U.S. Department of Education
Richard Riley
Secretary

Office of Educational Research and Improvement
Ricky Takai
Acting Assistant Secretary

National Institute for Postsecondary Education,
Libraries, and Lifelong Learning
Carole Lacampagne
Director

National Institute for Science Education,
The University of Wisconsin-Madison, Funded by
The National Science Foundation
Andrew Porter and Terrence Millar
Co-Directors

The research reported in this monograph was supported, in part, by an interagency agreement between the National Science Foundation and the United States Department of Education (No. 9453866), and by a cooperative agreement between the National Science Foundation and the University of Wisconsin-Madison (Cooperative Agreement No. RED-9452971). At UW-Madison, the National Institute for Science Education is housed in the Wisconsin Center for Education Research and is a collaborative effort of the College of Agricultural and Life Sciences, the School of Education, the College of Engineering, and the College of Letters and Science. The collaborative effort is also joined by the National Center for Improving Science Education, Washington, DC. Any opinions, findings, or conclusions are those of the author and do not necessarily reflect the view of the supporting agencies.

Acknowledgments

Executive Summary

Introduction: Metaphors of Passage and Participation

Part 1: Engineering Paths as Established by Students

Part 2: The Content of Their Curriculum

Part 3: Engineering and Science: Confusing Signs Along the Path

Part 4: Antecedents of the Engineering Path

Part 5: Choosing the Engineering Path

Part 6: Leaving Engineering: Migration and Traffic

Part 7: Experiencing Engineering: Classroom Environments, Credit Loads and Grades

Conclusion: What Did We Learn? Where Do We Take the Information?

Notes

References

Appendix: Technical Notes and Guidance

This document is the third to emerge from a cooperative relationship between the Office of Educational Research and Improvement (OERI) of the U.S. Department of Education and the National Science Foundation (NSF). Women and Men of the Engineering Path owes much to the support of NSF's Division of Science Resource Studies, and the to encouragement and feedback of the "College Level I" team of NSF's National Institute for Science Education (NISE) at the University of Wisconsin-Madison. I am particularly grateful to Mary Golladay of NSF and Prof. Arthur Ellis of the Department of Chemistry at UW-Madison for their faith in and patience with the production of this study. These relationships demonstrate how federal agencies and the research centers they sponsor can work together to address problems of common concern.

The idea of exploring the "engineering path" through an empirical examination of a national college transcript sample was presented first to the "College Level I" team of NISE in 1995. Sample transcripts, tentative decision-rules for classification, and a preliminary taxonomy were set before the team, and indispensable suggestions for the subsequent investigation were offered, particularly by Denise Denton, Dean of the School of Engineering at the University of Washington and Sheilah Tobias.

The most critical preliminary task consisted of the line-by-line reading of college transcript records for 8,395 students in the High School & Beyond/Sophomore Cohort to determine engineering path status. The task took two readers, and without the assistance of Catherine Mobley (currently an Assistant Professor at Clemson University), the assignment of engineering path destinations would not have been either possible or reliable.

In the course of many drafts of this study, I have received helpful feedback and suggestions from reviewers: Elaine Seymour of the University of Colorado, Jerilee Grandy of the Educational Testing Service, Mike Corradini of the College of Engineering at the University of Wisconsin-Madison, Dennis Carroll of the National Center for Education Statistics, and my colleagues Nevzer Stacey, Sandra Garcia, Carole Lacampagne, and especially Harold Himmerfarb. Thanks, too, to special assistance along the way from Maresi Neirad of the University of California/Berkeley, Charles Ratliff of the California Postsecondary Education Commission, and most of all to my younger son, Nicholas Adelman, who began his college career as a chemical engineering major and is finishing in chemistry, for truly enlightening discussions along the path he discovered.

Lastly, all of us working in the vineyards of higher education owe more than some will ever admit to the National Center for Education Statistics (NCES), which had the wisdom to include national college transcript samples in its longitudinal studies, thus providing a resource of knowledge that is simply unique in this world. With NCES came not only the data, but also my extraordinary colleagues and teachers, Nabeel Alsalam (now at the Congressional Budget Office) and C. Dennis Carroll. Without them, our national knowledge would be so much less.

This monograph seeks to provide college academic administrators, institutional researchers, professional and learned societies, and academic advisers with a tapestry of information to improve their understanding of the paths students take through higher education. It begins with the observation that of those students who earn bachelor's degrees by age 30, 16 percent entered with no particular major in mind, and only 42 percent of the balance earned degrees in their intended field. These data indicate a considerable degree of student field migration.

The study demonstrates that migration rates are by-products of factors in students choice of field including curricular momentum and quality of academic performance carried forward from high school, the growing trend toward multi-institutional attendance, the nature of community college curricula for transfer students, credit loads and stop-out behavior, classroom experiences, changing student perceptions of the labor market, and student misconceptions of what given fields of study and occupations are all about.

Engineering was chosen as a case because it brings all the variables affecting choice, persistence, and migration into play. And because undergraduate engineering programs are offered in a limited number of institutions, we can offer a sharper primary story line about student history and choice. Engineering was also chosen because, while the overall "attrition" from the field is not high after students reach the "threshold" of the field, it is much higher for women than men, an unfortunate situation in a discipline with a historically severe gender imbalance.

The evidence used in Women and Men of the Engineering Path comes principally from the 11-year college transcript history (1982-1993) of the High School & Beyond/Sophomore Cohort longitudinal study (HS&B/So), as well as the high school transcripts, test scores, and surveys of this nationally representative sample.

