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Effects of Check and Connect on Attendance, Behavior, and Academics: A Randomized Effectiveness Trial


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Objectives This study examined the effects of Check & Connect (C&C) on the attendance, behavior, and academic outcomes of at-risk youth in a field-based effectiveness trial. Method A multisite randomized block design was used, wherein 260 primarily Hispanic (89%) and economically disadvantaged (74%) students were randomized to treatment or control conditions within 14 urban middle and high schools. The social service organization Communities In Schools implemented C&C in each of the schools, and the effects were compared to those of typical Communities In Schools services. Hierarchical linear modeling was used to account for the nested or random school-level effects when modeling student-level responses to the intervention. Results Controlling for pretest performance and all relevant student- and school-level characteristics, C&C was significantly related to improvements in academic performance and reductions in disciplinary referrals. No significant effects were found for attendance. Conclusions C&C is a promising intervention to improve outcomes for at-risk youth in school settings. Application to social work practice and research are discussed.
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Research on Social Work Practice
The online version of this article can be found at:
DOI: 10.1177/1049731513497804
published online 8 September 2013Research on Social Work Practice
Brandy R. Maynard, Elizabeth K. Kjellstrand and Aaron M. Thompson
Effects of Check and Connect on Attendance, Behavior, and Academics: A Randomized Effectiveness
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Research Article
Effects of Check and Connect on
Attendance, Behavior, and Academics:
A Randomized Effectiveness Trial
Brandy R. Maynard
, Elizabeth K. Kjellstrand
, and
Aaron M. Thompson
Objectives: This study examined the effects of Check & Connect (C&C) on the attendance, behavior, and academic outcomes of
at-risk youth in a field-based effectiveness trial. Method: A multisite randomized block design was used, wherein 260 primarily
Hispanic (89%) and economically disadvantaged (74%) students were randomized to treatment or control conditions within
14 urban middle and high schools. The social service organization Communities In Schools implemented C&C in each of the
schools, and the effects were compared to those of typical Communities In Schools services. Hierarchical linear modeling was
used to account for the nested or random school-level effects when modeling student-level responses to the intervention.
Results: Controlling for pretest performance and all relevant student- and school-level characteristics, C&C was significantly
related to improvements in academic performance and reductions in disciplinary referrals. No significant effects were found for
attendance. Conclusions: C&C is a promising intervention to improve outcomes for at-risk youth in school settings. Application
to social work practice and research are discussed.
Check & Connect, dropout, attendance, randomized trial
Educational achievement and school completion are basic
components of the healthy development of children and youth
and of the success of young adults across their lives. Further, to
be competitive in global markets, local, state, and national
economies need an educated and skilled workforce. Despite the
obvious benefits of academic success and school completion
for both youth and society, too few students graduate from high
school—particularly low-income students and students in
racial and ethnic subgroups. For the class of 2007–2008, the
proportion of public high school freshman who graduated with
a regular diploma within 4 years of entering high school was
74.9%, with state averages ranging from 51.3% to 89.6%
(Chapman, Laird, & KewalRamani, 2010). In 2008, the event
drop-out rate—an estimate of students who left high school
without earning a high school diploma or passing the General
Educational Development tests—was 3.5%. Higher rates were
found for Black (6.4%) and Hispanic (5.3%) students compared
to White (2.3%) students. Students living in low-income fam-
ilies were also more likely to drop out of school. These students
dropped out at a rate of 8.7% compared to 2.0% of students in
high-income families (Chapman et al., 2010). In 2008, the sta-
tus drop-out rate, which reflects the percentage of individuals
in a given age range who are not in high school and have not
earned a high school diploma or passed the General Educa-
tional Development tests, was reported at 8%, or approximately
3 million 16- to 24-year-olds. Status drop-out rates were higher
for males than for females and higher for Black and Hispanic
students than for White students (Chapman et al., 2010).
Although drop-out rates in the United States have been trending
downward since 1972 (Chapman et al., 2010), current rates remain
a serious social and economic issue. Dropout and poor academic
achievement are related to numerous negative outcomes for indi-
viduals and society. High school dropouts typically have poorer
physical and mental health (Vaughn, Salas-Wright, & Maynard,
in press), are less likely to get a job, and earn significantly less rela-
tive to those who complete high school (Rouse, 2007). High school
dropouts also are more likely to engage in criminal activity, be
arrested, and be incarcerated (Bureau of Justice Statistics, 2004;
Lochner & Moretti, 2004); less likely to engage in civic activity
(Dee, 2004; Milligan, Moretti, & Oreopoulos, 2004); and less
School of Social Work, Saint Louis University, St. Louis, MO, USA
Department of Counseling, Leadership, Adult Education, & School Psychol-
ogy, Texas State University, San Marcos, TX, USA
School of Social Work, University of Missouri, Columbia, MO, USA
Corresponding Author:
Brandy R. Maynard, School of Social Work, Saint Louis University, Tegeler Hall,
3550 Lindell Boulevard, St. Louis, MO 63103, USA.
Research on Social Work Practice
00(0) 1-14
ª The Author(s) 2013
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likely to report positive well-being (Oreopoulos & Salvanes, 2011;
U.S. Department of Commerce, 2009) than those who graduate
from high school. Moreover, it is estimated that high school drop-
outs cost society close to $240,000 over their lifetime in lower tax
contributions, higher reliance on social welfare, and higher rates of
criminal activity (Chapman et al., 2010; Levin & Belfield, 2007;
Rouse, 2007). The high costs of dropout to individuals and societ-
ies; the rates at which U.S. high school students drop out; and the
increased emphasis on academic achievement, test scores, and gra-
duation through federal initiatives and the No Child Left Behind
Act have highlighted the need to further understand predictors of
dropping out as well as develop interventions to reduce dropout.
Although the causes of dropout are complex and several factors
have been implicated, four of the most salient malleable student-
level factors linked to dropout are academic achievement, atten-
dance, engagement, and behavior (Rumberger, 2011). Poor grades
and course failures are highly predictive of dropout. In a study of
students in the Chicago Public School system, Allensworth and
Easton (2007) found that grade point average and course failures
were the most accurate predictors of nongraduates. Other studies
have produced similar findings (see Balfanz, Herzog, & Mac Iver,
2007; Kurlaender, Reardon, & Jackson, 2008; Silver, Suanders, &
Zarate, 2008). Rumberger and Lim’s (2008) review of the literature
found that grades were a more consistent predictor of dropout than
test scores, concluding that ‘grades are a more robust measure of
academic achievement than test scores’ (p. 19), because grades
reflect both effort and ability throughout the school year, whereas
test scores reflect ability measured on one or two days.
Attendance, engagement, and school behavior are additional
factors consistently linked to dropout. For example, students who
are regularly absent from school are more likely than consistent
attenders to drop out (Henry, Knight, & Thornberry, 2012). In a
study of ninth-grade students in the Chicago Public Schools, atten-
dance was found to be highly predictive of course failure and drop-
out (Allensworth & Easton, 2005). In fact, freshman absences
were 8 times more predictive of course failure than eighth-
grade test scores and correctly identified nongraduates 77% of the
time. In a review of 25 years of research related to why students
drop out of school, Rumberger and Lim (2008) reported that
the majority of the 35 studies measuring attendance found that
high absenteeism predicted dropout. Likewise, engagement—
often reflected by attendance among other social, behavioral,
emotional, and cognitive indicators—has emerged as a strong
predictor of dropout (Fall & Roberts, 2011; Rumberger, 2011;
Rumberger & Lim, 2008). Of the 60 studies in Rumberger and
Lim’s review, the majority found that engagement signifi-
cantly predicted dropout. In addition, school misbehavior is
associated with dropout. That is, students who misbehave in
school are more likely to drop out than students without disci-
plinary problems (Ou, Mersky, Reynolds, & Kohler, 2007;
Rumberger & Lim, 2008).
