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Curriculum Tracking and Status MaintenanceAuthor(s): Beth E. Vanfossen, James D. Jones, Joan Z. SpadeSource: Sociology of Education, Vol. 60, No. 2 (Apr., 1987), pp. 104-122Published by: American Sociological AssociationStable URL: http://www.jstor.org/stable/2112586Accessed: 08/07/2009 12:39

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CURRICULUM TRACKING AND STATUS MAINTENANCE

BETH E. VANFOSSEN

State University of New York at Brockport

JAMES D. JONES

East Texas State University

JOAN Z. SPADE

State University of New York at Brockport

Sociology of Education 1987, Vol. 60 (April):104-122

Prior studies of the role of curriculum tracking in status maintenance have offered contradictory results, suggesting either that (1) tracking sorts children from different backgrounds into different curricular programs where they receive differential educational treatments; (2) tracking sorts children on the basis of ability rather than class background, thus facilitating social mobility; or (3) while tracking may sort children, it has little effect upon educational outcomes and thus has no role in status maintenance. This paper uses 1980s data from the High School and Beyond Study to estimate the effects of curriculum tracking over a two-year period on a number of dependent variables for students who have experienced only one track placement. The results show that there are substantial differences among students from different socioeconomic origins in ultimate track destination. Track location is modestly to moderately related to number of courses taken,. academic performance, educational and occupational aspirations, satisfaction with school, perceived values of friends, self-esteem, extracurricular participation and leadership, enrollment in postsecondary education, disciplinary climate, and teacher treatment. A reopening of the status maintenance hypothesis is suggested.

Curriculum tracking is the grouping of students into course sequences and classrooms on the basis of personal qualities, performances, or aspirations. It is a significant feature of the social organization of schools: Around 90 percent of high schools engage in some form of tracking (National Education Association 1968). Sociologists have found it to be a particularly interesting variable because of its possible role in stratification maintenance and status attainment. Over a decade of research has examined whether tracking facilitates the transmission of class status from one generation to another by providing different educational treatments for children of different socioeconomic backgrounds, or whether it instead promotes social mobility by according differential treatments on the basis of ability rather than social class.

The issue has not been entirely resolved because the findings of prior research are particularly contradictory concerning not only the basis of assignment to curricular programs

but also the effects of the curricular programs on achievement and other educational outcomes. The lack of agreement revolves around two questions: (1) Is there a class bias in track selection or track placement? (2) Does tracking in fact have any noteworthy impact on educa, tional outcomes? This paper attempts to reopen discussion of the first question and presents new evidence regarding the second.

Prior research findings pertinent to the role of curriculum tracking in status maintenance fall into three categories. One set of authors have presented evidence that tracking helps to maintain and perpetuate class status from one generation to another by sorting children from different backgrounds into different curricular programs. In these programs, children are accorded differential treatments and encounter different learning environments (Breton 1970; Schafer and Olexa 1971; Rosenbaum 1975, 1976; Alexander and McDill 1976; Persell 1977; Alexander, Cook, and McDill 1978; Alexander and Eckland 1980; Eder 1981; Oakes 1982; Morgan 1983; Jones, Vanfossen, and Spade 1985).

A second category of studies suggests that tracking plays a minimal role in status maintenance because students are placed into tracks more on the basis of ability and motivation than on the basis of their class membership (Jencks et al. 1972; Rehberg and Rosenthal 1978; Heyns, 1974; Davis and Haller 1981; Alexander and Cook 1982). If this is the case, then any differential treatment accorded is unrelated to

An earlier version of this paper was presented at the annual meetings of the American Sociological Associa- tion, August 1986. The research was funded by National Science Foundation grant no. SES-8310687. We thank Edward C. Lehman and Shepard Kellam for their helpful comments and advice, Fred C. Halley for creative technical assistance, and anonymous reviewers for constructive suggestions. Address all correspondence to Beth E. Vanfossen, Department of Sociology, State University of New York, College at Brockport, Brock- port, NY 14420.

104

CURRICULUM TRACKING AND STATUS MAINTENANCE 105

class bias in placement, and tracking should be viewed not as an instrument of status maintenance but as an opportunity for status mobility. Differential treatment can thus be seen as a mechanism for overcoming initial status inequal- ities by concentrating educational resources on students of higher ability regardless of origin.

The third category implies that the debate may be irrelevant, because tracking in high school does not have a significant impact upon achievement, values, and educational outcomes (Jencks et al. 1972; Sewell and Hauser 1980; Alexander and Cook 1982; Kulik and Kulik 1982). For example, Jencks et al. (pp. 34, 107) concluded that "neither track nor curriculum assignment seems to have an appreciable effect on students' cognitive development." Sewell and Hauser similarly postulated that curriculum tracking may not be a significant mechanism of social stratification. Although Alexander and McDill (1976) and Alexander et al. (1978) observed effects of high school curriculum tracking, Alexander and Cook (1982) concluded that the impact of tracking upon achievement is attenuated, in some cases to insignificance, when 9th-grade social-psychological factors, coursework, socialization, and grades are included in the equations.

In this paper, we reexamine these arguments and present new evidence to reduce the inconclusiveness of earlier findings. We address the role of tracking in the perpetuation of status advantage by focusing on three questions. First, does the pattern of recruitment of students into the different curricular programs reveal a class bias? This question addresses the first two categories of past research listed above. Second, does tracking at the high school level have any significant impact on achievement, values, and educational outcomes? Third, are there any concrete classroom or school experiences related to achievement that vary by track location? The second and third questions address the impact of tracking. If we not only find that there are associations among background, track location, and behavioral outcomes but also begin to identify concrete experiences that contribute to the production of differential outcomes, we will achieve considerable progress toward understanding the nature and importance of tracking. With this latter question, we thus seek to trace out the links between socioeconomic origin, track membership, school experiences and treatments, and educational outcomes.

SAMPLE AND METHODS

This study uses data from the first follow-up of the High School and Beyond Study (HSB). The first follow-up, conducted in 1982, used a

subsample of 14,825 high school students from an original sample of 29,737, drawn in 1980.

The HSB is a two-stage stratified probability sample. In the first stage, 1,100 schools were selected. In the second stage, 36 students within each school were selected. The response rate for those students still in school during the follow-up testing was 90 percent. In the fall of 1982, high school transcripts were collected for a subsample of 18,427 members of the 1980 sophomore cohort. Weighting values were devised to take account of both differential selection probabilities for sample members and differential response rates for different types of schools and students.'

