Research in Higher Education, Vol. 33, No. 4, 1992

JOINT DETERMINATION OF COLLEGE STUDENT ACHIEVEMENT AND EFFORT: IMPLICATIONS FOR COLLEGE TEACHING

O. Homer Erekson

° , , ° . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , ~ ,

in this paper, a four-equation simultaneous equations model is developed with col- lege student effort and achievement being jointly determined. The model is then tested using a sample of students from one comprehensive university. The empirical results provide evidence that student efforts with respect to faculty interaction, but not with respect to library usage or course effort, significantly affect achievement.

. . . . . . . . . . . ° ~ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ° , , ® . . . . . . . . . . . . . . .

You know, teachers sometimes think they are all that happen in class. But I have to take what a teacher is trying to teach me and translate it into something I learn, and that has little to do with the teacher. The learning is what I do.

- -A student named Susan, from On College Teaching, by Ohmer Milton and Associates

At its best, an undergraduate college or university is a place where students are actively engaged in learning. Presumably this outcome is most likely to occur in an environment where students and faculty share common learning objectives, incentive mechanisms are in place for faculty and students to work at their teaching and learning, and cocurricular experiences reinforce the learn- ing process. Increasingly, however, members of the higher education commu- nity and the general public express concern over the outcomes of higher educa- tion. It is common to read of "evidence of decline and devaluation . . . everywhere [while] the business community complains of the difficulty in re- cruiting literate college graduates" (Association of American Colleges Commit- tee, 1985).

With the increased call for accountability in higher education, it is incumbent on members of the academic community to take seriously the determinants of

An earlier version of this paper was presented at the Lilly Conference on College Teaching at Miami University in November 1989.

Address correspondence to: O. Homer Erekson, Professor of Economics, 208 Laws Hall, Miami University, Oxford, OH 45056

433

0 3 6 1 - 0 3 6 5 / 9 2 / 0 8 0 0 - 0 4 3 3 5 0 6 . 5 0 / 0 © 1 9 9 2 H u m a n S c i e n c e s P r e s s , I n c .

434 EREKSON

successful outcomes in higher education. This study is an attempt to provide some evidence as to the determinants of achievement by college students. This analysis will involve an extension of the substantial literature that economists have devoted to studying the production of education and the determinants of student achievement (Hanushek, 1979). In particular, a four-equation simul- taneous equations model is developed that argues that college student effort and achievement may be jointly determined. The model is then tested using a sam- ple of students from one comprehensive university. Following discussion of the empirical results, some conclusions with regard to implications for college teaching are developed.

THE MODEL

Previous research by economists on educational outcomes in higher educa- tion has typically assumed that student achievement is determined by combin- ing student ability and characteristics with university resources (McGuckin and Winkler, 1979; Endo and Harpel, 1982; Ayres and Bennett, 1983; Chizmar and Zak, 1984; Dolan, Jung, and Schmidt, 1985). This has led economists to use a single-equation educational production function relating "inputs" to "output." However, researchers in economics and other fields have properly noted that although students may have access to the same university resources, they use them in widely disparate ways. Thus, to accurately capture the importance of the various educational inputs in determining college student achievement, it is necessary to treat student choices endogenously (McGuckin and Winkler, 1979; Dolan, Jung, and Schmidt, 1985).

In this paper, college students are assumed to maximize the utility received from achievement (A) and from consumption of a composite commodity (X):

U = U(A,X) (1)

For simplicity, students will be assumed to have previously made the human capital choice determining the amount of time to devote to education. Thus, it is appropriate to focus directly on the student's maximization of achievement.

The basic production relationship is then posited to be:

A = A(E; ~,~) (2)

where E is student effort, c~ is student ability, and -r is a vector of technological factors that may affect the production of achievement.

In this formulation, student achievement is affected by the student's ability and technological factors, exogenous to the student when deciding how to allo- cate his or her time, and the student's use of these exogenous factors. Below

ACHIEVEMENT AND EFFORT 435

the measures of effort used in the empirical analysis will be specified. For now, suffice it to say that students may expend energies studying, doing library work, and working with faculty, formally or informally. Notice, unlike most other studies, in this formulation of the educational production function, family characteristics and student characteristics (other than ability) are assumed to not directly affect achievement.

