Journal of Emotional
and Behavioral Disorders
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Patterns and Predictors of Disciplinary Exclusion Over Time: An Analysis of the Seels National Data Set
Lisa Bowman-Perrott, Michael R. Benz, Hsien-Yuan Hsu, Oi-man Kwok, Leigh Ann Eisterhold and Dalun Zhang
Journal of Emotional and Behavioral Disorders published online 13 October 2011
DOI: 10.1177/1063426611407501
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an-Perrott et al.Journal of Emotional and Behavioral Disorders
© Hammill Institute on Disabilities 2010
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Patterns and Predictors of Disciplinary
Exclusion Over Time: An Analysis of the
SEELS National Data Set
Journal of Emotional and Behavioral Disorders
XX(X) 1­–14
© Hammill Institute on Disabilities 2011
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sagepub.com/journalsPermissions.nav
DOI: 10.1177/1063426611407501
http://jebd.sagepub.com
Lisa Bowman-Perrott1, Michael R. Benz1, Hsien-Yuan Hsu2,
Oi-man Kwok1, Leigh Ann Eisterhold1, and Dalun Zhang1
Abstract
Disciplinary exclusion practices are on the rise nationally, as are concerns about their disproportionate use and lack
of effectiveness. This study used data from the Special Education Elementary Longitudinal Study to examine patterns
and predictors of disciplinary exclusion over time. Students with emotional/behavioral disorders were most likely to be
excluded and be excluded multiple times, followed by students with attention deficit-hyperactivity disorder and students
with learning disabilities. For all student groups, being excluded in the first wave was a strong predictor of being excluded
at later points in time. Student gender (male students) and ethnicity (African American students) were associated with a
greater probability of exclusion over time. Students with higher social skills, as reported by teachers, had a lower probability
of being excluded over time. Implications for practice, policy, and future research are discussed.
Keywords
disciplinary exclusion; emotional disturbance; disorders/disabilities; ADHD; learning disability; research/statistical methods
Disciplinary exclusion measures such as suspension and
expulsion are common responses to student misbehavior in
school. While these practices are more often applied at the
middle and high school levels, they have been observed and
studied across both elementary and secondary school settings (Skiba & Sprague, 2008). In 2003, 11% of all K-12
students had been suspended from school (in- or out-ofschool), and 2% had been expelled (U.S. Department of
Education, 2007a). Furthermore, according to the U.S.
Department of Education (2007b), during the 2005-2006
school year 74% of all “serious” disciplinary consequences
assigned were suspensions of 5 days or more; 5% were
expulsions from school.
While the practice of disciplinary exclusion has existed
for decades (Grosenick et al., 1981), its use has increased,
particularly with the introduction of zero tolerance policies
in schools (Krezmien, Leone, & Achilles, 2006; Skiba &
Sprague, 2008). This increased use has given rise to two
major concerns. First, disciplinary exclusion measures have
been applied disproportionately to students with disabilities
(Krezmien et al., 2006). For example, students with emotional/behavioral disorders (EBD), learning disabilities
(LD), and attention deficit-hyperactivity disorder (ADHD)
experience higher rates of exclusion than their peers in
other disability categories and students in general education
(Achilles, McLaughlin, & Croninger, 2007; Wagner,
Newman, & Cameto, 2004; Zhang, Katsiyannis, & Herbst,
2004). Specifically, Cooley (1995) found that students with
disabilities (most of whom were identified as EBD or LD)
were more than twice as likely as their peers without disabilities to be suspended or expelled. Students who are culturally and linguistically diverse (CLD)—namely, African
American and Hispanic students (Gregory, Skiba, &
Noguera, 2010; Rocque, 2010)—have been overrepresented
as well. Second, disciplinary exclusion measures are not an
effective strategy for problem behavior. That is, suspension
and expulsion practices do not improve problem behavior
or promote school safety (Skiba, 2000). In reality, these
measures are associated with a number of undesirable consequences. Skiba, Peterson, and Williams (1997) found that
most suspensions were linked with “disrespectful” and
“disobedient” behaviors rather than violent behaviors.
Skiba (2000) also reported that past suspensions can predict
future ones; up to 40% of suspensions have been attributed
to repeat offenses. Moreover, these practices have been
1
Texas A&M University, College Station, Texas, USA
National Taiwan Normal University, Taipei, Taiwan
2
Corresponding Author:
Lisa Bowman-Perrott, 4225 TAMU, Texas A&M University, College Station,
TX 77843-4225.
Email: lbperrott@tamu.edu
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Journal of Emotional and Behavioral Disorders XX(X)
associated with future adverse consequences, including
school disengagement (Reschly & Christenson, 2006), academic failure (Brown, 2007), school dropout (Christle,
Jolivette, & Nelson, 2005), homelessness (Whitbeck &
Hoyt, 1999), running away from home (Tyler & Bersani,
2008), and involvement in the juvenile justice system
(Leone et al., 2003).
In 2007, Achilles et al. provided a cross-sectional examination of disciplinary exclusion using the Special
Education Elementary Longitudinal Study (SEELS)
national data set. Achilles et al. investigated sociocultural
correlates of the likelihood of disciplinary exclusion among
students with EBD, LD, and ADHD, students the authors
identified as being at high risk of disciplinary exclusion.
Among their recommendations for future research was a
call for longitudinal research that examined disciplinary
exclusion over time. To date, this knowledge base is limited to cross-sectional research with nationally representative samples that examines disciplinary exclusion at one
point in time (Achilles et al., 2007), 1-year state-level studies (Rausch & Skiba, 2004; Skiba, Wu, Kohler, Chung, &
Simmons, 2001; Sundius & Farneth, 2008), 1-year districtlevel studies (Raffaele Mendez & Knoff, 2003), and stateand district-level studies that have examined patterns of
disciplinary exclusion over time (Arcia, 2007; Krezmien et
al., 2006). No longitudinal research has been conducted
that examines patterns and predictors of disciplinary exclusion over time with a nationally representative sample of students with disabilities. Thus, research is needed that (a)
investigates the influence of early exclusion on later exclusion and (b) identifies variables associated with higher rates
of exclusion over time (U.S. Department of Education, 2002).