In 1984, a third subsample of the original 1980 sophomores was surveyed in a second follow-up, and a 92 percent response was obtained. Since we are interested in one of the variables measured in the second follow-up, we used the smaller subsample of 14,825 students rather than the larger original sample. In all analyses reported herein, the appropriate weighting factor has been applied to approximate the distributions of relationships in the population from which the sample was drawn. For the multiple regression equations, we restricted the sample to those students whose self-reported sophomore and senior track locations were the same. (Because we eliminated those students who may have switched from one track to another, the regression results are uncontamin- ated by distortion of tracking effects due to the presence of students who had experienced two different tracks.)

Following convention, we have included in the regression model those variables that were found by prior research to have an impact upon academic performance and other educational outcomes. We assume that socioeconomic background, race, sex, and educational expectations in the 8th grade are exogenous variables that may have strong independent relationships to the dependent variables. We have also included 10th-grade social-psychological variables (friend's plans to go to college, educational expectations) and 10th-grade academic characteristics (grades so far, courses completed in the subject area of the dependent variable) (see Alexander and Cook 1982). The sophomore measure of the dependent variable is included to control for all causal effects that may have occurred prior to the sophomore testing. All analyses use standard multiple regression with

' For an extensive discussion of weighting procedures, see Jones et al. (1983). Also, interested readers may contact the first author for information on the weights and weighting formulas used in the different analyses reported here.

106 VANFOSSEN, JONES AND SPADE

all variables entered simultaneously and listwise deletion of missing values.

Measures

A description of the variables used in the analysis and the zero-order correlation matrix are presented in Appendices A and C. The measurement of the track variable warrants additional explanation, however.

Track location. Research on tracking has had to grapple with several difficulties in measuring track location. In particular, two problems have surfaced. First, most studies of tracking have had to rely upon student reports of their track location. It is possible that students are not always aware of the track they are in (Ros- enbaum 1980). Second, the two-category variable most frequently used in studies of tracking-academic track versus all other tracks- oversimplifies the nature of tracking, which usually incorporates at least three tracks and may include as many as six.

The validity of the track measure. The issue of validity raised by Rosenbaum (1980) is important. Most studies of tracking have used student reports, since data on track location are rarely found in student transcript records. Using data on 1,554 students from the National Longitudinal Survey of the High School Class of 1972, Rosenbaum found that the correlation between students' reports of their track locations and official school records of their locations is only .60. He concluded that there may be a "real limitation of taking students' track perceptions as indicators of actual tracks" (Rosenbaum 1980, p. 85).2 Rosenbaum does not indicate the percentage of students who report a track location different from that on official school records, but a reanalysis conducted by Fennes- sey et al. (1981, Table 1) suggests that 19.7 percent of the students misreported their track location.

In the HSB data, there are no measures of track position in the transcript data, so we must use student reports. It is fortunate for an examination of validity that students were asked to indicate their track location in both their

sophomore and senior years. We compared the responses in these two years and found that 60 percent of the students indicated the same track location in both years. It is quite possible that most of the students who reported the same track in both years were correctly aware of the curricular path they were following. However, a more reliable method of assessing validity is desirable. We used criterion-related validity measures to assess the value of this measure. From the transcripts collected for the HSB study, two variables were constructed: math course concentration patterns and science course concentration patterns. Students in an academic track either concentrate in (major in) mathematics or science or take four-year college-oriented mathematics and science courses. Students in a general or vocational track either take general math and science courses, limited math and science courses, or no math and science courses at all.

Because of these associations between course patterns and curriculum enrollment, we are able to use the course concentration patterns as our external variables to test the validity of student reports of track location. We computed a new variable, coding students who took college- oriented courses in both mathematics and science as "courses definitely college-oriented," students who took no college-oriented courses in either mathematics or science as "courses definitely not college-oriented," and students who took college-oriented courses in one subject but not in the other as "courses possibly college-oriented." Students were divided into two groups: those whose reported sophomore and senior track locations were the same (track stayers) and those whose sophomore and senior track locations were different (track movers). Then, we determined the course patterns in mathematics and science for students in both of these groups, each differentiated further by self-reported senior track location. The results are presented in Table 1.

We define as errors those students who reported that they were in the academic track but who had taken no college-oriented courses and those students who reported that they were in the general or the vocational track but who had taken many college-oriented courses. We see that the highest levels of error are found among track movers who reported in the senior year that they were in the academic track but who reported in the sophomore year that they were in the general or vocational track. Twenty-eight percent of these students had taken no college- oriented courses in mathematics and science. The error rates are 17.8 percent for the track movers and 10.3 percent for the track stayers, which is somewhat below the 20 percent figure calculated by Fennessey et al. (1981, Table 1).

2 Rosenbaum does not examine the validity of the official school records. It is possible that use of school records on which the student is designated as having followed only one track would present errors for certain analyses. For example, a discrepancy between the student report and the school record is likely to exist for students who switched track locations before their senior year. In these cases, school records that assign a single track pattern to each student would be less valid than student reports if the research is examining educational outcomes occurring while the student is still in school, such as educational expectations, friends' values, and grades.


Table 1. Course-Taking Patterns in Mathematics and Science, by Senior Track Location, for Track Stayers and Track Movers

Courses Courses Courses Senior Definitely Possibly Definitely Percentage Track College College Not College of Students Location Oriented Oriented Oriented Total in Track N

Track stayers Academic 67.5 23.4 9.1 100.0 44.4 2,736 General 14.6 24.9 60.5 100.0 37.3 2,301 Vocational 4.4 17.7 77.9 100.0 18.3 1,132

Total 100.0 6,169 Track movers

Academic 44.7 27.1 28.2 100.0 32.5 1,361 General 17.2 27.5 55.3 100.0 29.6 1,235 Vocational 9.3 25.5 65.2 100.0 37.8 1,580

Total 100.0 4,176

While the error rate for the track stayers is larger than we might wish, we do not deem it too large to invalidate track effects that might be found. (In any case, error is more likely to reduce the size of the track coefficients in the analyses than to inflate them.)

The number of track categories. The second problem in the traditional measure of track location alluded to above-the oversimplifica- tion of track location by the use of only two categories-is partially alleviated in this study by the inclusion in the HSB data of a multiple- category track measure. In this analysis, three categories were used: academic, general, and vocational. Inclusion of the vocational track allows us to compare options taken by students who do not enroll in the academic track.