Rather a student is presumed to expend effort in the following manner:

E = g(A; zs, zf, -Oz) (3)

where zs is a vector of student characteristics, zf is a vector of family charac- teristics, and ~z is a vector of exogenous time allocations for the student.

This formulation of an effort function explicitly endogenizes achievement. That is, higher academic achievers are hypothesized, ceteris paribus, to be will- ing to devote more energies to their educational pursuits. Thus, in this model, achievement and effort are endogenously determined. 2 The effects of student characteristics such as marital status or family characteristics such as parents' education are assumed to affect effort directly and achievement only indirectly through effort. Thus, these effects are assumed to be exogenous to the student allocation of time for a particular time period. Similarly there are other reasons that may restrict the amount of time a student allocates to academic achieve- ment. For instance, participation in an intercollegiate athletic program will af- fect the amount of effort a student can devote to academic achievement, but it will not vary over a one-semester period and may be assumed to be exogenous to the student's time allocation within that time period?

This model then suggests joint determination of achievement, as modeled in equation (2), and effort, as modeled in equation (3). This two-step and yet joint determination of achievement and effort is a more realistic depiction of the educational process than is true of the typical one-equation educational produc- tion function. To the extent that achievement and effort are simultaneously determined, estimation of a single-equation production function would be sta- tistically improper. In the section that follows, the model proposed here is empirically tested as to its usefulness in understanding the educational process for an institution of higher education.

EMPIRICAL ANALYSIS

Model Specification

The empirical analysis must begin with specification of a production (achieve- ment) function and effort functions. Following the production function sug- gested by equation (2) above, we must first choose a measure of achievement. There is no standard measurement of achievement in the education production

436 EREKSON

function literature. Choices for higher education have included performance on examinations for entry in graduate education (such as the Graduate Record Examination) or for licensing purposes (such as the National Teacher Examina- tions) (Hanushek, 1979; Ayres and Bennett, 1983), performance on subject tests (such as the Test of Understanding of College Economics) administered before and after a course (Chizmar and Zak, 1984), or grade point average (McGuckin and Winkler, 1979; Lave, Cole, and Sharp, 1981). In this analysis, grade point average at the end of the semester of the sample period (GPA) is used. For the university involved in this analysis, GPA is believed, ceteris paribus, to be an accurate indicator of academic achievement.

To measure effort, three indices developed by C. Robert Pace were utilized (Pace, 1979, 1980). These indices were three of fourteen quality-of-effort scales used by Pace to contribute to the understanding and explanation of stu- dent learning and development in college.' The effort scales chosen were usage of library resources (LIBEFF), course learning (CRSEFF), and contact with faculty (FACEFF). These three scales were chosen because they have the most direct relationship to academic achievement. Each scale requires students to indicate how frequently they engage in a series of activities that require increas- ingly greater effort. For the library scale, the activities ranged from routine use of the library (such as using the card catalog) to more independent exploration and focused activity (such as developing a bibliography). For the course learn- ing scale, the activities ranged from simple cognitive activities (such as taking notes) to higher-level cognitive activities (such as efforts to explain, organize, and go beyond assignments). For the faculty scale, the activities ranged from routine and casual contacts (talking with a faculty member after class) to more serious contacts (such as discussing career options or asking for an evaluation of one's work).

The frequency of responses for the effort scales for the sample in this paper are given in Table 1. The experiences of the students were scored as never worth one point, occasionally worth two points, often worth three points, and very often worth four points. The result is that scores on each scale can range from ten to forty points, with a high score possible only when students engage in higher-quality activities with some frequency. Pace (1979) reports the psy- chometric properties of the scales demonstrate their reliability as measuring instruments.

