high school guidance counselor recommendations: the role of student race, socioeconomic status, and...

High School Guidance Counselor Recommendations:The Role of Student Race, Socioeconomic Status,

and Academic Performance

Frank Linnehan1

Drexel UniversityChristy H. WeerSalisbury University

Paul StonelyNational Commission of Cooperative Education

Boston, MA

This study explored the relation between student characteristics and counselor rec-ommendations. Based on a sample of 1,713 students, the results indicate that coun-selors recommended community colleges to students from lower socioeconomicstatus (SES) backgrounds more strongly than to students from higher SES back-grounds and recommended 4-year institutions more to students from higher SESbackgrounds than to students from lower SES backgrounds. White counselors weremore likely to recommend admission-related activities toward 2-year colleges toWhite than to Black students. In addition, for upper-class students, recommenda-tions toward community college were stronger for Whites with low academic per-formance than for Blacks with low performance; the reverse was true for upper-classstudents with strong academic performance. Areas for future research are identified.jasp_725 536..558

Research has suggested that high school students make postsecondaryeducational decisions based on information gained from a variety of sources,including peers and parents (sources of human, financial, and social capital totheir children; Kerckhoff, 1995), as well as teachers and guidance counselors(Royster, 2003). High school counselors provide educational planning andguidance to students and, therefore, are likely to be an important source ofinformation for students contemplating their postsecondary options (Hoyt,2001; Jenkins, 1987; O’Dell, Rak, Chermonte, Hamlin, & Waina, 1996). Inaddition, many counselors know college recruiters personally and interact ona regular basis to connect students to schools (Royster, 2003).

A study of North Carolina students by Knox, Pratto, and Mann Callahan(1974) showed that while 9% of students indicated a school counselor was the“most helpful person” in deciding their postsecondary educational plans,

1Correspondence concerning this article should be addressed to Frank Linnehan, DrexelUniversity, 101 N. 33rd Street Academic Building, Philadelphia, PA 19104. E-mail: [email protected]

536

Journal of Applied Social Psychology, 2011, 41, 3, pp. 536–558.© 2011 Wiley Periodicals, Inc.

72% of students who decided to go to college late in their high school careersindicated that they did or would use the assistance of a guidance counselor.Moreover, the counseling literature strongly promotes the role of counselorsin helping students explore their postsecondary educational options (Hoyt,2001; O’Dell et al., 1996).

Although past research has shown evidence that counselor advice hasrelevance to students’ postsecondary educational decisions, little, if any,research has looked at antecedents of this advice. In particular, we wereunable to find research exploring a possible relation between counselor rec-ommendations and characteristics of the high school student. The purpose ofthe current study is to address this gap in the literature, and since research hasshown a significant relation between race, socioeconomic status, and aca-demic performance among U.S. high school students (Alexander, Entwisle, &Bedinger, 1994; Sirin, 2005), our study focuses on these characteristics.

As there are significant economic consequences associated with the choiceof attending a 2-year or a 4-year institution, we include counselor advicetoward both types of institutions. These consequences stem from the positiverelation between the amount of education received and one’s future earnings(Hoyt, 2001). Additionally, it is unlikely that those earning a 2-year degreewill complete another degree in a 5-year period; in fact, only 10% of thosestudents will earn a bachelor’s degree in a 5-year period, as compared to 57%who attend a 4-year institution (Hoyt, 2001).

Our hypotheses focus on the roles that race and socioeconomic status playin influencing guidance counselors’ advice to high school students. We relyon embeddedness racial deprivation approaches as our theoretical founda-tion, and because we suggest that the racial composition of the counselor–student dyad will influence counselor recommendations, we include theoriesof relational demography and aversive racism (Dovidio & Gaertner, 2000;Gaertner & Dovidio, 2005).

Theory and Hypotheses

Race, Racism, and Postsecondary Education

While a number of reasons have been proposed to explain racial inequal-ity in U.S. education (Gamoran, 2001), it has been argued that racism con-tinues to be an important factor contributing to the academic achievementdisparity between Whites and Blacks. Research has suggested that racismremains a part of U.S. society and that the “victories” of the Civil Rights erahave not resulted in increasing percentages of Blacks gaining access to acollege education (Kalmijn & Kraaykamp, 1996; Royster, 2003).

RACE, SOCIOECONOMIC STATUS, AND ACADEMIC PERFORMANCE 537

Not only is there a disparity in academic achievement, but other educa-tional inequities between Whites and Blacks also exist. For example, Blackstudents are not equally represented at selective institutions (Kao & Thomp-son, 2003; Karen, 2002) and, along with other students of color, Blacks makeup a greater proportion of students attending community colleges, as com-pared to 4-year institutions (Kao & Thompson, 2003). In 2006, Black, non-Hispanic students represented 13% of all students enrolled in communitycolleges, while this percentage was 9% at 4-year institutions. Enrollment ratesfor Hispanic students were 14% and 7%, respectively (National Center forEducation Statistics, 2006). As such, many Blacks today begin their careerswith educational credentials that are not equal to those of Whites (Royster,2003).

