

 

(0.007)

(0.006)

(0.001)

(0.007)

(0.008)

(0.037)

OH GENmajor

-0.114***

-0.099***

-0.021***

-0.101***

-0.095***

-0.154***

 

(0.010)

(0.009)

(0.002)

(0.010)

(0.012)

(0.058)

LI GENmajor

-0.140***

-0.125***

-0.025***

-0.126***

-0.127***

-0.102

 

(0.012)

(0.011)

(0.002)

(0.012)

(0.015)

(0.063)

       

Student Controls

Y

Y

Y

Y

Y

Y

Classroom FE

Y

Y

Y

Y

Y

Y

       

N

2,321,219

2,321,219

2,321,219

2,021,854

1,528,767

76,173

Panel B

      
       

ED

0.244***

0.228***

0.049***

0.230***

0.235***

0.254***

 

(0.008)

(0.008)

(0.002)

(0.008)

(0.009)

(0.054)

LD

0.154***

0.137***

0.028***

0.142***

0.137***

0.191***

 

(0.006)

(0.006)

(0.001)

(0.007)

(0.007)

(0.037)

SI

0.103***

0.086***

0.018***

0.089***

0.091***

0.122***

 

(0.007)

(0.006)

(0.001)

(0.007)

(0.007)

(0.038)

OH

0.172***

0.148***

0.031***

0.153***

0.161***

0.184***

 

(0.009)

(0.009)

(0.002)

(0.009)

(0.011)

(0.056)

LI

0.186***

0.160***

0.035***

0.167***

0.171***

0.208***

 

(0.010)

(0.009)

(0.002)

(0.010)

(0.011)

(0.061)

ED GENmajor Full Time

-0.107***

-0.099***

-0.023***

-0.101***

-0.100***

-0.113

 

(0.014)

(0.013)

(0.003)

(0.015)

(0.017)

(0.073)

ED GENmajor Part Time

-0.153***

-0.145***

-0.030***

-0.147***

-0.141***

-0.264***

 

(0.012)

(0.012)

(0.002)

(0.013)

(0.015)

(0.076)

LD GENmajor Full Time

-0.055***

-0.058***

-0.012***

-0.048***

-0.048***

-0.124***

 

(0.007)

(0.006)

(0.001)

(0.007)

(0.008)

(0.040)

LD GENmajor Part Time

-0.099***

-0.095***

-0.020***

-0.088***

-0.091***

-0.145***

 

(0.007)

(0.006)

(0.001)

(0.007)

(0.008)

(0.040)

SI GENmajor Full Time

-0.088***

-0.077***

-0.016***

-0.076***

-0.083***

-0.107***

 

(0.007)

(0.007)

(0.001)

(0.007)

(0.008)

(0.038)

SI GENmajor Part Time

-0.126***

-0.108***

-0.023***

-0.110***

-0.121***

-0.140***

 

(0.007)

(0.007)

(0.001)

(0.007)

(0.008)

(0.036)

OH GENmajor Full Time

-0.095***

-0.081***

-0.017***

-0.080***

-0.079***

-0.131**

 

(0.011)

(0.010)

(0.002)

(0.011)

(0.013)

(0.056)

OH GENmajor Part Time

-0.125***

-0.109***

-0.023***

-0.113***

-0.105***

-0.172***

 

(0.010)

(0.010)

(0.002)

(0.010)

(0.012)

(0.059)

LI GENmajor Full Time

-0.136***

-0.122***

-0.024***

-0.124***

-0.131***

-0.104

 

(0.017)

(0.015)

(0.003)

(0.017)

(0.023)

(0.069)

LI GENmajor Part Time

-0.144***

-0.129***

-0.026***

-0.128***

-0.125***

-0.104

 

(0.012)

(0.011)

(0.002)

(0.013)

(0.015)

(0.065)

       

Student Controls

Y

Y

Y

Y

Y

Y

Classroom FE

Y

Y

Y

Y

Y

Y

       

N

2,321,219

2,321,219

2,321,219

2,021,854

1,528,767

76,173

Standard errors in parentheses and clustered at the school level. * p < 0.10, ** p < 0.05, *** p < 0.01

Notes: All models include grade and year effects; as well as student controls for gender, race, age, foreign-born status, LEP, and FRPL.