With GPA and the three quality-of-effort variables defined, we may then specify the full system as:

GPA = f (FACEFF, LIBEFF, CRSEFF; oL, "0 FACEFF = gf (GPA, LIBEFF, CRSEFF; Zs, zf, ~z) LIBEFF = gt (GPA, FACEFF, CRSEFF; zs, zf, -~z) CRSEFF = gc (GPA, FACEFF, LIBEFF; z~, zf, ~z)

(4a) (4b) (4c) (4d)

ACHIEVEMENT AND EFFORT

TABLE 1. Effort Style

437

Percentage of respondents who indicated the following experiences during the school year:

Very Often Often Occasionally Never Experiences with Faculty

25.7 32.9 39.9 15.1 29.8 47.8

11.8 19.5 53.1

8.8 17.6 56.6

5.5 18.9 52.6

5.9 13.6 47.6

5.0 18.2 46.7

1.7 4.0 15.1

0.6 1.7 9.2

0.9 2.6 21.3

21.5 21.1 45.0

6.3 13.8 67.1

2.8 8.8 63.6

7.9 18.0 44.1

7.0 20.0 54.0

6.3 18.0 50.0

3.3 11.2 37.3

1.5 Talked with a faculty member. 7.4 Asked your instructor for information related

to a course you were taking (grades, make- up work, assignments, etc.).

15.6 Visited informally and breiefly with an instructor after class.

16.9 Made an appointment to meet with a faculty member in his/her office.

23.0 Discussed ideas for a term paper or other class project with a faculty member.

32.9 Discussed your career plans and ambitions with a faculty member.

30.1 Asked your instructor for comments and criticisms about your work.

79.2 Had coffee, soda, or snacks with a faculty member.

88.6 Worked with a faculty member on a research project.

75.2 Discussed personal problems or concerns with a faculty member.

Library Experiences

12.3 Used the library as a quiet place to read or study materials you brought with you.

12.9 Used the card catalogue to find what materials there were on some topic.

24.8 Asked the librarian for help in finding material on some topic.

30.0 Read something in the reserve book room that was assigned by a faculty member.

t8.9 Used indexes (such as the Reader's Guide to Periodical Literature) to journal articles.

25.7 Developed a bibliography or set of references for use in a term paper or some other class assignment.

48.2 Found some interesting material to read just by browsing in the stacks.

438 EREKSON

TABLE 1. (continued)

Percentage of respondents who indicated the following experiences during the school year:

Very Often Often Occasionally Never Experiences with Faculty

3.7 9.2 37.3

4.2 8.5 27.6

0.9 2.2 31.8

66.7 28.1 4.2 56.4 40,4 3.1 58.5 25.7 11.8 41.2 41 ~7 16.4

33.6 41.7 23.7

30.5 34.0 30.7

29.8 36.0 29.4

23.3 43.9 31.8

20.4 21.9 37.5 2.6 9.2 52.2

49.8 Ran down leads, looked for further references that were cited in things you read.

59.7 Used specialized bibliographies (such as Chemical Abstracts, Psychological Abstracts, etc.).

65.1 Gone back to read a basic reference or document that other authors had often referred to.

Course Learning

0.9 Took detailed notes in class. 0.0 Listened attentively in class meetings. 4.0 Underlined major points in the readings. 0.7 Tried to see how different facts and ideas fit

together. 0.9 Thought about practical applications of the

material. 4.8 Worked on a paper, project, or assignment

where you had to integrate ideas from various parts of the course.

4.8 Summarized major points and information in your readings or notes.

0.9 Tried to explain the material to another student or friend.

20.2 Made outlines from class notes or readings. 36.0 Did additional readings on topics that were

introduced and discussed in the class.

For the production function equation, the three jointly dependent effort vari- ables would be expected to be directly related to GPA. The student ability variable a is measured by the ACT (American College Test) score of the stu- dent, and is intended to capture the human capital endowment or ability of the student upon entering college, and is expected to be directly related to GPA.

Two groups of variables are included to reflect any technological factors ('r) that may affect the production of education. First are several variables relating

ACHIEVEMENT AND EFFORT 439

to sociocultural or psychological factors affecting achievement. There is some evidence that male and female students differ in their academic achievement, at least as conventionally measured. Whether due to sociocultural reasons or dis- crimination, female students have often been found to have significantly lower achievement (Siegfried, 1979; Lave, Cole, and Sharp, 1981; Dolan, Jung, and Schmidt, 1985). Therefore, GENDER is a dummy variable equal to one for males and zero for females, and is expected to be positively related to GPA. Similar arguments may be made with respect to racial differences and achieve- ment. Therefore, RACE is an included dummy variable equal to one for white students and zero for nonwhite students, and is expected to be positively related to GPA.

