The School Counselor Caseload and the High School-to ... · the relationships between the school...

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Teachers College Record Volume 116, 100301, October 2014, 30 pagesCopyright © by Teachers College, Columbia University0161-4681

The School Counselor Caseload and the High School-to-College Pipeline

CHENOA S. WOODS

University of California, Irvine

THURSTON DOMINA

University of California, Irvine

Background: Advising students on the transition from high school to college is a central part of school counselors’ professional responsibility. The American School Counselor Association recommends a school counselor caseload of 250 students; however, prior work yields incon-clusive evidence on the relationship between school counseling and school-level counseling resources and students’ college trajectories.

Focus of Study: This study evaluates the relationship between access to school counselors and several critical indicators of student transitions between high school and college.

Research Design: The study utilizes the Education Longitudinal Study of 2002 to explore the relationships between the school counselor caseload and students’ progress throughout the high school-to-college pipeline. The key indicator is the counselor caseload for students at a given high school, measured as the number of 10th graders per counselor at the high school at which each student is enrolled. The outcome variables are students’ college expectations, whether students spoke with a counselor about college, taking the SAT, and college enrollment. Logistic and multinomial logistic regression analyses are applied to examine the relationships between these variables.

Findings: Students in schools with small counselor caseloads enjoy greater success at navi-gating the high school-to-college pipeline. Controlling for student- and school-level charac-teristics, students in schools where counselors are responsible for advising a large number of students are less likely to speak with a counselor about college, plan to attend college, take the SAT, and enroll in a four-year college.

Conclusions: The findings support the claim that a smaller school counselor caseload may increase students’ access to key college preparation resources and raise four-year college enroll-ment rates.

Teachers College Record, 116, 100301 (2014)

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Although college attendance rates have increased greatly in the past sev-eral decades, issues of equity in the college preparation and admissions process persist. A large majority of high school students expect to attend college, yet fewer students enroll, and even fewer students graduate with a baccalaureate degree (e.g., Solorzano, Villalpando, & Oseguera, 2005). The road from high school to college is particularly perilous for students from families with limited college experience, because many of these stu-dents lack access to information about college entrance requirements, selection criteria, and cost (Kirst & Venezia, 2004). Without this critical information, it is difficult for students to realize their college expectations, and even high-achieving students fail to make the transition from high school to a postsecondary educational institution (Plank & Jordan, 2001).

High school counselors have many duties, including providing students with informational and other assistance in college preparation and at-tendance. The American School Counselor Association (ASCA), which recommends that high schools maintain a ratio of 250 students per coun-selor, suggests that the role of school counselors includes responding to students’ academic, career, and personal/social development. High school counselors face the task of guiding students through adolescence into early adulthood, preparing them to be future citizens, college stu-dents, employees, and parents; they implement their counseling program by providing classroom guidance, individual student planning, responsive services, and by collaborating with other educators and community mem-bers (ASCA, 2012c, 2012d).

Although counselors are charged with the task of equitably serving all students, many scholars argue that counselors in schools that serve un-derrepresented students are often unable to effectively advise students because their caseloads are too large (Cabrera & La Nasa, 2000; Corwin, Venegas, Oliverez, & Colyar, 2004; Farmer-Hinton, 2008; Farmer-Hinton & McCullough, 2008; González, Stoner, & Jovel, 2003; Hamrick & Stage, 2004; V. E. Lee & Ekstrom, 1987; McDonough, 1997, 2005; Muhammad, 2008; Perna et al., 2008; Rosenbaum, Stephan, & Rosenbaum, 2010). The counselor caseload, sometimes referred to as the student–school coun-selor ratio, is usually measured by the number of students in a school di-vided by the number of counselors in a school. Many counselors manage caseloads double or triple ASCA’s recommended caseload. Nationally, the counselor caseload at the average high school is 471 students, and in California, the state with the largest caseload, counselors work with more than 1,000 students (ASCA, 2012b). Reasoning that manageable school counselor caseloads may facilitate more efficient counseling, several states, including Indiana, Maine, and New Hampshire have passed legisla-tion limiting the size of counselor caseloads (ASCA, 2012a). Nevertheless,

TCR, 116, 100301 The School Counselor Caseload and the High School-to-College Pipeline

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with caseloads varying in size it is apparent that access to individualized college counseling is not available to all.

J. M. Lee, Edwards, Menson, and Rawis (2011) provide a slightly better picture, stating that as of 2008 the student-to-“college counselor” ratio was 320:1. “College counselors” are those who are responsible for providing college counseling, either as their sole responsibility or in combination with other counseling tasks. Although the size of average caseloads has decreased by 49 students between 1998 and 2009, most caseloads remain well above the recommended ratio of 250:1 (Clinedinst & Hawkins, 2011; J. M. Lee et al., 2011).

The purpose of this paper is to examine the relationship between stu-dents’ access to college guidance, as measured by the size of the school counselor caseload, and college-going behaviors. Situated in the hypoth-esis that a more manageable school counselor caseload leads to additional opportunities for students to build social capital and progress through the high school-to-college transition, this study uses the Educational Longitudinal Study of 2002 (ELS:2002) to address how the size of the counselor caseload affects students’ postsecondary educational expecta-tions, preparation, and attendance patterns. The research questions are as follows:

1. What are the relationships between the counselor caseload and stu-dents’ college planning, information-gathering, and college-prepa-ratory behavior in 10th and 12th grade?

2. What are the relationships between the counselor caseload and stu-dents’ college enrollment patterns?

THE INFLUENCE OF SCHOOL COUNSELORS ON HIGH SCHOOL-TO-COLLEGE TRANSITIONS

The high-school-to-college pipeline begins as students develop their col-lege expectations, long before students enroll in college. Perna (2006) presents a model of college choice, which draws from both economic and sociological approaches. This model has four layers: habitus (Layer 1); school and community context (Layer 2); higher education context (Layer 3); and social, economic, and policy context (Layer 4). The out-er layers inform the inner layers, which then influence college choice. This study focuses primarily on the school and community context, but it stands to reason that the higher education context and macrolevel influ-ences have real impacts for counselors’ practices in schools. For example, universities’ changing admissions policies (Level 3) and state funding for school counselor positions (Level 4) can impact the extent to which


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school counselors can engage in precollege counseling. The school envi-ronment helps to develop one’s college-going habitus, or their beliefs and worldview, which influence their attitudes, goals, and in this case, educa-tional expectations (McDonough, 1997).