The purpose of the present study was to address this need by
conducting a comprehensive examination of patterns of disciplinary exclusion over time and factors associated with
higher rates of exclusion. The SEELS data set was used,
drawing information on patterns and predictors from all three
waves of data. Several variables have been identified as being
associated with disciplinary exclusion that generally can be
thought of as falling into the following categories: student
demographic characteristics, family/household characteristics, student academic and social skills, and school characteristics. A discussion of each of the areas follows.
Four student demographic variables have been identified
consistently in the literature as being associated with disciplinary exclusion: disability, age, gender, and ethnicity.
Disciplinary exclusion disproportionately affects students
with disabilities. Of students with disabilities ages 6 to 17,
9% to 33% were suspended during the 2000-2001 school
year (U.S. Department of Education, 2003). Among these,
students with EBD, ADHD, and LD are most likely to be
excluded (Achilles et al., 2007; U.S. Department of Education,
2007c). For students with and without disabilities, older
students and males are more likely to be suspended and
expelled (Blackorby et al., 2007; Skiba, 2000). Males are
also more likely to be excluded at earlier grades and to lose
more days of school instruction (Clark, Petras, Kellam,
Ialongo, & Poduska, 2003). African American youth are
suspended and expelled at rates greater than their peers
from other ethnic groups (U.S. Department of Education,
2007a). Furthermore, African American students are also
more likely to be given out-of-school versus in-school suspension as a disciplinary consequence (McFadden, Marsh,
Price, & Hwang, 1992). Finally, findings from some studies
have indicated that students’ ethnicity is significant even
after controlling for socioeconomic status (SES; Skiba,
Michael, Nardo, & Peterson, 2002).
Previous research has identified several family/household characteristics as being associated with higher rates of
disciplinary exclusion, including SES, family structure, student (family) mobility, and parent involvement and expectations. Achilles et al. (2007) found that students from lower
SES families were at greater risk for exclusion than their
peers from higher SES families. They also found that SES
impacted the likelihood of exclusion more than students’
ethnicity when all variables were accounted for in their
model. Similarly, in their study of elementary and middle
school students, Blackorby et al. (2007) found that students
from families with higher household incomes (more than
$50,000) were involved in fewer disciplinary actions than
students from families with household incomes below
$20,000. Family structure can hinder or facilitate how parents influence the future outcomes of their children (Amato,
2001). In examining data from the National Health Interview
Survey on Child Health, Dawson (1991) found that, in general, children under age 18 from “disrupted marriages”
(whose mothers had been previously married or who lived
with their mother and stepfather) or whose mothers never
married were significantly more likely to be suspended or
expelled. With respect to disability, Achilles et al. (2007)
found that family structure was associated with the likelihood of being excluded for students with EBD.
Student mobility, defined as students changing schools
for reasons other than promotion to the next school level,
has been shown to be related to behavior problems (Simpson
& Fowler, 1994). In the Achilles et al. (2007) study, student
mobility (multiple school changes) was associated with disciplinary exclusion for students with EBD.
Furthermore, there is evidence to suggest a relationship
between student disciplinary exclusion, parent expectations, and parent involvement in and satisfaction with his
or her child’s school. Blackorby et al. (2007) found that
high parent expectations were associated with fewer disciplinary actions for the students in their study, but this relationship did not retain significance when other factors
(e.g., student demographics and family characteristics)
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3
Bowman-Perrott et al.
were considered. Findings of a relationship between parent
involvement in school and student exclusion have been
mixed. Christle, Nelson, and Jolivette (2004) found that
low parental involvement was associated with higher rates
of both suspension and expulsion. In contrast, Achilles et
al. (2007) found no relationship between parent involvement and exclusion. However, in the Achilles et al. study,
there was a relationship between parent satisfaction and
disciplinary exclusion. Parents of students with EBD and
LD who had been excluded reported less satisfaction with
their child’s school.
While much of the current literature has examined relationships between disciplinary exclusion practices and student demographic and family/household variables, a few
studies have examined relationships between disciplinary
exclusion and various discrete variables that generally
could be considered as illustrative of student skills and
school characteristics. Blackorby et al. (2007) found that
students who were involved in fewer disciplinary incidents
were reported by their parents to have high social skills and
were reported by teachers to cooperate with peers, follow
directions, and complete homework on time. In addition,
there is some evidence to suggest that rates of disciplinary
exclusion are higher in schools with a greater number of
students living in poverty (Christle et al., 2005; Raffaele
Mendez, Knoff, & Ferron, 2002) and in schools located in
urban settings (Achilles et al., 2007).
In summary, current research indicates that disciplinary
exclusion practices are: (a) used with increasing frequency
nationwide, (b) of questionable effectiveness, (c) associated with later adverse consequences, and (d) applied disproportionately among certain student groups. Students
with EBD, LD, and ADHD are among students at high risk.
A majority of the research that has examined correlates of
disciplinary exclusion has focused on student demographic
variables (e.g., gender and ethnicity) and family/household
characteristics (e.g., SES), with relatively less attention
given to other potentially important factors such as student
skills and school characteristics. Some state-level research
suggests that early disciplinary exclusion is a predictor of
later exclusion.
For these reasons, a comprehensive examination of the
patterns of disciplinary exclusion over time is needed that
includes an investigation of patterns of exclusion for highrisk students with disabilities and factors associated with
higher rates of exclusion over time. Using the national, longitudinal SEELS data set, the current study was conducted
to explore two research questions: (a) Are students who are
excluded in Wave 1 more likely to be excluded in later
waves (i.e., Wave 2 and Wave 3)? If yes, are there differences in the probability of exclusion for the three groups of
students (EBD, ADHD, and LD) in later waves?; and (b)
what are the effects of selected predictors on student exclusion status over time? Do predictors perform consistently to
explain student exclusion status for students with EBD,
ADHD, and LD?
Method
Participants and Procedures
Participants and data for this study were drawn from the
Special Education Elementary Longitudinal Study (SEELS),
a nationwide study of the characteristics, experiences, and
outcomes of elementary and middle school students with
disabilities. Information on the study design is summarized
here and provided in detail elsewhere (e.g., Wagner, Kutash,
Duchnowski, & Epstein, 2005; Wagner, Marder, Blackorby,
& Cardoso, 2002). SEELS was funded by the Office of
Special Education Programs of the U.S. Department of
Education and conducted by SRI International.