Another variable that may capture important student differences in ability to achieve is FROSH, a dummy variable equal to one for freshmen and zero otherwise. First-year students often face adjustment struggles in coping with college life that may result in an equal ability upperclassman having higher academic achievement. Finally, in the achievement equation, it is important to take account of differences in grading practices and level of rigor among differ- ent departments within a university (Astin, 1977). Therefore, five dummy vari- ables (ARTS, SSCIENCE, BUSINESS, SCIENCE, EDUCATION) are in- cluded to account for grading differences among divisions within the university in question?

For the effort equations, GPA enters each equation as a jointly determined dependent variable. The expected effect of GPA on each of the effort variables is ambiguous. One might expect high academic achievers to put forth more effort in library usage, faculty contact, and course effort. However, the ex- pected effect on achievement of high achievers spending time in these ways may be small or even negative, to the extent that advanced learning is self- learning.

Four variables are included in the effort equations to account for differences in student characteristics that may affect effort. The dummy variable FROSH is included along with a dummy variable SENIOR (equal to one for seniors and zero otherwise). Freshmen may not score as highly on the effort scales because of lack of familiarity with the library, a feeling of being intimidated by the faculty, and being not as far down the learning curve with respect to effective effort in coursework. MARITAL STATUS is included (a dummy variable equal to one for married students and zero otherwise) to capture the possibility that responsibilities of marriage may make it difficult for students to put forth as much academic effort as single students. Finally, the variable GENDER is included in the FACEFF equation, since given the preponderance of male fac- ulty members, female students may not feel as comfortable developing working relationships with male faculty.

440 EREKSON

Two other variables are included in the effort equations to capture the effect of supportive home environments. Past research on educational production functions suggests that learning environments and attitudes toward education are highly correlated with socioeconomic status (Hanushek, 1979). As a proxy for socioeconomic status and orientation toward education, FAMINC (family income) and FEDUC (father's years of education) are included, and are ex- pected to be positively related to the effort measures.

Also included in the effort equations is a series of variables, denoted by ~ above, intended to reflect activity (or lifestyle) choices made by the student that are fixed for a given semester, but will have exogenous effects on the amount of time the student can give to the different effort activities. These variables are GREEK (a dummy variable equal to one if the student is a member of a frater- nity or sorority and zero otherwise), DORM (a dummy variable equal to one if a student lives in a residence hall and zero otherwise), STUDY (hours of study per week), WORK (hours worked per week), ATHLETICS (hours spent partic- ipating on a varsity athletic team per week), and PERFORM (hours spent in a performing arts activity per week).

The Sample

The basic sample for the empirical analysis consists of a sample of 544 undergraduate students from Miami University out of a random sample of 1,000 who were asked to return a mail survey giving basic demographic infor- mation as well as completing the effort scales from the Pace College Student Experiences survey. Miami University is a comprehensive doctoral-granting university that emphasizes excellence in undergraduate education. Enrollment is approximately 15,000 students on its main campus.

Table 2 gives values for the three effort scales used in this paper for this sample (Erekson--Miami) and those reported by Pace (1979) for three college and university types. Values for the sample used in this paper are consistent with those reported for similar institutions in the Pace study.

TABLE 2. Comparative Effort Mean Scores

Faculty Library Course Effort Effort Effort

Erekson--Miami 19.5 19.1 29.8 Pace

Doctoral Univ. 18.2 18.6 29.8 Comprehensive Univ. 18.9 18.9 29.5 Liberal Arts College 21.0 2t.0 29.8

ACHIEVEMENT AND EFFORT 441

EMPIRICAL RESULTS

Because GPA and the effort variables are simultaneously determined, esti- mating each equation separately would result in simultaneous equations bias for the coefficients of these variables. Therefore, the parameters of the four-equa- tion system were estimated using three-stage least squares (3SLS), since each equation was overidentified. Using 3SLS improves the efficiency of the esti- mates (Kmenta, 1986). Table 3 contains the results of the 3SLS estimation. Although t-statistics are not strictly valid for this estimation procedure, "ap- proximate" t-values are included to aid in interpreting the results.