From a racial embeddedness perspective, this educational disparity maybe the result of a lack of social capital available to Black students, in com-parison to those that are available to White students. According to Putnam(2000), social capital refers to “connections among individuals—social net-works and the norms of reciprocity and trustworthiness that arise fromthem” (p. 19). Social capital is developed through personal and institutionalnetworks and is deemed necessary for learning about and preparing studentsfor opportunities such as getting a job or selecting a college. Moreover, thesenetworks are seen as necessary for fully exploiting opportunities once theyhave emerged (Royster, 2003).

In essence, an embeddedness perspective suggests that opportunities existfor those who know the right people (Royster, 2003), and Black students arein less desirable positions, compared to White students, in that they may notbe affiliated with well-placed institutions or embedded in powerful informa-tion networks from which to develop social capital. Evidence from a study ofhigh school students’ transitioning from school to work provides indirectevidence that Black students may be less “embedded” than White studentsand, as a result, receive lower quality assistance regarding post-graduationopportunities (Royster, 2003). Specifically, as compared to Black students,White students received more effective job-related advice and material assis-tance from family, friends, and teachers. Black students received only verbalencouragement from teachers and relied on advice from more poorly situatedBlack family members and friends. In addition, Black students tended to bedisproportionately guided toward general and remedial tracks in schools,while Whites were steered toward academic tracks.

Given the persistence of racist views in U.S. society—and the differencesin minority populations at 2-year and 4-year institutions—it is likely thatcounselors’ recommendations are influenced by students’ race. Specifically,an embeddedness perspective suggests that counselors are more likelyto recommend admission activities toward community colleges to Black

538 LINNEHAN ET AL.

students than to White students, and admission activities toward 4-yearinstitutions to White students than to Black students. As such, we proposethe following:

Hypothesis 1a. Counselors will be more likely to recommendadmission-related activities for community college to Black stu-dents than to White students.

Hypothesis 1b. Counselors will be more likely to recommendadmission-related activities for a 4-year college or university toWhite students than to Black students.

Relational Demography and Postsecondary Education

While it is likely that the race of a student may be related to counselors’postsecondary educational recommendations, theories of relational demog-raphy would suggest that the race of the counselor and the race of the studenttogether may also play a role in explaining differences in counselor recom-mendations for students of different races. Relational demography refers tothe degree to which individuals are similar or different in their demographicmakeup. Demographically similar individuals are thought to establish posi-tive relationships more readily than individuals who differ in their demo-graphic characteristics and, therefore, may develop stronger internal socialcapital (Leana & Pil, 2006). Social identity theory (Tajfel, 1981; Tajfel &Turner, 1979) and the similarity-attraction paradigm (Byrne, 1971) have beenused as theoretical support in identifying the causal mechanisms throughwhich demographic similarity and differences result in attitudinal and behav-ioral outcomes.

According to the similarity-attraction paradigm, individuals who aredemographically similar are likely to have corresponding values, beliefs, andexperiences (Byrne, 1971). Individuals tend to be attracted to and influencedby those whom they perceive to be similar to them, a belief that then rein-forces social interaction and positive rapport among demographically similarindividuals (Cunningham & Sagas, 2006; Riordan, 2000). Further, socialidentity theory (Tajfel, 1981; Tajfel & Turner, 1979) argues that people definethemselves not just in terms of the individuals with whom they are alike, butalso according to the groups to which they belong. Individuals categorizethemselves and others based on readily available, socially significant cues(e.g., race) and seek membership in groups that are deemed as highlyregarded (Crocker & Luhtanen, 1990).

These social identity and categorization processes are likely to evoke asimilarity bias such that individuals will identify more closely with others


who share their racial category memberships (Elsass & Graves, 1997). Apotential outcome of this similarity bias is favoritism and interpersonalattraction toward demographically similar individuals (Ashforth & Mael,1989; Brewer & Miller, 1984; Cunningham & Sagas, 2006; Schwarzwald,Koslowsky, & Allouf, 2005; Tsui & Gutek, 1999; Tsui & O’Reilly, 1989).Thus, racially similar counselor–student dyads are more likely to developstronger interpersonal relationships and build stronger internal social capital(Leana & Pil, 2006) than racially dissimilar dyads. Based on this relationship,counselors are more likely to provide stronger advice toward a postsecondaryinstitution when the counselor and the student are racially similar than whenthey are racially dissimilar. As such, we propose the following:

Hypothesis 2. White counselors will be more likely to recom-mend admission-related activities to an educational institutionto White students than to Black students, while Black counse-lors will be more likely to recommend the same activities toBlack students than to White students.

Socioeconomic Status and Postsecondary Education

Scholars have questioned the degree to which educational institutionsserve to reproduce social order (Mills, 2008; Naidoo, 2004; Robbins, 2005)through sorting mechanisms based on socioeconomic status. Parental incomeas an indicator of socioeconomic status reflects the potential social andeconomic resources available to students, and studies have indicated a posi-tive relation between socioeconomic status and participation in post-secondary education (Dika & Singh, 2002).