SEELS data were collected in three waves between the
1999-2000 school year and the 2004-2005 school year on a
nationally representative sample of students who were
between ages 6 and 12 at the start of the study. Data were
collected through parent interviews, teacher and school
administrator questionnaires, and direct student assessments. Data were collected over a 6-year period with 1 to 2
years between waves. SEELS participants were selected
through a two-stage sampling process to produce a nationally representative sample. In the first stage, a stratified
(geographic region, size, wealth) random sample of 1,124
local education agencies (LEAs) was selected. A total of
245 LEAs and 32 special schools agreed to participate in
the study and provided rosters of students receiving special
education services. From these schools, a stratified (primary disability) random sample of 11,512 students was
selected to participate in the study. Responses to the Wave 1
parent interview resulted in a final sample of 9,824 students
(Wagner et al., 2002).
The current study focused on three disability categories
associated with high rates of suspension and expulsion
(U.S. Department of Education, 2007c): LD, EBD, and
ADHD. Primary disability roster information contained in
the SEELS data set was used to select participants. Drawing
on previous research (Achilles et al., 2007; Schnoes, Reid,
Wagner, & Marder, 2006), roster information on ADHD
was confirmed by parent report that a given student was
identified as having ADHD. These procedures resulted in a
final sample of 2,597 students (LD = 1,047; EBD = 874;
ADHD = 676) (see Table 1).
Measures
Dependent variable: Student disciplinary exclusion. The
dependent variable for both research questions was the
number of student disciplinary exclusions. Two items were
extracted from the SEELS parent interview data as
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4
Journal of Emotional and Behavioral Disorders XX(X)
Table 1. Demographic Characteristics by Disability Group
Characteristic
Ethnicity
Caucasian
African American
Hispanic
Other
Gender
Male
Female
Age
6 to 9
10 to 12
13 to 14
Living in poverty
Yes
No
Exclusion rate
Wave 1
Wave 2
Wave 3
LD (n = 1,047)
EBD (n = 874)
67.8%
16.1%
13.5%
2.7%
59.7%
27.5%
10.3%
2.5%
67.5%
32.5%
78.9%
21.1%
25.9%
61.0%
13.1%
32.0%
56.1%
11.9%
25.2%
74.8%
32.2%
67.8%
7.4%
10.6%
17.1%
30.3%
38.3%
37.8%
ADHD (n = 676)
81.0%
11.9%
5.1%
2.0%
76.7%
23.3%
30.0%
58.4%
11.6%
14.2%
85.8%
15.7%
20.9%
18.1%
Entries are percentages based on the n for each disability group.
measures of exclusion. The first item asked parents to
report whether their child had been suspended during the
past school year (yes = 1, no = 0), and the second item asked
parents whether their child had been expelled during the
past school year (yes = 1, no = 0). Parents were asked to
report on each item in each of the three waves of data collection. According to parent report, 17.14%, 22.32%, and
23.57% of students had been either suspended or expelled
at Wave 1, Wave 2, and Wave 3, respectively.
Predictor variables. To identify factors associated with
exclusion, a two-step process was used. First, drawing
upon the conceptual framework and previous data reports
from the SEELS study and the literature reviewed earlier,
items were extracted from the SEELS data set that were
determined in previous research to be potentially related to
higher rates of exclusion.
Theoretically relevant items were selected from the
Wave 1 parent interview data, teacher survey, and school
characteristics survey. Selected items were grouped into
four conceptual categories for analysis purposes: (a) student
demographic characteristics, (b) family/household characteristics, (c) student academic and social skills, and (d)
school characteristics.
Second, univariate relationships among the predictor
variables and between the predictor variables and the
dependent variable were examined. This was done for the
sample as a whole and separately for students with EBD,
ADHD, and LD. Given the size of the sample, only variables with statistically significant correlations of .15 or
higher (with the dependent variable) were retained for
further consideration. Analyses were also conducted to
check for potential outliers. No extreme outliers or non-normal variables potentially caused by any outliers were found
(see Note 1). A discussion of the four factors follows.
Student demographic characteristics. Four demographic
variables were extracted from the SEELS database: disability, ethnicity, age, and gender. Only participants who had a
primary disability of LD (40.32%), EBD (33.65%), or
ADHD (26.03%) were selected for inclusion in the current
study. “Ethnicity” included three major racial/ethnic groups:
Caucasian (68.51%), African American (AA; 18.83%), and
Hispanic (HIS; 10.23%). Other racial/ethnic groups (2.43%)
were recoded as “other” due to the small number of members in those groups. Caucasian was used as the reference
group based on previous research on the disproportionate
use of exclusion policies with students from ethnically/linguistically diverse groups.
Three dummy-coded variables were created to represent
the three ethnic groups in the analyses. “Age” reported in
the SEELS data set was also used and included ages 6
through 14 in the current study (M = 10.51, SD = 1.71).
“Gender” of the student was used and included two levels,
male (coded as 1; 73.71%) and female (coded as 0; 26.29%).
Family/household characteristics. Seven theoretically relevant variables were examined to investigate the role of family/household characteristics on rates of exclusion over
time: SES risk, family structure, mobility, parent expectations, parent involvement in school, parent report of child’s
school experiences, and parent satisfaction with school.
Three variables—parent involvement in school, parent
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5
Bowman-Perrott et al.
report of child’s school experiences, and parent satisfaction
with school—were all fitted as latent variables.
Examination of the univariate relationships revealed that
two latent variables, child’s school experiences and parent
satisfaction with school, were highly correlated (r = .81). To
prevent the plausible multi-collinearity caused by these two
highly correlated latent factors, only satisfaction with
school was included in these analyses since parent satisfaction with school was of interest.
“SES Risk” (Achilles et al., 2007) was a composite score
including three indicators of socioeconomic disadvantage:
(a) whether family income was below the poverty level (a
variable in the SEELS data set that was coded “yes” or
“no”; 24.19% of students in the sample came from families
in poverty), (b) whether a member of the family utilized
benefits from any of three federal benefit programs during
the previous 2 years (Temporary Assistance to Needy
Families, Food Stamp, and Supplemental Security Income;
27.76% of students were from families that participated in
one or more of these programs), and (c) whether the education level of the head of household was less than high school
(17.19% of students).
Each SES risk indicator was scored yes = 1 or no = 0,
resulting in an overall risk composite score of 0 to 3 (M =
0.65, SD = 0.89). That is, a student received a score of 3 on
the SES risk composite variable if he or she lived in a family with an income below the poverty level, in which a family member participated in any of the three federal benefit
programs, and in which the head of household had an education level less than high school.
