ArticlePDF Available

School Disciplinary Responses to Truancy: Current Practice and Future Directions


Abstract and Figures

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 effective 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.
Content may be subject to copyright.
This article was downloaded by: [Pennsylvania State University], [Jennifer Frank]
On: 03 October 2013, At: 07:04
Publisher: Routledge
Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered
office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Journal of School Violence
Publication details, including instructions for authors and
subscription information:
School Disciplinary Responses to
Truancy: Current Practice and Future
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
To link to this article:
Taylor & Francis makes every effort to ensure the accuracy of all the information (the
“Content”) contained in the publications on our platform. However, Taylor & Francis,
our agents, and our licensors make no representations or warranties whatsoever as to
the accuracy, completeness, or suitability for any purpose of the Content. Any opinions
and views expressed in this publication are the opinions and views of the authors,
and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content
should not be relied upon and should be independently verified with primary sources
of information. Taylor and Francis shall not be liable for any losses, actions, claims,
proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or
howsoever caused arising directly or indirectly in connection with, in relation to or arising
out of the use of the Content.
This article may be used for research, teaching, and private study purposes. Any
substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,
systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
Conditions of access and use can be found at
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
Educational and Community Supports, University of Oregon, Eugene, Oregon, USA
Prevention Research Center, Pennsylvania State University, University Park,
Pennsylvania, USA
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:
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
School Disciplinary Responses to Truancy 119
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
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
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?
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
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
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.
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 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, &
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
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.
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%).
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
School Disciplinary Responses to Truancy 123
TABLE 1 Distribution of Disciplinary Responses to Student Skipping Class at High School
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
Consequence for referral results in student being
unable to participate in some type of privilege
10 <1.0
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
872 10.3
Expulsion Consequence for referral results in student being
dismissed from school for one or more days
Consequence for referral results in 1 to
3-day-period when student not allowed on
the bus
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
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
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.
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
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
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
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
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
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
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
School Disciplinary Responses to Truancy 125
TABLE 2 (Continued)
Response type % Recidivism Mdays to recidivism SE 95% CI
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
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
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).
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
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.
50 100 150
Duration in Da
s to Second Skip
Survival Functions
Cumulative Survival
200 250 300
In-sch susp
Out-sch susp
Sat sch
FIGURE 1 Survival curves of time to truancy re-offense.
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
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).
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
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
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
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
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
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
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.
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.
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
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
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
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
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
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.
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
School Disciplinary Responses to Truancy 131
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
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
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
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
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.
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.
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
134 K. B. Flannery et al.
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.
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.
We have no conflicts of interest to disclose.
Alexander, K., Entwisle, D., & Kabbani, N. (2001). The dropout process in life course
perspective: Early risk factors at home and school. The Teachers College Record,
103, 760–822. doi:10.1111/0161-4681.00134
Appleton, J. J., Christenson, S. L., & Furlong, M. J. (2008). Student engagement
with school: Critical conceptual and methodological issues of the construct.
Psychology in the Schools,45, 369–386.
Archambault, I., Janosz, M., Fallu, J., & Pagani, L. S. (2009). Student engagement
and its relationship with early high school dropout. Journal of Adolescence,32,
651–670. doi:10.1016/j.adolescence.2008.06.007
Baker, M. L., Sigmon, J. N., & Nugent, M. E. (2001). Truancy reduction: Keeping stu-
dents in school. Washington, DC: U.S. Department of Justice, Office of Juvenile
Justice Programs, Office of Juvenile Justice and Delinquency Prevention.
Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications
and data analysis methods. Chicago, IL: Sage.
Catalano, R. F., & Hawkins, J. D. (1996). The social development model: A theory
of antisocial behavior. In Delinquency and crime: Current theories,Cambridge
criminology series (pp. 149–197). New York, NY: Cambridge University Press.
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
School Disciplinary Responses to Truancy 135
Chin, J. K., Dowdy, E., Jimerson, S. R., & Rime, J. (2012). Alternatives to suspension:
Rationale and recommendations. Journal of School Violence,11, 156–173.
Cooper, J. O., Heron, T. E., & Heward, W. L. (1987). Applied behavior analysis.