Counselors can potentially play an important role as students devel-op their educational plans and prepare for life after high school. They can influence students’ college predispositions, either by encouraging students to consider college and providing information or by acting as gatekeepers and redirecting students with unrealistic educational expec-tations away from higher education. Counselors can choose the amount and types of college and financial aid information to provide, effectively “channeling” students towards or away from different types of colleges (Freeman, 1997).

Stanton-Salazar and Dornbusch (1995) describe counselors as “institu-tional agents”: people who not only have the ability, but also have the dedication to communicate “institutional resources and opportunities” (p. 117). One way that counselors do so is by influencing students’ col-lege-going habitus; primarily shaped by the individual’s background and socialization, habitus defines a student’s view of his or her realistic pos-sibilities (Massé, Perez, & Posselt, 2010). Counselors can influence stu-dents’ habitus directly, using their face-to-face interactions to encourage students to formulate high expectations and provide relevant informa-tion. Additionally, counselors can shape the college-going culture of the school itself, creating an institutional-level atmosphere that supports col-lege preparation and attendance for all students (Jarsky, McDonough, & Nunez, 2009; McDonough, 1997).

In addition, counselors may increase students’ social capital. Coleman (1988) defines social capital as relationships that lead to action. When a student develops a relationship with a school counselor, he or she gains access to college knowledge, resources, and advice, which can play an important role in shaping a students’ future. Social capital factors such as encouragement from others or assistance on college applica-tions are closely linked to students’ likelihood of attending a four-year college; and for black and Latino students, these social capital factors are as important in predicting college attendance as academic ability (Perna, 2000). Counselors may have more influence in fostering college expectations for first-generation college students and students from low socioeconomic backgrounds compared to their peers with more college-related social and cultural capital (McDonough, 1997). However, nega-tive interactions between students and school counselors can dissuade students from approaching counselors for guidance, thus limiting stu-dents’ access to the potential social and cultural capital counselors hold.


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Additionally, the degree to which school counselors are able to engage in quality precollege counseling and act as positive institutional agents may be mitigated by larger school-, district-, or state-level regulations or resource limitations.

To effectively influence students’ habitus and social capital, coun-selors need to be involved consistently with students throughout their educational careers. Farmer-Hinton and McCullough provide a partic-ularly evocative description of this process, reporting that counselors at a college-prep charter school “infuse college by engaging students in informal, yet repetitive conversations about college” (2008, p. 85). Accordingly, the relevant literature generally assumes that counselors use their influence to broaden college access, and working with counsel-ors will have positive effects at each stage of the college choice process (Bryan, Moore-Thomas, Day-Vines, & Holcomb-McCoy, 2011). These effects may be especially pronounced for students who have few other sources of college knowledge (Eccles, Vida, & Barber, 2004; Freeman, 1997) because these students are more dependent on their school coun-selor to “help them receive insider information” and guide them through the application process (McDonough, 1997, p. 101; Rosenbaum, Miller, & Krei, 1996).

However, counselors may also play a gate-keeping role, providing con-crete encouragement and information to students that they view as “col-lege material” and withholding information from students that they be-lieve are less likely to succeed in higher education. Studies of students in college prep programs, magnet schools, and charter schools that are organized around college preparatory missions provide some evidence to support this claim (V. E. Lee & Eckstrom, 1987). When students enter into these programs and complete the advanced courses that are required for selective college admissions, they may signal their commitment to higher education to counselors, creating a continual loop in which counselors provide encouragement and information, which in turn helps students in these programs advance toward higher education (Farmer-Hinton & McCullough, 2008; Tuitt, Van Horn, & Sulik, 2011). Honors track stu-dents report that their counselors “knew all of us by name” (González et al., 2003, p. 162). In these settings, counselors regularly provide students detailed information about scholarships, internships, and jobs as well as guidance on where to apply and attend college (Freeman, 1997). On the other hand, lower-achieving students report limited access to college re-sources (Kimura-Walsh, Yamamura, Griffin, & Allen, 2009).


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THE CONSEQUENCES OF HIGH COUNSELOR CASELOADS

Encouraging students to develop college expectations and advising stu-dents as they progress through the high school-to-college transition pro-cess is time-consuming work, particularly for counselors working with stu-dents who have access to few other reliable sources of information about college. Counselors faced with large student caseloads, therefore, may find it especially difficult to effectively assist all students. In such a setting, counselors may be more likely to serve a gate-keeping role as they make strategic decisions about which students to devote college advising time and energy toward (Corwin et al., 2004; González et al., 2003; Kimura-Walsh et al., 2009). If this is the case, school-level counselor caseloads may be negatively associated with student college expectations, access to infor-mation and, ultimately, students’ odds of enrolling in higher education.

One explanation for providing students with limited college counseling is that, generally, counselors tend to wear many hats and have a diverse range of responsibilities. Public high school counselors report relatively equal amounts of time on counseling for preparing for postsecondary education, high school course scheduling, and personal–social counsel-ing, and an additional 14% of their time is allotted to testing (Clinedinst & Hawkins, 2011). School counselors are responsible for a variety of tasks including nonguidance activities and counselors’ role confusion may lead to discrepancies between accepted counseling models and counselors’ ac-tual duties (Burnham & Jackson, 2000; J. M. Lee et al., 2011).