“Family Structure” was a binary variable describing
whether a student lived in a two-parent household, no
(coded 1, 36.86%) or yes (coded 0, 63.14%). “School
Mobility” was a measure of the number of times a student
changed schools since kindergarten, excluding changes due
to promotion to the next grade level (M = 1.10, SD = 1.37).
“Parent Expectations” was a measure of parents’ expectations that their child would graduate from high school with
a regular diploma. Parent Expectations was scored on a
scale of 1 (definitely won’t) to 4 (definitely will), with a
score of 4 indicating the highest expectations for graduation
from high school (M = 3.52, SD = 0.66).
“Parent Involvement in School” was fitted as a latent
variable and was made up of four items. For each item, parents were asked to rate how frequently they were involved
at their child’s school in the past year. Frequency was measured by a scale of five choices: never (coded as 0), one to
two times (coded as 1), three to four times (coded as 2), five
to six times (coded as 3), and more than six times (coded as
4). Parents reported whether they (or another adult in the
household) had: (a) attended a general school meeting (M =
1.66, SD = 1.20), (b) attended a school/class event (M =
1.51, SD = 1.30), (c) volunteered at their child’s school
(M = 0.77, SD = 1.20), or (d) attended a parent/teacher
conference other than an IEP (individualized education
program) meeting (M = 1.60, SD = 1.09).
“Satisfaction With School” was fitted as a latent variable
that included six items. For each item, parents were asked to
rate their level of satisfaction in the past year using a 4-point
scale: very dissatisfied (coded as 1), somewhat dissatisfied
(coded as 2), somewhat satisfied (coded as 3), and very satisfied (coded as 4). Parents rated their satisfaction with the
following: (a) their child’s school (M = 3.18, SD = 0.92),
(b) their child’s teacher (M = 3.37, SD = 0.86), (c) special
education services (M = 3.32, SD = 0.89), (d) other education received (M = 3.32, SD = 0.78), (e) amount and difficulty of homework (M = 3.03, SD = 0.91), and (f) level of
information they received from the school about their child’s
behavior and academic performance (M = 3.37, SD = 0.91).
Student academic and social skills. Three theoretically relevant variables were examined to investigate the role of
academic skills on rates of exclusion over time: student
academic achievement, extracurricular activities, and
grade retention. “Academic Achievement” (M = 87.39, SD
= 13.38) was a composite score computed by averaging
two items (i.e., a standard score on math calculation and a
reading passage comprehension score from the direct
assessment data set) and could range from 25.5 to 121.0 for
this sample. The average composite score mean for this
sample was lower than that of the entire SEELS sample.
“Extracurricular Activities” (M = 0.36, SD = 0.48) referred
to student involvement in extracurricular activities and was
scored yes = 1 and no = 0. “Grade Retention” (M = 0.27,
SD = 0.44) was based on parent report of whether the child
was ever held back a grade in school, which was scored yes
= 1 and no = 0.
Three theoretically relevant variables were also used to
investigate the role of student social skills on rates of exclusion over time: peer interactions, teacher interactions, and
social adjustment. “Peer Interactions” (M = 3.13, SD =
0.80) was based on the parent report of how well the student
got along with peers; “Teacher Interactions” (M = 3.35, SD
= 0.76) referred to how well students got along with teachers. For both variables, parent responses were based on their
perceptions of student interactions in the previous year and
were measured on a 4-point scale: not at all well (coded as
1), not very well (coded as 2), pretty well (coded as 3), and
very well (coded as 4).
The third variable, “Social Adjustment,” was an existing
composite variable in the SEELS data set. It was fitted as a
latent variable consisting of 11 items measuring teacher rating of the following student behaviors: (a) joins group
activities without being told to (M = 2.25, SD = 0.65), (b)
makes friends easily (M = 2.17, SD = 0.68), (c) starts conversations rather than waiting for others to talk first (M =
2.28, SD = 0.67), (d) acts impulsively (M = 1.87, SD =
0.76), (e) invites others to join in activities (M = 1.98, SD =
0.62), (f) fights with others (M = 2.32, SD = 0.66), (g)
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Journal of Emotional and Behavioral Disorders XX(X)
controls his or her temper in conflict situations with other
students (M = 2.26, SD = 0.67), (h) avoids situations that are
likely to result in trouble (M = 2.18, SD = 0.62), (i) cooperates with peers without prompting (M = 2.29, SD = 0.56), (j)
responds appropriately when pushed or hit by another student (M = 2.11, SD = 0.71), and (k) argues with others (M =
2.06, SD = 0.70). For each item, teachers rated the frequency with which these behaviors occurred in the previous
year using a 3-point scale: never (coded as 1), sometimes
(coded as 2), and very often (coded as 3).
School characteristics. Three theoretically relevant variables were examined to investigate the role of school characteristics on student exclusion over time: urban school,
school climate, and school risk. “Urban School” (M = 0.32,
SD = 0.47) referred to whether the child’s school was in an
urban location (yes = 1 and no = 0). “School Climate” (M =
3.45, SD = 0.67) referred to teacher ratings of whether the
school climate was positive (a created variable in the
SEELS data set comprised of teacher ratings of the quality
of the school leadership and whether the school was a safe
place for students). It was measured on a 4-point scale:
strongly disagree (coded as 1), disagree (coded as 2), agree
(coded as 3), and strongly agree (coded as 4).
“School Risk” was fitted as a latent variable that included
four items: (a) percentage of students eligible for the free or
reduced-price lunch program (M = 2.08, SD = 1.05), (b)
percentage of students enrolled who moved away from the
school during the school year (M = 2.34, SD = 1.21), (c)
total number of suspensions/expulsions for all students at
the school in the last year (M = 2.49, SD = 1.12), and (d)
percentage of enrolled students absent from school on a
typical day (M = 2.32, SD = 1.13). Each item was recoded
into four categories: less than 25% (coded as 1), 26% to
50% (coded as 2), 51% to 75% (coded as 3), and more than
75% (coded as 4).
