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Journal of School Violence
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School Disciplinary Responses to
Truancy: Current Practice and Future
Directions
K. Brigid Flannery a , Jennifer L. Frank b & Mary McGrath Kato a
a Educational and Community Supports, University of Oregon,
Eugene, Oregon, USA
b Prevention Research Center, Pennsylvania State University,
University Park, Pennsylvania, USA
Accepted author version posted online: 20 Jan 2012.
To cite this article: K. Brigid Flannery , Jennifer L. Frank & Mary McGrath Kato (2012) School
Disciplinary Responses to Truancy: Current Practice and Future Directions, Journal of School Violence,
11:2, 118-137, DOI: 10.1080/15388220.2011.653433
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Journal of School Violence, 11:118–137, 2012
Copyright ©Taylor & Francis Group, LLC
ISSN: 1538-8220 print/1538-8239 online
DOI: 10.1080/15388220.2011.653433
School Disciplinary Responses to Truancy:
Current Practice and Future Directions
K. BRIGID FLANNERY
Educational and Community Supports, University of Oregon, Eugene, Oregon, USA
JENNIFER L. FRANK
Prevention Research Center, Pennsylvania State University, University Park,
Pennsylvania, USA
MARY MCGRATH KATO
Educational and Community Supports, University of Oregon, Eugene, Oregon, USA
Truancy, or unexcused absence, is a common problem facing
nearly all high schools across the United States and other nations.
Understanding how schools typically respond to student truancy
and the relative effectiveness of these responses is an important, yet
relatively unexplored area. Using a national extant dataset, this
study examined which school disciplinary responses are most effec-
tive in reducing the reoccurrence and growth in truancy among
ninth-grade students. Results revealed group differences in the
odds of truancy reoccurrence. After controlling for student-level
factors, out-of-school suspension (OSS) was found to significantly
decrease the probability of future truancy. However, longitudinal
growth models revealed that repeated and ongoing exposure to OSS
actually accelerated the growth in truancy. Implications for schools
and directions for future research are discussed.
KEYWORDS truancy, absenteeism, suspension, high school,
school discipline
Received September 5, 2011; accepted December 15, 2011.
Address correspondence to K. Brigid Flannery, Educational and Community Supports,
1235 University of Oregon, Eugene, OR 97403, USA. E-mail: brigidf@uoregon.edu
118
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School Disciplinary Responses to Truancy 119
SCHOOL DISCIPLINARY RESPONSES TO TRUANCY: CURRENT
PRACTICE AND FUTURE DIRECTIONS
Truancy, or unexcused absence, is a common problem facing nearly all
high schools across the United States (Baker, Sigmon, & Nugent, 2001).
Although estimating U.S. national truancy prevalence rates is difficult due to
inconsistent definitions, most current surveys have found that 4.3% of ninth-
grade students, 7.5% of 10th-grade students, 8.7% of 11th-grade students,
and 13.0% of 12th-grade students self-reported skipping 1 or more days of
school during the previous 30 days (National Center for Education Statistics,
2007). Absenteeism has been identified as the most frequently reported dis-
ciplinary infraction among high school age youth (Kaufman et al., 2010;
Spaulding et al., 2010), and as one of the top three most commonly reported
student disciplinary problems by high school principals (Heaviside, Rowan,
Williams, & Farris, 1998).
In addition to being a relatively common problem, truancy has been
linked to a variety of negative short- and long-term consequences for students.
A growing body of research links early truancy with grade retention, school
failure, and dropping out of school (Heck & Mahoe, 2006; Heilbrunn, 2007),
delinquency (Garry, 1996; Henry & Huizinga, 2007), early initiation of sexual
behavior and unwanted pregnancy (Hibbert & Fogelman, 1990), involvement
with the juvenile justice system (Newsome, Anderson-Butcher, Fink, Hall, &
Huffer, 2008), and 97% of first-time marijuana use (Seeley, 2008).
Historically, research to understand the correlates and causes of truancy
has focused on individual demographic, familial, and academic characteris-
tics of chronic truants. Results from these studies found that truancy tends to
be more prevalent among males (Garry, 1996), and is most prevalent among
some racial minority students (Teasley, 2004). Other correlates include the
presence of family problems, low socioeconomic status, and access to fewer
positive adult relationships (Veenstra, Lindenberg, Tinga, & Ormel, 2010).
In addition, students with poor academic performance (Henry & Huizinga,
2007), school disengagement risk factors (Henry, 2007), those who have
recently been retained, or who participate in special education programs
tend to have higher truancy rates as compared to their same-age peers
(Alexander, Entwisle, & Kabbani, 2001). Truancy rates increase with age
(Catalano & Hawkins, 1996) and peak during the transition from middle
school to high school (Archambault, Janosz, Fallu, & Pagani, 2009).