Counselors work with a diverse group of students who may be gifted, special needs, low income, English language learners, immigrants or chil-dren of immigrants, and/or of another population requiring individual-ized guidance. Counselors report that a wide range of tasks compete with college counseling for their time, including individual counseling; group counseling; classroom guidance; consultation with community agencies; parent groups; support, prevention and intervention strategies; and the administration of diagnostic and other standardized tests, thus some admit to serving only portions of the population, often relatively at-risk students (Burnham & Jackson, 2000; Perna et al., 2008; Sears, 1993). These find-ings vary with enrollment and the size of the counselor caseload and have changed over time. Counselors in schools with caseloads of 500 students or more spend substantially less time on college counseling than coun-selors in smaller schools and schools with smaller caseloads (Clinedinst & Hawkins, 2011). Nationally, school counselors’ time spent on college advising decreased between 2006 and 2009 (J. M. Lee et al., 2011).

Counselors in schools with unmanageably large student caseloads may focus their energy on advising students at one point in the college choice


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process at the expense of other points. There is some evidence to suggest that overburdened counselors reserve counseling resources for upper-classmen, leaving fewer resources (i.e., time) for 9th and 10th grade stu-dents (Corwin et al., 2004). To the extent to which counselors respond to large caseloads by adopting such an approach, we would expect caseloads to be particularly negatively associated with students’ odds of meeting with counselors in the early years of high school and their odds of forming early college predispositions.

On the contrary, Rosenbaum et al. (1996), argue that counselors ar-ticulate a “college-for-all ethos” and encourage all students to formulate college expectations, regardless of their assessment of students’ ability to realize these expectations. Small counselor caseloads may be an indicator of a college-going culture on campus, raising all students’ educational ex-pectations. Nevertheless, high expectations alone do not communicate to students the details of how and when to prepare for college; professionals are needed to guide students through the relevant college information. While this strategy results in universally high expectations, Rosenbaum et al. argue that counselors do not provide students with adequate informa-tion and preparation for success in higher education. As a result, they argue, many students move from high schools to nonselective community colleges, where they lack the skills to succeed academically. Rosenbaum et al.’s argument implies that counselor caseloads should correlate nega-tively with students’ odds of attending two-year colleges but not their odds of four-year college enrollment.

Despite these conflicting hypotheses, we currently have very little in-formation on the relationship between counselor caseload and student transitions between high school and college. Carrell and Carrell (2006) demonstrate that large caseloads undermine the extent to which coun-selors can prevent disciplinary problems in schools. However, this analy-sis provides no evidence about college outcomes. Engberg and Wolniak (2010), meanwhile, examine the effects of counselor caseloads and other indicators of school learning environments on students’ two- and four-year college enrollment. This study finds that student–counselor ratios have no direct effect on college outcomes. However, Engberg and Wolniak’s analy-ses include controls for student course taking and social capital. If these factors are mechanisms through which caseloads influence college out-comes, this study may underestimate the relationship between counselor caseloads and student college going.


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METHOD

DATA SOURCE AND MEASURES

We use the ELS:2002 to explore the impact of the school counselor casel-oad on students’ educational expectations and their progress throughout the high school-to-college pipeline. The ELS:2002 is based on a stratified sampling design. In the first stage, a sample of 750 schools was drawn from 1,220 eligible schools. Sampled schools provided a roster of their 10th grade students from which the study sampled approximately 30 students from each school. In spring of 2002, when students were sophomores in high school, all sampled students completed paper and pencil question-naires and standardized mathematics and reading tests. In addition, the study collected transcripts, course catalogues, and information about edu-cational resources and school security from administrators at all sampled high schools. In 2004, when most students were high school seniors, all participants were resurveyed and retested. In 2006, two years after most students have graduated from high school, students were again resurveyed to provide information about their postsecondary experiences (Ingels et al., 2007).

Our analyses use these data to investigate the relationship between counseling resources in students’ high schools and their progress on the high school-to-college pipeline. Each of these analyses takes the following general form:

In this equation, by indicates that the variable was taken from the base year; f1 indicates that the variable was taken from the first follow-up, and f2 indicates that the variable was taken from the second follow-up. The CASELOAD variable is the standardized counselor caseload and CASELOAD2 is the squared version of this variable. The STUCNTRL term includes the student and family background variables, and the SCHCTRL term captures schools’ urbanicity.

SCHOOL COUNSELOR CASELOAD

The key indicator in this study is the counselor caseload. This figure, which represents the number of 10th graders per counselor in surveyed students’ schools, was created by taking the 10th grade enrollment vari-able (BYG10ER) and dividing it by the number of school counselors em-ployed by a school (BYA23K):

p. 12/280:

p. 12/280:


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For example, if there are 300 10th grade students in the school and 5 counselors, the student–counselor ratio is 60:1 or 60 students per counselor. The 10th grade enrollment and school counselor count vari-ables are both reported in the school administrator’s questionnaire in the base year and the caseload variable is standardized in the regression equations. Note that this variable only accounts for 10th grade students, resulting in smaller ratios than what may be expected if calculated us-ing a school-wide enrollment variable. We maintain that this is the best way to calculate the counselor caseload, given that other measures of school enrollment in ELS: 2002 are not continuous. A squared version of the counselor caseload variable is included to account for nonlinear relationships.

COLLEGE EXPECTATIONS

College expectations and plans are measured in 10th and 12th grade, re-spectively. Two outcome variables, having expectations to attend a post-secondary educational institution (PEI; 10th grade; BYSTEXP) and hav-ing plans to attend college directly after high school (12th grade; F1S45) are examined. These dummy variables are coded as one if the student has expectations/plans for college attendance or zero if the student does not have expectations or does not know if he or she plans to attend college; a code of one includes any amount of college attendance, rang-ing from some college through advanced degree attainment.

COLLEGE INFORMATION AND READINESS

Speaking with a school counselor about college admissions information is one way students gain college knowledge. Logistic regression (Cabrera, 1994) explores the impact of the counselor caseload for whether stu-dents discuss college admissions information with counselors in the base year and in the first follow-up (BYS59B and F1S48A, respectively). Dummy variables for 10th and 12th grade are coded as one if the student has spoken with a school counselor about college at least once. The next step along the education pipeline is information gathering and college preparation. Plank and Jordan (2001) determined that preparing for, receiving guidance about, and taking the SAT/ACT increases students’ chances of enrolling in a PEI. This dummy variable is set equal to one if the student has taken the SAT/ACT by 12th grade (F1S21C).