Data Analysis
Structural Equation Modeling (SEM) was used to analyze
the data. SEM permits the simultaneous examination of
numerous relations between predictor and dependent variables. In addition, it allows a model with both observed and
latent variables to be estimated simultaneously. Mplus
(Version 5.2; Muthén & Muthén, 2007) was adopted for all
the analyses in this study. To account for the dependency
among the observations (i.e., students nested within school
districts), analyses were conducted using the “Type =
Complex” feature in Mplus (Muthén & Muthén), which
adopted the Huber-White correction for the standard errors
of the parameter estimates. In addition, to analyze discrete/
categorical dependent variables, a maximum likelihood
estimation with standard errors was used along with a chisquare test statistic—both of which are robust to non-normality and non-independence of observations (MLR) with
logit link. Missing data were handled by using the “Type =
missing” feature in Mplus, which adopted the full information maximum likelihood (FIML) approach to incorporate
information from incomplete responses and retain all 2,597
participants in the analyses.
Results are reported as odds ratios. An odds ratio of 1.0
indicates there is no relationship between a predictor and
the outcome variable. The greater the departure from an
odds ratio of 1.0, the greater the relationship between the
two variables. For example, if Predictor X1 (e.g., gender;
coded 1 = male, 0 = female) has an odds ratio of 2.0, then a
participant in the study with X1 = 1 (a male student) is two
times more likely to be excluded than a participant with X1
= 0 (a female student). Conversely, if the odds ratio of
Predictor X2 is .50, then a participant with characteristic X2
is two times less likely to be excluded than a participant
without characteristic X2. In this sense, the odds ratio serves
as an easy-to-interpret description of the magnitude of the
relationship between the predictor variable of interest and
disciplinary exclusion.
Results
Probability of Exclusion Over Time
Students who were excluded in the first wave were more
likely to be excluded again in the second wave (i.e., odds
ratios from Wave 1 to Wave 2 ranged from 2.45 to 8.29)
(see Figure 1). Similarly, students who were excluded in
Wave 2 were more likely to be excluded again in Wave 3,
although the odds ratios from Wave 2 to Wave 3 were relatively smaller (range from 3.25 to 5.83) than those from
Wave 1 to Wave 2. Finally, as Figure 1 depicts, exclusion
status at Wave 1 predicted exclusion status at Wave 3 for
students with LD and students with ADHD at conventional
levels of significance (p < .05), but not for students who
have EBD. For all three disability groups, students excluded
in Wave 1 were much more likely to be excluded in Wave
2 and being excluded in Wave 2 increased the probability
of being excluded in Wave 3.
The pattern of exclusion over time (i.e., the odds ratios
between each pair of waves) was the same across the three
disability groups. Several pairwise comparisons between disability groups on each of the path coefficients were conducted
with Satorra-Bentler scaled (mean-adjusted) chi-square tests,
given that MLR was used for estimating all the models.
The LD group had a statistically significant higher odds
ratio in the pattern of exclusion from Wave 1 to Wave 2 than
the EBD and ADHD groups (the chi-square difference statistics ranged from 6.15 to 41.47). Furthermore, the ADHD
group had a statistically significant higher odds ratio in the
pattern of exclusion from Wave 2 to Wave 3 than the LD
and EBD disability groups (the chi-square difference statistics ranged from 4.62 to 6.21). Apart from these two
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Bowman-Perrott et al.
LD
Wave 1
Exclusion
8.29*
Wave 2
Exclusion
3.25*
Wave 3
Exclusion
2.02*
EBD
Wave 1
Exclusion
2.45*
Wave 2
Exclusion
3.74*
Wave 3
Exclusion
5.83*
Wave 3
Exclusion
1.22
ADHD
Wave 1
Exclusion
4.14*
Wave 2
Exclusion
1.75*
*p<.05
Figure 1. Odds Ratios for Disciplinary Exclusion Over Time by
Disability Group
discrete patterns of exclusion, no statistically significant
differences on any other patterns of exclusion were found
among the three disability groups (the chi-square difference
statistics ranged from 0.48 to 3.83).
Predictors of Exclusion Status Over Time
Student exclusion status across the three waves was predicted by identified variables in four conceptual categories:
student demographic characteristics, family/household
characteristics, student academic achievement and social
skills, and school characteristics. As described earlier, the
predictors included observed variables as well as unobserved or latent variables.
For the latent variables, a confirmatory factor analysis
(CFA) was conducted to examine whether the observed
items loaded onto the corresponding latent variables. All of
the factor loadings were significant, and the standardized
coefficients ranged from .47 to .96 across the four latent
variables. Although the overall model chi-square (see Note
2) was significant, χ2(486) = 3,574.851, p < .05, various fit
indices (CFI = 0.95, RMSEA = 0.05, SRMR = 0.04) still
suggested that the proposed measurement model with the
four factors fitted the data adequately (Hu & Bentler, 1999).
The factor scores for each of the four latent variables were
subsequently computed and used in the analyses (Thompson,
2004). In other words, parental involvement, parent satisfaction with school, social adjustment, and school risk were
represented by the corresponding factor scores and entered
in the model.
The hypothesized model with student exclusion status at
different waves as the dependent variable was analyzed for
the overall sample and separately for the three disability
groups. Each odds ratio was transformed into effect size dHH
(Hasselblad & Hedges, 1995) by LOR * 3 , where LOR is
π
the natural logarithm of the odds ratio and π = 3.14159
(Chinn, 2000). Given the relatively large sample size, many
path coefficients were found to be statistically significant
even though some of them might carry minimal effects. To
filter out these minimal effects, results (see Note 3) are presented in Figure 2 and Table 2 only for odds ratios (a) that
are statistically significant (α < .05) and (b) with effect sizes
dHH > 0.10.
Overall model. Three student characteristics variables
were related to a higher probability of being excluded in
Waves 1 and 2: disability status (EBD and ADHD), being
African American, and gender (males) (see Figure 2).
Among all of the student academic- and social-skillsrelated variables examined, social adjustment was the
only variable that predicted exclusion status in both Waves
1 and 2. Students with higher social adjustment scores
were less likely to be excluded. Two family/household
characteristics variables, family structure and parent
involvement, were associated with higher rates of exclusion in Wave 1. Students from non-two-parent families
and students whose parents were more involved in school
had a higher probability of being excluded. With all theoretically relevant variables accounted for in the model,
early exclusion remained a statistically significant and
strong predictor of later exclusion, with odds ratios of 2.45
and 3.72, as depicted in Figure 2.
Disability status. Similar to the findings for the overall
sample, for each disability group, males were more likely
than females to be excluded in Waves 1 and 2 (see Table 2).