Through his interdisciplinary model for use with monitoring and
intervening on school absenteeism, Kearny (2008) emphasized the need
for comprehensive consideration of the different variables or influences
of absenteeism. Absenteeism occurs not only because of student vari-
ables but also parent, peer, school, and community influences (e.g., low
levels of school responsiveness to student academic issues, inconsistent
or highly punitive consequences by school). Consistent with Kearny’s
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120 K. B. Flannery et al.
recommendation, Appleton, Christenson, and Furlong (2008), emphasized
the need to attend to not only the indicators of school engagement but
to contextual factors (e.g., school discipline practices) that influence the
strength of the student–school connection. Kearny (2008) also took into
account the importance of behavioral change over time (e.g., across days
of week, following a specific event) by recommending that responses to
absenteeism be fluid and flexible.
Focus of the Current Study
Although a considerable amount of work has been done to understand how
individual student-level risk factors relate to truancy, less work has exam-
ined how school responses may affect future reoccurrence or growth in
truancy rates. Rather than focus on individual–student factors, we examine
the effectiveness of types of school disciplinary responses in order to draw
conclusions about their impact on truancy rates. This shift from a student-
focused to a school-focused approach could be important, because it may
help schools to move away from punitive responses (McEvoy & Welker,
2000; Skiba & Rausch, 2006) toward preventive and proactive strategies
that address the broader school culture by providing a system of support
(McIntosh, Horner, & Sugai, 2009). Although high schools can select from a
variety of disciplinary options in response to student truancy, differences in
how schools collect and report disciplinary data limit what is known about
how high schools respond to truancy (see Irvin, Tobin, Sprague, Sugai, &
Vincent, 2004). Moreover, it is unclear whether one disciplinary response
might be more or less effective than another at reducing future reoccur-
rences of truancy. The purpose of this study was to address this gap through
investigation of the following research questions:
1. What are the most common types of school disciplinary responses to
first-time student truancy offenses in high school settings?
2. Are the most common types of school disciplinary responses differ-
entially effective in preventing the reoccurrence of truancy?
3. Do school disciplinary responses that prevent the reoccurrence of
truancy for most students inhibit truancy rates among at-risk students
over time?
METHOD
Participants and Settings
Participants in this study were 8,457 ninth-grade students who had at
least one or more office disciplinary referrals (ODRs) for missing one or
more classes without permission during the first semester of their freshman
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School Disciplinary Responses to Truancy 121
year. Students were nested within 193 public high schools in the United
States. Schools were nested within 150 school districts across 31 states,
and all agreed to share their data for research purposes. All data were
collected concurrently during the 2007/08 school year. Private schools,
alternative/juvenile justice schools, and year-round schools were excluded
from analyses. In the final sample, 17.8% of schools were located in an
urban city (n=34), 23.0% were suburban (n=44), 25.7% were located in
atown(n=49), and 33.5% were located in a rural locale (n=64). Average
school enrollment was 1,008 students (SD =696). The average number of
full-time classroom teachers was 61 (SD =40) and student–teacher ratios
were 16 full time equivalent (FTE) teachers for every student (SD =3). With
regard to socioeconomic status, 3.2% were low-poverty schools with 10% or
less of the total student population eligible for free or reduced price lunch
(FRL), 22.3% of schools had between 11% and 25% eligible for FRL, 54.8% of
schools had between 26% and 50% of students eligible for FRL, 14.6% had
51%–75% of students eligible for FRL, and 5.1% were high-poverty schools
with more than 75% of students eligible for FRL. Of the students included in
the sample, 56.3% were male, and 7.8% had a formal individual special edu-
cation plan (IEP). Finally, 1.1% were Native American, <1.0% were Asian,
8.8% were Latino, 21.5% were African American, 26.4% were Caucasian, and
39.5% identified as an “other” racial category. Race was unknown or miss-
ing for 2% of sample. Due to the relatively small amounts of missing data,
list-wise deletion methods were employed.
Measures
DISCIPLINE REFERRALS
ODRs related to missing class were used as the primary data source for
the analytic model. Although the validity of ODRs as a direct measure of
complex student behavior has been debated (Kern & Manz, 2004; Nelson,
Gonzalez, Epstein, & Benner, 2003; Rusby, Taylor, & Foster, 2007), standard-
ization of referral practices and training greatly enhances the reliability of
ODRs (G. M. Morrison, Peterson, O’Farrell, & Redding, 2004). To ensure the
maximum level of data collection integrity, all schools utilized the same data
collection system (School-Wide Information System [SWIS]). Unlike unstruc-
tured disciplinary data systems, SWIS uses a set of operationally defined
and mutually exclusive codes to describe student behavior and disciplinary
responses, thus reducing ambiguity associated with this form of data (see
www.swis.org for code definitions). The current study included data from
two codes: skip (miss class without permission) and truant (unexcused
absence for half day or more). These combined ODRs are referred to here-
after as “truancy” in this study. In addition, each school was required to
meet the criteria listed in the SWIS Readiness Checklist (Todd, Horner, &
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122 K. B. Flannery et al.