We would expect all SWDs to be more (less) likely to be chronically absent, compared to GENs, if the threshold was reduced (increased). The estimates in the first two columns of Table 6 (where chronic absenteeism is redefined with 9% and 11% cutoffs) reflect this intuition, although the coefficients are not statistically different from the original estimates using a 10% cutoff (Table 5, Column 2); thus, our results are not sensitive to small changes in the cutoff determining which students are chronically absent. In Column 3, when we use a continuous measure of absenteeism, our results again are similar to previous estimates. For example, in Panel B, Column 3, EDs in SWD-majority classrooms are 4.9 percentage points more absent than GENs, but those in part-time GEN-majority classrooms are less absent than those in SWD-majority classrooms.  


Our results are also insensitive to each modification of the analytic sample. None of the estimates in the final three columns of Table 6 are statistically different from those in the final column of Table 5. This would imply that the incidence of chronic absenteeism among SWDs, compared to GENs, is independent of grade included in the analyses as well as of relative level of poverty among students in the same school.


Overall, the results in Table 6 directly reflect what has already been found in Table 5: EDs are the most at-risk group of SWDs, SWDs receiving part-time services in GEN-majority classrooms are less likely to be chronically absent compared to students diagnosed with a similar disability and receiving full-time services in GEN-majority classrooms, and SWDs receiving full-time services in GEN-majority classrooms are less likely to be chronically absent compared to students diagnosed with a similar disability but in SWD-majority classrooms.


DISCUSSION


Past research shows that absenteeism is linked to lower academic performance and weakened social and behavioral outcomes, on average. Before this study, however, little was known about SWD-GEN disparities in missing school, particularly in urban districts where the rates of missing school are higher across all students. Moreover, the changing landscape of special education policy is increasingly placing SWDs in GEN-majority classrooms, but we do not know how the classroom setting is associated with absenteeism.


 This study helps address these research gaps. We explored detailed patterns in student-level, longitudinal data including information on chronic absenteeism for all students, distinguishing disability classifications and indicating types of classrooms in which they were educated. Notably, these data represented entire cohorts of elementary school children in NYC – the nation’s largest school district.


We draw several key implications. First, overall SWD-GEN chronic absenteeism patterns in NYC reflect national trends. In line with national estimates, NYC SWDs are 65.6% more likely to be chronically absent than GENs. But, disaggregating by disability reveals considerable heterogeneity. Students with EDs miss a tremendous amount of school compared to other SWDs and as compared to GENs, and students with LDs, the largest group of SWDs, miss a considerable amount as well.


Second, SWDs in GEN-majority classrooms were less likely to be chronically absent than those in SWD-majority classrooms. Disaggregating GEN-majority classrooms into receiving full- and part-time services shows SWDs receiving part-time services in GEN-majority classrooms were less likely to be chronically absent compared to SWDs receiving full-time services in GEN-majority classrooms. Regardless of full- or part-time, however, students diagnosed with an ED remain the most at-risk to be absent, across all models in our analysis.


These findings have several implications for future policy, practice, and research. First, since our findings suggest that a SWD-GEN gap varies across disability categories, we urge that further attention be paid to the possibility that aggregate student data has obscured important heterogeneity. That is, aggregate attendance rates, such as school daily averages, mask critical detail about who is most at risk of missing school. Even the “SWD” category itself may be too broad to detect heterogeneity in absenteeism. Without further details, the profile of which students are absent in school is too coarse to guide practice and develop supports.


Second, this study provides new descriptive evidence regarding one important school practice through which chronic absenteeism gaps between and among SWDs and GENs might be reduced. The SWD-GEN gap was smaller for SWDs in a classroom composed of majority of GEN classmates. Thus, this new evidence provides insight into how school structures themselves might be related to absenteeism, as proposed by Chang and Davis (2015). There are several reasons to hypothesize that SWDs in GEN-majority classrooms might have lower absences. For instance, in more traditional classrooms settings, some SWDs have increased feelings of belonging with their teachers. Linking this to the results of this present study, it has been shown that as belonging increases, school engagement follows or school refusal declines, which in turn is linked to a decline in chronic absenteeism. In large urban systems, like NYC, where schools educate hundreds of thousands of students each year, feeling and being included might be especially salient for those with unique needs. Hence, this is one plausible explanation for the patterns found in this study, which merit further exploration.