Columbus, OH: Merrill. doi:10.1080/00405848209542992
Dishion, T. J., & Dodge, K. A. (2005). Peer contagion in interventions for chil-
dren and adolescents: Moving towards an understanding of the ecology and
dynamics of change. Journal of Abnormal Child Psychology,33, 395–400.
Efron, B. (1988). Logistic regression, survival analysis, and the Kaplan-
Meier curve. Journal of the American Statistical Association,83, 414–425.
Garry, E. M. (1996). Truancy: First step to a lifetime of problems [Bulletin]. Retrieved
Hanley, G. P., Iwata, B. A., & McCord, B. E. (2003). Functional analysis of prob-
lem behavior: A review. Journal of Applied Behavior Analysis,36 , 147–185.
Heaviside, F., Rowan, C., Williams, C., & Farris, E. (1998). Violence and discipline
problems in U.S. public schools: 1996–1997. Washington, DC: National Center
for Educational Statistics. Retrieved from
Heck, R. H., & Mahoe, R. (2006). Student transition to high school and persis-
tence: Highlighting the influences of social divisions and school contingencies.
American Journal of Education,112, 418–446. doi:10.1086/500715
Heilbrunn, J. (2007). Pieces of the truancy jigsaw: A literature review. Denver,
CO: National Center for School Engagement. Retrieved from http://
Henry, K. L. (2007). Who’s skipping school: Characteristics of truants in 8th
and 10th grade. Journal of School Health,77 , 29–35. doi:10.1111/j.1746-
Henry, K. L., & Huizinga, D. H. (2007). Truancy’s effect on the onset of drug
use among urban adolescents placed at risk. Journal of Adolescent Health,40,
e9–e17. doi:10.1016/j.jadohealth.2006.11.138
Hibbert, A., & Fogelman, K. (1990). Future lives of truants: Family formation and
health-related behavior. British Journal of Educational Psychology,60, 171–179.
Irvin, L. K., Tobin, T. J., Sprague, J. R., Sugai, G., & Vincent, C. G. (2004). Validity of
office discipline referral measures as indices of school-wide behavioral status
and effects of school-wide behavioral interventions. Journal of Positive Behavior
Interventions,6, 131–147. doi:10.1177/10983007040060030201
Kaufman, J. S., Jaser, S. S., Vaughan, E. L., Reynolds, J. S., Di Donato, J., Bernard,
S. N., & Hernandez-Brereton, M. (2010). Patterns in office referral data by
grade, race/ethnicity, and gender. Journal of Positive Behavior Interventions,
12, 44–54. doi:10.1177/1098300708329710
Kearny, C. A. (2008). An interdisciplinary model of school absenteeism in youth to
inform professional practice and public policy. Educational Psychology Review,
20, 257–282. doi:10.1007/s10648-008-9078-3
Kern, L., & Manz, P. (2004). A look at current validity issues of school-wide behavior
support. Behavior Disorders,30, 47–59.
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
136 K. B. Flannery et al.
Klein, J. P., & Moeschberger, M. L. (2003). Survival analysis: Techniques for censored
and truncated data. New York, NY: Springer Verlag.
Maag, J. W. (2001). Rewarded by punishment: Reflections on the disuse of positive
reinforcement in schools. Exceptional Children,67 (2), 173–186.
Matson, J. L., & Minshawi, N. F. (2007). Functional assessment of challenging
behavior: Toward a strategy for applied settings. Research in developmental
disabilities,28, 353–361. doi:10.1016/j.ridd.2006.01.005
Mayer, G. (1995). Preventing antisocial behavior in the schools. Journal of Applied
Behavior Analysis,28, 467–478. doi:10.1901/jaba.1995.29-467
McEvoy, A., & Welker, R. (2000). Antisocial behavior, academic failure, and
school climate. Journal of Emotional and Behavioral Disorders,8, 130–140.
McIntosh, K., Horner, R. H., & Sugai, G. (2009). Sustainability of systems-level
evidence-based practices in schools: Current knowledge and future directions.
In Handbook of positive behavior support, issues in clinical child psychology
(pp. 327–352). New York, NY: Springer. doi:10.1007/978-0-387-09632-2_14
Morrison, G. M., Peterson, R., O’Farrell, S., & Redding, M. (2004). Using office refer-
ral records in school violence research: Possibilities and limitations. Journal of
School Violence,3, 39–62.