Research has explained this relation as a result of disparities in socialcapital across socioeconomic classes (Coleman, 1988; Dika & Singh, 2002).Bourdieu (1986) highlighted the importance of economic capital and sug-gested that it provides opportunities to gain other forms of capital, such ascultural and social capital. He suggested that the economically privilegedhave the financial resources to develop cultural capital, and their economicposition can be used to create social capital. As such, as compared to indi-viduals from higher socioeconomic backgrounds, individuals from lowersocioeconomic backgrounds have fewer opportunities to build social capital,thus perpetuating their status (Coleman, 1988).

From a deficits perspective (as described in Royster, 2003), disparityamong educational and career achievement results not only from differencesin personal and social networks from which individuals may develop andbuild social capital, but also from the lack of or deficiency in the skills and

540 LINNEHAN ET AL.

values necessary to gain such capital. It has been suggested that, if available,social capital is only beneficial to individuals if such capital is “mobilisable”(Anthias, 2007), and those from lower socioeconomic backgrounds may nothave the same opportunities to mobilize or leverage their social capital as dothose from higher socioeconomic backgrounds (Coleman, 1988).

Hossler, Braxton, and Coopersmith (1989) reported that students fromhigher socioeconomic backgrounds are more likely to continue their educa-tion after high school than are students from lower socioeconomic back-grounds. Moreover, students whose parents are from a lower socioeconomicclass are more likely to attend community colleges than are students whoseparents are from a higher socioeconomic class (Bers & Galowich, 2002).Given the idea that educational institutions serve to reproduce social orderthrough mechanisms based on socioeconomic status (Mills, 2008; Naidoo,2004; Robbins, 2005) and that guidance counselors play an important rolein providing high school students with information regarding their post-secondary educational opportunities, it is likely that students’ socioeco-nomic class may influence counselors’ recommendations. As such, wepropose the following:

Hypothesis 3a. High school guidance counselor recommenda-tions to attend a community college will be negatively related tothe student’s socioeconomic status. Counselors will be lesslikely to recommend admission-related activities for a commu-nity college to students from a higher socioeconomic class thanto students from a lower socioeconomic class.

Hypothesis 3b. High school guidance counselor recommenda-tions to attend a 4-year college or university will be positivelyrelated to the student’s socioeconomic status. Counselors willbe more likely to recommend admission-related activities for a4-year institution to students from a higher socioeconomic classthan to students from a lower socioeconomic class.

Race, Socioeconomic Status, and Academic Performance

The term aversive racism describes the seemingly paradoxical state ofconsciously maintaining and projecting a nonracist self-image, while uncon-sciously harboring negative feelings and beliefs about members of groups inthe minority (Dovidio & Gaertner, 2000). These unconscious beliefs developas a result of normal human cognitive processes that categorize others basedon easily observable cues, such as race (Gaertner & Dovidio, 2005). Indivi-duals tend to classify others into in-groups and out-groups. In the U.S., some


Whites may have the tendency to categorize people on the basis of raceautomatically, which ultimately elicits evaluative racial biases and stereo-types (Gaertner & Dovidio, 2005).

Since aversive racists sincerely believe that they hold egalitarian beliefs,they do not intentionally discriminate against members of minority groups.However, since aversive racists unconsciously hold negative views of under-represented minority groups, discrimination is likely to occur if a discrimi-nating decision or behavior may be attributed to a factor or factors otherthan race. For example, Dovidio and Gaertner (2000) found that Whiterespondents in a simulated employment context discriminated against Blackswhen the decision context was ambiguous (i.e., there was an unclear matchbetween a candidate’s qualifications and position criteria), but did not dis-criminate against Black candidates relative to White candidates when thecandidates’ qualifications were clearly either strong or weak. Similarly, in astudy examining the influence of race on employee assessment, Gilbert andLownes-Jackson (2005) found that White respondents rated Black womenlower than White women on “soft” work characteristics, including serious-ness about work and emotional stability. However, no significant differenceswere found in the way Whites rated Blacks and Whites with respect to“harder” outcomes, such as hiring, salary, and benefits.

In the current context, an aversive-racism viewpoint would lead to theexpectation that when nonracial characteristics of a student create an unam-biguous situation, race will not play a role in counselors’ advice as a resultof the strength and clarity of these nonracial factors. For example, as wehypothesized, it is likely that counselors will be more likely to recommendstudents from higher socioeconomic backgrounds to attend 4-year institu-tions than students from lower socioeconomic backgrounds. However,when nonracial characteristics create an ambiguous situation for the coun-selor, there will be a difference in counselors’ advice by race, such thatadvice to White students will be stronger than advice to Black students, asthe racial difference in the counselors’ recommendations may be subcon-sciously attributed to the ambiguity of the situation and not to racial bias.As such, counselors will be more likely to differentiate their advice betweenBlacks and Whites in this ambiguous situation (Gilbert & Lownes-Jackson,2005).