Tobin, 2010) before beginning data collection. The checklist documented
that all schools utilized standardized referral forms compatible with SWIS
referral entry, adopted a coherent ODR procedure, engaged in timely data
entry, identified a school data facilitator, and participated in ongoing train-
ing related to SWIS procedures. The checklist was completed by a district
facilitator who was certified in SWIS procedures and trained to work with
school personnel on data collection and decision making procedures.
Data Analysis Plan
The analysis proceeded in multiple interconnected stages. First, we exam-
ined the frequency with which various disciplinary responses to first-time
student truancy were applied. Next, we conducted a series of nonparamet-
ric Kaplan-Meier survival analyses followed by logistic regression to explore
whether these most common disciplinary responses were differentially effec-
tive in preventing the single-event reoccurrence of truancy among different
populations of students. We selected logistic regression as an alternative to
Cox proportional-hazards regression method because the assumption of pro-
portional hazards over time for the different stratified groups could not be
met for these data and such methods can provide reasonable estimates under
such conditions (Klein & Moeschberger, 2003). Finally, we used hierarchical
linear modeling (HLM) with full maximum likelihood estimation method to
examine whether disciplinary methods significantly related to the absence
of single-event reoccurrence were also related to the rate or growth in tru-
ancy offenses over time. HLM was selected over other analytic options given
its flexibility in accommodating repeated measures data with nonequidistant
time points.
RESULTS
Disciplinary Responses to First-Time Truancy
Table 1 provides the occurrence of disciplinary responses applied in
response to first-time truancy offenses for ninth-grade students. The most
common type of disciplinary response was detention (26.0%), followed
by in-school suspension (ISS; 25.5%), Saturday school (16.4%), and out-
of-school suspension (OSS; 10.3%). More proactive forms of discipline that
required a higher degree of coordination such as arrangement of student
conferences (8.4%) and parent contact (5.4%) were less common. Similarly,
forms of discipline that often require identification of a specific approach
for the student and 1:1 interaction with an adult (e.g., restitution, loss of
privileges, instruction) were all exceedingly rare (<1.0%).
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School Disciplinary Responses to Truancy 123
TABLE 1 Distribution of Disciplinary Responses to Student Skipping Class at High School
Level
Response type SWIS operational definition n%
Time in office Consequence for referral results in student
spending time in the office away from
scheduled activities/classes
29 <1.0
Loss of
privileges
Consequence for referral results in student being
unable to participate in some type of privilege
10 <1.0
Student
conference
Consequence for referral results in student
meeting with administrator, teacher, and/or
parent (in any combination)
714 8.4
Parent contact Consequence for referral results in parent
communication by phone, e-mail, or
person-to-person about the problem
455 5.4
Detention Consequence for referral results in student
spending time in a specified area away from
scheduled activities/classes
2, 198 26.0
Instruction Consequence for referral results in student
receiving individualized instruction specifically
related to the students problem behaviors
17 <1.0
ISS Consequence for referral results in a period of
time spent away from scheduled
activities/classes during the school day
2, 158 25.5
OSS Consequence for referral results in a 1 to
3-day-period when student is not allowed on
campus
872 10.3
Expulsion Consequence for referral results in student being
dismissed from school for one or more days
4<1.0
Bus
suspension
Consequence for referral results in 1 to
3-day-period when student not allowed on
the bus
6<1.0
Saturday
school
Consequence for referral results in student
attending classes on a Saturday
1, 384 16.4
Restitution Consequence for referral results in apologizing
or compensating for loss, damage, or injury
6<1.0
Other/unknown Consequence for referral results in
administrative decision that is not listed above
604 5.7
Note. SWIS =school-wide information system; ISS =in-school suspension; OSS =out-of-school
suspension.
Effect of Disciplinary Practices on Preventing the Reoccurrence
of Truancy
In examining the effect of disciplinary practices on the probability of tru-
ancy reoccurrence, the analysis was focused on the most frequently utilized
school discipline responses (e.g., detention, ISS, Saturday school, OSS, con-
ferences, and parent contact). Results were stratified by student race, gender,
and special education status (i.e., student had an IEP). The results of Kaplan-
Meier analyses (Klein & Moeschberger, 2003), including the probability of
the reoccurrence of truancy, and average duration to reoffending (in days),
are provided in Table 2.
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124 K. B. Flannery et al.