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COLLEGE ATTENDANCE

Finally, we examine the relationship between the counselor caseload and students’ college attendance. Specifically, multinomial logistic regression assesses the relationship between the caseload and the type of PEI attend-ed (a two-year college or a four-year college as compared to not attending a degree-granting institution; F2PS1LVL). This outcome is measured in the third wave, or two years after high school.

DEMOGRAPHIC AND CONTROL VARIABLES

All multivariate models include controls for students’ gender and race, their family’s socioeconomic background, their prior academic achieve-ment, and school’s urbanicity. We operationalize gender as a dummy variable, based on the categorical ELS: 2002 BYSEX variable. Similarly, we operationalize race as a series of dummy variables. Using data re-ported in BYRACE, we categorize students as African American, white, Hispanic, Asian, or other race. For ease of interpretation we use the modal category—white—as the omitted category in all analyses (Hardy, 1993). We measure students’ family background using maternal edu-cational attainment (BYMOTHED) and family income (BYINCOME). Maternal education is a series of dummy variables with “some college” as the reference; income is a standardized continuous variable. Students’ standardized math (BYTXMSTD) and reading (BYTXRSTD) scores are included as standardized continuous variables in the model to account for past academic performance. School urbanicity (BYURBAN) is in-cluded as a dummy variable (with suburban schools as the reference category) to account for schools’ local context.

ANALYSES

For the purposes of this study, the sample is restricted to public school students who did not transfer schools between the base year and the first follow-up and who have nonmissing counselor caseload data (N = 7,800). Due to transferring schools, attending private schools, and/or missing data for the counselor caseload variable, 8,397 students were dropped from the original sample. Despite small differences in the current sample and the full sample, there were no significant differences between the two groups on any of the dependent variables. Including students who had transferred schools may result in including students who were enrolled in schools with different counselor caseloads between the first and second wave of data collection, and restricting the sample in this way holds the counselor caseload constant over the first two waves.


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We impute data for cases missing data on one or more control variables using Markov chain Monte Carlo multiple imputation. All analyses were conducted using logistic regression and multinomial logistic regression techniques and were run only for students who have data on the counsel-or caseload variable and the dependent variable of interest, resulting in different sample sizes depending on the equation. Data are weighted for representativeness to allow for generalizing to the national population. For analyses using only base-year variables, the base-year student prob-ability weight (BYSTUWT) is used, for analyses using variables from the first and second wave the first-year/follow-up panel weight (F1PNLWT) is used, and for analyses using variables from the first and third wave, second follow-up/base-year probability weight is used (F2BYWT).

RESULTS

SCHOOL POPULATIONS

Our preliminary analyses describe student populations at schools with counselor caseloads of varying sizes. Table 1 separates the counselor caseload into quartiles. The mean counselor caseload for schools with the smallest ratios (the schools at the 25th percentile and below) is 50 students per counselor; the mean for the second quartile is 74 students per counselor; the third quartile’s mean is 95 students per counselor; and the mean in the top quartile is 136 students per counselor. White students tend to be concentrated in schools with the smallest caseloads, whereas black and Latino students are concentrated in schools with larger caseloads. Students whose mothers have less than a high school education are more concentrated in the quartile with the largest case-loads, but students whose mothers have an advanced degree represent the smallest proportion of students in the highest quartile. Similarly, stu-dents with the lowest family income are much more concentrated in the schools with the largest caseloads. Not surprisingly, schools with the largest enrollment also have the largest counselor caseloads, but the size-able standard deviations across all four quartiles indicate that schools of various sizes can be found in any of the caseload quartiles.

Table 1 also demonstrates differences on student outcomes for students in the various quartiles. The rate of speaking with a counselor about col-lege in the 10th grade is approximately seven percentage points higher for students in schools with the smallest counselor ratios compared to their peers in schools with the largest caseloads. By 12th grade, that gap ex-pands to 10 percentage points. Similarly, only 59% of students in schools with the largest caseloads take the SAT/ACT, compared to nearly 70%


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Tab

le 1

. Des

crip

tive

Sta

tist

ics

of S

tude

nt P

opul

atio

ns o

f th

e C

ouns

elor

Cas

eloa

d Q

uart

iles

Sm

alle

st C

asel

oad

Low

er M

idsi

zed

Cas

eloa

dU

pper

Mid

size

d C

asel

oad

Lar

gest

Cas

eloa

d

%

Mea

n St

d. D

ev.

%M

ean

Std.

D

ev.

%M

ean

Std.

Dev

.%

Mea

n St

d.

Dev

.

Rat

io49

.511

.874

.26.

394

.56.

213

5.5

29.4

Mal

e48

.548

.750

.650

.3

Rac

e

Asi

an11

.68.

78.

415

.9

Lat

ino

8.6

11.2

15.8

17.4

Bla

ck9.

011

.713

.014

.4

Oth

er5.

84.

25.

25.

4

Wh

ite

65.0

64.1

57.5

47.0

Mot

her

’s e

duca

tion

Les

s th

an h

igh

sch

ool

9.0

11.0

15.2

16.1

Hig

h s

choo

l dip

lom

a29

.129

.328

.727

.7

Som

e co

llege

34.8

33.5

33.6

33.6

Col

lege

gra

duat

e18

.117

.615

.416

.4

Adv

ance

d de

gree

9.0

8.7

7.5

6.2

Fam

ily in

com

e in

do

llars

6210

8.7

4243

8.6

6259

4.6

4617

0.0

5698

0.6

4257

2.7

5545

7.0

4234

8.0

Stan

dard

ized

rea

din

g sc

ore

0.1

1.0

0.1

1.0

−0.1

1.0

−0.1

1.0

Stan

dard

ized

mat

h

scor

e0.

21.

00.

11.

00.

01.

0-0

.11.

0


13

Sm

alle

st C

asel

oad

Low

er M

idsi

zed

Cas

eloa

dU

pper

Mid

size

d C

asel

oad

Lar

gest

Cas

eloa

d

%

Mea

n St

d. D

ev.

%M

ean

Std.

D

ev.

%M

ean

Std.