Morrison, B. E., & Vaandering, D. (2012). Restorative justice: Pedagogy, praxis, and
discipline. Journal of School Violence,11, 138–155.
National Center for Education Statistics. (2007). School survey on crime and
safety (SSOCS). Washington, DC: National Center for Education Statistics,
U.S. Department of Education. Retrieved from
Neild, R. C., Stoner-Eby, S., & Furstenberg, F. (2008). Connecting entrance and depar-
ture: The transition to ninth grade and high school dropout. Education and
Urban Society,40, 543–569. doi:10.1177/0013124508316438
Nelson, J. R., Gonzales, J. E., Epstein, M. H., & Benner, G. J. (2003). Administrative
discipline contacts: A review of the literature. Behavioral Disorders,28,
Newsome, W. S., Anderson-Butcher, D., Fink, J., Hall, L., & Huffer, J. (2008). The
impact of school social work services on student absenteeism and risk factors
related to school truancy. School Social Work Journal,32(2), 21–38.
Railsback, J. (2004). Increasing student attendance: Strategies from research and
practice. Portland, OR: Northwest Regional Educational Laboratory. Retrieved
Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., & Congdon, R. (2008). HLM 6.06:
Hierarchical linear & non-linear modeling. Lincolnwood, IL: Scientific Software
Rusby, J. C., Taylor, T. K., & Foster, E. M. (2007). A descriptive study of school
discipline referrals in first grade. Psychology in the Schools,44, 333–350.
Seeley, K. (2008). Truancy prevention: Research, policy and practices. Denver, CO:
National Center for School Engagement. Retrieved from http://www.cdhs.state.
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
School Disciplinary Responses to Truancy 137
Shannon, G. S., & Bylsma, P. (2003). Helping students finish school: Why stu-
dents drop out and how to help them graduate. Olympia, WA: Office of
Superintendent of Public Instruction. Retrieved from
Skiba, R. J., & Peterson, R. L. (2000). School discipline: From zero tolerance to early
response. Exceptional Children,66 , 335–347.
Skiba, R. J., & Rausch, M. K. (2006). Zero tolerance, suspension, and expul-
sion: Questions of equity and effectiveness. In C. M. Evertson & C. S.
Weinstein (Eds.), Handbook of classroom management: Research, practice, and
contemporary issues (pp. 1063–1089). Mahwah, NJ: Erlbaum.
Spaulding, S. A., Irvin, L. K., Horner, R. H., May, S. L., Emeldi, M., Tobin, T.
J., & Sugai, G. (2010). Schoolwide social-behavioral climate, student prob-
lem behavior, and related administrative decisions. Journal of Positive Behavior
Interventions,12, 69–85. doi:10.1177/1098300708329011
Sutphen, R. D., Ford, J. P., & Flaherty, C. (2010). Truancy interventions: A review
of the research literature. Research on Social Work Practice,20, 161–171.
Teasley, M. L. (2004). Absenteeism and truancy: Risk, protection, and best practice
implications for school social workers. Children & Schools,26 , 117–128.
Testerman, J. (1996). Holding at-risk students. Phi Delta Kappan,77(2), 364.
Todd, A. W., Horner, R. H., & Tobin, T. (2010). SWIS documentation project refer-
ral form definitions (Version 4.4). Retrieved from
Veenstra, R., Lindenberg, S., Tinga, F., & Ormel, J. (2010). Truancy in late elementary
and early secondary education: The influence of social bonds and self-
control—the TRAILS study. International Journal of Behavioral Development,
34, 302–310. doi:10.1177/0165025409347987
Walker, H. M., Ramsey, E., & Gresham, F. M. (2004). Antisocial behavior in school:
Evidence-based practices. Belmont, CA: Thomson/Wadsworth.
Wu, W., West, S. G., & Taylor, A. B. (2009). Evaluating model fit for growth curve
models: Integration of fit indices from SEM and MLM frameworks. Psychological
Methods,14, 183–201. doi:10.1037/a0015858
Downloaded by [Pennsylvania State University], [Jennifer Frank] at 07:04 03 October 2013
... The starting point to curb the compounding effects of this phenomenon at universities is to propose and investigate proper solutions and practices Flannery, et. al. (2012). These solutions must address the causes of this alarming issue. ...