Intervening With Students at Risk of Dropping Out
Numerous programs have been designed to improve achievement,
attendance, engagement, and behavior for students at risk of
dropout. For school practitioners, identifying the most effective
programs for reducing dropout and improving graduation rates can
be a daunting task. To assist school practitioners in locating
evidence-based interventions, national databases such as the What
Works Clearinghouse, Blueprints for Violence Prevention, and the
National Dropout Prevention Center are available. In addition, sys-
tematic reviews and meta-analyses have become more prevalent as
a means to assist practitioners in making evidence-based decisions.
The Campbell Collaboration is an international research network
that prepares, maintains, and disseminates systematic reviews in
education, social welfare, crime and justice, and international
development to help practitioners and policy makers make well-
informed decisions (see The
Campbell Collaboration has produced and published on its web-
site several systematic reviews of education and social welfare
interventions relevant to dropout and related risk behaviors (see
Maynard, McCrea, Pigott, & Kelly, 2012, 2013; Wilson,
Tanner-Smith, Lipsey, Steinka-Fray, & Morrison, 2011). One
large systematic review published by the Campbell Collaboration
examined effects of dropout intervention programs (Wilson et al.,
2011). Findings from this review indicated that dropout programs
were on average effective in reducing dropout regardless of the
type of program. In addition to national databases and research
synthesis to help guide intervention dissemination, some dropout
prevention programs have been developed and disseminated on a
national scale. One of the largest and most widely disseminated
dropout prevention programs is Communities In Schools (CIS;
CIS, founded in 1977 by Bill Milliken, is a nonprofit, nation-
wide network of nearly 200 independent affiliates delivering a
dropout prevention and intervention model in more than 3,000
schools across 28 states. The CIS network communicates the
practice model by coordinating national, state, and local affili-
ate efforts around a mission to ‘surround students with a com-
munity of support, empowering them to stay in school and
achieve in life’ (Communities In Schools, n.d., heading sec-
tion). The CIS model uses school-based case managers, here-
after referred to as site coordinators, to develop community
partnerships, bring local resources to school campuses, and pro-
vide direct services to schools and students at risk of dropout.
CIS site coordinators function as a main point of contact for
students and their families, connecting them with resources and
supports to address both academic and nonacademic needs. As
a primary element of the CIS model, site coordinators conduct
annual needs assessments to identify the school’s and its stu-
dents’ risk factors. The needs assessments are then used to
select evidence-based services and organize schoolwide and
individualized student service plans to diminish those con-
cerns. School-level services include schoolwide activities or
services accessible to all students, regardless of risk status, such
as career days, college awareness activities, uniform or school
supply assistance, and social service assistance. Individua-
lized case management services provided to at-risk students
include basic needs and resources, academic assistance (tut-
oring), mentoring, enrichment and motivation, life skills and
social development, family engagement and strengthening,
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behavior interventions, community service and service learn-
ing, college and career preparation, and professional health and
mental health services (CIS, 2011; ICF International, 2010).
CIS uses student and school performance data (i.e., grades,
promotion rates, graduation rates) to report outcomes at the
local affiliate, state, and national levels. The most recent CIS
national annual report suggested that among students exposed
to CIS case management services, 97% stayed in school, 84%
were promoted to the next grade, and 88% of eligible seniors
graduated (CIS, 2011). The data reported in the CIS national
annual report, however, are aggregate data from CIS affiliates
reported to CIS national; the method by which CIS collects
and analyzes the data reported is unclear. In 2011, the results
of a 5-year national evaluation of CIS were released, which
included a quasi-experimental and three randomized studies
conducted in Austin, Texas; Wichita, Kansas; and Jackson-
ville, Florida (ICF, 2010). The quasi-experimental study—
which comprised 602 CIS schools matched with 602 comparable
non-CIS schools across the United States, using a propensity
score matching method—examined school-level effects of CIS.
Small, but positive effects on dropout, attendance, and some aca-
demic outcomes were found (ICF, 2010). Effects varied, how-
ever, based on the CIS affiliates level of implementation, with
larger effects found at high-implementing sites on many out-
comes. For example, standardized mean difference effect sizes
for dropout (.21) and graduation (.08) rates were smaller when
calculated with data from all CIS schools than effect sizes cal-
culated for high-implementer sites (.36 for dropout and .31 for
graduation rates; ICF, 2010).
Although positive effects were found in the quasi-experimental
study, results from the three randomized studies were mixed on
most outcomes measured. From baseline to end of year 1, signif-
icant positive effects for dropout were found in Austin; positive,
though not significant effects were found in Jacksonville; and null
or negative effects were found in Wichita. On attendance, null or
negative effects were found in Jacksonville and Wichita, and
positive, significant effects were found in Austin. Similarly,
positive, but not significant, effects on behavioral problems
were found in Jacksonville, but negative or null effects were
observed in Austin and Wichita. Effects for academics and stu-
dent attitude and behavior outcomes were similar (i.e., signif-
icant; positive, but not significant; and null or negative), both
within sites and between sites (ICF, 2010). Although this
national 5-year evaluation, which used quasi-experimental and
randomized designs, was an improvement over reports of
within-group change from data received from local affiliates
or states, the evaluation did not meet the What Works Clearing-
house evidence criteria standards, indicating substantial
threats to internal validity and thus could not be fully reviewed
(see Institute of Education Sciences, n.d.).
The national office of CIS, recognizing that needs and issues
of students, schools, and communities vary across regions and
states, encourages affiliates to use evidence-based interven-
tions to address local school and individualized student needs.
CIS specifies the service delivery model and process; however,
the CIS model does not prescribe the specific interventions.
Thus, local affiliates are able to select empirically supported
interventions based on school and student needs and staff pro-
fessional expertise and preference.
One local CIS affiliate in Texas sought an intervention to
reduce attendance problems—a significant risk factor predicting
dropout. The local affiliate partnered with The Meadows Center
for Preventing Educational Risk to implement a dropout and
engagement intervention, Check & Connect (C&C). C&C was
selected, because it was an empirically supported intervention;
had been successfully implemented in a prior study with research-
ers from The Meadows Center for Preventing Educational Risk;
was recognized by CIS as an exemplary program to reduce drop-
out (Hammond, Linton, Smink, & Drew, 2007); and aligned with
local goals to improve attendance, behavior, and academics.
C&C (Christenson, Sinclair, Thurlow, & Evelo, 1999; Sin-
clair, Christenson, Evelo, & Hurley, 1998) is a widely used inter-
vention that is often cited in the literature as a promising
intervention for improving school engagement and reducing
dropout (Alvarez & Anderson-Ketchmark, 2010; Kelly, Raines,
Stone, & Frey, 2010; Lehr, Johnson, Bremer, Cosio, & Thomp-
son, 2004; Stout & Christenson, 2009). The What Works Clear-
inghouse reviewed evidence of C&C on two occasions related to
(1) dropout prevention and (2) students classified as emotion-
ally disturbed (What Works Clearinghouse, 2006, 2011). With
regard to dropout prevention, two studies of C&C met evidence
standards for reducing dropout—with one study meeting stan-
dards and the other meeting standards with reservations. What
Works Clearinghouse rated C&C as having positive effects on
staying in school, potentially positive effects on progressing in
school, and no discernible effects on completing school. Four
additional studies were identified for the dropout review; how-
ever, those studies did not meet relevance (conducted with ele-
mentary students and outcomes were not relevant to the
review) or evidence (no control group or nonequivalent com-
parison group) standards to be considered in the review. With
regard to students classified as emotionally disturbed, 24 stud-
ies were reviewed; however, no studies met What Works Clear-
inghouse standards of relevance for the review or evidence for
improving outcomes. A recent, but yet unpublished, rando-
mized study on the effects of C&C on school engagement
found positive effects on behavioral and psychological engage-
ment but not for cognitive engagement (Roberts, Vaughn,
Vaughn, Wexler, Coleman, & Maynard, in press).
In summary, both CIS and C&C are well-known and widely
adopted interventions to improve attendance and engagement
among students at risk of school dropout. However, indepen-
dent researchers have not rigorously evaluated either program.