Dev

.%

Mea

n St

d.

Dev

.

Sch

ool u

rban

icit

y

Rur

al35

.822

.817

.320

.5

Urb

an14

.420

.230

.429

.9

Subu

rban

49

.856

.952

.249

.6

Sch

ool e

nro

llmen

t88

9.2

778.

312

22.4

608.

512

77.3

700.

613

96.9

670.

1

Bas

e ye

ar d

epen

den

t va

riab

les

Spok

e w

ith

cou

nse

lor

47.9

46.9

42.4

40.7

Exp

ects

to a

tten

d co

llege

94.5

94.4

93.6

91.4

Firs

t fol

low

-up

depe

n-

den

t var

iabl

es

Spok

e w

ith

cou

nse

lor

83.3

81.8

78.1

73.1

Plan

s to

att

end

colle

ge80

.480

.976

.578

.1

Took

SA

T/A

CT

69.6

71.4

61.8

59.2

Seco

nd

follo

w-u

p de

-pe

nde

nt v

aria

bles

Att

ende

d 2-

year

co

llege

26.9

25.6

29.2

33

Att

ende

d 4-

year

co

llege

49.2

50.3

41.3

39.3


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of students in schools with the smallest caseloads. Again, students in schools with the largest caseloads attend 4-year colleges at the lowest rates (39%) compared to students in schools with the smallest caseloads (49%). These findings provide initial descriptive evidence to suggest that students in schools with small counselor caseloads are advantaged in the high school-to-college transition relative to their peers in schools with higher counselor caseloads.

TENTH-GRADE OUTCOMESTable 2 focuses on the relationship between counselor caseloads and 10th graders’ odds of speaking with a counselor about college and ar-ticulating college expectations. This table suggests that there is a sig-nificant negative relationship between counselor caseload and students’ odds of meeting with their counselor prior to 10th grade. This relation-ship persists even after controlling for student and school background characteristics, suggesting that the relationship is robust. Furthermore, the squared term in both of these models is not significantly different from one, suggesting that the odds of speaking with a counselor de-crease incrementally as caseloads increase.

However, the next two models suggest that the associations between counselor caseloads and 10th graders’ college expectations are much weaker. We recognize that expectations develop through relationships with multiple people and influences that may begin well before high school, yet the number of students school counselors work with does not appear to impact 10th grade students’ educational expectations. Neither the reduced-form model nor the model with controls for student and school characteristics return a significant relationship between caseload and expectations.

Figure 1 helps to illustrate these findings. The figure represents the predicted probability that students at schools with varying counselor case-loads will speak with a counselor and articulate college expectations in 10th grade, with all controls held constant at the sample mean. Students’ probability of speaking with counselors declines dramatically as school counselor caseloads increase, pointing to a negative and significant ef-fect of the caseload on students’ access to conversations with counselors about college. The probability of speaking with counselors for students in schools with the smallest caseloads is nearly 60%, but less than 40% for students in schools with the largest caseloads. By contrast, there is virtually no relationship between counselor caseloads and students’ col-lege expectations, illustrating the insignificant effect of the counselor caseload on this outcome. In sum, these findings suggest that counselor caseloads influence students’ odds of speaking with their counselors, but


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nearly all students expect to attend college, regardless of their access to counselors. This finding is surprising, given scholars’ attention to the role that counselors can play in forming students’ college expectations (Krei & Rosenbaum, 2001; McDonough, 1997; Rosenbaum et al., 1996). In light of limited time and resources, school counselors should focus their efforts not solely on encouraging students to attend college but on providing useful assistance about the specific steps students need to take to be adequately prepared.

Figure 1. Predicted probabilities of the effect of the counselor caseload on students’ 10th grade outcomes: whether students expect to attend college and whether a student spoke with a school counselor about college. Predicted probabilities from the full models in Table 2, with control variables held at the mean.


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Table 2. The Counselor Caseload and the Likelihood of Speaking With a Counselor About College and Expecting to Attend College

10th Grade Outcomes

Spoke With Counselor Expects to Attend College

Counselor caseload0.814*** 0.816*** 0.843 0.867(0.045) (0.046) (0.085) (0.081)

Counselor caseload squared1.043 1.045 1.019 1.045

(0.032) (0.032) (0.058) (0.070)

Male0.788*** 0.403***(0.046) (0.053)

Racea

Asian1.454** 1.678(0.179) (0.468)

Latino0.872 1.784**

(0.103) (0.379)

Black1.068 2.182***

(0.124) (0.430)

Other1.093 1.184

(0.156) (0.327)Mother’s educationb

Less than high school0.822 0.675*

(0.087) (0.133)

High school diploma0.850* 0.807(0.061) (0.120)

College graduate0.888 1.215

(0.079) (0.285)

Advanced degree0.975 1.546

(0.115) (0.689)

Standardized family income1 1.509***

(0.044) (0.171)

Standardized reading score1.064 1.913***

(0.048) (0.203)

Standardized math score1.052 1.913***

(0.051) (0.199)School urbanicityc

Rural0.882 0.811

(0.085) (0.122)

Urban0.941 1.09

(0.096) (0.191)N 6300 6300 6780 6780

Note. Odds-ratios presented. Standard error located directly under coefficients. *p < .05; **p < .01; ***p < .001. aReference category for race is white. bReference category for mother’s education is some college. cReference category for school urbanicity is suburban school. Analysis is for public students who have remained in the same school between 10th and 12th grade, weighted using BYSTUWT, and clustered by school.


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12TH-GRADE OUTCOMES

In Table 3, we examine the relationship between counselor caseloads and 12th grade students’ odds of speaking with counselors about college, odds of articulating college plans, and odds of taking the SAT or the ACT, a ma-jor hurdle to four-year college enrollment. The first set of analyses suggests that there is a robust negative association between counselor caseload and 12th graders’ odds of speaking with their counselor. Furthermore, this association is much stronger than the association between caseloads and 10th graders’ odds of talking with their counselors. This finding runs con-trary to the idea that overburdened counselors focus their attention on students at the end of their high school careers.