... nd a moral character class. The students were assessed before and after the applying the intervention program. The results indicated students who joined the intervention program showed lower rates and inclinations towards truancy. Additionally, the program increased educational expectations, and leveraged attitudes toward education and engagement. Flannery, et. al. (2012) explores the effectiveness of schools' disciplinary response towards chronic truancy among high schools in the United States. The results of the analysis of the data of concern revealed that of all the controlling factors including out-of-school suspension was found to significantly decrease the probability of future truancy. Neverthele ...
Full-text available
Objectives: This paper examines the perspectives of academic staff and students at the American University of Madaba (AUM) on cyber-truancy during the COVID-19 pandemic, addressing challenges and proposing solutions to enhance student engagement in online learning. Methods: Case-study methodology requires detailed investigation of a situation. To attain the required information, two methods of data collection were utilized, namely, a structured (Likert scale) questionnaire and an interview distributed to a representative sample of AUM’s academic staff and students. The data was analyzed by using SPSS. Results: The study found the following results. First, all respondents show a high level of awareness of the cyber-truancy. Second, technological and behavioral factors contribute to increasing truancy rates among students. Third, the novelty of the experience as well as enforcing strict policies are crucial challenges that hinder online learning. Conclusions: The paper concludes with suggested solutions, which include enforcing strict policies to curb cyber-truancy, enhancing parental control, and spreading awareness among students regarding the detrimental effects of cyber-truancy.
... Research indicates that exclusion, suspension and punishment fail to teach appropriate behaviour, because the resulting rejection exposes students to environments conductive to crime and reinforces misdemeanour by attracting attention. (Child Welfare League of America [CWLA], 2012[CWLA], -2016Flannery, et al. 2012;Gazeley, 2010;Lacoe & Steinberg, 2019;Noltemeyer, et al. 2015;O"Malley & Austin, 2014). ...
... These methods focus on prevention and early positive intervention, that establish trusting student-teacher relationships, improve student social behaviour and academic performance and prevent negative effects on peers. (Flannery, et al. 2012;Gazeley, 2010;Lacoe & Steinberg, 2019;Noltemeyer, et al. 2015). Same of the most effective of them are proven School-wide Positive Behavioural Interventions and Supports (SWPBIS) and Social Emotional Learning (SEL) (Emmer & Sabornie, 2014;Mergler, et al. 2014; Technical-Assistance-Centre PBIS, n.d). ...
Full-text available
A literature review on effective student behaviour management methods shows a shift from punishment to prevention and positive intervention. However, teacher trainings in these areas are proven to be insufficient as they fail to meet special needs and change in-service teachers" practices. The present study, based on effective student behaviour management methods (Positive Behaviour Support and Social Emotional Learning approaches) and evidence of successful professional training (derived from Adult Learning Theories), proposes a model of teacher training in managing student behaviour.The model consists of three complementary axes applied simultaneously: live instructional group meetings, distance learning and individual coaching sessions. The adaptation and pilot implementation of the model in a middle school in Thessaloniki-Greece are described as well as the methodological design for the evaluation of it simpact on teachers and their students. If beneficial, this model could become a stepping stone to effectively training teachers in managing student behavior in Greece and internationally.
... Over the past 20 years, academics have been calling to reorganize policies and provisions, emphasizing a shift from seeing school absenteeism as an issue to be counteracted with punitive strategies (Grewe, 2005) to seeing it as an issue to be acknowledged with more proactive measures (Hennemann et al., 2010;Flannery et al., 2012;Ricking & Hagen, 2016;Stamm, 2007). This means that practice of educational institutions should focus on actions of prevention, intervention and reintegration to effectively address school absenteeism (Gren Landell, 2021;Palmu et al. 2021). ...
