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Learning Disabilities Research & Practice, 29(4), 150–159
C 2014 The Division for Learning Disabilities of the Council for Exceptional Children
Salient Predictors of School Dropout among Secondary Students with
Learning Disabilities
Bonnie Doren
University of Wisconsin-Madison
Christopher Murray
University of Oregon
Jeff M. Gau
Oregon Research Institute
The purpose of this study was to identify the unique contributions of a comprehensive set of
predictors and the most salient predictors of school dropout among a nationally representative
sample of students with learning disabilities (LD). A comprehensive set of theoretically and
empirically relevant factors was selected for examination. Analyses were conducted to explore
the unique contribution and relative importance of these factors in predicting dropout. Results indicated that the most salient predictors of school dropout included a set of malleable
individual (grades, and engagement in high-risk behaviors), family (parent expectations), and
school (quality of students’ relationship with teachers and peers) factors. The findings validate
multicomponent dropout prevention and intervention models for this population while at the
same time illuminating specific key components that appear to be of particular importance in
school dropout among students with LD.
Data suggest that students with disabilities are not experiencing the same level of progress as other high school
students. According to the Condition of Education 2013, 7
percent of public high school students dropped out during
the 2009–2010 school year (Aud et al., 2013). During this
same period, 21 percent of students with disabilities dropped
out (United States Department of Education, 2012). Students with learning disabilities (LD) make up approximately
62 percent of secondary students with disabilities (Wagner,
Newman, Cameto, Levine, & Marder, 2003). Furthermore,
students with LD consistently experience the second highest
dropout rate among students with disabilities, surpassed only
by students with emotional disturbance (National Center for
Learning Disabilities, 2013; U.S. Department of Education,
2012). Students with LD, irrespective of dropout, are less
likely to be employed, to earn a living a wage, and to be
enrolled in or complete postsecondary education in a 4-year
institution than students in the general population (Newman,
Wagner, & Knokey et al., 2011). Students with LD who
dropout experience significantly poorer postchool outcomes
in all the above areas compared to peers with LD who graduate (Newman, Wagner, & Knokey et al., 2011; Sitlington &
Frank, 1993).
A robust set of findings examining the determinants of
school dropout in the general adolescent population conRequests for reprints should be sent to B. Doren, University
of Wisconsin-Madison. Electronic inquiries should be sent to
[email protected].
curs that dropout is a consequence of several factors across
multiple contextual settings (e.g., individual, family, school;
e.g., Alexander, Entwisle, & Horsey, 1997; Janosz, LeBlanc,
Boulerice, & Tremblay, 1997, 2000; Rumberger, 1995). Despite decades of national attention and efforts to reduce
dropout rates for students in the general population, little
is known about the specific factors and processes that predict school dropout for students with LD. The purpose of the
current study was to identify a set of theoretically and empirically derived primary factors that directly predict school
dropout among students with LD.
LITERATURE REVIEW
Consistent with prior theory and research highlighting the
multideterminant nature of the school dropout experience,
the selection and examination of predictors was grounded in
an ecological approach. This perspective acknowledges the
influence of an array of personal and contextual factors on
experiences and outcomes of individuals. Bronfenbrenner
(1977, 1979) codified the ecological approach by defining
a unique set of social systems that interact with individuals to shape their experiences and outcomes. Brofenbrenner’s model is often depicted as a series of nested circles
with the individual residing in the inner most circle. Immediately surrounding the individual is the microsystem that
includes the most proximal settings and social systems in
which individuals are directly involved (i.e., families, peers,
LEARNING DISABILITIES RESEARCH
and schools). Because of the sustained proximity of the microsystem to the individual, the interactions and experiences
that occur have the potential to have the greatest direct impact on individual’s development, behavior, and outcomes
(Bronfenbrenner, 1994). Surrounding the microsystem is the
mesosystem. The mesosystem includes interactions and relationships between and among microsystems. An example of
this type of linkage is the participation of parents in parent–
teacher conferences. A critical feature of Brofenbrenner’s
ecological model is a focus on malleable factors within and
across systems. This feature is important because it allows
researchers to identify critical factors that are amenable to
prevention and intervention efforts. However, the importance
of sociodemographic factors cannot be discounted because
these factors can affect interactions within and across these
systems.
This study capitalized on a comprehensive nationally representative longitudinal dataset of secondary students with
disabilities in the United States. The dataset contained information on student characteristics (e.g., skills, attitudes,
behaviors), settings and interactions contained within students’ microsystems (e.g., families, peers, teachers, classroom) and to some extent students’ mesosystems (family–
school interactions). Next, we summarize the findings on
school dropout organized by sociodemographic, individual,
family, and school-based factors.