The first and second implications point to a third when we consider heterogeneity by disability type. Our results suggest that additional attention and supports are needed to address the disproportionally high rates of chronic absenteeism faced by students with EDs. While our study suggests that the patterns of absences for students with EDs mirror patterns for other SWDs in the study (i.e., students in part-time classrooms are the least likely to be chronically absent), students with EDs are still missing the largest amount of school regardless of classroom setting. Therefore, our findings call for the need to conduct additional research to identify why more school is missed by students with EDs, across all classroom settings.


Finally, on a broader level, as the nationwide trend of providing SWDs with more education in GEN-majority classrooms continues to press forward, our study shows that there are non-achievement outcomes to consider for such settings. In this case, increasingly heterogeneous settings are associated with fewer absences for SWDs. Thus, while accountability policies tend to focus on testing outcomes, our study shows that measuring student success can encompass non-academic measures, like attendance. And while school attendance, among other non-achievement outcomes, are not the primary focus of IDEA, our findings point to how some school structures might be beneficial to some students at risk.


LIMITATIONS


There are limitations with this study that can guide future research. These findings highlight the need to conduct additional research to identify what factors within the school setting might cause SWDs, and more importantly students with EDs, to miss more school. While many biases are eliminated by controls in our models, still selection on unobserved variables into GEN-majority and SWD-majority classrooms probably remain. Thus, more quasi-experimental work would help to bolster the findings in this present study. The work conducted in this study also could be complemented with qualitative research, where the mechanisms particular to students with EDs could be elaborated. Third, this study cannot make conclusions about the efficacy of SWD- versus GEN-majority classroom settings with regards to academic proficiency and growth metrics, which are often used for school accountability. Additionally, this study does not examine the role of teacher qualifications. Future work might compare these findings with regards to academic performance as well as to teacher effectiveness. Finally, this study relied on information for a specific group of the public-school population in the United States: elementary school children in NYC. Even with our highly detailed student-level, longitudinal dataset, future research might corroborate our findings using detailed data from other districts or entire states. In this way, we can continue building a portrait of absenteeism, risk factors, and supports for SWDs.



Notes


1. Note that blaming health for missing school is also a common misunderstanding for GEN students as well. Recent findings suggest that the majority of elementary school students who reported being sick as a reason for being absent were indeed not sick at all (Kerr et al., 2011).


2. Chronic absenteeism is an extreme form of absenteeism and as of August 2017, 14 of the 17 states that had submitted their ESSA plans to the federal government had used it or absenteeism as one indicator of school performance. Research, however, is more prevalent on effects of absenteeism in general, and thus we review the general absenteeism literature here.


3. Students are coded ungraded if they are eligible to participate in the New York State Alternate Assessment (NYSAA) or ineligible for NYSAA and their instructional level is significantly below (three or more years) the grade-level coursework in English language arts (ELA) and mathematics of their GEN peers (United States, New York City Department of Education, Office of Accountability, 2008).   


4. IEPs are required for each student identified with a disability under federal legislation.


5. These disabilities include autism, deaf-blindness, deafness, hearing impairment, intellectual disability, multiple disabilities, orthopedic impairment, traumatic brain injury, and visual impairment. Other authors have also combined low incidence classifications in order to achieve analytically manageable subgroups. See, for example, Shogren, Kennedy, Dowsett, & Little (2014).


6. More specifically: counseling, school health services, hearing education services, occupational therapy, physical therapy, speech/language therapy, vision education services, orientation and mobility services, and “other support” services. A student’s primary assigned service is designated RS if it is the only service provided by the district.


7. SETSS are provided at a minimum of three hours a week and a maximum of 50% of the school day. The special education teacher can provide instruction directly (to a single individual or a group no larger than eight SWDs) or indirectly (in collaboration with the student’s general education teacher).


8. Recall that our sample is elementary school students, who remain most, if not all, of the day together in the same classroom.


9. When D75 students are included, the percent of SWDs is 17.6.


10. Recall that these classroom settings are defined as follows: SWDs receiving one of the three less restrictive services (RS, SETSS, or ICT) were coded as 1 for being educated in a GEN-majority classroom and SWDs receiving SC were coded as 0, meaning that they were in a SWD-majority classroom.