All of the underlying evidence supporting the efficacy of CIS
was commissioned by the CIS national office or derived from
within-group program evaluation designs reported by local CIS
affiliates. Furthermore, the one study of CIS that was reviewed
by What Works Clearinghouse did not meet evidence criteria.
As such, many of the claims that CIS is associated with impr-
ovements in engagement, attendance, behavior, and academic
performance lack internal validity to make any causal infer-
ences. Similarly, the C&C program developers have conducted
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the few rigorous studies evaluating the effects of C&C. To
bridge this critical gap in the literature, the present study used
a randomized design to examine the effects of CIS compared to
CIS plus C&C on attendance, academics, and behavior for
youth referred for absenteeism.
Purpose of the Present Study
Absenteeism was a significant problem for the schools served
by a local CIS affiliate in Texas. To address this concern, CIS
staff members partnered with The Meadows Center for Pre-
venting Educational Risk at The University of Texas at Austin
to implement and conduct a field-based trial of C&C with stu-
dents exhibiting absenteeism. As such, the study reported here
positions the observations associated with C&C within the
broader CIS service delivery model. That is, all participating
CIS schools implemented universal, schoolwide interventions
and individualized case management services to improve aca-
demics, behavior, and attendance. However, within participat-
ing CIS schools, students were randomly placed in one of the
two conditions: (1) CIS services plus C&C or (2) CIS-only ser-
vices. The research question guiding this study is as follows:
Are there differences in effects on attendance, academics, and
behavior for students who receive C&C in addition to CIS from
those who receive only CIS services?
Study Design and Procedures
Design. This study used a randomized block design to examine
the effectiveness of C&C on academic performance, behavior,
and attendance with at-risk middle and high school students
during the 2011–2012 school year. Eligible students were ran-
domly assigned to the treatment or control condition within
each of the 14 participating CIS schools (9 middle schools, 4
high schools, and 1 middle/high school). We randomized stu-
dents to condition within schools, as opposed to randomizing
schools to condition, for several reasons. Because experiments
estimate the average causal effect on the units that have been
randomized, and we were interested in estimating effects on
student outcomes, it was necessary to randomize students to
condition. Moreover, randomizing at the lowest level possible
allows for more precise estimates and greater power to detect
effects (Rhoads, 2011; Shadish, Cook, & Campbell, 2002).
Recruitment of study participants occurred at each school
between September and November 2011. The intervention was
delivered between November 2011 and May 2012.
Study Inclusion Procedures. Study inclusion criteria were applied
at both the school and the student levels. At the school level,
three factors determined school inclusion: (1) the school had
contracted with CIS to provide case management and dropout
prevention services, (2) the school was either a middle or high
school, and (3) the school approved both the implementation of
the C&C program and the study to assess the effectiveness of
the program.
At the student level, CIS site coordinators and school staff
members identified students to participate. To be eligible for par-
ticipation in this study, students must have (1) not been previously
enrolled in CIS services, (2) met eligibility criteria for CIS ser-
vices at the time of study enrollment (i.e., met one or more of the
criteria on the Texas Education Agency at-risk eligibility list or
referred for family crisis; Texas Education Code §33.151 and
§29.081), and (3) demonstrated absenteeism (defined by 20 or
more absences during the prior school year or 2 or more absences
during the previous month). After school staff members identified
and referred students to CIS, the CIS site coordinators at each
school provided information to the eligible students and parents
about CIS services and the study, including information about
participants’ rights to not participate in the study and voluntarily
terminate participation at any time. For students from whom both
parent consent and student assent were obtained, CIS conducted
its standard assessment, which was not part of the study, with all
participating students. Following the assessment, students were
randomly assigned to treatment or control conditions.
Randomization Procedures. The institutional review board of the
university where the study originated, the CIS affiliate, and
administrators in all participating schools approved the study
and randomization procedures. Randomization occurred within
schools at the student level and was conducted by using an
online random number generator (Haahr & Haahr, 2010). Stu-
dent identification numbers (N ¼ 260) were entered into the
program that randomly generated numbers. Students were then
sorted in ascending order of the randomly generated numbers.
The list was then divided in half, with the first half assigned to
the CIS plus C&C treatment group (n ¼ 134) and the second
half assigned to the CIS-only control group (n ¼ 126). An
on-site CIS coordinator trained on C&C acted as the C&C
‘monitor,’ delivering C&C along with typical CIS services
to students randomly assigned to the treatment group. A second
site coordinator in each school who did not participate in the
C&C training delivered only typical CIS services to students
randomly assigned to the control condition.
Intervention Procedures. Because all participants received CIS ser-
vices, this study examined the benefit of combining C&C with
CIS compared to CIS alone. C&C is a manualized dropout pre-
vention and intervention program originally developed to reduce
drop-out rates for middle school students with emotional and
behavioral disabilities (Sinclair et al., 1998). C&C is designed
to promote students engagement in school through a targeted and
individualized approach (for a full description of C&C, see http:// and Sinclair et al., 1998). The C&C
model comprisestwo primarycomponents. The check component
involves regularly monitoring studentdata related to alterable risk
indicators. The connect component involves building relation-
ships with students and families and facilitating basic or intensive
interventions based on student data.
The C&C model is delivered by an adult ‘monitor who uses a
case management approach to work with students and their fam-
ilies for the duration of the intervention. The primary goal of the
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monitor is to keep education a salient issue for the student, his or
her family members, and teachers, and to reduce and prevent the
occurrence of absenteeism, suspensions, failing grades and other
warning signs of school withdrawal (Sinclair et al., 1998, p. 10).
The monitor works with students and families on his or her case-
load to promote school engagement by building and maintaining
relationships, monitoring student data related to alterable risk
indicators, and implementing individualized interventions with
students and families, based on the data.
In this study, CIS site coordinators acted as the C&C moni-
tors. The C&C monitors were school-based practitioners who
had been employed with CIS for a mean of 4.38 years. All
C&C monitors were female (100%) with education and experi-
ence in the fields of psychology, counseling, or social work.
Most of the C&C monitors had a master’s degree (64%) and the
remaining 36% held a bachelor’s degree. None of the C&C
monitors had delivered the C&C intervention prior to the train-
ing for the present study. The monitors provided C&C to up to
12 students, in addition to providing CIS services to their reg-
ular caseload. The monitors recorded attendance, tardiness,
behavioral referrals, and academic performance weekly on a
form adapted from Sinclair et al. (1998) and sent the form to
the CIS program manager for fidelity monitoring. The data
were used to provide feedback to students, determine students’
level of risk, and develop and implement individualized inter-
ventions based on risk indicators. Monitors met with students
weekly to discuss progress, discuss the importance of staying
in school, and assist students with problem solving related to
current or ongoing issues (Sinclair et al., 1998). For students
exhibiting high risk on the indicators being monitored, indivi-
dualized interventions were developed according to student
risk factors and needs. The student and C&C monitor devel-
oped these additional interventions, based on the monitor’s pro-
fessional expertise and judgment, student and family input, and
resources in the school and community (Sinclair et al., 1998).
Training. All CIS site coordinators selected for implementation
of the intervention received a full-day training on the C&C
intervention. The training consisted of didactic components and
role-playing. In addition, the session provided training on pro-
cesses and requirements of the research, including protection
of human subjects, informed consent, study and data collection
procedures, and problem solving regarding implementation bar-
riers. Additionally, interventionists were provided with a half-
day booster training and support session in December 2011, and
the co-principal investigator was available throughout the study
period to provide consultation and assistance as needed.
Fidelity. Due to limited resources, we could not comprehensively
or rigorously measure fidelity of implementation; however, we
used several strategies to promote and monitor fidelity over the
course of the study to ensure the intervention was being imple-
mented as intended. C&C is a well-specified intervention with
an intervention manual that provides for standardization,
reduces variability of implementation, and provides sufficient
information for the intervention to be replicated and compared
across the studies, thus enhancing both internal and external
validity (Gearing, El-Bassel, Gesquire, Baldwin, Gillies, &
Ngeow, 2011; Smith, Daunic, & Taylor, 2007). As mentioned
previously, all CIS site coordinators who implemented C&C
were trained and monitored to enhance the competence and
adherence of the implementers, reduce implementer drift, and
correct any deviations from the intervention in real time over
the course of the study (Bellg et al., 2004; Gearing et al.,
2011; Perepletchikova & Kazdin, 2005).