The nominal coding scheme applied to track location for the regression analysis is called effects coding (Cohen and Cohen 1983). The academic track variable is coded 1 if the student's track location is academic, 0 if it is general, and -1 if it is vocational. The general track variable is coded 1 if the student's track location is general, 0 if it is academic, and - 1 if it is vocational. Effects coding is particularly appropriate for nominal scales when each group is most conveniently compared with the entire set of groups, rather than with a single reference group, as is the case with the more frequently used dummy-variable coding. The statistical impacts on R2 are the same regardless of which type of coding is used.

FINDINGS

The Structure of Tracking

In their sophomore year, 98 percent of the 14,825 high school students were asked to choose a response that best described their high school program. One third of the students indicated an academic program, nearly half a general program, and one fifth a vocational

program. Most of those in the vocational program were in either business- or trade- oriented course sequences.

By the senior year, 14 percent of the original sample had dropped out of school. Sixty percent of the dropouts had reported enrollment in a general curriculum. Of the students remaining by the senior year, only one third (compared to one half in the sophomore year) were in a general curriculum. The sophomore general track is huge, and it contributes more than twice as many dropouts as the other two tracks.

Track, ability, and SES. Our knowledge of the possible status maintenance functions of tracking is increased by an examination of the relationships among sophomore curriculum location, measured test performance, and socioeconomic origin. We use gamma to measure the degree of relationship between the academic track variable and SES (.375), between SES and measured test pprformance (.505), and between measured test performance and the academic track variable (.546). The high degree of intercorrelations indicate that one set of relationships should not be interpreted without consid- ering the other set.

Figure 1 portrays track location by socioeconomic background for three different categories of measured test performance. Consistent with most earlier research, academic performance has a strong relationship to track location. Sixty-one percent of those whose test scores were in the highest quartile had entered the academic track by the sophomore year, compared to 12 percent of those in the lowest quartile-nearly a 50 percent difference.

Figure 1 also indicates that socioeconomic origin has an effect on track location independent of its effect on academic performance. Within the top ability quartile, for example, there is a 16 percent difference between the


10

70 - \

80-

50

30 -

10

jt .eaU.a IO -it AWm a4O -G Figure 1.Sphmrtakloain y w test p c i c

Figure 1. Sophomore track location by sophomore test performnance and socioeconomic origin.

lowest- and highest-SES quartiles in the number who report that they are in an academic track. (By the senior year, this difference has increased to 28 percent: Only 52 percent of the high- performance students from the lowest-SES quartile but 80 percent of the high-performance students from the highest-SES quartile end up in the academic track.)

Finally, it is important to recognize that the long-term indirect effect of socioeconomic origin upon track location through its effect upon measured test performance is unknown, but it conceivably may be quite high. For example, one of the ways that affluent parents may help their children benefit from educational inequality is by ensuring that they perform well academically. The importance of such an indirect path may be underestimated in the traditional regression analysis, particularly when the relationship between socioeconomic background and track location is estimated by an equation that includes a performance or ability measure as codeterminant. Because the track- ability correlation is higher than the track-SES correlation, much of the explained variance that is jointly shared by ability and SES would be reflected in the ability coefficient.

Further, as Gordon (1968) has pointed out, the relative size of a variable's regression coefficient depends to a considerable extent upon the number of other variables with which it is correlated in the equation. In such a case, there is no statistical rule for attributing controlled covariation to the influence of one rather than another of the independent variables, regardless of the disparity in size between their partial correlations. The degree to which the size

of the coefficient for SES is reduced because of its correlation with a large number of the other variables typically included in curriculum tracking analyses (for example, educational expectations, grades, and friends' values) is unknown.

For these reasons, it is important to note the differences among social classes in track location without controlling for test performance. These are graphically portrayed in Figure 2. For example, the chances that a student will be in the academic track are 53 percent in the top-SES quartile and only 19 percent in the bottom-SES quartile; the chances that a student will be in a vocational track are only 10 percent in the top-SES quartile and 30 percent in the bottom-SES quartile. Regardless of the reasons for getting there (prior academic performance, grades, teachers' recommenda- tions, or educational aspirations, all of which may be influenced by socioeconomic background), there are substantial differences among social classes in ultimate track destination. Consequently, whatever the differential treatments of students in separate tracks are, students of varying socioeconomic backgrounds have dissimilar probabilities of experiencing them.

The Consequences of Tracking

We turn now to the second question: Does tracking at the high school level have any significant impact on achievement, values, and educational outcomes? To answer this question, we confine our analyses to the track stayers, as discussed- above, thereby avoiding the contami- nation due to the inclusion of students who have


90

70

50K 40 N

30-

20

10

Figure 2. Sophomore track location by socioeconomic origin.

experienced two track treatments.3 Appendix B presents the means of the dependent variables by track location. In general, the means differ substantially between groups; the academic- track means are the highest, followed by the general-track means and the vocational-track means. For two of the variables-extracurricular activities and perceived teacher treatment-the

means for the general and vocational tracks are similar, but they are lower than the means for the academic track.

Course selection. Tables 2-4 present the estimates of the models of track consequences. The results in Table 2 suggest that curriculum tracks do what they are supposed to do-funnel students into different sequences of study. In all three regressions of mathematics, science, and business courses on the track variables and control variables, both track variables are substantial and statistically significant. Track location appears to have an influence on course enrollments that is above and beyond, and even greater than, the influence of prior academic performance and interests. Thus, as an organi- zational property of the process of schooling, tracking is effective in channeling students into different areas of study and specialization.

Academic performance. Table 2 also presents the predictions of senior test performance in mathematics, science, and a composite (a combination of the scores on tests of vocabulary, reading, and mathematics). In all three cases, effects of the academic track variable are statistically significant at the .05 level or less.4 The regression results suggest that the increase for the academic-track students occurs not only because they take more advanced courses but also because of other hitherto unidentified factors related to their track location.

Because of the history of debate concerning

I While it would be interesting to investigate more fully the characteristics of the track movers, who constitute 40 percent of the sample, their inclusion might divert us from our primary interest, i.e., the effects of tracking. Track movers experience the effects of two tracks, one in the sophomore year and a different one in the senior (and perhaps junior) year. Even if the track movers were examined separately, the results would be basically uninterpretable, because we could not deter- mine to which track we should attribute any differences in student characteristics that developed between the sophomore and senior year measures. An examination of what happens to students in high school would be a worthy subject for another paper. We compared the track stayers to the track movers on several other characteristics. Those movers who end up in the academic track are not as prepared for academic work as the track stayers, scoring nearly half a standard deviation below the track stayers on the sophomore composite test of academic performance. Lower academic readiness does not charac- terize the movers to the general or vocational tracks, however. The movers to the vocational tracks scored nearly a third of a standard deviation higher than the stayers. Track movers are also a little more likely than track stayers to have inconsistent rankings on the performance and socioeconomic status variables. Finally, the majority of the movers (53.3 percent) indicated a general track program in their sophomore year rather than the more goal-oriented academic or vocational programs, which suggests that many track movers are basically undecided about their track placement and their career destinations in their sophomore year.