Students with LD who were reported to have higher social
adjustment scores had a lower probability of being excluded
in Waves 1 and 2 than their counterparts with lower reported
social adjustment scores. For students in the EBD and
ADHD groups, few other variables in the model consistently predicted exclusion status over time. Notably, similar
to the findings for the sample as a whole, for each disability
group, early exclusion was a statistically significant and
strong predictor of exclusion over time—even with all theoretically relevant variables included in the model.
Predictors that did not meet dual criteria. It is worth noting
for future research that two variables identified consistently
in previous studies as associated with disciplinary exclusion—student age and SES risk—were significantly related
to exclusion status over time in this study, but they were
excluded from reported findings because their effect sizes
were below .10. Older students were more likely to be
excluded in Wave 1 (α < .05; dHH = .04) and Wave 2 (α <
.05; dHH = .03). Students from families with higher scores
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8
Journal of Emotional and Behavioral Disorders XX(X)
Disability Group
EBD
ADHD
2.49
2.03
2.04
School Characteristics
Family/Household Characteristics
SES
Risk
FS
SM
1.93
PE
†PI
Urban
†PS
SC
SR
1.75
3.02
2.45
Wave 1 Exclusion
1.83
AA
1.55
HIS
2.28
AGE
3.72
Wave 2 Exclusion
1.97
MALE
0.61
0.56
ACH
Student Demographic Characteristics
Wave 3 Exclusion
EA
GR
PeI
TI
†SA
Student Academic and Social Skills
Figure 2. Odds Ratios for Predictor Variables Over Time
Note. Only statistically significant (< .05) odds ratios with effect size dHH > 0.10 are presented. Odds ratios with values larger than 1.0
indicate higher probability of being excluded. †Variables are unobserved and measured by several observed variables. EBD = emotional/
behavioral disorders; ADHD = attention-deficit/hyperactivity disorder. FS = Family Structure. SM = Student Mobility. PE = Parent
Expectations. PI = Parent Involvement. PS = Parent Satisfaction with School. Urban = Urban School. SC = School Climate. SR = School
Risk. AA = African American. HIS = Hispanic. ACH = Academic Achievement. EA = Extracurricular Activities. GR = Grade Retention. PeI
= Peer Interactions. TI = Teacher Interactions. SA = Social Adjustment.
on the SES risk variable also were more likely to be
excluded at Wave 1 (α < .05; dHH = .06) and Wave 2 (α < .05;
dHH = .04).
Discussion
The purpose of this study was to examine patterns and predictors of disciplinary exclusion over time for students with
EBD, ADHD, and LD—three student groups that are consistently identified in the literature and in national reports
to Congress as being most at risk of disciplinary exclusion.
The longitudinal, nationally representative SEELS data set
was used to: (a) investigate the relation of early exclusion
(Wave 1) to later exclusion (Waves 2 and 3), and (b) examine the influence of student demographic characteristics,
family characteristics, student academic and social skills,
and school characteristics on rates of disciplinary exclusion
over time. SEELS data were collected over a 6-year period
with 1 to 2 years between waves. Given the study’s sample
size, a conservative decision rule was adopted that included
both statistical significance at conventional levels and an
effect size greater than .10 in reporting the results.
The findings from the current investigation provide the
first national picture of the powerful predictive effect of
early exclusion on later exclusion. Previously these data
were only available from state- and district-level studies
(Arcia, 2007; Krezmien et al., 2006). The present study
extends the work of Achilles et al. (2007) by: (a) examining
many of the same SEELS variables across all three waves
of data (versus one point in time), (b) examining additional
variables that have implications for educational practice
(e.g., social adjustment), and (c) calculating and presenting
effect sizes for all theoretically relevant variables to distinguish between statistical significance and the magnitude of
the relationships between potential predictor variables and
disciplinary exclusion over time.
For the sample overall, and for the three disability
groups separately, students excluded at Wave 1 were two to
five times more likely to be excluded in Waves 2 and 3.
Early exclusion was a statistically significant and strong
predictor of later exclusion even when all theoretically relevant variables were accounted for in the model. This is
significant, as it points to the impact of initial exclusion on
a pattern of exclusion over time. That is, students who are
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9
Bowman-Perrott et al.
Table 2. Odds Ratios and Effect Sizes for Predictors of Interest
Predictor
DV = Time 1 Exclusion
Disability (vs. LD)
EBD
ADHD
Ethnicity (vs. Caucasian)
African American
Gender (Male)
Family/Household Characteristics
Family Structure
Parent Involvementa
Student Academic/Social Skills
Peer Interactions
Social Adjustmenta
School Characteristics
Urban School
School Climate
DV = Time 2 Exclusion
Time 1 Exclusion
Disability (vs. LD)
EBD
ADHD
Ethnicity (vs. Caucasian)
African American
Gender (Male)
Family/Household Characteristics
Parent Involvementa
Student Academic/Social Skills
Social Adjustmenta
DV = Time 3 Exclusion
Time 2 Exclusion
Ethnicity (vs. Caucasian)
African American
Hispanic
Gender (Male)
Student Academic/Social Skills
Extracurricular Activities
School Characteristics
Urban School
Overall
LD
EBD
3.02 (0.26)
2.03 (0.17)
1.83 (0.15)
2.28 (0.20)
4.31 (0.35)
1.81 (0.14)
1.93 (0.16)
1.75 (0.13)
1.87 (0.15)
2.15 (0.18)
2.24 (0.19)
0.56 (0.14)
5.62 (0.41)
2.70 (0.24)
0.56 (0.14)
0.41 (0.22)
1.81 (0.14)
0.37 (0.24)
1.65 (0.12)
2.45 (0.21)
ADHD
4.80 (0.38)
1.97 (0.16)
1.55 (0.11)
1.97 (0.16)
2.41 (0.21)
1.74 (0.13)
0.61 (0.12)
0.51 (0.16)
3.72 (0.32)
2.54 (0.22)
2.49 (0.22)
2.03 (0.17)
3.95 (0.33)
2.01 (0.17)
0.57 (0.14)
2.02 (0.17)
1.67 (0.12)
1.67 (0.12)
2.18 (0.19)
2.08 (0.18)
0.56 (0.14)
4.96 (0.38)
Table entries are statistically significant (a< .05) odds ratios with effect size dHH > 0.10. Odds ratios with values larger than 1.0 indicate higher probability
of being excluded. Effect size dHH is computed using Hasselblad and Hedges’s (1995) equation. LD = learning disabilities; EBD = emotional/behavioral
disorders; ADHD = attention-deficit/hyperactivity disorder; SES = socioeconomic status.
a.Variables are unobserved and measured by several observed variables.
suspended or expelled once are likely to be suspended or
expelled multiple times.