All students participating in this study met Texas Education
Agency criteria for eligibility for CIS services (see Texas Edu-
cation Code sections 29.081 and 33.151). Participating students
averaged 5.06 days absent and .23 disciplinary referrals at base-
line. About half (56%) of the participants were female, and the
majority (89%) of the students were Hispanic. The average age
of the participants was 15.1 years, with the majority of students
in grades 6 through 9. Students participating in this study were,
by large, economically disadvantaged as evidenced by the
majority (74%) receiving free or reduced-price lunch.
Attrition. Due to attrition resulting from varied and sometimes
unspecified reasons including, but not limited to mobility,
transfer to alternative schools, early graduation, suspension/
expulsion, incarceration, and other reasons, posttest data were
available for only 189 of the 260 students randomized to the
treatment and control groups. The final analytic sample con-
sisted of 89 students in the treatment group and 100 students
in the control group (see Figure 1). The total (27%) and differ-
ential (13%) attrition rates were relatively high, although not
uncommon in school-based field research with at-risk students
in urban settings (see Wilson & Lipsey, 2006; Wilson et al.,
Analytic Sample. Analysis of selection bias and pretest equiva-
lency of the analytic sample suggested that randomization was
successful; that is, the analytic treatment and control groups
appear statistically balanced at pretest on all demographic char-
acteristics and outcomes. As seen in Table 1, for students rando-
mized to the C&C or the control condition, contingency tables
and w
tests for dichotomous variables suggest the groups were
similar with regard to sex, grade, ethnicity, and free and reduced-
price lunch status. Likewise for continuous variables, t-tests sug-
gest the treatment and control groups were statically balanced at
pretest by age, grade, family income, and on all outcome vari-
ables (i.e., academic performance, discipline, attendance).
Outcomes of interest in this field-based trial were selected
based upon prior studies examining the effects of C&C, the
priorities and interests of CIS, and the availability of data
already being collected by the school or the CIS. It was impor-
tant for us to measure proximal outcomes that did not impose
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additional burdens on the school or staff in terms of time or
money and that practitioners could sustain beyond the involve-
ment of external researchers. More specifically, we focused on
the intervention’s effect on average student academic perfor-
mance (Lehr, Sinclair, & Christenson, 2004; Sinclair et al.,
1998), student behavior (Todd, Campbell, Meyer, & Horner,
2008), and student attendance (Lehr et al., 2004; Sinclair,
Christenson, Lehr, & Anderson, 2003).
Dependent Outcomes. Three dependent variables were used as
outcomes in the present study. First, to assess the effect of the
intervention on academic performance, we generated perfor-
mance composites for all students in the data set, using English,
mathematics, science, and social studies grades. Pretest perfor-
mance composites (a ¼.73) were generated, using grades earned
during the marking period prior to enrollment in the study. Postt-
est performance composites were generated from the grades
earned during the study period (a ¼ .82). Second, to assess the
intervention effects on student discipline, the total count of office
referrals was tallied for each student. That is, the pretest discipline
outcome was generated using the total number of office referrals
for the marking period prior to enrollment in the study; posttest
discipline outcomes consisted of the number of referrals received
during the last marking period. Third, to assess the impact of the
intervention on student attendance, the pretest measure consisted
of the total number of days absent in the marking period prior to
the study, whereas the posttest measure consisted of the number
of days missed during the last marking period.
Covariates. Covariates were modeled at the student and school
levels. At the student level, all models controlled for student free
and reduced-price lunch status, race, sex (nominal, 0 ¼ no;
1 ¼yes), age, grade, and family income. School-level covariates
included school size; the percentage of students considered at
risk, highly mobile, disadvantaged, or of limited English profi-
ciency; and the percentage of students meeting standards on the
state-level achievement test. Following conventions in multile-
vel modeling, all continuous student- and school-level predic-
tors were grand mean centered and each model controlled for
pretest functioning on the outcome of interest (Raudenbush,
Bryk & Congdon, 2002; Singer, 1998).
Analysis Strategy: Hierarchical Linear Modeling (HLM)
Because randomization to treatment and control groups occurred
at the individual level within staff who provided C&C services
in each school setting, the student-level data used to analyze treat-
ment effects were nested within each school to account for varia-
tion in outcomes that may have occurred due to school- or staff-
level effects. In addition, prior research suggests schools have
contextual factors (e.g.,average school-levelpoverty, schoolsize,
mobility) that strongly influence individual student performance
and behavior (Raudenbush, Bryk, & Congdon, 2002; Singer,
1998). Therefore, HLM was used to account for the nested or ran-
dom school-level effects when modeling student-level responses
to the intervention.
A stepwise HLM model estimation procedure was used to fit
each dependent variable (Raudenbush et al., 2002). In Step 1,
an unconditional, fixed-effects model was fit to each outcome
to estimate the intraclass correlation (ICC)—or the proportion
of variance in outcomes attributed to school-level effects. The
ICC for academic performance (r ¼ .09), discipline (r ¼ .15),
and attendance (r ¼ .12) indicated that 9%,15%, and 12% of
the variation in posttest outcomes were attributed to school-
level effects, respectively—sufficient magnitudes to warrant
the use of HLM.
In Step 2, conditional models were fit to test the intervention
effects. In Figure 2, the Level-1 equation fit each outcome
)—average school performance and total number of disci-
pline referrals—for each student (i) in each school (j). Each
estimated outcome was derived from the sum of the intercept
) as a condition of student pretest scores (b
), treatment
assignment (b
), student-level covariates (b
), and a
fixed error term that refers to the unexplained residual within
schools (r
). The Level-2 equation models the random intercept
for the Level-1 equation as a function of the school average
effect (p
) conditioned by school size (p
) and school-level
proportions of students considered disadvantaged (p
), of lim-
ited English proficiency (p
), highly mobile (p
), academi-
cally proficient (p
), and at risk (p
). The Level-2 equation
also models a between-school residual term (e
In Step 3, random slope and intercept models were tested by
fitting cross-level interactions and random effects. Cross-level
interactions were represented by product terms created by multi-
plying the treatment assignment variable (i.e., CIS þ C&C ¼ 1;
CIS only ¼ 0) and student- and classroom-level variables to
examine whether the effects of C&C were moderated by those
characteristics. Random effects and interaction terms were
retained in the models only if significant. All associations were
Figure 1. Participant flowchart. Participant percentages reported at
each stage are calculated using the number of participants in prior
stage. N(n) ¼ number of students; J(j) ¼ number of schools.
6 Research on Social Work Practice 00(0)
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assessed using a two-tailed test and a ¼ .05. Model estimates
were generated in STATA 11.0 (StataCorp, 2005), using
xtmixed and a maximum likelihood estimator—which performs
well when the number of Level-2 units is not large (Bryk & Rau-
denbush, 1992).
In Step 4, following model estimation, effect sizes were esti-
mated for all outcomes significantly associated with C&C
(Cohen, 1988). Effect sizes were calculated for average aca-
demic performance and discipline using approaches suggested
for multilevel data (Spybrook, Raudenbush, Congdon, & Martı´-
nez, 2009). Figure 3 represents the effect size equation, where b
is the multilevel coefficient for the treatment variable on the out-
come of interest, t
is the residual variance between schools, and
represents the residual variance within schools.
The multilevel model estimates are offered in Table 2. No associ-
ation was observed between study conditions for the attendance
outcome. In addition, no significant random effects or associations
were observed for all interactions between C&C and student-level
fixed effects. As such, random effects and product terms were not
retained in the final models and will not be reported here.