4 Inspection of the means of the performance variables in both the sophomore and senior years (not shown) indicates that increases in academic performance among academic-track students are substantial, that increases among general-track students are moderate, and that increases among vocational-track students barely exist.


Table 2. Metric Coefficients, Standard Errors, and Standardized Coefficients (in parentheses) of the Effects of Background, School Performance, and Tracking on Seniors' Course-Taking Patterns and Achievement Test Scores, for Track Stayers

Math Science Business Math Science Composite Courses Courses Courses Score Score Score

SES -a -a -.186*** .416** .183** .235** .038 .120 .063 .088

(- 070) (.029) (.030) (.020)

White -a _a .370*** 1.483*** 1.660*** 1.229*** .012 .285 .152 .210

(.052) (.039) (.104) (.039)

Female -a _,122*** 1.162*** -1.430*** -.765*** -.471*** .020 .049 .155 .082 .113

(- 070) (.296) (-.067) (-.088) (-.027)

8th-grade educational -.020* -a .087** -a -a -a expectations .009 .029

(-.024) (.041)

10th-grade educational .034*** .054*** - .029* .254*** .094*** .191*** expectations .005 .006 .014 .046 .024 .033

(.110) (.148) (- .036) (.057) (.052) (.052)

Friend's educational .071*** .076** .131* -a _a -a expectations .019 .023 .039

(.043) (.040) (.030)

Courses taken by .086*** .146*** .260* .111* .160* I0th grade .010 .011 .101 .047 .073

(.088) (.146) (.019) (.023) (.014)

Grades by 10th grade .070*** .104*** -.093*** .440*** .193*** .354*** .006 .008 .020 .064 .032 .046

(.143) (.181) (- .073) (.063) (.067) (.062)

Academic track variable .268*** .348*** -.788*** 1.500*** .514*** 1.404*** .014 .017 .044 .145 .075 .105

(.263) (.284) (- .296) (.103) (.086) (.116)

General track variable -.106*** -.137*** -.293*** -a -a -.249** .012 .014 .036 .084

(-.100) (-.109) (-.105) (-.020)

10th-grade academic per- .021*** .029*** -.004 .699*** .562*** .736*** formance (math, science, .001 .003 .004 .011 .011 .009 or composite score) (.272) (.145) (- .021) (.638) (.569) (.739)

Courses taken by 1.783*** .522*** 12th grade .140 .060

.125 .105

Constant 3.070*** 3.204*** .181*** 12.306*** 6.600*** 14.440*** SE constant .073 .089 .281 .835 .418 .689 R2 (adjusted) .482 .440 .241 .767 .609 .796

a Variable was included but was not statistically significant. * p<.05.

** p<.01. p<.001.

the effects of tracking, the model employed here merits elaboration. All three of these regressions include the sophomore test score in the same subject. Thus, the equations present the relationship of the senior dependent variable to other independent variables while controlling for the sophomore level of the dependent variable. As we expected, the sophomore variable explains most of the variance in the senior variable.

(Thus, those who perform well in the sophomore year tend also to perform well in the senior year, and the Beta coefficient of this relationship is very high.) Since the independent variables might also have had a prior relationship to the sophomore variable, the coefficients will in some cases underestimate the actual degree of relationship.

The standardized Betas for the academic track


variable in the prediction of the performance variables in Table 2 range from .09 to .12. They show about the same degree of unique influence (i.e., influence not shared with and not working through other variables) on the dependent performance variables as the Betas for the course pattern variables. While statistically highly significant, these Betas indicating unique association are relatively small, showing that a change in the track variable of only one standard deviation (which- is about three fourths of the difference between one track and another) is over the two-year period associated with one tenth of a standard deviation change in the performance variable.

The models used in this study and in previous studies are cautious because they distribute joint influences-those shared by tracking and such variables as college expectations, grades, courses taken, or ability-to the coefficients for all interrelated variables. Thus, not indicated by the multiple regression technique is the total effect of the track variable, including its indirect effect working through other variables. We have already seen a strong relationship (r = .54) between the academic track variable and mathematics and science course-taking patterns. Other intercorrelations are probably important as well. A zero-order correlation between the academic track variable and educational expectations of .56, for example, suggests the possibility of joint and mutually reinforcing influences. Educational expectations may influence a student to enroll in a particular track, and once there, the student's experiences in that track may reinforce and strengthen the original expectations. Thus, we should be conscious of the intercorrelations among the independent variables.

As we examine the findings, we should also note whether the academic track variable has a consistent relationship to the multiple dependent variables under study. A single small relationship between track location and a dependent variable, which could be the result of chance, is less impressive than a number of small relationships between track location and many dependent variables.

Finally, we should be aware that a small unique influence over a two-year period may signify a larger influence over the total period in which students are enrolled in school. A number of authors have suggested that tracking often begins as early as the first grade and that tracking decisions at higher levels may be based on tracking patterns established earlier. The cumulative impact of tracking may thus be substantial even when its unique impact over a two-year period is relatively minor. It is for this reason that evidence of a tracking impact, even if minor, is notable.

Attitudes toward school, aspirations, and postsecondary education. Having seen that academic performance is related to track location, we now ask, Are the other variables that are usually associated with performance also affected? Here, we move from a consideration of academic performance to a focus on attitudes toward school and educational and occupational goals.

Students were asked in both the sophomore and senior years if they were satisfied with the way their education was going and if they were interested in school. We measured a student's liking for school by combining these two items. The regression presented in Table 3 indicates that track location does modestly affect changes in satisfaction with school between the sophomore and senior years net of background and other academic and social-psychological measures. The direction of the relationship between changes in satisfaction and track location, revealed in the regression analysis, is made more concrete by comparing within each track the mean of the satisfaction index in the sophomore year to the mean in the senior year. This comparison (not shown) indicates that satisfaction with the education received and interest in school increased among those enrolled in an academic track, did not change among those in the general track, and declined among those in the vocational track. These findings are consistent with those reported by Kulik and Kulik (1982) in a meta-analysis of prior research.

Table 3 reveals a moderate relationship between the academic track variable and senior educational aspirations. The metric coefficient indicates, for example, that the change in the difference between the educational aspirations of academic-track students and the aspirations of general-track students is one quarter of a standard deviation, and the change in the difference between the aspirations of academic- track students and the aspirations of vocational- track students is one half of a standard deviation. These findings are consistent with those of earlier researchers, most notably Alexander and Eckland (1980), Breton (1970), and Alexander and Cook (1982).