11. Note that first through fifth graders are in one classroom the majority of the day, while some sixth graders may change classes (although usually as a group). In our sensitivity analysis, we remove sixth graders from the sample and find that results so not change.


12. We show only two results (9% and 11%), although the results were also tested at 5% and 15%. All cutoffs give similar results.


13. Six-year averages across all student-year observations.


14. Seven-year averages across all student-year observations.


References


Alexander, K. L., Entwisle, D. R., & Horsey, C. S. (1997). From first grade forward: Early foundation of high school dropout. Sociology of Education70(2), 97–107.


Anderman, E. M. (2002). School effects on psychological outcomes during adolescence. Journal of Educational Psychology94(4), 795–809.


Balfanz, R., & Byrnes, V. (2012). The importance of being in school: A report on absenteeism in the nation’s public schools. Baltimore: Johns Hopkins University Center for Social Organization of Schools.


Biordi, D. L., & Nicholson, N. R. (2012). Social isolation. In I. M. Lubkin & P. D. Larsen (Eds.), Chronic illness (pp. 97–115). Burlington, MA: Jones and Bartlett Learning.


Broadhurst, K., Paton, H., & May-Chahal, C. (2005). Children missing from school systems: Exploring divergent patterns of disengagement in the narrative accounts of parents, careers, children and young people. British Journal of Sociology of Education26(1), 105–119.


Chang, H. N., & Davis, R. (2015). Mapping the early attendance gap.


Coie, J. D., Dodge, L. A., & Kupersmidt, J. B. (1990). Pee group behavior and social status. In S. R. Asher & J. D. Coie (Eds.), Peer rejection in childhood: Cambridge studies in social and emotional development (pp. 17–59). New York: Cambridge University Press.


Connolly, F., & Olson, L. S. (2012). Early elementary performance and attendance in Baltimore City schools’ pre-kindergarten and kindergartenBaltimore Education Research Consortium. Baltimore, MD: Baltimore Education Research Consortium.


Cortiella, C., & Horowitz, S. H. (2014). The state of learning disabilities: Facts, trends and emerging issues. New York: National Center for Learning Disabilities.


Dexter, D. D., Park, Y. J., & Hughes, C. A. (2011). A meta-analytic review of graphic organizers and science instruction for adolescents with learning disabilities: Implications for the intermediate and secondary science classroom. Learning Disabilities Research & Practice26(4).


Dryfoos, J. G. (1990). Adolescents at risk: Prevalence and prevention. New York: Oxford University Press.


Finn, J. D. (1989). Withdrawing from school. Review of Educational Research59(2), 117–142. http://doi.org/10.3102/00346543059002117


Finn, J. D. (1993). School engagement and students at risk. Washington, DC: U.S. Department of Education.


Fitch, E. F. (1999). Special education and social censure: A case study of an inclusion program. Unpublished doctoral dissertation. Oxford, Ohio.


Freeman, S. F. N., & Alkin, M. C. (2000). Academic and social attainments of children with mental retardation in general education and special education settings. Remedial and Special Education21, 3–18.


Frostad, P., & Pijl, S. J. (2007). Does being friendly help in making friends? The relation between the social position and social skills of pupils with special needs in mainstream education. European Journal of Special Needs Education, 22, 15–30.


Gershenson, S., Jacknowitz, A., & Brannegan, A. (2016). Are student absences worth the worry in U.S. primary schools? Education Finance and Policy.


Goodman, J. (2014). Flaking out: Student absences and snow days as disruptions of instructional timeWorking Paper 20221. Cambridge, MA: National Bureau of Economic Research.


Gottfried, M. A. (2009). Excused versus unexcused: How student absences in elementary school affect academic achievement. Educational Evaluation and Policy Analysis, 31(4), 392–415. 


Gottfried, M.A. (2011). Absent peers in elementary years: The negative classroom effects of unexcused absences on standardized testing outcomes. Teachers College Record, 113(8), 1597–1632.


Gottfried, M.A. (2014) Chronic absenteeism and its effects on students’ academic and socioemotional outcomes, Journal of Education for Students Placed at Risk (JESPAR), 19(2), 53–75


Gottfried, M. A., & Kirksey, J. J. (2017). When students miss school: The role of timing of absenteeism on students’ test performance. Educational Researcher, 46, 119–130.