Sociodemographic Factors
Although our focus was largely on malleable factors, one
cannot ignore the extensive findings regarding the association between sociodemographic factors and dropout rates
in the general population. For example, ethnic/minority status, poverty, limited English proficiency, and gender (i.e.,
males) have all been shown to be powerful predictors of
school dropout rate among secondary students in general
(Aud et al., 2013; Dalton, Glennie, Ingles, & Wirt, 2009).
Although, sociodemographic factors are important markers
of dropout, their predictive role for students with LD remains less clear. Several studies have reported that family
socioeconomic status (SES) and race/ethnicity are associated
with high school dropout for students with LD (Reschly &
Christenson, 2006; Wagner, Newman, Cameto, Garza, &
Levine, 2005; Zablocki & Krezmien, 2013). However, several studies have not found an association between SES and
race/ethnicity and high school dropout for students with LD
(Dunn, Chambers, & Rabren, 2004; Kortering, Haring, &
Klockars, 1992; Zablocki & Krezmien, 2013). Even less is
known about the predictive role of gender and limited English
proficiency.
Individual Factors
Individual risk factors associated with school performance
such as poor academic achievement, absenteeism and problem behaviors have been identified as powerful predictors
of dropout for students in the general population (BattinPearson et al., 2000; Janosz et al., 1997). However, the predictive role of these factors in school dropout has been in-
151
consistent in studies focusing on students with LD (Bear,
Kortering, & Braziel, 2006; Kortering et al., 1992; Reschly
& Christenson, 2006; Zablocki & Krezmian, 2013). Additional risk factors linked to dropout in the general population
include poor social skills and engagement in risk behaviors
such as substance use, criminal activities, and early sexual
activity (e.g., O’Donnell, Hawkins, Catalano, Abbot, & Day,
1995). Students with LD are significantly more likely to exhibit problems with social skills and engage in risk behaviors than are their nondisabled peers, which places students
with LD at greater risk of dropout (Kavale & Forness, 1996;
Svetaz, Ireland, & Blum, 2000). By contrast, positive individual skills and dispositions such as exhibiting high levels
of self-determination, self-concept, and self-advocacy may
promote high school completion among students with LD
(Morningstar et al., 2010; Murray & Naranjo, 2008). This
study examines the relative importance of multiple individual risk and promotive factors in the likelihood of school
dropout among students with LD.
Family Factors
Parent involvement in their child’s education has consistently
been shown to be associated with school dropout in the
general adolescent population (Battin-Pearson et al., 2000;
Dalton et al., 2009). Parent educational involvement typically
includes: (a) parent’s expectations of his/her child’s educational achievement, (b) home-based supports such as helping
with homework or discussing school-related issues with the
child, and (c) school-based involvement such as volunteering
at his/her child’s school or attending parent–teacher conferences (Chen & Gregory, 2010; Fan & Williams, 2010). In
several descriptive studies, students with LD identified lack
of parent involvement in their education at school or at home
and low parent expectations as potent reasons for school
dropout (Kortering & Braziel, 1999a,b; Murray & Naranjo,
2008). There is limited research on the predictive power of
parent educational involvement within the context of other
factors and settings on school dropout among students with
LD, and thus, further study is warranted.
School Factors
Several school-based factors are conceptually and empirically relevant to school dropout for students with LD including: (a) the quality of social relationships within the school
setting, (b) access to academic coursework, (c) inclusion in
general education classrooms, (d) class size, and (e) receipt
of instructional and testing accommodations.
Student perceptions of the quality of their relationships
with teachers and peers have emerged as an important and
consistent predictor of school dropout among students without disabilities (e.g., Croninger & Lee, 2001; Lee & Burkam,
2003). Descriptive studies focusing on students with LD reported that poor relationships with teachers and peers were
related to dropout. By contrast, studies reported that positive
relationships with teachers and peers were related to staying
in school (Dunn et al., 2004; Murray & Naranjo, 2008; Seidel
& Vaughn, 1991).
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DOREN ET AL.: PREDICTORS OF DROPOUT AMONG STUDENTS WITH LD
In addition to school-based relationships, access to academic coursework such as English Language Arts and mathematics is critical for students to gain a standard diploma
and be college and career ready (Newman, Wagner, Huang
et al., 2011; U.S. Department of Education, 2010). Newman,
Wagner, Huang et al. reported that students with disabilities
who completed high school were more likely than those who
dropped out to take academic rather than vocational courses.
Access to academic coursework required for graduation typically occurs as part of the general education curriculum in
general education classrooms. The promise of including students with disabilities in the general education classroom on
graduation rates has yet to be realized. According to Goodman, Hazelkorn, Bucholz, Duffy and Kitta (2011), although
inclusion rates increased by 62 percent during a 6-year period
between 2003 and 2008, graduation rates remained stagnant
for students with disabilities. During the same period, graduation rates increased for students without disabilities (Goodman et al., 2011). These findings suggest that students with
disabilities who are receiving all or most of their instruction
in general education settings may not be poised to graduate.
This may in part be due either to the lack of appropriate supports for accessing the general curriculum or to inappropriate
classroom placements (Goodman et al., 2011).