In the present study, in addition to socioeconomic status and students’race, we also included academic performance as a factor that is likely to betaken into consideration by the counselor when recommending 2-year and4-year institutions. This academic factor is representative of human capitaland has been shown to influence postsecondary educational plans and atten-dance for high school students (Hossler et al., 1989). Indeed, studies using theNational Education Longitudinal Study of 1988 (Karen, 2002) and 1994

542 LINNEHAN ET AL.

(Walter-Perna, 2000) have confirmed the importance of academically basedfactors (e.g., test scores, high school grades) on enrollment in college.

As such, academic performance and socioeconomic status may createboth ambiguous and unambiguous situations for counselors, and the impactof student race on counselor recommendations will differ depending on theambiguity of the situation. For example, when a student’s family is from ahigher socioeconomic class and the student has strong academic perfor-mance, race is less likely to play a role in counselor recommendations as aresult of the strength and clarity of nonracial factors. However, when asituation is more ambiguous—as in the case of a student whose familycomes from a higher socioeconomic class, but the student has weak aca-demic performance—it is likely that the student’s race will influence coun-selor advice. In this situation, counselors’ advice to White students will bestronger than advice to Black students since the racial difference in thecounselors’ recommendation may be subconsciously attributed to the ambi-guity of the situation and not to racial bias. As such, since counselors willbe more likely to differentiate their advice between Blacks and Whites inthis ambiguous situation (Gilbert & Lownes-Jackson, 2005), we propose thefollowing:

Hypothesis 4a. There will be a three-way interaction betweenstudent race, economic class, and academic background. Forupper-class students, counselors will be more likely to recom-mend admission-related activities to White students with lowacademic performance than to Black students with low aca-demic performance.

Hypothesis 4b. There will be a three-way interaction betweenstudent race, economic class, and academic background. Formiddle-class students, counselors will be more likely to recom-mend admission-related activities to White students with highacademic performance than to Black students with high aca-demic performance.

Method

Participants and Procedure

Data collection for the present study was sponsored by the NationalCommission of Cooperative Education (NCCE) as part of its annual surveyof high school guidance counselors. For the purposes of the present study,the commission purchased a mailing list with the addresses of all high schools


in the U.S. and, if known, the name of the head guidance counselor. If theguidance counselor’s name was not known, the labels were addressed to“Head of Guidance” at the school. A total of 19,855 surveys were mailed, and2,371 (response rate = 11.9%) were returned.

Based on the total number of high schools by region (Northeast, Midwest,South, and West) in the U.S. for the years 2003 to 2004,2 surveys were sent to67% to 80% of all public and private high schools nationally. Table 1 showsthe details of the total number of counselors in public high schools, the totalnumber of high schools in the U.S., the number of surveys distributed in eachregion, the total responses received, and those that were used in the presentanalyses.

We used an experimental, between-subjects design, manipulating fourstudent characteristics: gender, race, academic performance, and socioeco-nomic background. The counselors were told that the purpose of the studywas to identify factors that may influence the likelihood that they wouldsuggest or recommend different types of colleges to students. Each surveyincluded a description of a student, and the respondents were asked a seriesof questions about the likelihood that they would recommend certainadmission-related activities to the student described in the survey.

We used two categories for each of the four student characteristics tocreate 16 student profiles. Each survey described the student’s gender, race(Black or White), academic performance (two categories characterized bylower or higher SAT scores, and lower or higher class rank), and socioeco-nomic background (two categories characterized by different levels of familyincome and parents’ educational background).

The description of the stronger student academic performance profileincluded a 1225 SAT score and a class rank at the 85th percentile, while themore moderate performance profile described a student with a 1008 SAT anda class rank at the 50th percentile. These two profiles were based on the 2003SAT I results, prior to the writing component being added to the SAT (50th

percentile for 2003 = 1030 total SAT; “College-Bound Seniors,” 2003). Thehigher socioeconomic class background was indicated by a family householdincome of $95,000 and one parent having a master’s degree and the otherhaving a bachelor’s degree. The lower socioeconomic background profiledescribed a family income of $58,000 and one parent having an associate’sdegree and the other having a high school diploma. These two income-levelprofiles were based on the family income levels of students taking the SAT I

2These were calculated from the Digest of Education Statistics, National Center forEducational Statistics (http://nces.ed.gov/programs/digest/d05/tables/dt05_097.asp?referer=list)and the Profiles of USA Private Schools, Private School Review (www.privateschoolreview.com).

544 LINNEHAN ET AL.

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in 2003, which indicated that 53% of all test takers came from families withincomes not exceeding $60,000 (“College-Bound Seniors,” 2003).