As the counselor caseload increases, students’ likelihood of planning to continue their education directly following high school decreases after accounting for personal and school demographic controls. In stark con-trast to the lack of significant effects of the school counselor caseload on 10th grade students’ college expectations, 12th grade students’ plans are significantly negatively associated with an increasing counselor caseload.

Additionally, there is a significant negative association between counsel-or caseloads and 12th graders’ odds of completing the SAT or ACT. Each of these relationships remains significant after controlling for student and school background characteristics. Interestingly, the caseload squared term is significant and positive in each of the full models in this table, suggesting that for schools with particularly high counselor caseloads, the negative relationship between caseload and 12th graders’ college plans and behaviors is somewhat truncated.

Table 3. The Counselor Caseload and the Likelihood of Speaking With a Counselor About College, Planning to Attend College, and Taking the SAT/ACT

12th-Grade Outcomes

Spoke With a Counselor About College

Plans to Attend College

Took the SAT/ACT

Counselor caseload0.695*** 0.710*** 0.855** 0.868** 0.758*** 0.776***

(0.052) (0.053) (0.050) (0.047) (0.049) (0.050)

Counselor caseload squared

1.073* 1.070* 1.083** 1.096*** 1.05 1.063*

(0.030) (0.029) (0.032) (0.030) (0.030) (0.032)

Male0.696*** 0.484*** 0.497***

(0.053) (0.035) (0.033)


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12th-Grade Outcomes

Spoke With a Counselor About College

Plans to Attend College

Took the SAT/ACT

Racea

Asian1.238 3.083*** 1.813**

(0.193) (0.667) (0.358)

Latino0.891 1.428** 0.847

(0.125) (0.185) (0.106)

Black1.363* 2.358*** 1.580***

(0.176) (0.266) (0.185)

Other0.889 0.735 0.717*

(0.161) (0.119) (0.114)

Mother’s educationb

Less than high school

0.909 0.688** 0.669***

(0.117) (0.084) (0.080)

High school diploma

1.039 0.786** 0.851*

(0.101) (0.065) (0.066)

College graduate0.975 1.363* 1.531***

(0.111) (0.165) (0.173)

Advanced degree0.815 1.508* 1.957***

(0.122) (0.282) (0.319)

Standardized fam-ily income

1.017 1.428*** 1.564***

(0.059) (0.076) (0.097)

Standardized read-ing score

1.041 1.232*** 1.540***

(0.061) (0.068) (0.081)

Standardized math score

1.137* 1.447*** 2.006***

(0.068) (0.087) (0.111)

School urbanicityc

Rural0.976 1.024 1.141

(0.110) (0.096) (0.140)

Urban0.99 0.934 0.851

(0.121) (0.083) (0.099)

N 6060 6060 7720 7720 7620 7620

Note. Odds-ratios presented. Standard error located directly under coefficients. *p < 0.05; **p < .01; ***p < .001. aReference category for race is white. bReference category for mother’s education is some college. cReference category for school urbanicity is suburban school. Analysis is for public students who have remained in the same school between 10th and 12th grade, weighted using F1PNLWT, and clustered by school.


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Figure 2 represents these relationships graphically, by plotting predict-ed probabilities that students at schools with various caseload sizes speak with counselors about college and articulate college plans with control variables held at the sample mean. As this figure makes clear, 12th grad-ers in schools with large counselor caseloads are less likely to speak with their counselors than similar students in schools with smaller caseloads, although this relationship flattens somewhat at the top of the caseload dis-tribution. Similarly, the figure indicates that students’ probabilities of ar-ticulating college plans decline as counselor caseloads increase, although this relationship reverses among students in schools with the very highest counselor caseloads.

Two points are particularly worth noting here. First, the relationship between caseloads and students’ odds of speaking with counselors and their odds of articulating college expectations are both much more pro-nounced in the 12th grade than in the 10th grade. Although there was no relationship between caseload and expectations in the 10th grade, the counselor caseload is significantly negatively related to 12th graders’ odds of college plans. This finding suggests that even the most overbur-dened counselors attempt to discuss higher education with a broad array

Figure 2. Predicted probabilities of the effect of the counselor caseload on students’ 12th grade outcomes: whether students plan to attend college and whether a student spoke with a school counselor about college. Predicted probabilities from the full models in Table 3, with control variables held at the mean.


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of students at the beginning of their educational careers. In the last year of high school, however, many students in schools with large counselor case-loads do not discuss college with a counselor. One possible explanation for this finding is that overburdened counselors may invest their coun-selor resources more selectively among high school seniors. Second, the predicted probabilities graphed in Figure 2 suggest that in schools with an average counselor caseload or smaller, students have nearly the same probability of speaking with counselors about college as planning to at-tend college. However, in schools with larger than average caseloads, these probabilities are divergent, with more students articulating plans to attend college than speaking with a counselor.

COLLEGE ATTENDANCE

Finally, Table 4 considers the relationship between school counselor case-loads and students’ odds of enrolling in two-year and four-year colleges. These multinomial logistic models indicate that there is no significant as-sociation between counselor caseload and students’ odds of attending a two-year college. However, there is a negative association between coun-selor caseload and students’ odds of attending a baccalaureate degree-granting institution, even after controlling for student and school charac-teristics. This relationship is nonlinear, such that the negative association between counselor caseload and odds of attending a four-year college is less pronounced among schools with particularly high counselor casel-oads. This suggests that negative effects of large counselor caseloads on four-year college attendance begin to flatten in the schools with the larg-est caseloads.