Very few researchers have attempted to measure the influence of tracking upon occupational aspirations. In the HSB survey, students were asked to choose, from 17 categories, the type of job they would like to have at age 30. We recoded the categories so that they could be ordered along a continuum of occupational prestige. Table 3 indicates the results of the predictions of senior occupational aspirations by sophomore occupational aspirations, background controls, performance, sophomore educational aspirations, friend's plans for college, and grades. The relationship between track and


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occupational aspirations is modest in size and statistically significant at the .05 level or less. The metric coefficient indicates that enrollment in one track rather than another, net of sophomore aspirations and other factors, would produce an increase in the difference between tracks of .64 point on the occupational aspira- tion scale. (A difference of one point equals the difference between sales work and school teaching, for example, or between school teaching and management.)

In the second follow-up, conducted two years after the senior year, the former students were asked about their postsecondary educational attendance. Table 3 indicates that both the academic track variable and the general track variable are significant predictors of postsecondary educational experiences and that the academic track variable is particularly important in the equation, more so than any other variable in the model except 10th-grade educational expectations. Tracking thus appears to influence not only learning and other characteristics of student life but also adult outcomes.

Social-psychological effects. In this section, we examine the possible effects of tracking on self-concept, peer selection, and extracurricular activities. Much recent literature suggests that tracking in high school affects self-esteem, status among peers, and attachment to school (Findley and Bryan 1970; Schafer and Olexa 1971; Kelly 1975). On the other hand, Kulik and Kulik's (1982) meta-analysis concludes that while tracking affects a student's liking for school, it has little effect on self-esteem.

As shown in Table 3, net of sophomore self-esteem and other variables, the academic track variable has a modest and statistically significant effect upon senior self-esteem. Inspec- tion of the means of the self-esteem variable for the three track locations in both time periods (not shown) indicates that the self-esteem scores of students in the academic track increased slightly between the sophomore and senior years, that the scores of students in the general track remained stable, and that the scores of students in the vocational track declined. These findings are consistent with those of Oakes (1985), who concluded that students' self- concepts and educational plans are highly related to track level, while many other kinds of attitudes, such as perception of the quality of the school, interest in math and English, and attitudes about interpersonal relationships, are not.

Schafer and Olexa (1971, p. 42) reported that enrollment in an academic track encourages a student to become more involved in the extracurricular life of high school. Our measure of participation in nonathletic extracurricular activities is a composite index of items summing

the student's participation in student council or student government, honorary clubs, debating or drama, chorus or dance, school newspaper or yearbook, and cheerleading. The results reported in Table 3 indicate a very small but statistically significant effect of track location upon extracurricular participation. We created a similar measure of participation in sports and found a similarly small but statistically significant effect of track location upon participation in athletic activities. Students in the academic track were a little more likely than other students to be engaged in athletics in the senior year net of sports participation in the sophomore year.

When only the extracurricular items involving leadership (participation in student council or student government, in newspaper or yearbook) are used in the index, the tracking effect is a little stronger, as shown in Table 3.

As Hallinan and S0rensen (1985) found in their observations of 4th- 5th- and 6th-grade reading groups, tracking affects performance and aspirations partly by providing the opportunity for the formation of social networks that may be compatible with such performance. Membership in an instructional group may increase opportunities for student interaction, thus underscoring student similarities, produc- ing new similarities, and, in turn, fostering friendships. Thus, we ask, Does segregation into tracks encourage students to confine themselves increasingly to friendships with people whose values are consistent with the track population? The composite index of friends' values used in the equation is the sum of items asking if the student's closest friend gets good grades, is interested in school, attends class regularly, and plans to go to college. The actual values of the friends were not measured; thus, the index is more properly considered an index of respondents' perceptions of friends' values. Prior research has suggested that students overestimate the congruence between their own values and their friends' values. However, there has been no suggestion that students in a particular track overestimate this congruence to a greater degree than students in other tracks or that overestimation increases between the sophomore and senior years.

The results shown in Table 3 indicate that track location has a statistically significant effect on seniors' perceptions of friends' values net of sophomores' perceptions of friends' values. This finding suggests that enrollment in an academic track rather than a general or vocational track provides an environment more favorable to the development of friendships with peers who are academically oriented (see also Alexander and McDill 1976; Hauser, Sewell,


and Alwin 1976; Schwartz 1981; and Ros- enbaum 1976).

Track Experiences

How does curriculum tracking bring about these changes? Several paths immediately come to mind. Changes in course-taking patterns are generally built into the school's requirements for each track pattern. Course-taking patterns in turn influence how much students learn of subjects such as mathematics, science, or business, and also how much practice they obtain in reading and vocabulary. Segregation in multiple classrooms of students with similar goals and aspirations will lead to friendships between those students. But the results presented above suggest that tracking has an influence above and beyond that of merely getting students into courses. To explore this question more fully, we must look at the processes that go on within the classroom. The HSB instruments did not attempt to measure classroom processes per se, but there are several items that may permit inferences about those processes.

Student perception of discipline problems. In both the sophomore and the senior questionnaires, students were asked to indicate the extent of the following disciplinary problems in their school: students don't attend school; students cut classes, even if they attend school; students talk back to teachers; students refuse to obey instructions; students fight with each other; students attack or threaten to attack teachers. These items reflect the academic climate in a school, which has been found to be an important educational variable (see Rosenshine 1979 for a review). While the question asks about problems in the school, the items concern behaviors occurring in the classroom; hence, we expect students going to classes in different tracks to have different kinds of experiences, even if they are in the same school. The items can thus give us information on the extent to which students in the different tracks habitually encounter disciplinary problems that disrupt learning.

The regression results reported in Table 4 indicate that track location has a statistically significant relationship to senior perception of disciplinary climate. Seniors in the academic track report fewer incidents of students cutting classes, talking back to teachers, and refusing to obey instructions; seniors in the general and vocational tracks report more of these problem behaviors. (Appendix B shows that the average index score varies little between students in the general and vocational tracks but that the average scores of students in these two tracks differ by nearly one-half standard deviation from the average score of students in the

academic track.) These findings are consistent with other reports indicating that classes in the academic track are more serious, spend less time handling discipline, spend more time on task, and place a greater emphasis upon learning (Fisher et al. 1980; Bloom 1981; Stallings and Kaskowitz 1974; Metz 1978; Eder 1981; McDermott and Aron 1978, Schwartz 1981; Karweit and Slavin 1982; Oakes 1985).