In addition, each survey specified the student’s race (Black or White), aswell as the student’s gender. The student was described as being 18 years oldin all of the surveys. As the mailing labels were sorted by ZIP code, wecollated the 16 profiles and then randomly distributed them across themailing labels. Chi-square tests show very few differences in response ratesbased on student characteristics across and within each geographic region.Responses with the higher and lower student SES profiles (c2 = 0.87, p > .10),gender (c2 = 0.73, p > .10), and race (c2 = 0.23, ns) were not significantlydifferent across regions. However, more responses were received with thehigher academic profile than with the lower academic profile (c2 = 8.20,p < .01).

To ensure that respondents referred to the correct student profile whileanswering the questions, manipulation checks were placed at the end of thesurvey. Respondents were asked questions about the student’s characteris-tics described in the profile that they received. Of the 2,371 completedsurveys, 72% of the respondents answered all of the manipulation-checkquestions correctly. We eliminated all responses that answered any of themanipulation-check questions incorrectly, which reduced the sample size to1,717.

Of the counselors who responded, 51.8% were located in a rural area(n = 890), 30.6% (n = 526) were from a suburban area, and 16.3% (n = 279)were in an urban area (22 counselors did not indicate an area type). Withregard to type of school, 85% (n = 1,454) of respondents worked in publicschools, 10% (n = 175) worked in religiously affiliated private schools, and3% (n = 46) worked in non-religiously affiliated private schools (42 counse-lors did not indicate type of school). In addition, 75.6% of the respondentswere female (1,280 females, 414 males, 4 respondents did not report theirgender), and 91.8% were White. The race and gender of respondents byregion are presented in Table 1.

Measures

Counselor advice. For the present study, we created two multi-item mea-sures assessing counselor advice, specifically measuring the likelihood that acounselor would recommend a student to engage in certain admission-relatedactivities for a community college and the likelihood that the counselorwould recommend these same activities for a 4-year institution. To createthese measures, we identified a range of activities through conversations withtwo experienced guidance counselors. We asked each counselor to identify

546 LINNEHAN ET AL.

actions or activities that they would most likely suggest when counselingstudents about postsecondary educational options. We identified sixcommon counselor recommendations to students from these discussions:(a) consider a particular college; (b) learn more about the school; (c) visit thecampus; (d) speak with an admission counselor; (e) apply to the institution;and (f) attend the institution.

Respondents were asked to answer the following six questions based onthe student described in the survey: “In your discussions with this studenthow likely is it that you would suggest the student to . . . ?” (a) consider;(b) apply to; (c) visit; (d) attend; (e) speak with an admission’s counselor abouta 4-year college or university; and (f) learn more about a 4-year college oruniversity. These same six questions were used to assess the likelihood thatthe counselor would make these suggestions about a community college (thephrase 2-year community college was substituted for 4-year college or univer-sity). The responses to all 12 items (six items for the 4-year institution and sixitems for the community college) were rated on a 5-point Likert-type scaleranging from 1 (very unlikely to suggest) to 5 (very likely to suggest).

Since these items were created for the present study, we conducted twofactor analyses: the first on the six items focusing on community college, andthe second on the six items focusing on the 4-year institution. The results weresimilar for both; that is, a single factor emerged in each analysis. We foundthat 75% of the variance was explained by the factor for the 4-year institutionitems (eigenvalue = 4.49), while 85% of the variance was explained by thefactor for the community-college items (eigenvalue = 5.13). The means of theitems loading on their respective factors were used to measure the counselor’srecommendation to a community college (a = .97) and a 4-year college oruniversity (a = .93). As such, the study’s two outcome variables were asfollows: (a) the likelihood that the counselor would recommend to thestudent whose background was described in the survey admission-relatedactivities ranging from considering, applying to, and actually attending acommunity college; and (b) the likelihood that the counselor would recom-mend the same activities to the student for a 4-year institution.

Other measures. We created two dummy variables measuring the racialcomposition of the counselor–student dyad: the first for racially similarWhite counselor and White student dyads (1 = similar race, 0 = dissimilarrace), and the second for racially similar Black counselor and Black studentdyads (1 = similar race, 0 = dissimilar race). Since community colleges and4-year institutions are not dispersed equally throughout the U.S., it is likelythat school location (i.e., rural, urban, suburban) and region (i.e., East,Midwest, South, West) would be related to counselor recommendations.

We first ran a 4 (Region) ¥ 3 (Location) ANOVA testing for mean dif-ferences in counselor recommendations. As expected, there were significant


differences in counselor recommendations for community colleges acrossregions (East, M = 2.63, SD = 1.31; Midwest, M = 3.53, SD = 1.20; South,M = 3.21, SD = 1.31; West, M = 3.39, SD = 1.35), F(3, 1489) = 23.91,p < .001, h2 = .05; as well as school location (rural, M = 3.46, SD = 1.27;urban, M = 2.94, SD = 1.33; suburban, M = 3.04, SD = 1.32), F(2,1489) = 15.75, p < .001, h2 = .02. There were also significant differences incounselor recommendations for 4-year institutions across regions (East,M = 4.29, SD = 0.69; Midwest, M = 4.07, SD = 0.78; South, M = 4.12,SD = 0.85; West, M = 4.16, SD = 0.85), F(3, 1563) = 3.08, p < .05, h2 = .01;as well as school location (rural, M = 4.07, SD = 0.84; urban, M = 4.23,SD = 0.75; suburban, M = 4.21, SD = 0.76), F(2, 1563) = 5.37, p < .01,h2 = .01.