In Germany, penalties such as fines or police action are imposed for noncompliance with compulsory schooling. However, formal administrative procedures are often deemed inadequate by experts who advocate for school-wide multidimensional support in addressing school absenteeism. We analyze guidance documents of different federal states, examining their emphasis on formal and school-wide strategies. We specifically explore the report of Schleswig-Holstein to demonstrate the shifting focus from punitive measures to more supportive approaches in addressing absenteeism. [The Version of Record of this manuscript has been published and is available in EUROPEAN EDUCATION - 19.09.2023 -] || ACCESS via
... Exclusion, suspension and punishment reject students by exposing them to environments conducive to crime, reinforce misbehaviour by attracting attention and fail to teach proper behaviour. Instead there are alternative research-based disciplinary approaches that focus on prevention and early positive intervention, which establish trusting student-teacher relationships and improve student social behaviour and academic performance (Gazeley 2010;Flannery, Frank, and Kato 2012;Noltemeyer, Ward, and Mcloughlin 2015 Lacoe andSteinberg 2018). Two of the most effective of them are proven School-Wide Positive Behaviour Interventions and Supports (SWPBIS) and Social-Emotional Learning (SEL) (Emmer and Sabornie 2014; Mergler, Vargas, and Caldwell 2014; PBIS Technical Assistance Center n.d). ...
The study aimed to train teachers in managing student behaviour and to investigate the impact on teachers and their students. The training was based on adult-learning and group-leading strategies (development/application) as well as Social-Emotional-Learning and School-Wide-Positive-Behaviour-Supports approaches (content). It consisted of training meetings, coaching and distance learning and was implemented at Thessaloniki middle school, using a neighbouring middle school as the control school. The stability of impacts was checked by follow-up tests after four and twelve months accordingly. A convergent parallel mixed-methods design was used for data analysis. After completion of training and four months later, an effective behaviour-management methodology and corresponding strategies were developed in the intervention school. Teachers reported increased professional self-efficacy, teachers and students evaluated their school climate more positively and students’ office discipline referrals decreased, compared with the control school. Benefits had decreased a year later, however, they remained increased compared with the control school. Ways to maintain beneficial results and a sustainable in-service teacher professional development policy are discussed.
... Instead of being anxious about going to school, young people labeled as truant are understood to be expressing their autonomy or lack of concern for consequences and authorities or are motivated to participate in non-school activities. These interventions aim to reduce absenteeism through increased monitoring, parental awareness, school connectedness, and court and community involvement (Flanagan, 2006;Flannery et al., 2012;Gonzales et al., 2002). Less attention is often paid to the potential psychosocial stressors or the family context that may impact youth exhibiting truancy (Dembo et al., 2012). ...
Full-text available
Youth enrolled in intensive home-based treatments (IHBT) often present with concurrent mental health issues, complex multigenerational trauma, and broad psychosocial adversity that impacts functioning, including their ability to attend school. These young people stand to benefit from the protective factors associated with successfully adapting to school, including better mental and physical health outcomes in adulthood. This study examined the prevalence, clinical presentations, and treatment trajectories of chronically absent youth participating in the Yale IICAPS IHBT program in Connecticut. This study is a retrospective descriptive examination of records from 932 youth from the Yale IICAPS program. Two subgroups of youth were identified based on parental identification of school attendance as a clinical concern or not. Rates of absence, clinical diagnosis, severity and functioning, and treatment trajectories of these two groups were examined. More than one-third of youth (n = 362) were chronically absent, but a minority of parents (36%) identified school absences as a core concern at intake. All youth with chronic absenteeism experienced reduced school days absent during IICAPS treatment. However, those not explicitly identified by parents with attendance as a core concern at intake experienced a significantly greater improvement in attendance (38% reduction in days absent vs. 17%). Although potentially helpful in improving school attendance for all youth, the differential treatment effect observed in this study could indicate that IHBT are particularly well suited to addressing familial and system aspects of chronic school absenteeism among high-risk youth.
... This means that 93% of Queensland students did not receive a suspension and the number of students being suspended twice or more often in each year was less than 3% of total enrolments. Based on similar data (Flannery et al., 2012), Bear argues that for most students, rather than being associated with negative outcomes, suspension serves as an effective deterrent. ...