Table 4. Multinomial Logistic Regression of Students’ Attendance at a Postsecondary Educational Institution

College Outcomes

Model 1 Model 2

Two-Year College

Four-YearCollege

Two-YearCollege

Four-YearCollege

Counselor caseload0.983 0.789*** 0.997 0.798***

(0.061) (0.053) (0.062) (0.050)

Counselor caseload squared1.064 1.056 1.069* 1.084*

(0.037) (0.043) (0.031) (0.041)

Male0.630*** 0.443***

(0.050) (0.042)

Racea


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College Outcomes

Model 1 Model 2

Two-Year College

Four-YearCollege

Two-YearCollege

Four-YearCollege

Asian1.937** 2.802***

(0.453) (0.622)

Latino1.196 0.902

(0.155) (0.133)

Black1.297 2.144***

(0.179) (0.316)

Other0.588** 0.736

(0.120) (0.162)

Mother’s educationb

Less than high school0.513*** 0.471***

(0.071) (0.074)

High school diploma0.690*** 0.673***

(0.067) (0.071)

College graduate1.294 1.857***

(0.207) (0.291)

Advanced degree0.874 1.717*

(0.216) (0.396)

Standardized family income1.401*** 1.846***

(0.094) (0.127)

Standardized reading score1.176** 1.792***

(0.073) (0.115)

Standardized math score1.389*** 2.626***

(0.092) (0.187)

Urbanicity

Rural0.996 1.525***

(0.119) (0.177)

Urban0.955 0.805

(0.099) (0.097)

N 6970 6970 6970 6970

Note. Odds-ratios presented. Standard error located directly under coefficients. *p < .05; **p < .01; ***p < .001. Models are in comparison to no college attendance. aReference category for race is white. bReference category for mother’s education is some college. cReference category for school urbanicity is suburban school. Analysis is for public students who have remained in the same school between 10th and 12th grade, weighted using F2BYWT and clustered by school.


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Figure 3 illustrates these findings by graphing students’ predicted prob-abilities of enrolling in two-year and four-year colleges, with control vari-ables held at the sample means. This figure indicates that in schools with the largest counselor caseloads, students’ four-year college attendance rates are lowest and two-year college attendance rates are highest. Students in schools with the smallest caseloads are more than twice as likely (63%) to attend a four-year PEI as they are to attend two-year schools (28%), however students in schools with the largest caseloads are 3% less likely to attend 4-year PEIs (40%) than they are to attend two-year colleges (43%). The tapering of the slope for the line of students who do not attend col-lege indicates that students at schools with the average size caseload have about an 18% probability of not attending college, compared to 10% for students in schools with the smallest caseloads.

Figure 3. Predicted probabilities of the effect of the counselor caseload on students’ likelihood of attending college. Predicted probabilities from the full models in Table 4, with control variables held at the mean.


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DISCUSSION AND CONCLUSION

The purpose of this paper is to explore the relationship between the size of high school counselor caseloads and students’ college expectations, preparation, and enrollment patterns. A noticeable shortcoming in the current school counseling literature is the limited research on how coun-selor caseloads affect college access. Carrell and Carrell (2006) even state that as far as they are aware, “there is no direct empirical evidence in support of lower student to counselor ratios” (p. 2). Although there is the general belief that smaller counselor caseloads would affect the coun-seling services available (e.g., Bryan, Holcomb-McCoy, Moor-Thomas, & Day-Vines, 2009; Bryan et al., 2011; Clinedinst & Hawkins, 2011; Corwin et al., 2004), there is virtually no research to specify the ideal caseload size, which is surprising because ASCA (2012b) clearly communicates their rec-ommended caseload of 250 students for every counselor.

The descriptive analyses illustrate that students who are already disad-vantaged are concentrated in schools with larger counselor caseloads, further limiting their access to knowledgeable adults. Students whose mothers have the lowest educational attainment are most represented in schools with an average of 135 10th grade students per counselor; the findings are similar for students whose parents have the lowest incomes. Black and Latino students, who have been noted to be more likely to rely on school personnel for college information and guidance (Freeman, 1997; Perez & McDonough, 2008), are also more concentrated in the schools with the largest caseloads, whereas white students are overrep-resented in schools with the smallest caseloads. Students who already face significant barriers to college (such as limited finances or few col-lege-educated family or community members) also face limited access to their school counselors due to the fact that their counselors typically have many students to manage.

Our analyses indicate that these inequalities in access to counselors have important consequences for students as they prepare for college through-out high school and transition from high school to college. Even after con-trolling for student and school background characteristics, we find that students in schools with high counselor caseloads are less likely to speak to their counselors, less likely to formulate and act on college plans, and less likely to attend four-year colleges. Notably, our findings suggest that the association between counselor caseload and student behavior are more pronounced in the later stages of the high school-to-college transition than the early stages, net of the same control variables. We find that nearly all 10th graders, regardless of school-level counselor caseloads, articulate college expectations. However, as students progress through high school,


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large caseloads negatively impact students’ college preparation and at-tendance behaviors. Previous research indicates that early and consistent expectations for college are an important step in progressing through the preparation process and attending four-year institutions (Klasik, 2012). However, the current study indicates that students are formulating these expectations independently of their access to school counselors. Based on these findings and the extant literature, we advise counselors to guide students through the necessary steps of preparing for and enrolling in col-lege: taking requisite coursework and admissions examinations, applying for financial aid, applying to multiple institutions, and finally enrolling.

The counselor caseload was also significant in predicting students’ likeli-hood of taking the SAT/ACT. This is an important part of college prepa-ration because many colleges and universities, in particular closed-access institutions, use standardized tests to evaluate students’ likelihood for suc-cess at their school and may use scores as a placement tool (e.g., indicating whether a student needs to enroll in remedial courses). Taking these exams signals to both teachers and counselors a student’s intent to apply for and enroll in college (Tuitt et al., 2011). The results demonstrate that counsel-ors’ guidance impacts student outcomes when they are related to discrete college preparation and attendance milestones such as taking college ad-missions tests and actually attending college. These results align with the findings that college coaches can increase students’ enrollment in college by focusing efforts on distinct tasks, such as completing aid applications and applications to three or more institutions (Stephan & Rosenbaum, 2013).

While Rosenbaum et al. (1996) worry that the “college for all” message conveyed by U.S. high school counselors leads large numbers of unpre-pared students to enroll in community college, this finding indicates that counseling caseloads are unrelated to two-year college enrollment. However, students enrolled in schools with the lowest counselor caseloads are more than 50% more likely to enroll in four-year colleges than their peers in schools with the highest counselor caseloads, net of controls.