Given the regional and location differences, these controls were includedin the analyses. Since the size of the school may be related to the workload ofthe counselor, school enrollment was also used as a control variable. Theenrollment was transformed to a natural log because of its non-normaldistribution (Cohen, Cohen, West, & Aiken, 2003).

Data Analyses

We used ANCOVAs to test all of our hypotheses. In addition to controlsfor school region (East, Midwest, South, West) and location (rural, urban,suburban), we also included the natural log of the schools’ student popula-tions as a covariate. The first set of ANCOVAs tested for the main effect ofstudent race (Hypotheses 1a and 1b) and included counselor race (Black,White, Asian American, Hispanic, Native American, and Other) and gender.The next set of ANCOVAs included the counselor–student dyad similaritydummy variables (which were tested separately) in order to test Hypothesis 2.We tested Hypotheses 3a and 3b by entering the main effect of socioeconomicstatus. Our final series of ANCOVAs, which tested for Hypotheses 4a and 4b,included a three-way interaction (after controlling for all two-way interac-tions) between student race, socioeconomic status, and academic perfor-mance. Since our hypotheses predict specific directions of differences acrossour main effects, the significance is reported using one-tailed statistical tests(Davis, 1991; Konrad & Linnehan, 1995).

Results

Table 2 shows the descriptive statistics and correlations among the dicho-tomous and continuous variables of our study. Student race was negatively

548 LINNEHAN ET AL.

Tab

le2

Mea

nsan

dC

orre

lati

ons

for

Dic

hoto

mou

san

dC

onti

nuou

sV

aria

bles

Var

iabl

eM

SD

12

34

56

78

9

1.F

emal

eco

unse

lor

0.75

0.43

—2.

Log

scho

olsi

ze5.

650.

64-.

02—

3.M

ale

stud

ent

0.47

0.50

-.02

.01

—4.

Hig

her

acad

emic

perf

orm

ance

0.49

0.50

.02

.01

-.02

—

5.W

hite

stud

ent

0.50

0.50

-.01

.04

.01

.01

—6.

Hig

her

SES

stud

ent

0.51

0.50

-.04

.00

-.01

-.02

.01

—7.

Whi

tedy

ad0.

450.

50.0

1.0

3-.

03.0

2.9

1**

.00

—8.

Bla

ckdy

ad0.

020.

14-.

04.0

1.0

1.0

1-.

15**

.03

-.13

**—

9.R

ecom

men

dati

on:

Tw

o-ye

arin

stit

utio

n3.

241.

32-.

02.1

0**

.01

-.26

**-.

04-.

16**

-.01

-.02

—

10.

Rec

omm

enda

tion

:F

our-

year

inst

itut

ion

4.14

0.80

-.00

-.01

.04

.23*

*-.

09**

.08*

*-.

07**

.03

.14*

*

Not

e.N

=17

13.

Cor

rela

tion

sin

clud

eP

ears

on,

phi,

and

poin

t-bi

seri

alco

effic

ient

s.SE

S=

soci

oeco

nom

icst

atus

.W

hite

dyad

:1

=co

unse

lor

and

stud

ent

Whi

te,0

=di

ssim

ilar;

Bla

ckdy

ad:1

=co

unse

lor

and

stud

ent

Bla

ck,0

=di

ssim

ilar.

**p

<.0

1.


related to the outcome measure for 4-year colleges, indicating that counselorswere more likely to suggest these activities to a Black student than a Whitestudent. Correlations between socioeconomic status (SES) and the outcomemeasure for community colleges and 4-year schools were also significant andin opposite directions; higher SES was positively correlated with the likeli-hood of suggesting activities for a 4-year institution, but negatively related tosuggestions toward a 2-year institution. The size of the counselor’s schoolwas positively related to the likelihood measure for community colleges.

The results of the ANCOVAs testing our hypotheses are shown in Table 3.Hypotheses 1a and 1b, which predicted that student race would significantlyinfluence guidance counselors’ recommendations toward a community collegeor a 4-year college or university were not supported. Although the main effectswere significant for recommendations toward 4-year and 2-year institutions,the adjusted means were not significantly different. Hypothesis 2, whichpredicted that racial similarity between the counselor and the student would bepositively related to counselor recommendations, was partially supported.The White counselor–White student similarity dyad variable was significant inthe model predicting community college recommendations, and the adjustedmeans (Ms = 3.54 and 3.05, respectively) were also significantly different. TheBlack counselor–Black student dyad variable was not significant, but theadjusted mean for recommendations to Black students (M = 3.48) was higherthan the adjusted mean for recommendations to White students (M = 3.05).Neither similarity variable was significant in the models for counselor recom-mendations for 4-year institutions.3

As predicted in Hypothesis 3a, the counselors were more likely to recom-mend admission-related activities toward community colleges for studentsfrom lower socioeconomic backgrounds (M = 3.31) than to those fromhigher socioeconomic backgrounds (M = 2.87). Consistent with Hypothesis3b, SES was also significant for our 4-year institution outcome; however,after adjusting for the covariates, the mean difference between the outcomesfor lower SES students (M = 3.92) and higher SES students (M = 4.07) wasnot statistically significant.