Class size has received much attention in school reform
initiatives. Findings suggest students who learned in small
classes during primary grades experienced lower dropout
rates and higher on-time graduation rates compared with
students who were instructed in regular sized classes (Finn,
Gerber, & Boyd-Zharias, 2005). High schools typically have
larger average class sizes than elementary or middle schools.
Students with disabilities may be particularly disadvantaged
in high school classes due to the difficulty in obtaining individualized instruction and personal attention (Wagner, Newman, et al., 2003).
Finally, instructional and testing accommodations are important sources of support to access and benefit from the
general education curriculum for students with LD. Wagner,
Marder, et al. (2003) reported that students with disabilities
who received five instructional or testing accommodations
were approximately one year farther behind in both reading and math than peers who received no accommodations.
This finding suggests that those students in most need of
accommodations may be those with lower levels of achievement. The finding also suggests that significant improvement
in performance and outcomes may take time to appear as
teachers learn how to apply and students learn how to use appropriate accommodations. Neither the unique nor relative
contributions of these school factors have been examined in
explaining school dropout among students with LD.
contribution of a comprehensive set of predictors to school
dropout for high school students with LD, and to ascertain
the most influential predictors of school dropout among these
students.
METHOD
Participants and Procedures
Data for the current study were from the National Longitudinal Transition Study-2 (NLTS2). The NLTS2 is a nationally representative sample of over 11,000 13–17-year-old
students who were receiving special education services during the 2000–2001 school years. Data were collected over a
10-year period (2000–2010) in five waves. This study used
data contained in Waves 1–4. Several sources were used to
obtain the data included within this study. At Wave 1 parents/guardians were interviewed by telephone to ascertain
information regarding students’ school and nonschool experiences, personal and educational history (e.g., age disability first identified), household characteristics (e.g., socioeconomic status), and family processes. All parents who could
not be reached by telephone were mailed a self-administered
questionnaire (83% Wave 1 response rate). Parents/guardians
and youth were interviewed as part of Waves 2–4. All youth
who could not complete a telephone interview but were able
to complete a written version were mailed a self-administered
questionnaire. If youth were unable to complete the telephone
interview or questionnaire, then the parents/guardian continued the interview on behalf of the sample youth (61% Wave
2 response rate, 50% Wave 3 response rate, 50% Wave 4
response). Finally, a one-time direct face-to-face assessment
with a focus on academic achievement, self-determination
skills, self-concept, and attitudes toward school and learning was conducted when sample students were between ages
16–18 (56% direct assessment response rate). Response for
each sample member was weighted to represent the number
of students in his/her disability category and characteristics
of the LEA (e.g., regions, size, and wealth).
The study sample for this research included any NLTS2
participant identified with LD with follow-up data at Waves
2–4 (n = 725). Study demographic characteristics are shown
in Table 1. The study sample was compared to all NLTS2
participants identified with LD (n = 1,122). The 397 participants not included in this study had a modal age of 17 years at
Wave 1, were primarily male (66%), white (61%), and lived
in a suburban community (54%). The rates were statistically
similar to the study sample. However, participants with LD
not included in the study reported significantly lower household incomes (p < .001; 33% study sample reported $25,000
and under vs. 47% for excluded participants).
Summary and Current Study
Descriptive and correlational findings suggest that factors
within the individual, family, and school may affect the likelihood of school dropout among students with LD. However,
there is scant research examining both the unique and relative contributions of these factors concurrently. Therefore,
the purpose of this study was to examine both the unique
Measures
Outcomes
One dichotomously scored outcome (1 = yes, 0 = no) indicating whether or not the participant dropped out of high
LEARNING DISABILITIES RESEARCH
TABLE 1
Sample Characteristics
Female
Age
14 years
15 years
16 years
17 years
18 years
Household income
$25,000 and under
$25,001 to $50,000
$50,001 or more
Did not report
Ethnicity
White
African-American
Hispanic
Other
Did not report
City Designation
Rural
Suburban
Urban
Did not report
Na
%
240
33.1
140
170
160
200
55
19.3
23.5
22.1
27.6
7.6
200
190
230
100
27.8
26.4
31.9
13.9
460
130
110
20
10
63.0
17.8
15.1
2.7
1.4
90
330
220
90
12.3
45.2
30.1
12.3
a As per requirement of the Institute of Education Sciences restricted use data
agreement all unweighted sample size numbers are rounded to the nearest
ten.
school at Waves 1–4 was examined. Parents/guardians and/or
youth were asked at Waves 1–4 whether the youth dropped
out and did not graduate, received a certificate, took a test
for a high school diploma, or previously graduated. A youth
was scored as dropping out if dropout status was positive at
any wave.