In the achievement equation, the only effort variable to have a significant effect on GPA was FACEFF. That library effort did not have a significant effect on GPA is consistent with the finding by Ayres and Bennett (1983) that "no measure of library facilities is strongly related to achievement differences." However, the results here suggest that even more intensive u s e of these facili- ties did not significantly affect GPA. In fact, the coefficient was nearly signifi- cant and negative, suggesting that library use for the sample of students in this study may actually decrease GPA. It also was interesting, and surprising, that course effort did not have a significant effect on GPA. Most of the other vari- ables in the achievement equation had the hypothesized effect with increases in ACT resulting in increases in GPA, with freshmen having lower GPA, and with white students having significantly higher GPA. Interestingly, contrary to other research, male students had a significantly lower GPA than did female students. None of the dummy variables included to control for differences in grading practices or rigor between divisions were significant individually. However, a joint-F test indicated that with 95 percent confidence, these vari- ables were jointly significant and should be included as control factors. 6

The results for the effort equations were interesting. In all three cases, GPA was not a significant determinant of effort, although it was nearly a significant positive determinant of course effort. The interpretation of these results may simply be that high-achievement students find the marginal gain from pursuing these forms of effort to be low relative to the marginal gain for lower-achieve- ment students. For the effort variables, LIBEFF seemed to be complementary to both CRSEFF and FACEFF as increases in LIBEFF resulted in increases in the other effort variables. On the other hand, CRSEFF and FACEFF appear to serve as substitutes for students as increases in either one of them results in significantly lower values for the other.

For the independent variables in the effort equations, some interesting pat- terns emerge. GENDER, FROSH, and MARITAL STATUS did not have a significant effect on any of the effort variables. On the other hand, seniors had significantly higher values for FACEFF and CRSEFF, but lower values for LIBEFF. Perhaps, after four years seniors have become efficient in library us-

442 EREKSON

T A B L E 3 . 3 S L S R e s u l t s o n A c h i e v e m e n t a n d E f f o r t

Jointly Dependent and Predetermined Variables

Dependent Variables

GPA FACEFF LIBEFF CRSEFF

GPA

FACEFF

LIBEFF

CRSEFF

ACT

GENDER

RACE

FROSH

SENIOR

MARI T AL STATUS

FAM I NC

FEDUC

G R E E K

DORM

STUDY

W O R K

ATHLETICS

P E R F O R M

ARTS

SSCIENCE

0.06* (1.93)

- 0.07 ( - 1.43)

0.03 (0.80)

0 .08*** (8.08)

- 0.11"

( - 1.69) 0 .47**

(2.02)

- 0 . 1 7 " *

( - 2 . 5 3 )

- 0.03 ( - 0.28)

0.03 (0.25)

0.44 - 0.57 (0.40) - 0.62

0 .84***

(6.99)

1.10"** (7.13)

- 0.90** 0 .89***

( - 2.45) (3.61)

- 0.05 ( - 0 . 1 7 )

1 . 0 6

(1.38)

- 0 .53*** ( - 2.62)

0 .77***

(4.00)

0.20 - 0.16 0.07 (0.34) ( - 0 . 3 0 ) (0.14)

2.44*** - 2 .09*** 1.53"* (3.77) ( - 3.28) (2.17)

- 0.92 1.42 - 2.95 ( - 0 . 3 4 ) (0.64) ( - 1.55)

0.00 - 0.00 0.00003**

(1.07) ( - 1.29) (2.10) 0.32 - 0.31 0.28

(0.74) ( - 0.84) (0.69) - 0.29 0.27 - 0.24 ( - 0.70) (0.79) ( - 0.62)

1.18" - 1.17"* 1.12"*

(1.89) ( - 2.26) (2.23) 0.05* - 0 .05** 0 .05***

(1.88) ( - 2 . 1 9 ) (2.88) 0.04 - 0.04 0.05*

(0.98) ( - 1.19) (1.79) 0.07* - 0 .06** 0.05

(1.94) ( - 2.02) (1.50) 0 .25*** - 0 .20*** 0.09

(3.03) ( - 2.59) (0.99)