To test Hypotheses 4a and 4b—which predicted a significant three-wayinteraction between student race, socioeconomic class, and academic perfor-mance with counselor recommendations—we ran ANCOVAs that included athree-way interaction term between SES, student race, and academic perfor-mance and controlled for the two-way interactions (SES ¥ Race; SES ¥Academic Performance; Race ¥ Academic Performance). These hypotheses

3At the suggestion of an anonymous reviewer, we also tested a similarity variable ofcounselor–student gender. This was not significant for either the 2-year institution recommen-dation, F(1, 1428) = 0.34, ns; or the 4-year recommendation outcome, F(1, 1497) = 0.26, ns.

550 LINNEHAN ET AL.

Tab

le3

Res

ults

ofA

NC

OV

As

byH

ypot

hese

s

Mai

nef

fect

M90

%C

IM

90%

CI

F(d

f)

Tw

o-ye

arin

stit

utio

nsB

lack

Whi

teH

ypot

hesi

s1a

Rac

eof

stud

ent

3.14

a2.

88–3

.40

3.03

a2.

77–3

.29

3.22

(1,1

429)

*Si

mila

rD

issi

mila

rH

ypot

hesi

s2

Whi

teco

unse

lor

3.54

a3.

10–3

.98

3.05

b2.

79–3

.30

4.19

(1,1

429)

*B

lack

coun

selo

r3.

48a

2.94

–4.0

33.

05b

2.79

–3.3

11.

82(1

,142

9)L

ower

Hig

her

Hyp

othe

sis

3aSE

S3.

31a

3.05

–3.5

72.

87b

2.60

–3.1

248

.60

(1,1

429)

**F

our-

year

inst

itut

ions

Bla

ckW

hite

Hyp

othe

sis

1bR

ace

ofst

uden

t4.

06a

3.89

–4.2

23.

93a

3.77

–4.1

04.

19(1

,149

8)**

Sim

ilar

Dis

sim

ilar

Hyp

othe

sis

2W

hite

coun

selo

r4.

17a

3.89

–4.4

53.

98a

3.82

–4.1

41.

54(1

,149

8)B

lack

coun

selo

r4.

18a

3.84

–4.5

23.

98a

3.82

–4.1

41.

07(1

,149

8)L

ower

Hig

her

Hyp

othe

sis

3bSE

S3.

92a

3.75

–4.0

84.

07a

3.91

–4.2

415

.47

(1,1

498)

**

Not

e.M

eans

inro

ws

wit

hdi

ffer

ent

subs

crip

tsar

est

atis

tica

llysi

gnifi

cant

atp

<.0

5.SE

S=

soci

oeco

nom

icst

atus

.*p

<.0

5.**

p<

.01

(one

-tai

led)

.


were partially supported, as the three-way interaction for the 2-year institu-tion recommendations was significant, F(1, 1446) = 4.08, p < .05; but it wasnot significant for the 4-year institutional advice, F(1, 1515) = 0.40, ns.

Plots (Figures 1 and 2) were created to help understand the nature of thisinteraction for the community college outcome. As predicted by Hypothesis5a, for the upper-class students (Figure 2), counselor recommendations forWhite students with low academic performance (M = 3.32) were strongerthan for Black students with low academic performance (M = 3.18). Con-trary to Hypothesis 5b (Figure 1), counselor recommendations for middle-class students were stronger for Black students than for White students withhigh academic performance (Ms = 3.05 and 2.93, respectively).

Discussion

To our knowledge, this was the first study to use an experimental designto examine student characteristics of race, socioeconomic status, and aca-demic performance as antecedents of guidance counselor recommendationstoward postsecondary education. The likelihood that counselors wouldsuggest admission activities toward community college was greater forstudents from lower socioeconomic backgrounds than for students fromhigher socioeconomic backgrounds. This finding is consistent with the social

Figure 1. Relation of academic performance and race of student with counselor community-college recommendations for students from middle-class socioeconomic backgrounds.

552 LINNEHAN ET AL.

stratification perspective in education, which suggests that the U.S. educationsystem reinforces status inequality based on factors such as socioeconomicclass (Bowles & Gintis, 2003; Gamoran, 2001; Hallinan, 2001; Roscigno &Ainsworth-Darnell, 1999). As evidence exists that very few students whoattend a community college will complete a bachelor’s degree in a 5-yearperiod and the fact that there is a strong, positive relation between theamount of education received and one’s future earnings (Hoyt, 2001), ourresults support this pessimistic view of the U.S. educational system.