Predictors
Four domains of predictors were examined: sociodemographic, individual, family, and school-based factors. Sociodemographic predictors included gender, race, poverty
status, and English proficiency. Gender was coded as a dichotomous variable (1 = male, 0 = female). Race was collected as a six-point, single choice variable. Due to low cell
sizes for Asian/Pacific Islander, American Indian/Alaskan
native and “multiple races,” race was recoded (1 = European American/White, 2 = African American/Black, 3 =
Hispanic, 4 = other). Poverty status was a dichotomous variable indicating whether the respondent was above or below
the federal poverty threshold. English language proficiency
was coded as a single choice response in five categories but
due to low response rates for some categories was recoded to
native/bilingual (= 1) and “other” (= 0).
Individual predictors included academic factors, individual risk factors, and individual skills and dispositions.
Academic factors included academic achievement, grades,
number of absences, number of suspensions/expulsions and
classroom behavior. Academic achievement was assessed
153
using the research version of the Woodcock-Johnson III
Test of Achievement. Students’ academic achievement in
reading and math were included as predictors. Specifically,
standard scores were used on the two indicators of reading:
synonym/antonym and passage comprehension and two indicators of math: calculation and applied problems. The variable for grades was obtained from the Wave 1 cross instrument data file combining parent, teacher, and school program
responses into one variable categorizing a student’s typical
grades received in coursework (1 = mostly Ds and Fs, 2 =
mostly Cs and Ds, 3 = mostly Bs and Cs, 4 = mostly As and
Bs). The number of absences (both excused and unexcused,
excluding suspensions or expulsions), and number of suspensions/expulsions (including in-school suspensions, out-ofschool suspensions, and expulsions) were obtained from the
school program survey and were continuous variables. NLTS2 calculated the number of absences by taking the number of
days students were absent in February in the year the program
survey was collected (2002 or 2004) and then multiplied this
number by 9 for the average days absent in a school year.
The number of days students were absent due to suspensions
or expulsions was then subtracted from this value (Wagner,
Marder et al., 2003). The number of suspensions/expulsions
was the total number of these incidents across the school year
within which the program survey was collected. Classroom
behavior was an existing scale in the cross-instrument data
file. The scale was the sum of four items that described the
student’s behavior in the classroom (stays focused, completes
homework on time, does not withdraw, participates in discussions across settings), each rated on a four-point scale (1 =
rarely, 2 = sometimes, 3 = usually, 4 = almost always).
Individual risk factors included a scale of risk behaviors
and arrest history. The risk scale was developed for the purposes of the current study and summed the total number of
five risk behaviors in which the youth engaged (i.e., drinking alcohol, smoking, sexual intercourse, carrying a weapon,
illegal drug use) and was coded 0 = engaged in none to 5
= engaged in all five. Arrest history measured whether or
not the youth had ever been arrested (1 = yes, 0 = no).
Risk behaviors and arrest history were obtained from the
Parent/Youth surveys Part 2 for Waves 2, 3, and 4.
Individual skills and dispositions included social skills,
self-concept, self-advocacy, and self-determination. Three
subscales from the Social Skills Rating System included social assertion, self-control, and cooperation. The social skills
scale reports an internal consistency of .90 and test–retest
reliability of .80 (SSRS; Gresham & Elliot, 1990). Parents of
youth were asked to report how often their sons or daughters
engaged in these aspects of social skills. A scale for overall
social skills was the sum of the three subscales (0–10 = low
ability, 11–16 = medium ability, 17–22 = high ability). A
short version of the Student Self Concept Scale with reliability estimates reported between .79 and .92 (SSCS; Gresham,
1995) was used to measure student’s self-concept. The short
version included a 15-item subscale that measured students’
confidence in a number of academic and social areas. The
sum score of the 15 items with a three-point response option
(1 = not at all confident, 2 = not sure, 3 = confident) was
used. Self-advocacy was measured by one item that asked
parents/teachers how well students asked for what they need.
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DOREN ET AL.: PREDICTORS OF DROPOUT AMONG STUDENTS WITH LD
The item was measured on a four-point scale (1 = not at all
well and 4 = very well). Self-advocacy was obtained from
the cross-instrument data file. Self-determination was calculated as a sum score of a subset of the 72 items from the
Arc Self-Determination Scale with a coefficient α reported
of .90 (Wehmeyer & Kelchner, 1995) in the autonomy (15
items), self-realization (5 items), and empowerment domains
(6 items). Response options ranged from 1 = not when I have
the chance to 4 = every time I have the chance for the autonomy items; and from 1 = never agree to 4 = always agree
for the self-realization items. For the empowerment items
participants scored one if they chose the empowering option
or zero if they chose the nonempowering option when describing themselves on each item. Scores were summed with
a possible range of 20–86 with higher scores indicative of
greater levels of self-determination.