Similar to earlier research (e.g., Achilles et al., 2007;
Wagner et al., 2004), the present study documents that students with EBD were at the greatest risk of initial disciplinary exclusion, followed by students with ADHD and
students with LD. When patterns of exclusion were examined, few statistically significant differences between the
three student disability groups were found in their odds of
being excluded over time. There was one notable exception:
Students with LD had a statistically significant higher odds
ratio in their pattern of disciplinary exclusion from Wave 1
to Wave 2 than students in the EBD and ADHD groups. In
this study, students with LD excluded in Wave 1 were over
eight times more likely to be excluded again in Wave 2.
When other theoretically relevant variables were accounted
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Journal of Emotional and Behavioral Disorders XX(X)
for in the model, a slightly different perspective on disciplinary exclusion emerged. Students in the EBD and the
ADHD groups were significantly more likely than students
in the LD group to be excluded in Wave 1 and Wave 2.
These findings warrant further inquiry, as understanding the
types and consequences of behavioral infractions leading to
exclusion (and how they differ across the three disability
groups) is important for better understanding disciplinary
exclusion.
Several variables were related to higher rates of disciplinary exclusion over time: being male, being African
American, parent involvement, and poor social adjustment.
Each variable contributed unique and significant variance
to the prediction of disciplinary exclusion when all variables were accounted for in the model.
Being male was a strong consistent predictor of disciplinary exclusion for the sample as a whole and for each
disability group in Wave 1 and Wave 2 and for students with
LD in Wave 3. Males were two to four times more likely
than females to be excluded over time. For the sample overall, African American students were almost two times more
likely than students in other ethnic groups to be excluded in
Waves 1 and 2. African American ethnicity was also related
to exclusion for students in the ADHD group in Wave 1 and
for students in the LD group in Wave 3. The unique contribution of gender (males) and ethnicity (African American
students) to exclusion status over time extends current
research (Achilles et al., 2007; Skiba et al., 2002; Snyder &
Sickmund, 2006). Achilles et al. (2007) noted that “the relationship between African American ethnicity and school
exclusion has been repeatedly demonstrated but remains
poorly understood” (p. 41). Perhaps more closely examining additional student, family, and school variables will provide insight into other factors that may be at work in the
relationship between ethnicity and disciplinary exclusion.
In the current study, higher levels of parent involvement
were related to higher rates of exclusion for the overall sample and for students with LD and EBD in Wave 1 and for
students with ADHD in Wave 2. This finding was somewhat counterintuitive and may suggest that parents were
involved in ways (e.g., attending a conference related to
problem behaviors) that were not described in the definition
of the SEELS variable (e.g., attending an IEP meeting). In
fact, Duchnowski and Kutash (in press) surveyed parents
about their involvement in their children’s schools; results
indicated that parents attended meetings at their children’s
schools. However, the majority of those meetings were
described as “negative” and as being related to “discipline
issues” (p. 25). Results of their study point to “the need for
a more comprehensive measure of this construct” (p. 24).
Among the student academic and social skills variables
examined, positive social adjustment was strongly predictive of lower rates of disciplinary exclusion in Waves 1 and
2. Social adjustment was a composite variable extracted
from the SEELS data set. A student’s social adjustment
score was based on teacher ratings of 11 items that
addressed areas such as how well the student got along
with peers and how well the student avoided situations that
might result in trouble. Positive social skills have been
shown to be related to ease of transition into kindergarten
(Carlson et al., 2009) and middle school (Crockett, Losoff,
& Petersen, 1984) and transition to postsecondary life
(Kohler & Field, 2003). Some research (Meyer & Farrell,
1998; Raffaele Mendez et al., 2002) suggests that social
skills instruction is associated with lower suspension rates.
However, there is little empirical research on the relationship
between student social skills and disciplinary exclusion.
This is a topic worthy of further examination.
Limitations
There are two main limitations to the present study. First,
parent-report data from the SEELS data set provided information for several of the variables examined, including the
dependent variable disciplinary exclusion. However, this
method is used in virtually all follow-up studies conducted
in special education that have relied on self-report for a
majority of the data collected (Bullis, Bull, Johnson, &
Peters, 1994; Levine & Edgar, 1994). Furthermore, selfreport data from parents and from students with disabilities
who leave school will be used extensively by states as they
implement new postschool outcomes reporting requirements under Individuals with Disabilities Education Act
(IDEA) (Falls & Unruh, 2009).
Nevertheless, self-report data raise concerns about the
accuracy of the information provided. For example, accuracy can be affected if the respondent feels compelled to
provide a socially desirable response. In this study, to the
extent that some parents felt it was undesirable to report
their student as having been suspended or expelled in the
previous school year, it is possible that rates of disciplinary
exclusion are underreported. Accuracy of information can
also be affected by a lack of knowledge or recall by the
respondent. Research (e.g., Bullis et al., 1994) indicates that
while parents provide less accurate information on details
(e.g., hourly wages), they are good respondents for macroevents about their sons and daughters (e.g., employment),
as reflected in high levels of agreement among parents, students, and teachers for macro-events and factual information. Moreover, parents can accurately recall and report
factual information about their children for several years
with the highest rates of accuracy occurring for up to
2 years (Robbins, 1963). The variables investigated in this
study were largely factual (e.g., parent report of student
mobility) or the perceptions of respondents (e.g., parent
report of student interactions with peers), and the time
frame for recalling information for data collected in each
wave was no more than 1 to 2 years.
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Bowman-Perrott et al.
Second, the study used the extant data set from the
SEELS study to investigate patterns and predictors of disciplinary exclusion over time. Large, longitudinal data sets
permit multivariate investigations of important issues
across time. However, the breadth of such databases can
limit the depth of information on any specific variables of
interest. That was the case in this study. For example, the
SEELS data set did not include information on the reasons
for or length of time students were suspended or expelled.
Such information could permit a more in-depth understanding of patterns of exclusion over time. Nonetheless, the
present study provides a “first look” at patterns and predictors of disciplinary exclusion over time based on a longitudinal, nationally representative data set that extends current
research on disciplinary exclusion and provides several
implications for policy, practice, and future research.