Perception of teacher treatment. In the senior questionnaire only, students were asked a series of questions concerning teacher behavior. A factor analysis revealed that four items form a factor, and these were summed to form a composite index of perception of teacher

Table 4. Metric Coefficients, Standard Errors, and Stan- dardized Coefficients (in parentheses) of the Effects of Background, School Performance, and Tracking on Classroom Environment, for Track Stayers

Discipline Teacher Problems Treatment

SES -a -a

White -a -a

Female -a - .375** .126

(- .042)

8th-grade educational expectations -a -a

10th-grade educational expectations -a -a

Friend's educational .518** expectations .152

(.053)

Grades by 10th grade -a - .232*** .051

(- .079)

Academic track variable - .350*** .519*** .051 .115

(-.107) (.084)

General track variable .108* - .267** .042 .094

(.031) (-.041)

10th-grade academic -.014** .041*** performance (composite .004 .010 score) (-.053) (.081)

10th-grade disciplinary .510*** climate .012

(.502)

Constant 5.925*** 20.958*** SE constant .343 .729 R2 (adjusted) .304 .053

a Variable was included but was not statistically significant.

* p<.05. ** p<.Ol.

*** p<.001.


treatment. Seniors were asked how many teachers in their school (1) were patient and understanding, (2) treated everyone with respect, (3) were clear in their presentations, and (4) enjoyed their work.

The regression results reported in Table 4 indicate that track location is significantly related to seniors' perceptions of teacher treatment. The direction of that relationship is suggested in Appendix B, which shows that the average scores on the indices of perception of teacher treatment for general- and vocational- track students are similar and that the average score for academic-track students is nearly two fifths of a standard deviation higher. These results are only suggestive; i.e., they are based on student reports of teacher behavior, not on direct measures of teacher behavior. However, they are consistent with results reported by Oakes (1985) using secondary school data from the Study of Schooling, which show that students in the lower tracks rate their teachers lower on teacher enthusiasm and clarity and higher on punitiveness. Oakes (p. 110) concludes, "The instructional environments of high-track classes were more characterized by a whole set of teacher behaviors thought to promote learning than were those of low-track classes."

DISCUSSION

Curriculum tracking is a pervasive feature of school organization in most American high schools. One reason school officials maintain curriculum tracking is that they think it makes teaching and maintaining order easier and more manageable. Many believe that teaching and learning are facilitated if students of similar performance potential are grouped together so that teachers can focus on their particular needs. Yet, while students end up in their tracks partly on the basis of their performance, performance is not the only basis of track location. A very good student from a lower-SES background has only a 52/48 chance of ending up in an academic track (see Figure 1). Track locations of students are affected by a number of individual and school-level variables other than prior performance, such as socioeconomic background, preferences, the number of seats available in track-related courses, and teachers', counse- lors', and school administrators' perceptions of students' abilities.

Thus, the strong relationship between socioeconomic origin and track location (gamma = .38), which may be even larger than it appears at first glance because of the influence of socioeconomic origin upon test performance (gamma = .51), lends support to the status maintenance hypothesis. There are substantial

differences among students from different socioeconomic origins in ultimate track destination. Even if we discount entirely the joint variation of SES and performance in their relationship to track location, these results still support the argument that tracking helps to maintain and perpetuate class status from one generation to another by sorting children from different backgrounds into different curricular programs.

Does it matter? Once students are committed to their curricular programs, do they take different courses, learn differently, develop different social-psychological traits, perceive their friends as having different values, face different classroom environments, and perceive teacher treatments differently? This research has examined the relationship of the track location of high school students who stayed in the same track to changes in their behavior, performance, and expectations over a two-year period, a period that for many is the culmination of five or more years of tracking. Because all equations (where data are available) include the sophomore level of the senior dependent variable, our model focuses on change, and we accordingly look for modest evidence pertinent to the question.5 This we have found for a number of the outcome variables measured: Over the two-year period, there is a moderate effect of track location on the types of courses taken in mathematics, science, and business and on educational aspirations, and there are more modest effects of track location on academic performance, educational and occupational aspirations, satisfaction with school, perceived values of friends, self-esteem, and extracurricular participation and leadership. The tracking effects extend beyond the high school years to enrollment in postsecoildary education. Improve- ments in the social-psychological variables- liking for school, self-esteem, and perceived values of friends-are not only outcomes of tracking but also reinforcements of tracking, since they are likely to elevate even further the academic achievement and aspirations of students in the academic track.

Two other ways in which tracking may exert its impact are suggested by the data on student perceptions of disciplinary climate and teacher treatment. In contrast to students in the general and vocational tracks, students in the academic track report greater commitment of students to academic goals, better classroom discipline, and more positive treatment by teachers. Such school-level variables have not been studied in most prior research. (One exception is Oakes

I Equations that do not include sophomore levels of the senior dependent variables produce much higher coefficients for the track variables.


[1982, 1985], who found variations in classroom climate in secondary schools by track location.) Though classroom climate is not directly measured here, these data on student perception suggest that there may be true variations between tracks in discipline problems and in teacher treatment of students.

Further, even though tracking does not create totally homogeneous ability groups, it does shift the balance of ability levels. At the classroom level, such a disproportion may preclude the development of a critical mass of interested and enthusiastic learners in the general and vocational tracks, an ingredient that some have suggested is necessary for providing peer support for commitment to educational goals (Coleman et al. 1966).

The learning of basic subjects such as mathematics, science, and vocabulary may be facilitated for college-bound students if they are segregated from other students and then given special treatment in the classroom. However, students in nonacademic tracks are not given an environment that encourages them to increase their performance and their educational and occupational aspirations. Rather, they are less likely to select college-oriented courses, and, as the data on student perceptions suggest, they are segregated in the classroom with peers who have lower aspirations and who are more likely to disrupt classroom proceedings. The benefits that accrue to academic-track students by virtue of their track location would be beneficial to students in other tracks, particularly to those students who score well on academic tests, but even to those who do not test so well.6

It is interesting to compare some of our findings to the findings of Alexander and Cook (1982), whose research is considered by at least one author to be the "most sophisticated study to date" (Hum 1985, p. 179). Alexander and Cook concluded that "tracking in all probability is less influential in school achievement processes than earlier studies, our own included, had led us to believe." Our findings lead us to a different conclusion. We find consistent, small to moderate effects of tracking over a two-year period not only on achievement but also on a broader array of dependent variables than those considered by Alexander and Cook. Further, we found that these effects occur within a shorter time span (the last two years of high school) and extend to postsecondary education, even when social

background and prior educational resources are controlled.