Family predictors included home-based support for
schooling, parent involvement in their child’s school, parent involvement in their child’s IEP, and parent expectations
for their child’s future. Home-based support was a sum of
two items (how often the parent spoke with the youth about
school experiences and how often they helped the youth with
homework), with one item collapsed into a four-point scale
for equivalence (2–5 = very low, 6 = low, 7 = medium,
8 = high). Parent school involvement was a sum of three
items and included (a) “attended general school meetings,”
(b) “attended school or class events,” or (c) “volunteered at
the school.” The range for this scale was from 0 = no involvement to 12 = high involvement. Parent involvement in child’s
IEP was a dichotomous variable indicating participation (1 =
yes, 0 = no). Parent expectations for the student focused on
achievement in the areas of education and independence and
were a sum of four items about parent expectations related to
whether their child would achieve the following: obtain a high
school diploma, attend postsecondary education, live away
from home without supervision, and earn enough money to
support themselves without parental support. Each item was
rated on a four-point scale (1 = definitely will to 4 = definitely
won’t).
School-based predictors included: (a) the quality of students’ relationship with teachers and peers, (b) whether or
not students received instruction in language arts, math or
study skills, (c) accommodations received, (d) class size, and
(e) time in special education class. The quality of students’
relationships was obtained from a created variable from the
parent/youth survey, and was the sum of two items (“gets
along with teachers” and “gets along with other students”).
Each item was rated on a four-point scale (1 = not at all
well, 2 = not very well, 3 = pretty well, 4 = very well).
Data on the remaining school predictors were obtained from
the school program survey. Receipt of language arts, math,
and study skills instruction was scored as either (1 = yes,
0 = no). The rating of accommodations was a continuous
variable of whether or not the student received accommodations for such activities as tests, assignments, and instruction. Class size was the number of students in the child’s
regular classes. Time in special education classes referred to
whether or not the child spent any part of the day in a special
education class, noted as a dichotomous variable (1 = yes,
0 = no).
Data Analytic Strategy
An initial analysis was conducted to assess the unique contribution of each study predictor using univariate logistic regression models to predict the dichotomous student dropout
outcome. Next, recursive multivariate logistic regression
models were run. All predictors were considered empirically or conceptually important, and thus included in the
preliminary multivariate model regardless of their univariate
relationship to dropout status. To obtain the most parsimonious model with the most influential predictors, the model
was trimmed by excluding the predictor with the highest nonsignificant p value (p < .05) and rerunning the model. Age
of the participant was considered a covariate and stayed in
the model regardless of p value in order to adjust for any
potential age-related effects. The final multivariate model
included only variables that significantly predicted student
dropout status and age of the participant. Odds ratios were
interpreted as the measure of effect size using the convention
1.48 small, 2.48 medium, and 4.28 large effect, for odds ratios greater than 1.0, and 0.68 small, 0.40 medium, and 0.23
large, for odds ratios less than 1.0 (Lipsey & Wilson, 2001).
Complex sample surveys like the NLTS2 dataset, deviate
from simple random sampling and require consideration of
the sampling strategy design features to ensure unbiased estimates of the population parameters. All models were run
with the SAS PROC SURVEYLOGISTIC procedures to accommodate the cluster, stratification, and sampling weights
used in the NLTS2 study. When data from multiple instruments are combined, it is appropriate to use the weight from
the instrument with the smallest sample size. The sampling
weight from the Wave 4 parent/youth assessment, the smallest sample size, was used for this study. If missing the weight
from Wave 4 assessment, then the weight from the Wave 3
parent/youth assessment, the next smallest sample size, was
used. The Taylor series linearization technique for variance
estimation was used to account for lack of independence due
to sampling within clusters.
Missing Data and Multiple Imputation Procedure
Rates of missing data on the study sample (n = 725) ranged
from 0% to 44% with 1% missing the study outcome, 0% to
35% for demographic predictors, 2% to 44% for individual
skill/risk predictors, 8% to 18% for family predictors, and
5% to 44% for school experience predictors. Of the 29 study
predictors 10 predictors were missing 10% or less data, 3
were missing between 10% and 29% data, 8 were missing
between 30% and 39% data, and 8 were missing between
40% and 44% data. Although it is not possible to know
for sure that data are missing at random (MAR) because
information about the value of the missing data is not
available, the MAR assumption can be made more tenable as
the imputation model is made more general. The inclusion
of additional predictors in the imputation model can reduce
bias and make the MAR assumption more plausible (Allison,
2009; He, Zaslavsky, & Landrum, 2009). Therefore, an
imputation model was used that included both auxiliary
variables correlated with the study outcome (e.g., self-care
LEARNING DISABILITIES RESEARCH
ability) and variables uncorrelated with the outcome (e.g.,
grades, IEP status). Multiple imputations for missing
data are also conditional on the sampling design (Reiter,
Raghunathan, & Kinney, 2006); therefore imputation models
included strata and clusters nested within strata. Sequential
regression multiple imputation (SRMI; van Buuren, 2007)
was used to impute 20 datasets using the IVEware software
V0.2 (Raghunathan, Solenberger, & Van Hoewyk, 2002).