TABLE 2 Survival Times in Days Until Reoccurrence of Truancy
Response type % Recidivism Mdays to recidivism SE 95% CI
Native American
Overall 66.3 37 5.38 26.66–47.77
Conference 85.7 12 2.92 6.62–18.05
Detention 66.7 42 6.80 28.25–54.92
ISS 60.0 36 11.55 12.96–58.24
OSS 66.7 6 6.00 0–17.76
Saturday school 100 77 70.50 0–214.68
Parent contact — — — —
Asian
Overall 26.0 45 13.75 17.75–71.64
Conference 11.0 — — —
Detention 28.0 7 3.5 0–13.36
ISS 42.9 60 44.41 0–146.05
OSS — — — —
Saturday school 37.5 43 11.16 20.63–64.37
Parent contact 33.3 — — —
Latino
Overall 37.8 55 3.36 48.14–61.31
Conference 37.7 55 12.99 29.78–80.69
Detention 31.6 49 7.19 34.48–62.68
ISS 37.8 52 7.24 37.62–66.01
OSS 31.1 47 8.12 31.04–62.88
Saturday School 48.7 58 5.61 46.75–68.74
Parent contact 35.4 73 16.06 41.88–104.83
African American
Overall 44.5 51 2.00 46.87–54.69
Conference 44.3 62 6.88 48.70–75.67
Detention 46.6 48 4.26 39.20–55.89
ISS 41.7 53 3.39 46.06–59.33
OSS 33.8 50 5.54 38.95–60.66
Saturday school 52.4 50 5.09 39.82–59.78
Parent contact 54.5 36 5.92 23.92–47.13
White
Overall 44.2 45 1.74 41.87–48.70
Conference 38.4 45 5.58 31.51–53.38
Detention 42.3 46 3.14 40.19–52.49
ISS 45.8 46 3.36 39.74–52.93
OSS 44.0 34 5.16 24.06–44.29
Saturday school 49.6 49 4.22 40.58–57.13
Parent contact 44.6 42 7.22 27.82–56.14
Male
Overall 43.0 49 1.25 46.49–51.38
Conference 40.6 51 4.55 42.11–59.96
Detention 43.0 47 2.27 42.27–51.15
ISS 43.2 48 2.43 43.01–52.52
OSS 37.5 49 3.76 41.65–56.40
Saturday school 48.5 53 2.92 47.09–58.53
Parent contact 43.9 49 4.77 39.36–58.05
(Continued)
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School Disciplinary Responses to Truancy 125
TABLE 2 (Continued)
Response type % Recidivism Mdays to recidivism SE 95% CI
Female
Overall 41.7 47 1.33 44.25–49.47
Conference 38.5 50 5.02 40.36–60.02
Detention 43.4 43 2.34 38.76–47.92
ISS 41.8 51 2.48 45.82–55.55
OSS 37.3 44 4.63 34.87–53.01
Saturday school 44.1 48 3.12 41.88–54.09
Parent contact 40.8 40 5.23 29.80–50.31
Has IEP
Overall 51.7 48 0.96 46.40–50.17
Conference 44.4 51 3.50 44.48–58.21
Detention 51.3 45 1.70 41.47–52.68
ISS 54.3 49 1.84 45.47–52.68
OSS 51.6 48 3.20 41.92–54.47
Saturday school 56.3 52 2.25 47.28–56.10
Parent contact 48.6 46 3.75 38.67–53.38
No IEP
Overall 41.6 46 2.85 40.02–51.20
Conference 39.3 42 12.44 17.42–66.19
Detention 42.6 50 5.74 39.03–61.16
ISS 41.5 49 5.33 39.00–55.56
OSS 35.7 41 7.21 27.31–55.56
Saturday school 45.9 38 6.12 26.11–50.11
Parent contact 42.1 39 11.36 16.62–61.16
Note. “—” indicates no cases were present. IEP =individual special education plan; ISS =in-school
suspension; OSS =out-of-school suspension.
Results from this analysis revealed that within the race strata, Asian stu-
dents had the lowest probability of truancy recidivism (26%, see Table 2).
Males had a slightly higher probability of truancy recidivism (43%) as com-
pared to females (41.7%). Students with IEPs also had higher probabilities
of truancy recidivism (51.7%) as compared to students without IEPs (41.6%).
The average number of days until truancy recidivism also varied within and
across stratified groups. Native Americans had the overall shortest latency
until the reoccurrence of truancy (M=37.2days,SE =5.4) followed by
Asians (M=44.7days,SE =13.8), Whites (M=45.3 days ,SE =1.7), African
Americans (M=50.8days,SE =2.0), and Latinos (M=54.7days ,SE =3.4).
Across stratified gender groups, males had a slightly longer latency until
truancy recidivism (M=48.9days,SE =1.3) as compared to females (M=
46.9days,SE =1.3). Students with IEPs had a longer latency to recidivism (M
=48.3days,SE =1.0) as compared to students without IEPs (M=45.6days ,
SE =2.9).
Prior to testing for group differences across these estimates, we first
visually inspected survival and hazard plots for possible violations of the
assumption of proportional hazards over time (Klein & Moeschberger, 2003).
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126 K. B. Flannery et al.