Technical Report
Full-text available
This report describes analyses of the Pathways to Prevention database to explore the impact of first school suspension on primary school-aged children's social-emotional wellbeing, academic performance and behaviour. It utilises outcome measures based on Clowning Around, the precursor to Rumble's Quest (
... One feature nested under the quantity of teaching is the extent to which student learning is hindered by "student truancy" (SC061Q01TA); this particular factor stands out as the top school-level factor that predicts high or low performance. Student truancy not only leads to less academic instruction but is also closely related to low levels of school responsiveness to student academic issues and insufficient disciplinary responses to truancy (Appleton et al., 2008;Flannery et al., 2012). As confirmed by previous research, students from schools with higher unexcused absence rates underperformed compared with students from schools with a lower proportion of truant students (Kearney, 2008). ...
Full-text available
This research intended to identify key contextual factors that synergistically influence high- and low-performing students’ science outcomes by drawing upon a dynamic model of educational effectiveness. The dataset, the Programme for International Student Assessment (PISA) 2015, consisted of 79,963 science scores for secondary students (49,924 high performers at proficiency Level 6 and 30,039 low performers at proficiency Levels 1a and 1b) from 53 countries/economies along with students’ and school principals’ responses to the PISA questionnaires. By applying a support vector machine (SVM) and SVM-recursive feature elimination (SVM-RFE) sequentially, this study successfully (a) identified 30 key factors of the total 127 contextual factors at the school, classroom, and student levels that synergistically differentiate high and low achievers and (b) provided evidence to support the validity of the dynamic model of educational effectiveness by recognizing the multidimensionality of the contextual factors.
Student absenteeism – particularly chronic absenteeism – is a cause for great concern among school leaders, policymakers, and all education stakeholders. The problem has grown larger in recent years and has been exacerbated further by the COVID-19 pandemic. To stem the tide of student absenteeism, policymakers – including the federal government – have designed systems to hold schools accountable for student attendance. Although well-intentioned, these systems add another layer of complexity to already overwhelmed school leadership. Further, the increased stakes associated with student absenteeism have put it into even greater focus for school leaders and policymakers. With this chapter, we explore the history, current context, and practical solutions to student absenteeism. First, we clarify key terms, followed by an overview of the history of accountability and absenteeism in the United States. Then we examine the current state of school attendance policy, including the influence of absenteeism on school funding formulas and how the Every Student Succeeds Act has heightened the attention and accountability associated with absenteeism. In the second section, we look at who misses school most often, when, and why. We also detail the academic and social consequences of excessive absenteeism. Following that, we review the factors that influence attendance at the elementary and secondary levels. Finally, we discuss school-, classroom-, and family-level strategies that have improved student attendance in specific cases.KeywordsStudent absenteeismAttendanceTruancyChronic absenteeismAcademic performance
School attendance and completion are significant indicators of health and positive psychosocial development in children and adolescents. School attendance and completion rates remain particularly stifled, however, for students of color, students in poverty, and students with disabilities, among other vulnerable groups. Researchers and educational agencies often focus on student and family mechanisms of school attendance problems with less attention to dominant systems that truly propel school absenteeism. To keep these themes “front and center” for stakeholders, this article summarizes key social forces and social justice issues that impact school attendance problems for marginalized students who are often excluded from many aspects of the educational process. These issues include education deprivation, migration and discrimination, school funding, school discipline, residential mobility and housing insecurity, adverse childhood experiences, school climate, school victimization, access to care, and other barriers to school attendance. Recommendations for research, policy, and intervention are presented as well. These recommendations partly include greater incorporation of various ecological levels, interaction effects, policies to promote school engagement, broader school accountability measures, proactive early warning systems, and culturally responsive multi-tiered system of support interventions.
Mirroring trends in the legal system, discipline within education has adopted zero-tolerance policies in an attempt to curb undesired behaviors in school. K–12 schools have expanded the use of exclusionary discipline policies for lesser offenses, giving way to the phenomenon known as the school-to-prison-pipeline (STPP), which disproportionately affects black and Latinx students. While much is known about the link between exclusionary discipline policies and subsequent involvement in the juvenile legal system, less attention has been given to other education policies and practices that may be criminalized in a similar manner. In this systematic review, we argue that truancy policies and practices contribute to the STPP for black and Latinx students. We synthesize the research on school truancy to better understand the characteristics of students who experience truancy; predictors of truancy; the relationship between truancy, truancy policies, and various student outcomes; and the impact of truancy interventions and school disciplinary responses. Implications for improving research on truancy and disrupting the relationship between truancy and the STPP for black and Latinx youth are discussed.