While there are several reasons for the different findings of the two studies, two seem especially significant. First, Alexander and Cook studied both track movers and stayers, but we focused exclusively on the latter. Our strategy was designed to eliminate the confound- ing effects of student experiences of multiple tracks during high school. Prior analyses of the HSB data not presented here on both movers and stayers revealed effects of tracking on only several dependent variables (Vanfossen, Jones, and Spade 1985). Had Alexander and Cook examined stayers only, their results might have been similar to ours.

A second possible reason for the difference between the findings of the two studies concerns sampling. It is plausible that the sample developed by Alexander and Cook overrepresented either high-SES students or students from schools with a heavy emphasis upon the academic curriculum. The proportion of their sample who were in the college track is 69.3 percent, which is substantially higher than the proportion of our sample in the college track (43.5 percent), and also considerably higher than the 50 percent of students in the college track in all 27 high schools in the original Growth Study sample from which the Alexander- Cook subsample is drawn (Hilton 1971).7 That the complete Growth Study sample may have overrepresented high-SES students is suggested by a comparison to the 1966 study by Coleman and associates (Hilton 1971).

The absolute difference in proportions is not as significant as what this difference may represent theoretically. If the overrepresentation of academic-track students signifies an overrepresentation of high-SES students, then we would expect to see smaller track effects, because high-SES students are less influenced by their track location than lower-SES students (Spade et al. 1985). If, instead, the overrepresentation of academic-track students signifies that these students are attending schools that are primarily oriented toward a college- preparatory curriculum, then again we would expect to find diminished track effects, because a school with a strong emphasis on academics may lead to considerably different educational outcomes for all students than a school with less emphasis on academic pursuits. A number of studies have found that the general academic

6 Other analyses, not presented here, show that even for the lower-performance students, the greater the number of courses taken in mathematics and science, the greater the cognitive growth in those areas (see Spade, Vanfossen, and Jones 1985).

7Alexander and Cook's analysis was based on a sample of 912 students for whom data were available on all the variables used in the analysis. These students came from 8 of'the 27 high schools located in three highly urbanized communities.


emphasis in a school has an important influence upon achievement, particularly for lower-SES and lower-ability students (see, e.g., Rock et al. 1985; for reviews, see Anderson 1982; Ralph and Fennessey 1983; Mackenzie 1983; Peng, Owings, and Fetters 1982; Rosenholtz 1985; Glenn and McLean 1981; and Purkey and Smith 1983). Thus, Alexander and Cook's sample may have revealed fewer tracking effects because a strong academic emphasis in schools diminishes the negative impact of placement in a general or vocational track.

A reopening of the status maintenance hypothesis seems in order. Prior research has ignored the substantial zero-order correlations between socioeconomic background and track location because the relationship between track location and ability is higher (Heyns 1974; Rehberg and Rosenthal 1978; Alexander and Cook 1982, p. 631). We have argued that ability measures reflect a sizable influence of socioeconomic background, which is manifested through indirect paths. However, even if we control for prior performance, we still find that students from higher socioeconomic origins are overrepresented in the academic track, where they undergo changes in their self-orientations, satisfaction with school, and extracurricular activities. Moreover, indirect student perception data suggest that academic-track students experience classroom environments more favorable to learning. The courses and treatments in turn lead to high school and postsecondary school outcomes conducive to a higher adult status attainment. The organization of students through tracking appears to provide greater opportunities for students from the middle to upper classes (because class is related to track location) and for some students of higher performance (because track location is related to performance) not only because it helps such students learn but because it affects course-taking patterns, aspirations, friendships, and classroom environment. Once children are distributed into tracks, they have systematically differing experiences, which are relevant to ultimate educational attainment, and they diverge in their development. The links connecting socioeconomic background, track placement, performance, attitudes, and behaviors become more evident. For these reasons, we become more convinced of the utility of a view of school as a social institution that, as McPartland and McDill (1982) put it, accentu- ates small initial student achievement differences deriving from social class background through the processes of differential selection and treatment.

APPENDIX A: MEASURES

Cognitive performance. In both the sophomore and the senior years, mathematics performance was measured by

two tests, Math I and Math II, which were combined into a composite score. Science performance was measured by a single test, taken in both the sophomore and senior years. The composite test score is a combination of the mathematics, reading, and vocabulary test scores. See Heyns and Hilton (1982) for an analysis of the reliability and validity of these measures. Formula scoring, used in the analysis reported here, tends to increase the variability of scores and tends to yield higher correlations between achievement and the independent variables of interest (Heyns and Hilton 1982). The use of composite scores also increases validity (Fetters, Stowe, and Owings 1984), and we are able to use composite scores in the measures of mathematics (by combining Math I and Math II) and the composite test score (by combining the scores for mathematics, reading, and vocabulary).

College expectations in 8th grade (item BB068A). Students were asked if they expected to go to college. Their responses were recoded as follows: 1 = "yes," 2 = "not sure," 3 = "hadn't thought about it," 4 = "no."9

Courses taken by 12th grade in mathematics or science. These variables (MATHPATN and SCIPATN) indicate the course patterns in mathematics and science. They were coded by NCES from the transcript data: 4 = "four or more credits in advanced mathematics or science," 3 = "four or more credits in standard mathematics or science," 2 = "one or two credits in mathematics and science with less than two in the college-preparatory courses," and 1 = "less than one credit in mathematics or science." (The codes were reversed as indicated for the purposes of this paper.)

Courses taken by 12th grade in business. This variable (HSBUS), coded by NCES from the transcript data, indicates the number of credits in business courses. These include trade, business, office, home economics, and industrial arts courses.

Courses completed by 10th grade in mathematics or science (items YBO06A and YBO06G). Sophomores were asked how many courses they would have taken by the end of the year in mathematics and science. Responses ranged from "none" (coded 1) to "more than 1 year" (coded 4). (The codes were reversed as indicated for the purposes of this paper, so that higher scores indicate higher grades.)

Perceived disciplinary climate. From the sophomore and senior questionnaires, a composite score based on the sum of scores for five questions was created. Students were asked to indicate the extent of the following discipline problems: (1) students don't attend school, (2) students cut classes, (3) students talk back to teachers, (4) students don't obey instructions, and (5) students fight with other students. Items were recoded so that high scores indicate high exposure to these problems. The items were summed to form an index of academic climate. The alpha reliability coefficient for the index is .788.