Throughout our analyses, we find evidence to suggest that the relation-ship between counselor caseloads and student outcomes are nonlinear, such that students in the schools with the highest caseloads are more likely to articulate college plans, take the SAT/ACT, and enroll in four-year col-leges than one might expect given the overall relationship between casel-oad and student college transitions. This finding may indicate that schools with the largest counselor caseloads have developed alternative mecha-nisms to provide students with college encouragement and information. One possibility is that teachers in these schools fill in for overburdened counselors and help students plan for college. Another possibility is that these schools rely heavily on extracurricular college outreach programs.


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IMPLICATIONS, LIMITATIONS, AND FUTURE RESEARCH

Our findings suggest that efforts to reduce counselor caseloads could lead to improved student outcomes and more equitable access to higher education. Smaller caseloads allow for a greater opportunity to develop social capital, providing students with sources of college knowledge and with each counselor potentially having increased availability for precol-lege counseling. Clinedinst and Hawkins (2011) argue that one way to increase college completion is to implement state policies that will im-prove middle and high school counseling, which includes maintaining reasonable school counselor and college counselor caseloads and man-aging counselors’ time on task. With this study and additional research, school counseling positions may become better defined, more valued and understood, and may enjoy greater support at district, state, and federal levels. By communicating the importance of small counselor caseloads, these findings may lead schools and districts to implement policies that protect counselors’ time, such as redistributing noncounseling duties to other employees or considering part-time or contracted employees (e.g., test administrators) during high-need times.

Counselors can help create and maintain a pervasive college-going cul-ture, which may increase students’ college knowledge and improve college attendance rates of traditionally underrepresented students, particularly in schools with new or low college-going traditions. Part of developing a college-going culture may include educating teachers, parents, and other community partners about the importance of preparing early for college and providing tools for how to talk with students about college. Districts, particularly those with a strong college-going culture, may provide addi-tional structural support for precollege counseling and prioritize counsel-ors’ efforts to maintain or increase access to college and college prepara-tion resources to all students.

However, these analyses are limited in several important ways. First, these correlational analyses cannot conclusively isolate the causal relationship between counselor caseloads and student outcomes. While our analyses control for a limited set of student demographic family background char-acteristics, as well as students’ prior achievement, other factors such as wealth, neighborhood quality, and commitment to college may confound observed associations between counselor caseloads and student outcomes. Second, as mentioned, this study takes into account the effects of several demographic differences by controlling for these variables, yet we do so in reference to white and female students and include a continuous vari-able for income. We call for future research to examine how the counselor caseload distinctly impacts outcomes for students in various racial/ethnic,


26

income, immigrant, and primary language subgroups and school settings (e.g., charter schools and school urbanicity) by analyzing subgroups sepa-rately. Third, although this study provides a view of how the size of the counselor caseload affects students’ postsecondary choice process, this da-taset does not have qualitative or frequency measures of students’ interac-tions with counselors. Because of this we cannot understand how these student–counselor interactions occurred, who initiated them, the duration or frequency of the meetings, or the type of guidance provided. It is plau-sible that several one-on-one meetings with a school counselor could have a very different effect than one large-group assembly. Additionally, students’ interactions with college coaches or other noncredentialed personnel would not be captured in the current study but could lead to sizable effects in student outcomes. Chicago Public Schools employed college coaches who assisted students in completing key actions in the college preparation process. In these schools more students completed the Free Application for Federal Student Aid (FAFSA) and three or more college applications, as compared to schools without college coaches (Stephan & Rosenbaum, 2013). Employing college coaches may improve college preparation and attendance rates without increasing the number of school counselors.

Using another dataset, such as the High School Longitudinal Study of 2009, which has a counselor survey in addition to the administrator in-strument, could aid our understanding of school counselors’ use of time, counselors’ caseloads, and schools’ college preparation resources. Follow-up studies using future waves of the ELS: 2002 data could also lead to a better understanding of long-term effects of the size of the counselor caseload regarding degree completion and earning potential.

Further investigation using a variety of analytical approaches and set-tings of how counselor caseloads affect student outcomes is warranted to better illustrate the effectiveness of the school counselor and the impacts of the size of the school counselor caseload. In addition to high schools, middle school counseling programs should also be examined; monitor-ing counselor caseloads in middle school may increase students’ access to academic pathways that lead to college eligibility and readiness (J. M. Lee et al., 2011). Furthermore, precollege counseling may occur in less formal spaces and from multiple sources. Exploring how counselors and schools partner with community resources such as afterschool programs or local community and industry leaders and how students use in-school and out-of-school resources can result in a better allocation of counselors’ time and other resources.

Mixed-methods research in this area could provide useful information on how counselors and counseling resources are allocated and utilized and may add to the generalizability of the current findings (Carter &


27

Hurtado, 2007). Comparative analyses between schools with large and small caseloads, and mixed-methods case studies in particular, may pro-vide insight on how counselors employ college guidance techniques, to which students, and the extent to which this guidance translates into ob-servable actions (i.e., ordering and submitting high school transcripts for college). Additionally, accounts from multiple school personnel, (e.g., ad-ministrators, counselors, teachers, and paraprofessionals) students, and parents could provide for a well-rounded understanding of how school counselors affect student outcomes. Student and counselor narratives may illuminate the college choice process even further, allowing for a rich un-derstanding of precollege counseling interactions and influences. These suggestions are not exhaustive but can address some limitations of the cur-rent dataset and the limited research in the field. This paper attempts to fill the current gap in research about the relationships between the school counselor caseload and student outcomes and may open the door to addi-tional empirical work exploring the profession of high school counseling in regards to college access and attendance, as well as other indicators of student success.


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CHENOA S. WOODS is a doctoral candidate in the School of Education at the University of California, Irvine. Her research explores how underrep-resented minority students and students from low-income backgrounds prepare for the transition between high school and college within their social contexts.

THURSTON DOMINA is an associate professor in the School of Education at the University of California, Irvine. His research pairs the demographic and economic empirical methods with sociological theory to better un-derstand the relationship between education and social inequity in the contemporary United States. His most recent publication explores the re-lationships between merit aid programs and students’ achievement and college trajectories.

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