Hallfors, D., Vevea, J. L., Iritani, B., Cho, H., Khatapoush, S., & Saxe, L. (2002). Truancy, grade point average, and sexual activity: A meta-analysis of risk indicators for youth substance use. The Journal of School Health72(5), 205–211.


Harris, K. D. (2016). In School + On Track: Attorney General's Report on California's Elementary School Truancy and Absenteeism Crisis. Retrieved October 24, 2016.


Johnson, G. M. (2005). Student alienation, academic achievement, and WebCT use. Education Technology and Society, 8(2), 179–189.


Kane, J. (2006). School exclusions and masculine, working-class identities. Gender and Education18(6), 673–685.


Kearney, C. A., Pursell, C., & Alvarez, K. (2002). Treatment of school refusal behavior in children with mixed functional profiles. Cognitive and Behavioral Practice, 8(1), 3–11.


Kerr, J., Price, M., Kotch, J., Willis, S., Fisher, M., & Silva, S. (2011). Does contact by a family nurse practitioner decrease early school absence? The Journal of School Nursing, 28(1), 2012th ser. 


Kozleski, E. B., & Jackson, L. (1993). Taylor’s story: Full inclusion in her neighborhood elementary school. Exceptionality4, 153–175.


Ladd, G. W., & Price, J. M. (1987). Predicting children's social and school adjustment following the transition from preschool to kindergarten. Child Development, 58(5), 1168–1189.


Murray, C., & Greenberg, M. T. (2001). Relationships with teaches and bonds with school: Social emotional adjustment correlates for children with and without disabilities. Psychology in the Schools38(1), 25–41.


National Center for Education Statistics. (2016, May). Children and youth with disabilities. Retrieved from http://nces.ed.gov/programs/coe/indicator_cgg.asp


Nauer, K., Mader, N., Robinson, G., & Jacobs, T. (2014). A better picture of poverty: What chronic absenteeism and risk load reveal about NYC’s lowest income elementary schools. New York: Center for New York City Affairs.


New York City Department of Education, Office of Special Education Initiatives. (n.d.). Special

education services as part of a unified service delivery system. Retrieved from http://www.uft.org/files/attachments/doe-continuum-of-services.pdf


NYC Student-level Administrative Data. (n.d.). Unpublished raw data.


Pearl, R., Farmer, T. W., Van Acker, R., Rodkin, P. C., Bost, K. K., Coe, M., & Henley, W. (1998). The social integration of students with mild disabilities in general education classrooms: Peer group membership and peer-assessed social behavior. Elementary School Journal99, 167–185.


Rogers, J. (1993). The inclusion revolution. Phi Delta Kappa Research Bulletin, 11.


Romero, M., & Lee, Y.-S. (2007). A national portrait of chronic absenteeism in the early grades. New York: Columbia University.


Rumberger, R. W. (1995). Dropping out of middle school: A multilevel analysis of students and schools. American Educational Research Journal32, 583–625.


Schnorr, R. F. (1990). “Peter? He comes and goes...”: First graders’ perspectives on a part-time mainstream student. Journal of the Association for Persons with Severe Handicaps15, 231–240.


Shogren, K. A., Kennedy, W., Dowsett, C., & Little, T. D. (2014). Autonomy, psychological empowerment, and self-realization: Exploring data on self-determination from NLTS2. Exceptional Children, 80, 221–235.


Siperstein, G. N., & Leffert, J. S. (1997). Comparison of socially accepted and rejected children with mental retardation. American Journal of Mental Retardation101, 339–351.


Stiefel, L., Shiferaw, M., Schwartz, A. E., & Gottfried, M. (2018). Who feels included in school? Examining feelings of inclusion among students with disabilities. Educational Researcher, 47(2), 105–120. 


Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High selfcontrol predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality, 72(2), 271–324.


Therrien, W. J., Taylor, J. C., Hosp, J. L., Kaldenberg, E. R., & Gorsh, J. (2011). Science instruction for students with learning disabilities: A meta-analysis. Learning Disabilities Research & Practice26(4), 188–203.


Turner, R. J., & Noh, S. (1988). Physical disability and depression: A longitudinal analysis. Journal of Health and Social Behavior, 23–37.


United States, New York City Department of Education, Office of Accountability. (2008). The Data Specialist’s Guide for New York City Schools 2008-2009 (Vol. 2).