Significant associations were observed between study condi-
tions on academic performance and discipline. The pretest cov-
ariate was significant for all outcomes, and—with the exception
of sex (male ¼ 1; female ¼ 0), race (African American ¼ 1;
Hispanic Latino ¼ 0), and average school-level mobility—the
student- and school-level covariates were not significantly asso-
ciated with the dependent variables.
Table 1. Demographic Characteristics and Pretest Equivalence of Treatment and Control Groups.
Total (N ¼ 189) C&C (n ¼ 89) Control ( n ¼ 100)
% N % n % n w
(df) p
Male 44 84 38 34 50 50 2.66 (1) .10
Female 56 105 62 55 50 50
Grade .10
6th 20 37 19 17 20 20 1.71 (6)
7th 15 28 15 13 15 15
8th 21 40 24 21 19 19
9th 23 43 21 19 24 24
10th 7 13 6 5 8 8
11th 6 12 8 7 5 5
12th 8 16 8 7 9 9
Ethnicity .09
African American 11 20 15 13 7 7 2.88 (1)
Hispanic 89 169 85 76 93 93
Free or reduced lunch .52
Yes 74 140 72 64 76 76 0.41 (1)
No 26 49 28 25 24 24
MSD MSDM SDT(df ¼ 187)
Age 15.1 2.02 15.2 0.20 15.0 0.21 0.51 .61
Grade 8.35 1.80 8.34 0.19 8.36 0.18 0.04 .97
Income 42.46 36.69 43.03 3.95 41.35 3.63 0.20 .84
Academic performance 75.63 8.70 76.56 7.73 74.78 9.46 1.36 .17
Discipline 0.23 0.65 0.169 0.65 0.30 0.72 1.38 .17
Attendance 5.06 4.47 4.63 3.83 5.44 4.97 1.25 .22
Note. C&C ¼ Check & Connect; df ¼ degrees of freedom; SD ¼ standard deviation.
Level 1: Y
¼ b
þ b
Level 2 : b
¼ p
þ p
Figure 2. Two-level random intercept model.
Effect size (d) ¼
þ s
Figure 3. Effect size estimate for multilevel data structures.
Maynard et al. 7
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Academic Performance
First, controlling for pretest performance and all relevant stu-
dent- and school-level characteristics, C&C was positively
related to improvements in posttest student academic perfor-
mance, 1.547 (p ¼ .043, 95% CI [.047, 3.048]). That is, on aver-
age, students randomized to the C&C condition, compared to
control students, evidenced 1.547 percentage points higher on
their average academic performance during the study compared
to preintervention levels of performance. In addition, the coeffi-
cients for age, grade, and free and reduced-price lunch were sig-
nificantly associated with the average academic performance.
The coefficient for the grand mean centered variable, age,
1.447 (p ¼ .012, 95% CI [2.575, .321]), suggests that the
average-age student in the study (15.07 years) scored 1.447
percentage points lower on their average academic perfor-
mance during the study compared to preintervention perfor-
mance. The coefficient for grade, 1.418 (p ¼ .05, 95% CI
[2.86, .028]), suggests the students in grade 8, the average
grade of students in the study, scored 1.418 percentage points
higher on their average academic performance during the study
compared to preintervention performance. Finally, the coeffi-
cient for free and reduced-price lunch, 2.773 (p ¼ .01, 95%
CI [4.978, .568]), suggests that students receiving free and
reduced-price lunches scored 2.773 percentage points lower on
their average academic performance during the study com-
pared to preintervention levels.
Discipline Referrals
Controlling for pretest performance and all relevant student-
and school-level characteristics, C&C was negatively associ-
ated with the total number of office referrals received during
the study period compared to the marking period before the
study, .363 (p ¼ .036, 95% CI [.703, .023]). That is, on
average, students randomized to the C&C condition, compared
to control students, had .363 fewer office disciplinary referrals
at posttest. In addition, students who identified as African
American, .544 (p ¼ .088, 95% CI [0.082, 1.17]), compared
to Hispanic Latino students, had .544 more office referrals than
during the preintervention marking period.
Effect Size Estimates and Improvement Index
The effect sizes representing the changes in the dependent vari-
able attributed to C&C were small, according to Cohen’s metric.
For the changes in academic performance, the effect size was d
¼ .07. For the reductions in disciplinary referrals for the study
period compared to the prior marking period, the intervention’s
effect size was d ¼.27. Although no significant effects were
observed for attendance, the effect size was d ¼.01.
To translate effect sizes into terms that illustrate the practical
meaning of the intervention’s effect, we used an improvement
index as suggested by the Institute of Education Sciences
(2008). An improvement index represents the change observed
between the percentile rank of the average student in the inter-
vention group compared to the average student in the control
group. Alternatively, an improvement index can be interpreted
as the expected change that would be observed if the average stu-
dent in the control group were to receive the intervention.
To convert an effect size to an improvement index entails
using a standard normal curve for z-scores. An improvement
index suggests the effect size of .07 for academic achievement
translates into a 3% improvement for the average student in the
intervention compared to a student in the control condition. An
improvement index for a .27 effect size for disciplinary
Table 2. Student-School Fitted Hierarchical Linear Models: The Effects of C&C (N ¼ 189).
Academic performance Discipline Attendance
Level Effect b SE b SE b SE
Student Conditional mean 14.025*** 7.262 2.019** 1.305 13.537* 7.776
Pretest .679*** .052 1.208*** .138 .731*** .123
Age 1.447** .576 .076 .127 .760 .761
Grade 1.418* .738 .188 .154 1.310 .907
Sex .142 .829 .149 .183 .796 1.093
Free lunch 2.773** 1.125 .070 .235 .551 1.392
Income .014 .011 .003 .003 .026* .015
African American 1.072 1.400 .544* .139 1.736 1.905
Tx (C&C) 1.547* .765 .363* .173 .577 1.033
School School size .002 .003 .000 .000 .002 .002
% disadvantaged .017 .263 .009 .039 .235 .224
% LEP .415 .255 .035 .036 .189 .202
% at risk .171 .113 .007 .016 .013 .089
% mobility .414** .155 .021 .021 .062 .117
% average performance .079 .112 .017 .017 .015 .095
Note. C&C ¼ Check & Connect; SE ¼ standard error; academic performance ¼ composite of English, mathematics, science, and social studies; discipline ¼ total
number of office referrals; attendance ¼ total number of days missed; LEP ¼ limited English proficiency. Tx ¼ C&C ¼ 1, control ¼ 0; income and percentage of
disadvantaged students, LEP, at risk, mobility, and average school-level performance all grand mean centered. All hypothesis tests are two tailed.
*p < .10. **p < .05. ***p < .001.
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referrals translates into an 11% reduction in disciplinary refer-
rals for the average student in the C&C group compared to a
student in the control group. Finally, an improvement index for
a .01 effect for attendance translates into a .04% improve-
ment in the average student in the intervention compared to one
in the control condition.
Discussion and Applications to Social Work
High school dropout is a significant social and public health
concern that affects not only dropouts across their life course
but also the society as a whole. The drop-out problem in the
United States has been referred to as a crisis, and substantial
attention and effort have been directed at reducing drop-out
rates (Rumberger, 2011). Although the drop-out rate has
declined over the past 2 decades, too many students continue
to drop out of school. As such, there is a significant need to
develop, implement, test, and refine interventions that seek to
reduce dropout and improve school engagement.
The present study examined the effects of C&C, a dropout
and school engagement intervention, with students at risk of
dropout, as defined by high rates of absenteeism, poor school
behavior, and below-average academic performance—three crit-
ical risk factors in predicting eventual dropout. The current study
adds to the evidence base for C&C, supporting the findings of
prior studies by the developers of C&C (Sinclair et al., 1998).
The current study, being the only independent randomized effec-
tiveness trial of C&C, not only adds rigor to the existing evi-
dence but also was conducted with a predominantly Hispanic
population—a student subgroup that prior studies suggest are
at an increased risk of dropout (Chapman et al., 2010).