SRMI specifies a multivariate model by separate conditional
models for each incomplete variable allowing for imputation
of variables with different distributional properties. For
this study, three models were specified: (a) a normal linear
regression model for continuous variables, (b) a logistic
regression model for binary variables, and (c) a generalized
logit regression model for variables with more than two
categories. Finally, the SAS PROC MIANALYZE was used
to combine the model effects across 20 imputed datasets.
RESULTS
The dropout rate for the study participants (n = 725) was
14.1%. Descriptive statistics identified a number of predictors that differed as a function of dropout status at a p value
less than .05. Students who dropped out had on average
lower social skills, higher risk scores, and got along less
with teachers and other students (Means = 12.2, 2.1, 5.6, respectively) compared to students who did not dropout (Means
= 14.3, 1.5, 6.5, respectively). Also, students who dropped
out showed a higher percentage of being arrested (26% vs.
10%), a lower percentage of having a parent involved in
his/her child’s IEP (24% vs. 12%), and a lower percentage
of being fully included in general education classes (9% vs.
15%).
The results of the univariate logistic regression models
predicting dropout status are shown in Table 2. None of the
demographic characteristics uniquely contributed to student
dropout status. Four of the individual factors significantly
predicted dropout: grades (OR = 0.42), social skills (OR =
0.84), risk behaviors (OR = 1.44), and ever been arrested
(OR = 2.98). The odds a student would dropout decreased
by 138% for each one unit increase in grades and by 19%
for each one unit increase in social skills. The odds a student would drop out increased by 44% for each one unit
increase in risk behaviors and by 198% for students who had
ever been arrested compared to those who had not. Two of
the family measures significantly predicted dropout status:
parent school involvement (OR = 0.88) and parent expectations (OR = 3.45). The odds of dropping out decreased by
13% for each unit increase in parent school involvement and
increased by 245% for each unit increase in negative parent expectations. Finally, one school measure significantly
predicted school dropout status—getting along with teachers
and other students (OR = 0.72). The likelihood of dropping
out decreased by 39% for every unit increase in getting along
with teachers and other students.
Results of the final multivariate model are shown in
Table 3. Four predictors remained significantly associated
with the likelihood of school dropout in the final model. Two
individual measures significantly predicted dropout: grades
155
(OR = 0.51) and risk behaviors (OR = 1.45). The odds of
dropping out of school decreased by 96% for each one unit
increase in grades and increased by 45% for each one unit
increase in risk behaviors. One family measure significantly
predicted dropout in the final model: parent expectations (OR
= 2.83). The odds of dropping out increased by 183% for
each one unit increase in negative parent expectations. Finally, one school experience measure significantly predicted
dropout: getting along with teachers and other students (OR
= 0.82). The odds of dropping out decreased by 22% for each
one unit increase in getting along with teachers/students.
DISCUSSION
Students with LD face an increased risk of school dropout
compared with students without disabilities. However, far
less is currently known about the factors and processes
that predict school dropout for students with LD than is
known about factors predicting dropout for adolescents in
the general population. The purpose of the current study was
to add to the research base on predictors of school dropout
among students with LD. The findings indicated that the
most salient predictors of school dropout among students
with LD included individual, family, and school factors.
Notably, fixed attributes related to sociodemographic characteristics of students did not uniquely contribute to school
dropout and did not remain in the final multivariate model.
These results are somewhat surprising given the significant
direct association often reported between sociodemographic
variables and dropout in the general population. But research
pertaining to students with high incidence disabilities has not
consistently found an association between these variables
and dropout (Dunn et al., 2004; Wagner et al., 2005;
Zablocki & Krezmien, 2013). These findings are promising
in the sense that they suggest that alterable variables such
as students’ individual behaviors, skills, dispositions, and
experiences within the contexts of families and schools may
be more important determinants of school dropout than are
fixed attributes and traits.
Two individual factors, grades and engagement in risk
behaviors, were significant in the final multivariate model.
The finding that grades were predictive of school dropout
are consistent with prior research of students with high incidence disabilities and with students own reports about why
they dropout of school (Bounds & Gould, 2000; Kortering & Braziel, 1999a,b; Scanlon & Mellard, 2002; Zablocki
& Krezmien, 2013). The importance of grades in predicting school dropout for students with LD reinforces the
need to identify strategies designed to improve the academic skills of students with LD. Dynarski et al. (2008)
identified several evidence-based academically focused recommendations for students in general that have moderate
support in preventing school dropout. These recommendations include providing academic support, personalized instruction, and rigorous and relevant instruction. The findings of this study support the need for these types of efforts
for students with LD. Our findings also highlight the importance of including grades as an indicator within early
warning systems that schools and districts are encouraged to
156
DOREN ET AL.: PREDICTORS OF DROPOUT AMONG STUDENTS WITH LD
TABLE 2
Results of Univariate Logistic Regression Model
95% CI
Demographics
Race
Caucasian vs. African American
Caucasian vs. Hispanic
Caucasian vs. other race
Gender
Poverty
English language proficiency
Individual risks/skills
Self-determination
Academic Knowledge
Synonym/antonym
Passage comprehension
Calculation
Applied problems
Self-concept confidence
Classroom behavior scale
Self-advocacy
Grades
Number of absences
Number of suspensions, expulsions
Social skills scale
Risk scale
Ever been arrested
Family
School support youth received from adults
Parent school involvement
Parent involvement in SPED/transition
Parent expectations
School Experiences/Context
Getting along with teachers and other
students
Language arts instruction
Math instruction
Study skills instruction
Number of modifications
Class size
Special education class
Est.