United States, New York City Department of Education, Office of Special Education initiatives. (2016). Special Education Services as Part of a Unified Service Delivery System.


U.S. Department of Education. (2016, October). Civil rights data collection: A first look (Rep.). Retrieved from United States Department of Education website.


U.S. Department of Education, Office of Special Education and Rehabilitative Services. (2007). 29th Annual Report to Congress on the Implementation of the Individuals with Disabilities Education Act, Volume 1.


U.S. Department of Education, Office of Special Education and Rehabilitative Services. (2015). 37th Annual Report to Congress on the Implementation of the Individuals with Disabilities Education Act.


Uwah, C. J., McMahon, G. H., & Furlow, C. F. (2008). School belonging, educational aspirations, and academic self-efficacy among African American male high school students: Implications for school counselors. Professional School Counseling11, 296–305.


Van Gundy, K., & Schieman, S. (2001). Looking inward: Introspectiveness, physical disability, and depression across the life course. International Journal of Ageing and Human Development53(4), 293–310.


 

APPENDIX


Table A1. Descriptive Statistics: SWDs Educated in District 75

2007–2012

  

All

Chronic

Not Chronic

Panel A (column percents)

    

ALL D75 STUDENTS14

Female

25.70

27.18

23.97

 

Hispanic

35.75

40.70

30.00

 

Black

44.72

44.24

45.27

 

White

13.25

10.13

16.88

 

Asian/Other

6.28

04.93

7.85

 

Native Born

94.51

94.68

94.33

 

FRPL

77.82

79.90

75.40

 

LEP

10.05

9.91

10.21

 

Age (years)

9.58

9.65

9.52

 

ED

30.96

37.49

23.44

 

LD

4.15

5.21

2.92

 

SI

2.11

1.36

2.98

 

OH

2.73

2.39

3.12

 

LI

60.05

53.54

67.54

Panel B (row percents)

    

ALL D75 STUDENTS13

ALL

 

53.79

46.21

 

ED

 

64.79

35.21

 

LD

 

67.26

32.74

 

SI

 

34.49

65.51

 

OH

 

46.87

53.13

 

LI

 

47.71

52.29

 

S-Y Obs.

137,305

73,860

63,445

 

N-students

40,880

30,147

23,007

 

N-schools

66

66

66





Cite This Article as: Teachers College Record Volume 121 Number 8, 2019, p. 1-34
https://www.tcrecord.org ID Number: 22755, Date Accessed: 12/2/2021 7:33:57 PM

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About the Author
  • Michael Gottfried
    University of California, Santa Barbara
    E-mail Author
    MICHAEL A. GOTTFRIED is an associate professor in the Gevirtz Graduate School of Education at UC Santa Barbara. His research interests pertain to attendance and truancy as well as students with disabilities. Recent publication: Gottfried, M.A. (2017). The role of attending center-based care for kindergarten-aged children with disabilities. Teachers College Record, 119(2), 1–37.
  • Leanna Stiefel
    New York University
    E-mail Author
    LEANNA STIEFEL is professor of economics and education policy at NYU’s Wagner and Steinhardt Schools. Her current research focuses on urban education policy in areas such as small high school reforms, student mobility, and special education policy. Recent publication: Stiefel, L., Shiferaw, M., Schwartz, A. E., & Gottfried, M. (2018) Who feels included in school? Examining feelings of inclusion among students with disabilities, Educational Researcher, 47(2),105–120.
  • Amy Schwartz
    Syracuse University
    E-mail Author
    AMY ELLEN SCHWARTZ is the Daniel Patrick Moynihan Professor of Public Affairs at the Maxwell School at Syracuse University. Her interests span issues in education policy, urban economics, and public finance and her current research focuses on special education policy, student mobility, and the relationship between neighborhoods, schools, and child health and education outcomes. Article: Schwartz, A. E., Stiefel, L., & Rothbart, M. W. (2016). Do top dogs rule in middle school? Evidence on bullying, safety, and belonging. American Educational Research Journal, 53(5), 1450–1484.
  • Bryant Hopkins
    New York University
    E-mail Author
    BRYANT HOPKINS is a doctoral candidate at NYU’s Wagner School of Public Service. His current research focuses on urban education policy in areas including teacher mobility and special education policy.
 
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