The findings summarized here extend empirical support for
C&C as a promising intervention for providing academic and
behavioral supports to at-risk students referred for absenteeism.
Specifically, students who received C&C had better grades and
fewer disciplinary referrals compared to the students in the
control group. However, no significant effects were found for
attendance. Although the findings are mixed and effects are
small, the findings are, in several ways, impressive, consider-
ing that C&C was compared in this trial to another dropout
prevention program, CIS; that C&C was implemented in a
real-world setting by practitioners with minimal support from
the university researchers; and that outcomes were assessed
after one semester of implementation versus the 2 years rec-
ommended by the C&C developers (Sinclair et al., 1998).
Two key outcomes for which we observed statistically signif-
icant effects, student grades and behavior, indicate promise in
preventing dropout, improving protective factors, and reducing
risk factors for at-risk youth. Of the known dropout risk factors,
student grades is the strongest predictor of dropout (Allensworth
& Easton, 2007), and student misbehavior is a well-established
risk indicator for dropout (Battin-Pearson et al., 2000; Rumber-
ger & Lim, 2008). Indeed, prior research on key proximal indi-
cators, such as academic performance and school behavior,
suggest that interventions seeking to alter the cumulative
impact of poor grades and disruptive behavior can positively
influence the developmental sequencing of risk across child-
hood. That is, despite a collection of risk factors beyond the
walls of a school, students who do well academically, have few
behavioral problems in school, and healthy relationships with
peers and teachers tend to experience proximal school success.
Proximal success in school can confer long-term benefits,
which promote the likelihood that students will graduate,
attend college, and participate in labor markets while simulta-
neously decreasing the likelihood that those at-risk students
drop out, subsist on welfare, or engage in criminal behavior
(Burt & Roisman, 2010; Heckman & Kautz, 2012; Wentzel,
2002). As observed in prior school-based prevention studies,
early intervention focused on both academic and nonaca-
demic risk factors may interrupt a cascade of events associ-
ated with economic disadvantage that eventually result in
costly social and health problems by adulthood (Bradshaw,
Reinke, Brown, Bevans, & Leaf, 2008; Bradshaw, Zmuda,
Kellam, & Ialongo, 2009; Hawkins, Kosterman, Catalano,
Hill, & Abbott, 2008). Indeed, as both grades and behavior are
significant risk factors for dropout, improving proximal func-
tioning related to these outcomes may translate into long-term
benefits, such as improved school-completion rates. These
findings are consistent with Sinclair, Christenson, & Thurlow
(2005) finding that youth who received C&C had lower rates
of dropout than a control group.
The absence of significant effects on attendance found in
this study, however, runs contrary to recent experimental stud-
ies of C&C that have measured behavioral engagement, of
which attendance is often considered an indicator. A prior
study observed a large treatment effect on behavioral engage-
ment, as assessed by the School Dropout Risk Inventory, a
self-report questionnaire that estimates students’ likelihood of
dropping out of school, based on dispositional and contextual
sources of risk (Roberts et al., in press). The behavioral engage-
ment subscale of the School Dropout Risk Inventory includes 6
items and measures the extent to which students conformed to
classroom rules and norms, such as completing homework, get-
ting in trouble in school, being absent from school, and skipping
class. Likewise, Sinclair et al. (2005) found that students who
received C&C were more likely to demonstrate greater consis-
tency in school attendance than the control group. In two
single-group pretest–posttest studies of C&C, Lehr et al.
(2004) found a decline in absences among the elementary stu-
dents who received C&C, and Christenson et al. (1999) found
decreased risk based on a composite measure of absences, course
grades, and behavior. The C&C studies that found positive
effects on attendance or behavioral engagement were implemen-
ted for at least 2 years. Because students in the present study
received the C&C intervention for one school semester, it is pos-
sible that C&C treatment effects on attendance require the inter-
vention be sustained over a longer period of time.
The positive effects on grades and behavior and the absence of
effects on attendance found in this study are somewhat puzzling,
given the significant correlation between absenteeism, grades,
and behaviorfound in longitudinal (Henry & Huizinga, 2007) and
population-based (Vaughn, Maynard, Salas-Wright, Perron, &
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Abdon, 2013) studies. As such, we would have expected that if a
reduction in disciplinary infractions occurred, an associated
increase in attendance would follow. Moreover, no discernible
increase in attendance would then result in a lack of effects found
for grades. Although attendance is associated with a number of
negative outcomes, including poor grades, externalizing beha-
viors, and dropout, increasing attendance does not necessarily
lead to improvement in problems correlated with attendance. For
example, although dropout prevention programs, overall, have
been found to be effective in reducing dropout (Wilson et al.,
2011), meta-analytic findings from a recent systematic review
found mixed effects of dropout programs on attendance outcomes
(Tanner-Smith & Wilson, 2013). The meta-analysis did not
directly test mediating effects of attendance on dropout; how-
ever, the lack of consistency in positive outcomes on atten-
dance and dropout in studies that measured both outcomes
‘cast doubt on the assumption that dropout prevention pro-
grams may also decrease absenteeism, or that absenteeism is
simply a point along the ‘dropout continuum’’ (Tanner-
Smith & Wilson, 2013, Discussion section, para. 7). Therefore,
an intervention that does not positively affect attendance may
nonetheless reduce dropout.
The present study is not without limitations, and findings
should be interpreted accordingly. First, an intent-to-treat anal-
ysis was used in this study. However, we were not able to com-
plete a full application of an intent-to-treat analysis as planned
due to missing data resulting from participant attrition from the
study. Total and differential attrition rates experienced in this
study were relatively high and exceeded acceptable attrition
rates established by the What Works Clearinghouse; thus, this
study would not earn the highest rating, ‘meets evidence stan-
dards’’ from the What Works Clearinghouse (U.S. Department
of Education, 2011). In the presence of missing outcome data,
an intent-to-treat analysis can yield biased results (Shadish
et al., 2002). We followed the What Works Clearinghouse
guidelines for randomized controlled trials with high attrition
and examined baseline equivalence on student characteristics
and outcome variables. Our results indicate that the analytic
treatment and control groups werecomparableonallobserved
variables. In addition, we controlled for demographic vari-
ables and pretest scores on outcome variables in our analyses.
Although the study estimates may be biased by the presence
of differential attrition or unobserved heterogeneity, the ana-
lytic sample was balanced on observed variables at pretest and
a randomized design minimizes threats to internal validity.
A second limitation is the lack of measurement of fidelity of
the intervention and rigorous assessment of the counterfactual,
due to limited resources. To begin, fidelity is a bifurcated con-
cept consisting of surface features (counting the number of
intervention elements students experience) and quality features
(a deeper understanding of implementation issues such as lan-
guage and examples used to teach that impacts student skill
acquisition). As such, future research may take into account the
bonding or quality of the relational supports provided to stu-
dents by C&C staff. It may be that certain staff behaviors may
lead to increased bonding for students. As a result, it is possible
that significant outcomes observed in this study could be attri-
butable to influences of unknown variables. Similarly, the lack
of positive effects on attendance and the small effects on beha-
vior and academics could be due to the intervention not being
implemented as designed. We did, however, implement several
strategies to promote, monitor, and enhance fidelity. These
strategies included having a well-defined, manualized, and
replicable intervention; providing initial and booster training
sessions to the implementers; and monitoring implementation
through weekly fidelity monitoring forms completed by the
implementers and reviewed by the coinvestigator.
A third limitation is the relatively short period within which
the interventionwas implemented. In this study, students received
the intervention for approximately 6 months, as opposed to the
2 years recommended by Sinclair et al. (1998). As a result,
students may not have received the full benefit of C&C, pos-
sibly explaining the smaller effects on behavior and grades
and null effects on attendance.
Our choice of outcomes—school-reported grades, office
disciplinary referrals, and attendance—was both pragmatic,
in that the data were readily available, as well as purposeful,
in that the schools and the CIS affiliate were interested in posi-
tively affecting these proximal outcomes. School data can,
however, be idiosyncratic to schools and may not always be
reliably tracked and reported, potentially introducing bias.