Full-text available
Dramatic incidents of school violence have thrust school discipline to the forefront of public consciousness. Despite a dramatic increase in the use of zero tolerance procedures and policies, there is little evidence demonstrating that these procedures have increased school safety or improved student behavior. Moreover, a punitive disciplinary climate may make any attempt to include more students with behavioral problems a cause for conflict between general and special educators. A preventive, early response disciplinary model increases the range of effective options for addressing violence and disruption across both general and special education. Ultimately, the effectiveness of any disciplinary system may be judged by the extent to which it teaches students to solve interpersonal and intrapersonal problems without resorting to disruption or violence.
Full-text available
Recent reports have demonstrated that the United States has a dropout crisis of alarming proportions. In some large-city school systems, more than 50% of students leave high school without a diploma. A large proportion of these dropouts have not accumulated enough credits to be promoted beyond ninth grade. Using survey and student record data for a cohort of Philadelphia public school students, the authors find that ninth-grade academic outcomes are not simply proxies for student characteristics measured during the pre—high school years and that ninth-grade outcomes add substantially to the ability to predict dropout. An implication is that efforts to decrease the dropout rate would do well to focus on the critical high school transition year.
The authors systematically review the available research on administrative discipline contacts to (a) identify the domain variables (e.g., gender ethnicity) that influence the use of administrative discipline contacts, (b) identify participant classification variables (e.g., antisocial) related to administrative discipline contacts, and (c) determine the validity of administrative discipline contacts. They drew from 20 independent samples published in 23 articles. A wide range of school (e.g., grade level, size) and student (e.g., achievement, abilities, socioeconomic status, ethnicity) domain variables appear to influence the use of administrative discipline contacts, whereas administrator/teacher (e.g., ethnicity, gender) and family (e.g., parenting style, education levels of parents) variables have less influence. Four participant classification variables appear to be related to administrative discipline contacts: participation in athletics, child neglect, antisocial behavior, and anecdotal suspension report. The concurrent and predictive validity of administrative discipline contacts appears to be relatively limited. The findings and future research needs are discussed.
Secondary schools can no longer ignore the affective domain, Ms. Testerman argues. Especially for atrisk students, schools must deal with head and heart. She tells the story of one school's attempt to do so.
The author discusses the context in which absenteeism and truancy occur through an analysis of risk and protective mechanisms and suggests best practice methods based on a review of literature and research on several successful absenteeism and truancy prevention and reduction programs. The author suggests ways that school social workers can participate in truancy prevention and reduction projects through collaborative efforts with other school professionals, community organizations, social services agencies, parents, and school children.
Perhaps the most “naturally occurring” data on school misbehavior and aggression are school discipline data, including office referrals, suspensions, and expulsion data. These data constitute the most common markers of school discipline status available on school campuses. There is, however, very little information available in professional or research literature about the reliability and validity of office referrals. This article examines the sources of error that enter into the collection and use of office referrals. Despite these sources of errors, this article documents the importance of considering how office referral data provide information about how discipline systems are functioning on a school campus. Guidelines are provided for utilizing disciplinary data for school safety and school policy planning.
From a life course perspective, high school dropout culminates a long-term process of disengagement from school. The present paper uses data from a representative panel of Baltimore school children to describe this unfolding process. Over 40% of the study group left school at some point without a degree, but this high overall rate of dropout masks large differences across sociodemographic lines as well as differences involving academic, parental, and personal resources. A sociodemographic profile of dropout for the study group shows how dropout rates vary across different configurations of background risk factors including family socioeconomic status (SES), family type, and family stress level. Dropout risk factors and resources in support of children's schooling then are examined at four schooling benchmarks: the 1st grade, the rest of elementary school (years 2-5), the middle school (years 6-8), and year 9 (the 1st year of high school for those promoted each year). Academic, parental, and personal resources condition dropout prospects at each time point, with resources measured early in children's schooling forecasting dropout almost as well as those from later in children's schooling. Additionally, evidence is presented that resources add on to one another in moderating dropout risk, including risk associated with family SES. These patterns are discussed in terms of a life course view of the dropout process.