ACHIEVEMENT AND EFFORT

TABLE 3. (continued)

443

Jointly Dependent and Predetermined Variables

Dependent Variables

GPA FACEFF LIBEFF CRSEFF

BUSINESS - 0.01 ( -0 .11)

SCIENCE 0.03 (0.32)

EDUCATION 0.09 (0.87)

Intercept - 0.10 ( -0 .15)

19.27"** - 17.80"** 17.81"** (2.63) ( -- 3.17) (4.92)

t-ratios in parentheses. Sample size is 544. ***Indicates coefficient is statistically significant, p-<.01, two-tailed test. **Indicates coefficient is statistically significant, p-<.05, two-tailed test. *Indicates coefficient is statistically significant, p-<. 10, two-tailed test.

age, but find faculty contact more comfortable and necessary as career planning and the desire for original research or independent study become more timely. The only family background variable that had a significant effect was family income, where increases in income resulted in increases in CRSEFF.

Turning to the effects of the variables that captured competing time de- mands, being in a fraternity or sorority had no discernible effect on effort. Living in a residence hall was associated with increases in FACEFF and CRSEFF, but decreases in LIBEFF. Perhaps this reflects a need by off-campus students to use the library for concentrated study. Moreover, this is consistent with Astin's (1977) finding that students who live in residence halls have more contact with faculty. Interestingly, hours spent studying, in athletics, or in the performing arts generally had similar results, with increases in them resulting in increases in faculty effort and course effort, but decreases in library effort. Finally, the only effect hours of work seemed to have was to increase course effort. This result contradicts that found by Wetzel (1977).

IMPLICATIONS FOR TEACHING AND CONCLUSIONS

In this paper, a model has been developed and estimated suggesting that educational production can usefully be divided into two interrelated compo- nents: achievement and effort. The usefulness of the model was illustrated by estimating a four-equation simultaneous equations system for a sample of stu- dents from a comprehensive university.

444 EREKSON

Hopefully, though, the importance of this analysis emerges as we consider the implications for college teaching. A smug look by faculty at the achieve- ment equation may lead one to self-satisfaction as increased student efforts in working with faculty increase their performance, as measured by GPA. How- ever, these results are open to differing interpretations. For example, the posi- tive effect of student efforts working with faculty may be a proxy for student interest in the subject matter, student ability to satisfy faculty grading nuances, or even student attempts to curry favor with faculty.

Further inspection of the results is more discouraging, with course effort and library effort having no significant effect on GPA, and with library effort al- most having a negative effect. To comprehend the sense of discouragement in these results, it is useful to consider arguments made by Machlup (1979). Mach- lup argues that it is perfectly understandable to have poor learning from good teachers. He argues that good teachers have a tendency to present course mate- rial "in an exciting way and with extraordinary lucidity . . . . [giving] students a feeling of comprehension and mastery of the subject; the result is that they can safely neglect the reading r e q u i r e d . . . [they] skimp on their reading or prob- lem-solving." To the extent that faculty "teach the textbook" and "test the notes," they reward note-taking and discourage more advanced course effort activities such as working on a writing project where students have to integrate ideas from various parts of the course or doing additional readings on topics introduced in class. These activities may provide the impression that students who work closely with faculty and who accordingly earn high course grades are attaining a high level of course achievement. Actually such efforts simply may reflect mastery of course materials a "good" teacher presents.

Similarly faculty may provide little incentive to use more advanced library effort activities such as consulting original sources or using specialized bib- iliographies. Boyer (1987) has argued that libraries at most institutions are "ne- glected resources" and little more than "a quiet place to study." This idea is reinforced from the earlier finding that off-campus students expend more li- brary effort than do on-campus students.

Rather than being satisfied with a high level of student effort with faculty as a positive determinant for student achievement, faculty are called to consider ways to modify the objectives and expectations of their courses to create an environment where student effort with coursework and usage of the library result in higher achievement as well. In addition, faculty should consider alter- nate means of evaluating student efforts that reflect achievement more fully than just grade point average. Ohmer Milton (1978) has suggested that course objectives should be developed jointly with students to help give students own- ership of the course and thus of the learning process. At its best, an under- graduate college or university is a place where students are actively engaged in all aspects of learning.