As Figure 1 illustrates, meaningful differences across disciplinary approaches
seemed plausible, but the assumption of proportional hazards across time
(i.e., nonoverlapping curves) was not met. Therefore, we conducted a
logistic regression to test for group differences in the probability of the
reoccurrence of a truancy event (Efron, 1988). As Table 3 illustrates, among
the stratified demographic variables tested, only IEP status and student race
were significant predictors of the reoccurrence of truancy. Specifically, after
controlling for other variables entered into the model, students with IEPs
were significantly more likely to have a reoccurrence of truancy as compared
to students without IEPs, β=0.50, p<.05. Native American students were
significantly more likely, β=1.11, p<.01, and Latinos were significantly
less likely, β=–0.31, p<.01, than Whites to have truancy recidivism. The
Gender, Gender ×Race, and IEP ×Race interaction terms were not signif-
icant. In examining the relative effectiveness of each commonly used disci-
pline response, after controlling for student-level characteristics, only the dis-
cipline responses of Saturday school and OSS had a significant relation with
the probability of future truancy within the same school year. Specifically,
Saturday school was associated with a significant increase of the probability
of truancy recidivism, β=0.18, p<.05, and OSS significantly associated
with a decrease of the probability of future truancy, β=–0.27, p<.01.
0
0.0
0.2
0.4
0.6
0.8
1.0
50 100 150
Duration in Da
y
s to Second Skip
Survival Functions
Cumulative Survival
200 250 300
Admin
Decision
Conf
Parent
Detent
In-sch susp
Out-sch susp
Sat sch
FIGURE 1 Survival curves of time to truancy re-offense.
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School Disciplinary Responses to Truancy 127
TABLE 3 Logistic Regression Predicting Reoccurrence of Truancy
Predictor βSE Wal d df p eβ(OR) 95% CI for OR
Constant −.26 .78 11.23 1 .00 .77 NA
Gender −.01 .09 .01 1 .91 .99 1.11–2.44
IEP .50 .20 6.11 1 .01 1.64 1.11–2.44
Race NA NA 26.04 5 .00 NA NA
Native American 1.11 .33 11.13 1 .00 3.02 1.58–5.78
Asian −.73 .45 2.59 1 .11 .48 .20–1.17
Latino −.31 .14 7.31 1 .01 .68 .52–.90
African American .038 .10 .13 1 .72 1.04 .85–1.27
Other −.13 .09 2.34 1 .13 .87 .74–1.04
Gender ×Race NA NA 4.5 5 .48 NA NA
IEP ×Race NA NA 6.12 4 .19 NA NA
Conference −.14 .09 2.57 1 .11 .87 .73–1.03
Parent contact .01 .11 .01 1 .94 1.01 .82–1.24
Saturday school .18 .07 6.26 1 .01 1.19 1.04–1.37
ISS −.03 .06 .18 1 .68 .97 .86–1.10
OSS −.27 .08 9.96 1 .00 .77 .65–.90
Note. IEP =individual special education plan; ISS =in-school suspension; OSS =out-of-school suspen-
sion. R2=0.03 (Cox & Snell), 0.02 (Nagelkerke). Model χ2(22) =99.78, p<.05. Reference categories
for contrasts: female, no IEP, White, detention.
Effect of Disciplinary Practices on Truancy Growth Over Time
Given the aforementioned findings, we then tested whether these same fac-
tors predicted growth in student truancy rate over time. Researchers have
advocated the use of individual growth curves to study change and have
demonstrated that HLMs are particularly well suited to the analysis of indi-
vidual growth over time (Bryk & Raudenbush, 1992). Although structural
equation modeling (SEM) has many advantages in the modeling of individ-
ual growth over time (e.g., estimation of multiple correlation structures and
fit indices), assumptions regarding the spacing between measurement time
points and limitations in accommodating Type III longitudinal data unbal-
anced on time made HLM a better fit for this particular dataset (see Wu, West,
& Taylor, 2009). To prepare the dataset, all continuous daily student ODR
data were aggregated to provide weekly ODR data counts for each student.
All multilevel analyses were conducted in HLM version 6.06 (Raudenbush,
Bryk, Cheong, & Congon, 2008).
LEVEL 1
The first stage of HLM modeling is called the within-subjects portion of
the model, or Level 1. During this first stage, a regression analysis was
performed on each participant. Repeated observations from each participant
on the number of instances of truancy (dependent variable) were regressed
on time (the independent variable) and an intercept and slope parameters
were obtained for each participant in the sample. An important feature of
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128 K. B. Flannery et al.
our model is the inclusion of exposure to OSS as a time-varying covariate.