However, because our sample was randomized within schools,
we believe that any bias resulting from idiosyncratic practices
at individual schools was balanced across the treatment and the
control groups. Moreover, school archival data are commonly
used in school-based research (Irvin, Tobin, Sprague, Sugai,
& Vincent, 2004). Thus, the limitations of this study from the
use of school-reported data are not particularly uncommon,
although they are important to note.
Despite the limitations, this randomized study is one of a
few rigorous intervention studies of C&C, provides evidence
of effects with a population different from prior C&C studies,
and adds a level of internal validity rarely found in studies of
school-based intervention research. Moreover, this study pro-
vides evidence of the effects of C&C implemented in a real-
world setting by school-based practitioners, situating effect
sizes within the context of C&C being implemented under con-
ditions that practitioners would normally experience.
Dropout and related risk factors, such as school disengagement,
absenteeism, behavioral problems, and poor academic perfor-
mance, are issues with which social workers, counselors, and
psychologists are frequently confronted and for which they
expected to intervene. Generating and using evidence for prac-
tice has been a growing mandate in social work and related
fields, and using evidence to address drop-out and related
issues is of no exception. Implementing evidence-informed
interventions to improve behavioral and academic outcomes
for at-risk students is of critical importance to reducing dropout
and improving social and behavioral health outcomes.
10 Research on Social Work Practice 00(0)
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C&C has received significant attention as a promising inter-
vention to improve engagement and reduce dropout but has
limited evidence of effects, particularly experimental evidence
and evidence with varying populations. This study provides
rigorous empirical evidence in support of C&C for a different
sample of at-risk students from prior studies of C&C, Hispanic
and absentee students. Thus, this study uniquely contributes to
the evidence of C&C and provides additional evidence that
school-based practitioners can use inform practice decisions.
Although this randomized study adds to the knowledge base of
social work intervention research, to fully engage in evidence-
based practice as a profession, we need a robust body of rigorous,
experimental evidence of effects of interventions (Soydan, 2008).
There is, however, a dearth of rigorous intervention research in
social work (Horton & Hawkins, 2010; Maynard, Vaughn, & Sar-
teschi, 2012; Rosen, Proctor, & Staudt, 1999). Challenges of con-
ducting rigorous intervention research in social work have been
discussed previously (see Fraser, 2004; Geierstanger, Amaral,
Mansour, & Walters, 2004). Although real challenges do exist,
real or perceived challenges are often too easily dispensed as bar-
riers, often prematurely thwarting attempts at rigorous interven-
tion research. Methodological and substantive advances, as well
as advances in implementation models, provide social work
researchers and practitioners with access to more tools and models
to conduct intervention research than ever before (Fraser, 2004).
Moreover, university–community partnership models have been
advanced as a means of bridging practice and research that social
work can use to build evidence-based practice and practice-based
evidence (Begun, Berger, Otto-Salaj, & Rose, 2010).
This study provides evidence that university–community
partnerships can work to build rigorous evidence of effects of
interventions in real-world settings, using existing resources.
Although this research was supported by a postdoctoral grant
through the Institute of Education Sciences to support the posi-
tion of the principal investigator, the principal investigator
could have done the same work as a faculty member at a uni-
versity without external funding. Resources beyond the time
and effort of the university and community personnel involved
to implement and conduct the study were not necessary, as the
team worked together to use and build upon existing resources,
processes, and systems. Although there are many challenges to
conducting rigorous research in school settings, the delivery of
interventions within the school context by school personnel
affords researchers an understanding of the limitations,
strengths, and real-world effects of a program. The data col-
lected from such research also position effect size estimates
in the context of a real school setting. Although randomiza-
tion strengthens internal study validity, alternative designs
(i.e., switching replications or regression discontinuity) can
simultaneously alleviate ethical and methodological issues
frequently raised regarding the randomization of children in
need of services. Despite potential challenges, conducting rig-
orous intervention research in social work is not only possible,
but critical to advancing the field and building the knowledge
needed to provide effective services to improve student
The authors would like to thank the local CIS affiliate for their commit-
ment to building and using evidence to improve practice, the CIS site
coordinators for their hard work throughout the implementation and
data collection process, participating schools for their support and com-
mitment to their students, and the students who participated in the study.
Authors’ Note
The content is solely the responsibility of the authors and does not
necessarily represent the official views of the supporting entities.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
The authors disclosed receipt of the following financial support for the
research, authorship and/or publication of this article: The authors are
grateful for support from the Meadows Center for Preventing Educa-
tional Risk at the University of Texas at Austin and the Institute of
Educational Sciences (grant #R324B080008).
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... show that C&C was significantly related to improvements in academic performance and reductions in disciplinary referrals, after controlling for pre-test performance and all relevant student-and school-level characteristics, but no significant effects were found for attendance. Maynard, et al. (2014) conclude that C&C is a promising intervention to improve outcomes for at-risk youth in school settings. ...
... Hakkarainen et al., 2015), while others seem to be more promising (e.g. Poirier, et al, 2013;Liabo, et al., 2013;Maynard, et al., 2014;Wahlgren & Mariager-Anderson, 2017;Bilodeau & Meissner, 2018) or show moderate to large effects (Lehr, et al., 2003;Magen-Nagar & Shachar, 2017). ...
... The interventions aimed at truancy are more extensive. They include different interventions aimed at preventing school refusal, truancy and absenteeism, or to promote attendance(Maynard, Kjellstrand & Thompson, 2014;Rogers, 2014; Cabus & Witte, 30 2015 ...
... Developing suitable interventions focusing on dyadic student-teacher relationships in secondary school therefore seems to be an important next step. This could be done, for example, by adapting existing programs (e.g., Check and Connect; Maynard et al., 2014;Sinclair et al., 2005) or by using principles from interventions for primary school teachers (e.g., Teacher Student Interaction Coaching; Bosman et al., 2021;Spilt, Koomen, et al., 2012). ...
Person-centered analyses were used to identify relationship profiles based on seventh graders’ perceptions of relationships with three of their teachers (homeroom teacher, teacher of the easiest subject, teacher of the hardest subject). Furthermore, we investigated whether students’ characteristics (gender and academic track) could explain relationship profile membership and how these relationship profiles were associated with students’ school engagement. Students ( N = 899, 47.8% girls) reported about their relationships (closeness, conflict) with their teachers. Students and their homeroom teachers reported about students’ behavioral and emotional engagement. Four relationship profiles were found: a no risk profile (high closeness, low conflict with all three teachers), two single risk profiles (low closeness, high conflict with only one teacher), and one multiple risk profile (high conflict with all three teachers). Furthermore, boys were underrepresented in the no risk profile. Finally, students in the no risk profile tended to be more behaviorally and emotional engaged.
... The mentor builds relationships between the student, school staff, and family members, regularly checks the student's absenteeism over time, and re-connects the student with his/her school. Check and Connect has shown its usefulness for increasing middle school students' attendance rates in some studies (Powers et al., 2017) but not in all (Maynard et al., 2014). ...
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This longitudinal study of 1,066 Finnish students examined bidirectional reciprocal relationships between changes in perceived supportive school climate and changes in self-reported truancy from the last year of primary school (Grade 6) to the last year of lower secondary school (Grade 9). The results of the random intercept cross-lagged panel model showed, first, that a decrease in positive perceptions of school climate at the previous time point predicted more self-reported truancy at the following time point. Second, an increase in self-reported truancy predicted less positive perceptions of supportive school climate. The study indicates that students’ perceptions of supportive school climate and truancy negatively predict each other over time. Thus, school climate can serve as a target for prevention and intervention efforts to tackle truancy. Moreover, effectively addressing truancy may lead to a more favorable student view of school climate.
... Such substantial investments are backed by a large body of research demonstrating the positive effects of mentoring. Experimental and quasi-experimental findings across studies show that mentored youth perform better academically, receive fewer disciplinary referrals in school, selfreport more positive perceptions of their abilities and college prospects (Herrera et al., 2011;Maynard, Kjellstrand, & Thompson, 2014;Woods & Preciado, 2016). ...
