ArticlePDF Available

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

September 2013
Research on Social Work Practice 24(3)

DOI: 10.1177/1049731513497804

Authors:

Brandy R Maynard

Saint Louis University

Elizabeth Kjellstrand Hartwig

Texas State University

Aaron M. Thompson

University of Missouri

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.

Participant flowchart. Participant percentages reported at each stage are calculated using the number of participants in prior stage. N ( n ) 1⁄4 number of students; J( j ) 1⁄4 number of schools.

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. Demographic Characteristics and Pretest Equivalence of Treatment and Control Groups.

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. Student-School Fitted Hierarchical Linear Models: The Effects of C&C (N ¼ 189).

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DOI: 10.1177/1049731513497804

published online 8 September 2013Research on Social Work Practice

Brandy R. Maynard, Elizabeth K. Kjellstrand and Aaron M. Thompson

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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

Abstract

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.

Keywords

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.

Email: bmaynar1@slu.edu

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 www.campbellcollaboration.org). 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;

www.communitiesinschools.org).

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

Maynard et al. 3

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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?

Method

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://

checkandconnect.umn.edu 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).

Participants

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.,

2011).

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).

Measures

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

Maynard et al. 5

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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

– 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.

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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.

Results

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.

Variable

Total (N ¼ 189) C&C (n ¼ 89) Control ( n ¼ 100)

% N % n % n w

(df) p

Sex

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

ðPre

Þþb

ðtx

Þþb

ðafam

Þþb

ðsex

Þþb

ðfrl

Þþb

ðage

Þþb

ðinc

Þþr

Level 2 : b

¼ p

þ p

ðsize

Þþp

ð%dis

Þþp

ð%lep

Þþp

ð%mob

Þþp

ð%prf

Þþp

ð%atrisk

Þþe

Figure 2. Two-level random intercept model.

Effect size (d) ¼

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

þ s

Figure 3. Effect size estimate for multilevel data structures.

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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.

Conclusion

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.

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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

outcomes.

Acknowledgments

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.

Funding

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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J EDUC BEHAV STAT

Judith Singer

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.

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