Implications for Policy and Practice
Without question, schools have a responsibility to create
safe and disciplined learning environments so that teachers
can teach and students can learn. Furthermore, school personnel have a responsibility to evaluate policies and practices to ensure they are achieving their intended outcomes.
The disproportionate application and multiple poor outcomes associated with disciplinary exclusion found in the
literature, as well as the patterns and predictors of exclusion
over time identified in the present study, highlight the
importance of implementing school policies and practices
that reduce the likelihood of initial disciplinary exclusion.
To start, district and school discipline policies should be
examined to ensure that (a) definitions of suspension and
expulsion are clear, (b) behavioral infractions that warrant
suspension and expulsion are clearly stated, (c) procedures
are clearly stated (e.g., written notification to parents/
guardians), (d) measures are in place to ensure that alternatives are exhausted so that disciplinary exclusion measures
are a “last resort,” and (e) disciplinary exclusion is part of
a comprehensive school-wide approach to behavior and
discipline that emphasizes prevention and creates options
for excluded students to remain connected to the learning
process (American Psychological Association [APA] Zero
Tolerance Task Force, 2008; Brown, 2007; Horner, Sugai,
Todd, & Lewis-Palmer, 2005).
Schools can play a major role in connecting the teaching
of social skills to instruction. Social skills are an important
component of social adjustment (Sumi, Marder, & Wagner,
2005). They include those communication, problem-solving, decision-making, self-management, and peer relations
abilities that allow students to initiate, build, and maintain
positive social relationships with others. These are skills
that can, and should, be taught in school. This is particularly
important for students with EBD and ADHD (Sumi et al.,
2005). Modeling and practicing these skills is critical, as
deficits or excesses in social behavior interfere with learning, teaching, and the classroom’s orchestration and climate. Students’ social competence is linked to peer
acceptance, teacher acceptance, successful inclusion efforts
with students with disabilities, and post-school success.
Implications for Research
Longitudinal, prospective research that examines the implementation and impact of disciplinary exclusion practices is
needed. Future research might further investigate the relationships among certain student and family demographic
characteristics and disciplinary exclusion and the circumstances that place some students at greater risk of exclusion
both initially and over time. Are some students in double or
triple jeopardy of exclusion? Wagner and Cameto (2004)
reported that youth with EBD are more likely to be
involved in fights or with bullying at, on their way to, or on
their way home from school. Some students identified with
EBD in later school years were first identified as LD in
earlier grades (Duncan, Forness, & Hartsough, 1995), some
have concurrent EBD and LD diagnoses (Lopes, 2005), and
some experience comorbid LD, EBD, and ADHD diagnoses (Dietz & Montague, 2006). Moreover, several demographic characteristics associated with disciplinary
exclusion (e.g., male gender, African American ethnicity,
low family SES) are also documented characteristics of
youth with EBD (Wagner, 2004). What circumstances in
schools place students with specific risk factors in jeopardy
of exclusion? What can schools do to create systems that
monitor discipline practices and their impact on students at
greater risk of exclusion?
Increasingly, it is being recommended that disciplinary
exclusion measures be implemented within the context of
multitier school-wide discipline models (e.g., APA Zero
Tolerance Task Force, 2008). Recent research indicates that
school-wide discipline systems, such as school-wide
Positive Behavioral Intervention and Support Systems, can
lead to reductions in suspensions and increases in academic
achievement among elementary school students (Bradshaw,
Mitchell, & Leaf, 2010). It is not clear, however, if the
implementation of school-wide discipline systems will necessarily lead to reductions in the disproportionate application of disciplinary exclusion practices among students
most at risk for exclusion (e.g., students with EBD, ADHD,
male students, and African American students). For example, some research suggests that school personnel do not
readily access and use information on rates of disciplinary
office referrals by ethnicity even when such reports are
available (Vincent, 2008). What conditions are necessary
for school personnel to use discipline data to evaluate the
implementation and impact of policies and practices on specific student groups? What impact does a multitier, schoolwide approach to discipline have on patterns of exclusion
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Journal of Emotional and Behavioral Disorders XX(X)
over time? What strategies within primary, secondary, and
tertiary tiers have the greatest success in reducing rates of
exclusion among students at highest risk of suspension and
expulsion? What strategies are most successful in keeping
excluded students connected to the learning process?
Finally, given the relation of parent involvement to disciplinary exclusion, examining factors such as the quality of
parent interaction or involvement should be taken into
account (Duchnowski & Kutash, in press). This multidimensional variable should be further investigated to go
beyond how many meetings or conferences parents attended
to include the purpose for and quality of such involvement—
as well as its impact on student outcomes.
Answers to questions such as these about the implementation and impact of disciplinary exclusion within the context of school-wide discipline systems may provide insights
into the patterns of exclusion over time and into strategies
that can reduce the disproportionate use of and adverse consequences associated with disciplinary exclusion.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interests with
respect to the authorship and/or publication of this article.
Funding
The authors received no financial support for the research and/or
authorship of this article.
Notes
1. A table detailing the items extracted from the SEELS data set,
including the item name, values, scoring, data source, and
correlation matrices for the present study, is available from
the first author upon written request.
2. All chi-square values presented in the results section were
adjusted for all other variables.
3. All of the findings (odds ratios and effect sizes) for the entire
sample, for each disability group, and for all three waves of
data are available from the first author.
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About the Authors
Lisa Bowman-Perrott, PhD, is an assistant professor of special
education at Texas A&M University. Her interests include school
discipline and interventions for students with or at risk for emotional and behavioral disorders.
Michael R. Benz, PhD, is a professor of special education and
Director of the Center on Disability and Development at Texas
A&M University. His interests include secondary, transition, and
postschool outcomes.
Hsien-Yuan Hsu, PhD, is an assistant research fellow in the
Center of Educational Research and Evaluation at National
Taiwan Normal University. His interests include large-scale data
analysis, multilevel modeling, and web survey.
Oi-man Kwok, PhD, is an associate professor of research, measurement, and statistics at Texas A&M University. His interests
include longitudinal data analysis using multilevel models and
structural equation models and the applications of these models in
educational and psychological research.
Leigh Ann Eisterhold, MEd, is a doctoral student at Texas A&M
University. Her interests include family involvement of students
with disabilities.
Dalun Zhang, PhD, is an associate professor of special education
at Texas A&M University. His interests include transition, selfdetermination, and prevention of juvenile offence.
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