Educational expectations in 10th grade (item BB065). Students were asked to indicate how far they thought they would go in school. Responses ranged from "less than high school" (coded 1) to "Ph.D., M.D., or other advanced professional degree" (coded 9).

Extracurricular leadership. Two items (FY38K and FY38I) were used to form the senior variable: participation in student council or student government and participation in newspaper or yearbook.

Extracurricular participation. For the senior measure


of extracurricular activity, we used a composite index of extracurricular participation identified by factor analysis to be internally consistent. The reliability coefficient for the combined items is .599. The items (FY38C, FY38D, FY38F, FY38H, FY38I, FY38K) measure participation in student council or student government, honorary clubs, debating or drama, chorus or dance, school newspaper or yearbook, and cheerleading. Not all of these items were on the sophomore questionnaire. Thus, the sophomore index includes only the items pertaining to cheerleading, debating or drama, and chorus or dance (BB032C, BB032D, BB032F). Factor analysis identified these items as a cluster.

Family socioeconomic background. This was measured by a composite score, based on famnily income, father's education, mother's education, father's occupation, and eight material possessions in the household. It is the simple average of nonmissing components. Items from the follow-up senior questionnaire were supple- mented with responses from the sophomore questionnaire if missing.

Sex. Sex is a two-category dummy variable, coded 1 for males and 2 for females.

Friends' values (items F64A-D and BBO5A-D). On both the sophomore and senior questionnaires, students were asked if their closest friend gets good grades, is interested in school, attends class regularly, and plans to go to college. Sophomore and senior indices of friends' values were formed by summing the responses to these items. The reliability coefficient for the sophomore index so formed is .653, for the senior index, .636.

Friends' educational expectations (item BBOSJD). Sophomores were asked whether their closest sophomore friend planned to go to college. Responses were coded as follows: 1 = "true," 2 = "false."

Grades by 12th grade. Used in the regression for postsecondary education, this variable (HSGRADES) was coded by NCES from the transcript data and indicates the student's high school GPA. Codes are similar to those used in BB007.

Grades by 10th grade (item BB007). Students were asked to indicate which response best described their grades so far in high school. Responses ranged from "mostly A's" (coded 1) to "mostly below D" (coded 8).

Liking for school. Students were asked whether they were satisfied with the way their education was going and whether they were interested in school. High scores indicate satisfaction and interest.

Occupational aspirations (items BB062 and FY77A). Sophomores and seniors were asked what type of occupation they expected to have at age 30. The 17 response categories were recoded into a continuum of occupational prestige consistent with the ranking devel-

oped by the National Opinion Research Center (1984, Apps. F and G). High scores indicate higher occupational aspirations.

Postsecondary education. In the second follow-up, respondents were asked the type and status of their current postsecondary activities. The responses were recoded as follows: 1 = "nonstudent," 2 = "part- or full-time other," 3 = "part- or full-time public two-year college," 4 = "part- or full-time public four-year college," 5 = "part- or full-time private four-year college. "

Self-esteem (items BBCONCPT and FYCONCPT). This variable is measured by responses to four items first developed in the Rosenberg scale, for which reliability coefficients between .85 and .92 have been obtained (Rosenberg 1965). Students were asked whether they had a positive attitude about themselves, whether they believed they had worth or were equal to others, whether they believed they were able to do things as well as most other people, and whether they were generally satisfied with themselves. Scores were recoded so that high scores indicate high self-esteem.

Sports participation (items FY38A and FY38B). The senior measure of sports participation is a composite index based on the sum of scores for two variables. The items measure participation and leadership in varsity athletics and other athletics. The sophomore measure is a single item measuring participation in athletics. High scores indicate high participation.

Perception of teacher treatment (items FY69A, FY69B, FY69D, FY69E). This variable is based on the sum of four senior items, which factor analysis identified as a cluster. Students were asked how many of their teachers (1) enjoyed their work, (2) were clear in their presentations, (3) treated everyone with respect, and (4) were patient and understanding. A composite index of teacher treatment was created, with an alpha reliability coefficient of .815. High scores indicate a positive perception of teacher treatment.

Track location. The academic track variable was coded 1 if the respondent indicated placement in an academic track in both the sophomore and senior years, 0 if the respondent indicated placement in a general track, and - 1 if the respondent indicated placement in a vocational track. The general track variable was coded 1 if the respondent indicated placement in a general track in both years, 0 if the respondent indicated placement in an academic track, and - 1 if the respondent indicated placement in a vocational track.

Race. Race is a two-category dummy variable coded 0 for blacks and 1 for whites. Other racial categories (e.g., American Indian, Asian and Pacific Islander) are excluded from the analysis.


APPENDIX B: Average Scores on Dependent Variables by Track Placement, for Track Stayers (Standard Deviations in Parentheses)

Academic General Vocational (N=2,904) (N= 2,543) (N= 1,234)

Math courses 3.079 2.271 2.094 (.656) (.596) (.480)

Science courses 2.980 2.072 1.873 (.783) (.702) (.593)

Business courses 1.124 1.603 2.863 (1.204) (1.639) (3.152)

Math score 23.299 12.332 8.225 (8.820) (9.065) (8.123)

Science score 12.664 9.354 7.538 (3.850) (4.192) (4.291)

Composite score 59.253 49.004 45.279 (7.086) (7.568) (7.113)

Liking for school 3.684 3.300 3.410 (.577) (.720) (.698)

Senior educational expectations 9.061 6.194 5.468 (1.673) (2.512) (2.221)

Senior occupational aspirations 13.354 10.084 9.482 (3.247) (4.584) (4.191)

Self-esteem .169 - .089 - .124 (.674) (.739) (.792)

Extracurricular activities 7.916 6.965 6.85 1* (1.947) (1.558) (1.547)

Extracurricular leadership 2.765 2.328 2.307 (1.030) (.721) (.731)

Senior perception of friends' values 7.559 7.062 6.895 (.843) (1.156) (1.095)

Postsecondary education 3.432 1.966 1.520 (1.370) (1.347) (1.005)

Senior sports participation 3.230 2.908 2.690 (1.218) (1.165) (1.042)

Sophomore sports participation 1.643 1.544 1.434 (.479) (.498) (.496)

Disciplinary climate 9.938 10.993 11.208 (2.417) (2.328) (2.460)

Perception of teacher treatment 24.227 22.644 22.519* (4.153) (4.584) (4.923)

* The difference in means between the vocational and general tracks is not statistically significant.


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Documents

Curriculum Tracking and Status Maintenance Author(s): … Grouping Sectioning/Curriculum... · Prior studies of the role of curriculum tracking in status maintenance have ... annual