By using the rates of student exposure to disciplinary events as covariates
at each time point, we can explore whether change in the growth of tru-
ancy over time is (or is not) related to changes in these school disciplinary
responses. We focused on OSS because our previous logistic regressions
found this to be the only predictor significantly associated with lower prob-
ability of future truancy. Variables were treated as random effects, and thus
allowed to vary across multiple levels of the model. Time was centered at
the first time point, such that the intercept can be interpreted as behavior at
the beginning of the school year. In notation, the Level 1 model is given as:
Level 1: π0+π1(time) +π2(OSS) +(quad) +e
LEVEL 2
In the second phase of the HLM model, the slope and intercept parameters
for participants were used as dependent variables in a series of analysis of
variance models. Factors that vary across students, such as race, gender, and
IEP status were used as independent variables in this between-subjects or
Level 2 portion of the model. Each Level 2 variable was coded using cate-
gorical dummy codes, with Caucasian, females, and students without IEPs
serving as the reference category for each predictor respectively. In notation,
the Level 2 model is given as:
Level 2: β0j +β1j (IEP) +β2j (gender) +β3j (race) +εij
LEVEL 3
Finally, to account for possible effects of school-level variables on growth
over time, we included a variety of Level 3 variables including student
socioeconomic status (SES), racial diversity, student–teacher ratio, school
geographic locale, and total enrollment. SES was indexed using the percent-
age of students at each school qualifying for free or reduced price lunch.
Racial diversity was indexed using the percentage of non-White students
attending the school. All continuous variables (SES, racial diversity, student–
teacher ratio, and total enrollment) were group mean centered. School locale
was coded as a categorical dummy code, with suburban schools serving as
a reference category for each predictor.
Level 3: γ00 +γ001 (SES) +γ002 (enrollment) +γ003 (%minority) +γ004
(student −teacher ratio) +γ005 (locale) +u00
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School Disciplinary Responses to Truancy 129
TABLE 4 Deviance Tests
Model Deviance χ2df p
Basic unconditional 37,634.25
Time 35,496.18 2138.07 5 <.001
Quadratic form 33,288.25 4,297.91 8 <.001
OSS exposure −18,739.80 55,009.72 6 <.001
Student characteristics −16,016.93 52,286.84 19 <.001
School characteristics −16,043.66 52,313.57 33 <.001
Note. OSS =out-of-school suspension.
SEQUENTIAL MODEL BUILDING PROCEDURE
As in multiple regressions, the effects of a particular variable are best con-
sidered in the context of other variables. Therefore, we conducted multiple
deviance tests and examined changes in χ2sequentially in order to specify
the final model tested previously. Models were tested in a nested fashion,
so the χ2change reflects variance above and beyond the previous model.
As Table 4 illustrates, as compared to the unconditional model, the addition
of time resulted in a decrease in deviance and improved model fit, χ2=
2,138.07, p<.001. We then tested the functional form of growth, and a
quadratic term was retained, χ2=4,297.91, p<.001. More complex cubic
growth terms were tested, but did not significantly improve overall fit. The
addition of the time-varying covariate OSS exposure, χ2=55,009.78, p<
.001, entry of student-level characteristics including student IEP status, gen-
der, and race, χ2=52286.84, p<.001, all resulted in sequential decreases
in deviance and improved model fit. The addition of Level 3 school factors
(e.g., SES, locale, and racial diversity) significantly improved model fit, χ2=
52,313.57, p<.001, but did not decrease deviance as a consequence of the
additional parameters added.
FINAL GROWTH MODEL PARAMETERS
Tables 5 and 6 provide parameter estimates and significance tests for each
of the intercept and slope terms included in the final growth model. With
regard to test of intercepts, gender, γ=0.007375, p<.01, IEP status, γ=
0.017695, p<.01, and being Native American, γ=0.007375, p<.01,
were all significant and positive, suggesting that initial levels of truancy
were significantly higher for these groups at time 1. With regard to slopes,
or growth over time, this pattern was reversed, as males, γ=–0.00246,
p<.01, and students with IEPs, γ=–0.000385, p<.01, had significantly
lower rates of growth in truancy over time. Although the locale intercepts
were not significant, the slope for urban school status was, γ=–0.000498,
p<.05, suggesting that although there were no locale-based differences
at Time 1, students attending urban schools generally had a slower rate of
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130 K. B. Flannery et al.
TABLE 5 Full Hierarchical Growth Model Predicting Truancy Intercepts
Var i a b le γSE t ratio df p
Constant 0.01 0.01 0.74 186 .46
Percent FRL −0.18 0.02 −1.12 186 .26
Total enrollment 0.00 0.00 0.47 186 .64
Percent minority −0.00 0.01 −0.22 186 .82
Student–teacher ratio 0.00 0.00 0.31 186 .76
Urban −0.01 0.01 −1.10 186 .27
Rural −0.00 0.01 −0.42 186 .67
Male 0.01 0.00 3.13 8448 <.01
IEP 0.02 0.00 3.99 8448 <.01
Race
Native American 0.01 0.00 3.13 8448 <.01
Asian 0.02 0.02 0.96 8448 .34
Latino 0.00 0.01 0.34 8448 .73
African American 0.01 0.01 0.69 8448 .49
Note. FRL =free and reduced lunch; IEP =individual special education plan.