This dissertation includes three manuscripts that evaluate career mentoring as an approach to enhancing Detroit students’ college readiness. Recent trends reveal that most Detroit high school graduates are not prepared for college, and so, not surprisingly, even though about half of all graduates pursue higher education, college persistence is low and the vast majority of students never earn degrees. Low rates of postsecondary educational attainment demand a response, and educational and community organizations, thus, aim to support Detroit young people on their paths towards higher education. Career mentoring is one way that community members have tried to support Detroit’s young people. Since 1994, the Winning Futures (WF) career mentoring organization has partnered with Metro Detroit schools to pair young people with professionals within their local communities. In partnership with WF, this dissertation investigated whether and how such an approach supports students. Specifically, I aimed to answer: How does career mentoring benefit students, with a particular focus on enhancing college readiness? What aspects of career mentoring seem particularly promising? And how might programs such as this one be improved to enhance the benefits for students? The first manuscript presents results from an experimental evaluation of the WF career mentoring program. Results of a randomized control trial assessed WF’s impact on students’ academic performance and college-ready attitudes. Classrooms participating during the 2019-2020 schoolyear were randomly selected. Outcomes measured included: students’ first semester grades, and self-reported self-efficacy, growth mindset, goal orientation, perseverance, and adult support. The results suggested that career mentoring enhances college readiness both attitudinally and academically, although many of the findings lacked statistical significance. The second manuscript presents a subjective evaluation of WF. Using a social validity framework, I share the perspectives of WF participants on the social importance of the program’s effects and the perceived mechanisms through which the program benefits students. I facilitated a total of fifteen focus groups and interviews with participating students, mentors, and classroom teachers. The results supported the quantitative findings that career mentoring enhances college readiness, particularly in its effects on non-cognitive and navigational factors. Additionally, the findings highlighted several avenues through which career interventions operate. Importantly, I found that strong mentoring relationships provide an essential foundation upon which the impacts of career mentoring depend. The third manuscript explored how career mentoring programs might enhance the benefits students receive by encouraging more frequent student-mentor virtual communication. I present the results of an experiment that tested a light-touch nudge intervention designed to encourage mentors to reach out more often via text message, email, or phone. Unexpectedly, the results showed that, although there were no differences in the overall frequency of mentors’ outreach, the intervention resulted in lower ratings of student responsiveness (both self-reported and mentor-reported) and less-frequent student-initiated outreach. Additionally, mentors who were encouraged to reach out gave worse ratings of their relationships with students at the end of the program, and students of treated mentors gained less from the program in terms of their college-ready attitudes. Exploratory analyses examined possible causal mechanisms driving the intervention’s negative impacts. Taken together, the study’s findings point to the limitations of behavioral interventions to encourage relational practices and underscore the importance of carefully considering the content of nudges to ensure that reminders messages ultimately enhance student support.
This quantitative study examined the influence of the Latinx cultural value of educación on high school students’ disciplinary behavior and its relationship to college degree expectations and academic achievement. Because student misbehavior is often identified as one factor that may contribute to low academic achievement, the study posited that the concept of educación, which encompasses a variety of respectful behavior expected of children, may be associated with the disciplinary behavior of students in schools and in turn may influence their educational expectations and achievement. Data were drawn from nearly 400 underrepresented minority students in the southwestern United States. Logistic and multiple regression were used to investigate the relationship between educación and academic achievement and college degree expectations. Findings indicated that students who exhibited positive behavior at school had increased odds of expecting to earn a four-year college degree and that as students’ behavior at school increased in a positive direction, so did their GPA. The results of this study suggest that adherence to the Latinx cultural value of educación may serve as a protective factor for Latinx students in regard to their academic expectations and achievement.
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This article reports a study assessing the contribution of current social work journal publications to a cumulative body of research that is capable of informing interventions. Thirteen journals published from 1993 to mid-1997 were reviewed. Analysis was guided by the view that the focus and design of studies should correspond to and supply the knowledge needed to perform different practice tasks. Accordingly, journal research articles were classified by their potential contribution to practice decisions requiring descriptive, explanatory, or control knowledge. Research aiming at control knowledge, informing the selection and implementation of interventions, was further evaluated by whether the interventions and outcomes studied were sufficiently specified to permit reliable research replication and application in practice. Very few of the published articles were found to report research on interventions, and even fewer used designs that enable replication. Factors affecting the rate and quality of research and interventions are also discussed.
Enhancing coping skills of students at high risk of school dropout is critical, particularly when school demands increase. The data-based Check & Connect school engagement model, originally designed to reduce the risk for dropping out among secondary/middle school youth with learning and emotional/behavioural disabilities, is described. Also described in this article are the students' patterns of school engagement over time, for whom national US statistics indicate only 42 per cent to 61 per cent typically complete high school. Results indicate that the levels of school engagement vary and change over time, among these high risk adolescents, supporting the importance of focusing on alterable indicators of risk, rather than status predictor variables. Furthermore, students' risk for disengagement from school was significantly lower for the youth who received sustained intervention throughout grade nine, compared to similar youth who received intervention support throughout grade eight.
The purpose of this study was to examine the efficacy of a sustained dropout prevention procedure that incorporated monitoring and school engagement strategies. Ninety-four students with learning and emotional/behavioral disabilities received interventions in Grades 7 and 8; half of the students (treatment group) continued to receive intervention through Grade 9. Results of this experimental study indicated that, for two of three measures, students in the treatment group were significantly more likely to be engaged in school than were control group students. The overall performance of both treatment and control students, however, points to the need for early and sustained support for students with learning and behavioral disabilities to attain academic and behavioral standards.
This study compared the adequacy of 5 theories to predict dropping out of high school before the 10th grade. These theories include full mediation by academic achievement and direct effects related to general deviance, deviant affiliation, family socialization, and structural strains. Nested latent variable models were used to test these theories on prospective data from an ethnically diverse urban sample. Poor academic achievement mediated the effect of all independent factors on school dropout, although general deviance, bonding to antisocial peers, and socioeconomic status also retained direct effects on dropping out. Therefore, none of the theories tested was fully adequate to explain the data, although partial support was obtained for each theory. Implications for prevention of early high school dropout are discussed.
An experimental research design was used to examine the effectiveness of a targeted, long-term intervention to promote school completion and reduce dropout among urban high school students with emotional or behavioral disabilities. African American (67%) males (82%) composed a large portion of the sample. This intervention study was a replication of an empirically supported model referred to as check & connect. Study participants included 144 ninth graders, randomly assigned to the treatment or control group. The majority of youth were followed for 4 years, with a subsample followed for 5 years. Program outcomes included lower rates of dropout and mobility, higher rates of persistent attendance and enrollment status in school, and more comprehensive transition plans.
SAS PROC MIXED is a flexible program suitable for fitting multilevel models, hierarchical linear models, and individual growth models. Its position as an integrated program within the SAS statistical package makes it an ideal choice for empirical researchers and applied statisticians seeking to do data reduction, management, and analysis within a single statistical package. Because the program was developed from the perspective of a "mixed" statistical model with both random and fixed effects, its syntax and programming logic may appear unfamiliar to users in education and the social and behavioral sciences who tend to express these models as multilevel or hierarchical models. The purpose of this paper is to help users familiar with fitting multilevel models using other statistical packages (e.g., HLM, MLwiN, MIXREG) add SAS PROC MIXED to their array of analytic options. The paper is written as a step-by-step tutorial that shows how to fit the two most common multilevel models: (a) school effects models, designed for data on individuals nested within naturally occurring hierarchies (e.g., students within classes); and (b) individual growth models, designed for exploring longitudinal data (on individuals) over time. The conclusion discusses how these ideas can be extended straighforwardly to the case of three level models. An appendix presents general strategies for working with multilevel data in SAS and for creating data sets at several levels.