Despite a significant race of student effect in both the 2-year and 4-yearinstitution models, after controlling for school size, counselor recommenda-tions did not differ across White and Black students. Race did, however, havean indirect effect on counselor advice. Specifically, White counselors’ recom-mendations toward 2-year institutions were significantly higher for Whitestudents than for Black students, suggesting a similarity effect for Whitecounselors. Interestingly, similar results did not emerge for Black counselors.However, it should be noted that after adjusting for school population, themeans were indeed significantly different—and in the predicted direction—forboth 4-year and 2-year institutions. Perhaps the lack of significance of theBlack similarity variable is a result of the limited number of racially similarBlack counselor–student dyads (n = 37) in our sample. If so, future research inthis area should include more racially similar Black counselor–student dyads.

Consistent with an aversive-racism perspective, we found that studentrace influenced counselors’ recommendations when information about the

Figure 2. Relation of academic performance and race of student with counselor community-college recommendations for students from upper-class socioeconomic backgrounds.


student was ambiguous. Counselors were more likely to recommendadmission-related activities toward community college for White studentswith low academic performance than Black students with low academicperformance when both students were from higher socioeconomic back-grounds. Under this ambiguous condition, these differences are attributed tostudent differences in academic performance, rather than race. This resultshould be viewed in the context that it was not replicated in our otherambiguous situation (i.e., students from the lower socioeconomic class withhigher academic performance). Additionally, the three-way interaction wasnot significant for the 4-year college/university outcome. As such, futureresearch should further explore these relations in other data samples.

Evidence that the race of the student had an effect, albeit indirect, oncounselor recommendations is of real interest to scholars studying the mecha-nisms that create societal structure, particularly in light of past research thathas often focused on the role that social class, rather than race, plays in collegeattendance. According to McDonough (1997), “The most stubborn barriers toparity in entrance to college, however, are in social class background, ratherthan race, ethnicity, or gender” (p. 3). These findings provide evidence thatrace may indeed play a role (albeit indirect) in the postsecondary educationalrecommendations that guidance counselors provide to students. Futureresearch in this area should focus on counselors’ underlying motivations.

The results of our study are valuable to those who have argued thateducational inequality based on social class and racial differences is alsoevident in the type of school students attend (Gamoran, 2001; Hallinan,2001). In our literature search, we were unable to locate other studies thatincluded both community colleges and 4-year institutions. Perhaps counse-lors see these institutions as serving very different populations.

Student social class was significantly related to the likelihood that coun-selors would suggest the admission-related activities. Recommendationstoward community college were negatively related to social class; that is,counselors were more likely to recommend admission-related activities for acommunity college to students from a lower socioeconomic class than tostudents from a higher socioeconomic class. Moreover, although not formallypredicted, our results indicate that the counselors were more likely to recom-mend admission-related activities toward a community college to studentswith lower academic performance and admission-related activities toward a4-year college to students with higher academic performance: 2-year institu-tion recommendation, F(1, 1429) = 122.90, p < .01 (lower academic perfor-mance, M = 3.44; higher, M = 2.74); 4-year institution recommendation,F(1, 1498) = 87.70, p < .01 (lower, M = 3.81; higher, M = 4.18). What is mostinteresting about these results is that while 2- and 4-year institutions do servedifferent populations, the profiles we used for the lower-class student and for

554 LINNEHAN ET AL.

the lower academic performer could not have been seen as one describing astudent who would not be able to complete a 4-year degree successfully. Yet,these factors influenced the strength of the counselor’s recommendation.These results can help to inform practitioners (counselors) about how suchdecisions may contribute to the societal stratification of education based onsocial class, rather than on academic ability.

Despite the strengths of the present study—including the use of an experi-mental design, the manipulation checks, and a large, national sample—ourstudy has limitations, as does all research. Because of the low response ratesof our mailing, self-selection bias may have been present. Moreover, becauseof differences in response rates for different regions of the country, thesample may not accurately reflect the national counselor population, thuslimiting extrapolation of the results beyond our study. In addition, while theANCOVA models explained 19% of the variance in community-college rec-ommendations, the analyses explain much less of the variance (9%) for 4-yearcollege/university recommendations. Other variables not included in ourstudy may offer more explanatory power and should be examined in futureresearch.

Finally, although our results show differences in counselor recommenda-tions across many of the demographic characteristics, the extent to whichthese recommendations will influence a student’s choice is outside the scopeof our study. Some scholars have proposed that counselor recommendationsinevitably reflect the expectations of the school and the community in whichit is located and, thus, act to limit the range of academic choices students willconsider (McDonough, 1997). If this is indeed the case, the role of counselorsin social sorting would be felt through the range of possibilities they chooseto suggest to students. This is another important area of focus for futureresearch in social stratification.

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