TABLE 6 Full Hierarchical Growth Model Predicting Truancy Slopes
Var i a b le γSE t ratio df p
Constant 0.00 0.00 13.28 8,448 <.01
Percent FRL −0.00 0.00 −0.22 186 .83
Total enrollment 0.00 0.00 1.17 186 .25
Percent minority −0.00 0.00 −0.69 186 .49
Student–teacher ratio −0.00 0.00 −1.62 186 .49
Urban −0.00 0.00 −2.33 186 <.05
Rural −0.00 0.00 −1.59 186 .11
Male −0.00 0.00 −3.25 8,448 <.01
IEP −0.00 0.00 −2.59 8,448 <.01
Race
Native American −0.00 0.00 0.52 8,448 .61
Asian 0.00 0.00 1.21 8,448 .23
Latino 0.00 0.00 0.78 8, 448 .43
African American 0.00 0.00 0.61 8,448 .54
OSS 1.06 0.01 232.95 380,533 <.00
Quadratic −0.00 0.00 −33.00 380,533 <.01
Note. FRL =free and reduced lunch; IEP =individual special education plan; OSS =out-of-school
suspension.
growth in the accumulation of truancy events as compared to students in
suburban schools. At Level 1, the effect of the time-varying covariate OSS
on truancy slopes was both positive and significant, γ=1.062208, p<
.01, suggesting that continued increased exposure to OSS over time actually
accelerates growth in the accumulation of truancies. The significant negative
quadratic term parameter, γ=–0.000082, p<.01, suggests that the growth
in truancies at the individual student level is not linear, but rather assumes a
more concave pattern of curvilinear growth over time.
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School Disciplinary Responses to Truancy 131
DISCUSSION
The research literature has documented the challenges that students face
during the transition from middle school to high school and truancy in par-
ticular. Heck and Mahoe (2006) found that students with high absenteeism
in ninth grade were almost six times more likely to be academically behind
their peers or to drop out of school by the 10th grade compared to their
peers with regular attendance. It is critical for high school administrators and
teachers to better understand the patterns of ninth graders who are skipping
school to develop interventions and successful monitoring strategies. Placing
truancy in social context by understanding the patterns and the effectiveness
of policies and practices, such as school discipline responses and their role in
contributing to future truant behavior, is important. The purpose of this study
was to describe the most common disciplinary responses to truancy currently
used in high school settings, to examine whether or not these responses
were differentially effective in preventing the reoccurrence of truancy, and
to examine whether responses effective in preventing reoccurrence also
impacted growth in truancy over time.
Types of Consequences Used by Schools and Effect on Reoccurrence
Descriptive analyses of patterns in high school disciplinary responses in this
study revealed that high schools currently select from a relatively limited
repertoire of school discipline responses to student truancy. The most com-
mon of these disciplinary responses are often quite exclusionary, with ISS
and OSS occurring in over 35% of truancy incidents. The punitive nature of
this pattern of responding is particularly noteworthy since our descriptive
analyses focused on exploring how schools responded to first-time truancy
offenses recorded for ninth-grade students. However, the most common
forms of discipline used by schools tend to be those that lend themselves
to being easily implemented. This finding suggests the need for the devel-
opment of nonexclusionary disciplinary responses that can also be easily
embedded into school systems.
Our examination of the differential effectiveness of common high
school discipline responses on the prevention of future instances of truancy
revealed, after controlling for student-level factors, that only two responses
had any significant effect on the probability that truancy would reoccur: OSS
and Saturday school. Interestingly, the effects on these probabilities were in
opposing directions with Saturday school significantly increasing and OSS
significantly decreasing the probability of future occurrences of truancy.
The finding that Saturday school increases the probability of future truancy
occurrences is consistent with peer deviancy training models (see Dishion
& Dodge, 2005, for a review), which maintain that naturally occurring
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132 K. B. Flannery et al.
peer interactions promoted through the congregation of problem youth
can inadvertently exacerbate the development of subsequent deviant behav-
ior. Although we did not gather the implementation data necessary to fully
explore these possible links within the context of this study, our preliminary
results suggest this may be an interesting area for future research. On the
surface, the findings indicated that OSS seems to work at reducing the future
reoccurrence of truancy. However, further examination through growth
modeling analyses revealed that while OSS may initially reduce the prob-
ability of recidivism, repeated ongoing exposure to OSS has a strong and
significant effect on the growth of truancy occurrences over time. It has been
speculated that suspension or expulsion of a student for truancy may reward
their desire to escape from or avoid school and does little to encourage them
to have consistent attendance (Railsback, 2004; Shannon & Bylsma, 2003).