SE
t
p
OR
LB
UB
−0.214
−0.401
−0.810
−0.227
0.189
−0.254
.374
.384
.504
.288
.300
.757
−0.57
−1.04
−1.61
−0.79
0.63
0.34
.567
.297
.108
.430
.529
.738
0.81
0.67
0.44
0.80
1.21
0.78
0.39
0.32
0.17
0.45
0.67
0.18
1.68
1.42
1.20
1.40
2.18
3.42
−0.179
.018
1.01
.315
0.98
0.95
1.02
−0.010
−0.009
−0.013
−0.017
−0.005
−0.105
−0.173
−0.879
0.062
0.011
−0.172
0.362
1.093
.011
.009
.009
.010
.029
.057
.168
.144
.034
.074
.039
.122
.315
−0.91
−0.93
−1.53
−1.67
−0.16
−1.86
−1.03
−6.10
1.81
0.15
−4.32
2.97
3.48
.363
.357
.128
.098
.869
.065
.305
<.001
.071
.880
<.001
.003
<.001
0.99
0.99
0.99
0.98
1.00
0.90
0.84
0.42
1.06
1.01
0.84
1.44
2.98
0.97
0.98
0.97
0.96
0.94
0.81
0.60
0.31
0.99
0.88
0.78
1.13
1.61
1.01
1.01
1.00
1.00
1.05
1.01
1.17
0.55
1.14
1.17
0.91
1.82
5.53
0.059
−0.124
−0.413
1.238
.094
.053
.420
.205
0.63
−2.31
−0.98
6.01
.528
.021
.327
<.001
1.06
0.88
0.66
3.45
0.88
0.80
0.29
2.30
1.28
0.98
1.51
5.16
−0.334
.080
−4.18
<.001
0.72
0.61
0.84
−0.210
0.575
0.181
−0.057
−0.013
0.596
.741
.676
.315
.073
.018
.391
−0.28
0.85
0.57
−0.78
−0.78
1.52
.778
.397
.566
.439
.434
.130
0.81
1.78
1.20
0.94
0.99
1.82
0.19
0.47
0.65
0.82
0.95
0.84
3.47
6.69
2.22
1.09
1.02
3.91
Est. = estimate, SE = standard error, t = t-value, p = p-value, OR = odds ratio, CI = confidence interval, LB = lower bound, UB = upper bound.
use to identify struggling students at-risk of school dropout
(Therriault, O’Cummings, Heppen, Yerhot, & Scala, 2013).
Some researchers speculate that grades may not only represent mastery of course content, but additional nonacademic
skills such as motivation, study skills, and perseverance
(Farrington et al., 2012). This perspective suggests that
grades carry additional information about students that can
be targeted in prevention and intervention efforts.
Engagement in risk behaviors remained a salient predictor
of school dropout for students with LD in the study sample.
Several studies found an association between engagement
in risk behaviors and lack of school bonding and disengagement, which in turn has been linked to school dropout
(Henry, Knight, & Thornberry, 2012; Reschly & Christenson, 2006). Although causal linkages were not disentangled
in this study, prior research indicates that the link between
risk behaviors and school disengagement may be connected
to school dropout for students with LD. One implication of
this potential linkage is that interventions targeting school
disengagement may reduce engagement in risk behaviors,
which in turn may reduce the likelihood of dropping out.
However, because of the ambiguity of the directionality of
this relationship, interventions may be more effective if both
indicators of school disengagement and engagement in risk
behaviors are targeted.
Parent expectations of their child’s future success was the
sole family predictor that remained significantly associated
with school dropout in the final multivariate model. Few studies have examined family factors as determinants of dropout
among students with LD. However, there are corollary findings pertaining to the important relationship between parent
expectations and a variety of postschool outcomes of students
with disabilities in general (Carter, Austin, & Trainor, 2011;
Doren, Gau, & Lindstrom, 2012). The findings from this
study are important because parent expectations predicted
school dropout among students with LD after controlling for
LEARNING DISABILITIES RESEARCH
TABLE 3
Results of Final Multivariate Logistic Regression Model
95% CI
Est.