ACHIEVEMENT AND EFFORT 445

Acknowledgments. The author gratefully acknowledges the Lilly Foundations for a grant in support of this research. Helpful comments were received from John J. Siegfried, James A. Dunlevy, and an anonymous referee.

NOTES

1. Since this study involves students at one rather homogeneous university, peer effects are as- sumed not to vary among students.

2. Allison (1982) constructed a three-equation model, jointly determining achievement, effort, and enjoyment. This important contribution is in the spirit of this research, but was focused on the value of self-paced instruction in economics principles classes.

3. An additional constraint that could be included is:

7" = Te(E) + ~

where 1" is the total, exogenously determined waking hours for the student, Te is the time devoted to effort activities, and T.- is the exogenously determined time on other predetermined activities such as intercollegiate athletics. If T~ is assumed to be strictly monotonically related to E, then this equation is redundant and not needed in the analysis.

4. The fourteen scales developed by Pace included scales related to the use of facilities and to the use of personal and interpersonal opportunities. These included scales relating effort to usage of the library, cultural facilities, science laboratories, student unions, athletic and recreation facili- ties, and residence units, course learning, contacts with faculty, club and organization activities, experiences in writing, self-understanding, student acquaintances, topics of conversation, and information in conversations. For a full description, see Pace (1979).

5. Miami University has another academic division (Western College School of Interdisciplinary Studies) that serves as the omitted dummy variable category.

6. The calculated F-statistic was 3.475. The critical F-value with 5 and 524 degrees of freedom with 95 percent confidence is 2.21.

REFERENCES

Allison, E. (1982). Educational production function for an introductory economics course. In R. Fels and J. Siegfried (eds.), Research on Teaching College Economics: Selected Readings. New York: Joint Council on Economics Education, pp. t71-194.

Association of American Colleges Committee (1985). Integrity in the college curricu- lum. Chronicle of Higher Education 29(22):12-30.

Astin, A. (1977). Four Critical Years. San Francisco: Jossey-Bass Publishers. Ayres, Q., and Bennett, R. (1983). University characteristics and student achievement.

Journal of Higher Education 54(5): 516-532. Boyer, E. (1987). College: The Undergraduate Experience in America. New York:

Harper & Row. Chizmar, J., and Zak, T. (1984). Canonical estimation of educational production func-

tions. Economics of Education Review 3(1): 37-43. Dolan, R., Jung, C., and Schmidt, R. (1985). Evaluating educational inputs in under-

graduate education. Review of Economics and Statistics 67(3): 514-520. Endo, J., and Harpel, R. (1982). The effect of student-faculty interaction on students'

educational outcomes. Research in Higher Education 16(2): 115-138. Hanushek, E. (1979). Conceptual and empirical issues in the estimation of educational

production functions. Journal of Human Resources 14(3): 351-388.

446 EREKSON

Kmenta, J. (t986). Elements of Econometrics. New York: Macmillan Publishing Com- pany.

Lave, C., Cole, M., and Sharp, D. (1981). Determinants of education achievement. Economics of Education Review 1 (2): 253-262.

Machlup, F. (1979). Poor learning from good teachers. Academe 65(6): 376-380. McGuckin, R., and Winkler, D. (1979). University resources in the production of edu-

cation. Review of Economics and Statistics 61 (2): 242-248. Milton, O., and Associates (1978). On College Teaching. San Francisco: Jossey-Bass

Publishers. Pace, R. (1979). Measuring quality of effort. Report to the Spencer Foundation. Los

Angeles: Laboratory for Research in Higher Education, University of California at Los Angeles.

Pace, R. (1980). Measuring the quality of student effort. Current Issues in Higher Education 2(1): 10-16.

Siegfried, J. (1979). Male-female differences in economic education. Journal of Eco- nomic Education 10(2): 1-11.

Wetzel, J. (1977). Measuring student scholastic effort. Journal of Economic Education 9(1): 34-41.

Received August 5, 1991.