School policies are often designed to provide efficient response to
behavior without consideration of the function the behavior serves (Maag,
2001; Mayer, 1995). However, actions intended to punish do not always
function effectively to decrease the targeted behavior (Mayer, 1995; Skiba
& Peterson, 2000). In fact, the results of the current study support the
conclusion that OSS may have been an antecedent or setting event for
more occasions of truancy. Effective alternatives to suspension have been
advocated for (Chin, Dowdy, Jimerson, & Rime, 2012; B. E. Morrison &
Vaandering, 2012) that include comprehensive social, emotional, or edu-
cational interventions and supports, which may interrupt the negative
behavioral trajectories of students. Specifically, early screening procedures
for risk factors, early preventative strategies, secondary and tertiary tier
interventions, formal adult mentoring, and comprehensive after school pro-
grams are among the strategies suggested (Kearny, 2008; Walker, Ramsey, &
Gresham, 2004). Although these interventions have been identified and val-
idated, they are not frequently used in schools and information to explain
lack of implementation is needed to improve use of screening and alternative
interventions to suspension.
The strict exclusionary school policies found to be used most fre-
quently by the schools in the current study also could have the unintended
consequence of pushing the student out of school. When designing conse-
quence systems, it is crucial to examine why a student might be engaging in
a repeated behavior as every behavior occurs for a reason. Behavior contin-
ues to occur because it is resulting in some desired outcome for the student
(Cooper, Heron, & Heward, 1987; Kearny, 2008). The use of policies with
a reliance on single solutions ignores the heterogeneity and complexity of
truancy. Schools need to consider the use of a function-based approach,
where antecedents to the problem behavior are identified and examined in
conjunction with events that frequently follow the behavior (consequences).
Once identified, this information can be used to identify the function of the
behavior so interventions can be delivered with such considerations in mind
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School Disciplinary Responses to Truancy 133
(e.g., Hanley, Iwata, & McCord, 2003; Matson & Minshawi, 2007). In the
case of skipping school, if the student is skipping school to avoid a diffi-
cult class, or to hang out with friends, using an OSS will only increase the
likelihood that the behavior will continue. Staff needs to take time to under-
stand why the student is skipping school, especially when it is an ongoing
behavior. Asking this question with the help of a brief functional behavioral
assessment allows for the assignment of a consequence that will (a) teach
the student acceptable replacement behaviors and (b) also may serve to
reinforce staying in school (Hanley et al., 2003).
There is recognition that students who are frequently truant from school
may be in need of academic, social, or personal assistance (Neild, Stoner-
Eby, Furstenberg, 2008; Sutphen, Ford, & Flaherty, 2010). Testerman (1996)
found that students leave school because they feel teachers are not inter-
ested in them or their success. It is not apparent that the schools, when
selecting consequences for truancy, attended to the established relationship
between the students’ success in academics and their absenteeism. Student
truancy is a complex behavior that requires continued research to identify
evidenced-based interventions, including those focused on the academic or
other supports that may be needed by students as alternatives to suspen-
sion. Schools need to develop a screening and team driven tiered approach
to intervention for absenteeism.
LIMITATIONS
The results of this study should be considered in light of several limitations.
First, although a strength of this study is a relatively large sample size (N=
8,457), we were unable to control for a variety of individual student-level risk
factors associated with truancy such as academic performance, school attach-
ment, and family relationships. Future research is needed to explore the pos-
sible interactive effects of disciplinary responses by individual student risk
variables on the reoccurrence and growth in truancy. Interactions between
these risk variables and school disciplinary responses are probable, and
likely to be a fruitful area for future research. Second, our study focused on
only a single academic year and ninth-grade students because of the particu-
lar relevance of this population for high school prevention efforts. However,
due to the focus on freshmen, future research may wish to explore whether
these findings generalize to other grade levels. It is quite possible the relative
effectiveness of various disciplinary practices change across time and student
development. The time of year in which the offense occurs was not explored
in this study, but may also factor into the probability of reoccurrence. Finally,
it is important to note that our extant dataset lacked information pertaining
to the general quality or integrity with which disciplinary responses were
implemented or the specific policy for recording truancy.
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134 K. B. Flannery et al.
CONCLUSION
Given the student risk factors that result from truancy (e.g., loss of instruc-
tional time, dropping out of school, criminal deviance) it is important to
continue to study the effectiveness of school policies on truancy rates. It is
critical to focus on school culture, and particularly what impact specific
school policies have on students and the choices they make. Understanding
the patterns of student behavior and the effectiveness of school poli-
cies, thereby placing behavior in social context, will improve the odds of
improved outcomes for students.
STATEMENT ON FUNDING
The development of this article was supported in part by grants
from the Institute of Education Sciences, U.S. Department of Education
(R324A070157). Opinions expressed herein are the authors’ and do not
reflect necessarily the position of the U.S. Department of Education, and
such endorsements should not be inferred.
COMPETING INTERESTS
We have no conflicts of interest to disclose.
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