SE
t
p
OR LB UB
Age
0.260 .109 2.38 .017 1.30 1.05 1.61
Grades
−0.676 .171 −3.95 <.001 0.51 0.36 0.71
Parent expectations
1.041 .207 5.02 <.001 2.83 1.89 4.25
Risk scale
0.373 .136 2.74 .007 1.45 1.11 1.90
Getting with teachers/students −0.195 .094 −2.07 .038 0.82 0.68 0.99
Est. = estimate, SE = standard error, t = t-value, p = p-value, OR = odds
ratio, CI = confidence interval, LB = lower bound, UB = upper bound.
other important factors and processes in multiple contexts.
Some researchers posit that parent expectations influence
their child through the parent’s behavior and activities (e.g.,
Alexander et al., 1997). Thus, parents with high educational
expectations may be parents that are more likely than those
with low expectations to advocate for their child’s educational
needs, provide educational support, and be more positively
involved in their child’s schooling (Alexander et al., 1997).
However, in this study, level of parent involvement in these
activities did not remain a salient predictor of school dropout.
Other researchers suggest that parent expectations may be
transmitted to their child more subtly through verbal or nonverbal messages and these messages may be internalized by
the child, and in turn, influence his/her own expectations,
attitudes, and behaviors (e.g., Eccles & Wigield, 2002). One
implication of this view is the need for prevention and intervention strategies to provide parents with knowledge and
skills to explore and reframe negative attitudes, messages,
and behaviors related to the expectations of their child’s future. The findings also highlight the importance of equipping
parents with explicit support strategies (including passive
and active verbal and behavioral support) to encourage their
child to stay in school. Moreover, intervention and prevention efforts can target students themselves by equipping them
with the knowledge and skills to buffer the effects of negative expectations. This could include matching students at
risk of dropout with a supportive adult in the school, and
providing students with curricula, instruction and opportunities to promote positive self-concept and self-determination
(Christenson & Thurlow, 2004; Eisenman, 2007).
Finally, students’ perceptions of the quality of their relationships with teachers and peers were associated with school
dropout for students with LD. This finding is consistent with
those reported by Seidel and Vaughn (1991) who found that
students with LD who dropped out of high school reported
having trouble with teachers and their peers. By contrast,
having a good relationship with teachers and peers has been
found to promote school completion among youth with LD
(Murray & Naranjo, 2008). Positive relationships with teachers and peers have been identified as critical to a student’s
sense of belonging and acceptance in school, which in turn
has been associated with higher levels of school engagement and negatively associated with school disengagement
and dropping out (Reschly & Christenson, 2006). The finding suggests that interventions directed at training teachers
157
to improve their interactions with students in the classroom
could be an effective strategy in dropout prevention efforts.
Furthermore, interventions that target students’ social skills
and encourage participation in social activities may facilitate
positive relationships between students with LD and their
teachers and peers.
It is important to note that the original intention of this
investigation was to examine the unique contribution and
relative importance of a constellation of factors within and
across settings. Our findings indicated key factors within the
individual, family, and school emerged as more salient than
others to explain dropout status among this population. Thus,
one implication of this work is the validation that multicomponent prevention and intervention efforts that target the multiple settings of a student’s context may be more efficacious
than efforts that target a singular setting. The findings provide specific targets for prevention and intervention efforts
that may be particularly relevant for students LD. Furthermore, the findings suggest that focusing on the four factors
emerging from this study may be an efficient way to identify
and screen students with LD who are at most risk of dropping
out.
Limitations and Future Research
This study has several limitations that should be noted when
interpreting the findings. First, as with any secondary analysis, the study is constrained by the design of the NLTS2
and the items available within the dataset. Second, the findings may not generalize to students with LD from low-income
families. As noted in the method section, a significantly lower
percentage of participants who met our inclusion criteria for
the study reported making an annual income of $25,000 or
less compared with participants excluded from the study.
Third, although our intent was to select a comprehensive
set of factors to predict dropout among students that was
grounded in prior research, there may be other measures that
may significantly influence dropout among this population
that we did not select. Fourth, the NLTS2 was not designed
specifically to investigate school dropout among students
with LD, and a prospective study of school dropout and LD
would potentially include different variables than those gathered through NLTS2. Thus, future research focused specifically on a comprehensive understanding of school dropout
among students with LD is certainly warranted. Fifth, we did
not investigate whether the factors we selected may function
as mediators or moderators of dropout and such an investigation would provide insight into the complexity of school
dropout among students with LD. Finally, the significant statistical relationships found within this study are correlational
and therefore, no inferences about causal relationships should
be attributed to the results.
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About the Authors
Bonnie Doren, Ph.D., is an Assistant Professor of Special Education at the University of Wisconsin-Madison.
Christopher Murray, Ph.D., is a Professor of Special Education at the University of Oregon.
Jeff M. Gau, M. S., is a Data Analyst at the Oregon Research Institute.

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