This is the first national tracking study of students in any undergraduate discipline that identifies attempted major field from the empirical evidence of college transcripts. A "curricular threshold" of engineering was defined, and the careers of students described with reference to that threshold. While 16 long-term "destinations" of students who reached the threshold are identified, they are collapsed into four for purposes of analysis:

• Thresholders, who never moved beyond the requisite entry courses.

• Migrants, who crossed the threshold of the engineering path, began to major in engineering, but switched to other fields or left college altogether.

• Completers, some of whom continued on to graduate school by age 30.

• Two-year-only students, whose college experience was confined principally to engineering tech programs in community colleges.

Attendance Patterns and Degree Completion

• Attending more than one institution is not a drag on degree-completion, either for students on the engineering path or anyone else. More than half of the HS&B/So college students attended more than one college, and 20 percent crossed state lines in the process.

• Community college transfer students evidence strong preparation, with degree completion rates equivalent to those of 4-year college students. The transfers constitute 1/6th of the degrees awarded in engineering.

• The bachelor's degree completion rates (in any field) of students who reach at least the threshold of the engineering path are much higher than those for anybody else.

• While there is a 20 percent gap between men and women on the engineering path who eventually earn degrees in engineering, among the most qualified students there is no difference in degree completion rates.

The Empirical Core Curriculum

• Changes in the empirical core curriculum of engineering students over two decades reflect increases in sub-field concentrations in mechanical and computer engineering and declines in civil and chemical engineering.

• No matter what a one's final destination on the engineering path--threshold, migrant, or completer--bachelor's degree recipients spent more time in calculus than any other course. For degree completers in engineering, one out of seven credits earned was in mathematics.

• Of the groups on the engineering path, the migrants have much higher course participation rates than others in physics, computer science, computer programming, and philosophy, providing some clues as to where these student go when they leave engineering.

• Among engineering degree completers, only four courses outside the sciences, mathematics, and technology--introduction to economics, English composition and technical writing, general psychology, and introduction to management--turn up frequently on transcripts.

High School Backgrounds

• The highest level of mathematics studied in secondary school is strongly correlated with bachelor's degree completion in any field. The correlation is stronger for men than women, and stronger, still, for students from the lowest SES quintile. But once students reach the threshold of the engineering path, these effects diminish.

• In terms of high school mathematics and science backgrounds, women and men who come to the engineering path look remarkably alike, yet very different from the women and men who never attempt to major in engineering. Women, however, have a higher academic performance profile (academic grade point average, class rank) than men, regardless of where they end up in college.

• Women who eventually complete engineering degrees had slightly higher SAT scores than male completers and were more uniform in test performance, whereas women who left engineering performed much worse than men on the SAT and evidenced greater variance in performance.

• About 4 percent of high school graduates with curricular momentum in mathematics and science and high quality academic profiles were not interested at all in engineering, rather, for the most part, in "pre-professional" preparation in college and (for women) in health sciences/services majors. Women constitute 60 percent of this high talent group, and among high talent students, very few input measures can be squeezed to explain nuances in student choice.

Choice and Attrition in Engineering

• As evidenced among labor market participants at age 28/29, engineering attracts a high proportion of people who had a consistent occupational goal starting in high school and a low proportion of people who were constantly changing their career objectives.

• Women who intended to major in engineering enjoyed the highest degree of parental support for bachelor's degree attainment among all women--or men--who intended to major in any field.

• Once in higher education, there is considerable "traffic" among the disciplines, some (but not all) of which can be explained by students' curricular momentum. Students who migrated from the engineering path did so primarily to disciplines requiring strong quantitative skills--computer science, business, and physical sciences--skills in which these students had made considerable investments.

• Credit loads in engineering are not much higher than those in other fields, though engineering students perceive overload because of a high ratio of classroom, laboratory, and study hours to credits awarded. The perception of overload is one of the major factors involved in decisions to leave engineering.

• Women and men earn similar grades in engineering courses; and the women who leave engineering have higher grades than the men who leave. Women who leave engineering do not leave because of poor academic performance, though they do evidence a higher degree of academic dissatisfaction.

Curricular momentum begins in secondary school, and sets up both trajectories and boundaries. Secondary school mathematics study is the key booster to these trajectories, with trigonometry the gate to likely science or engineering majors in college. The trajectories accelerate and the boundaries become more defined in college. Curricular momentum explains why half the students (and three quarters of the women) who leave engineering (the migrants) eventually earn bachelor's degrees in the physical sciences and computer science.

There are considerable differences between engineering and science that confuse students in high school and eventually come into play in field migration. Engineering practice, as students discover only in time, involves clients (and all the ambiguities, cultural contexts, and negotiations that come with clients) far more than the practice of science, and client specifications lie at the core of engineering design. The differences in the culture and texture of engineering and science are highlighted in women's experience in both the college laboratory and the workplace.

The metaphor of "paths" is a far more flexible and accurate way to describe student histories than "pipelines." We cannot micromanage choice, and judge a system to be deficient because students are constantly exploring, acquiring, and changing academic identity. "Pipelines" with "leaks" are convenient metaphors of institutional policy, but they neglect the texture of student histories, and the nature of the paths students discover, sometimes with many detours. What we can do is to improve the signs along the pathways, and, in the case of women in engineering, improve the quality of instruction and professorial sensitivity to women's minority status.

This monograph concludes with a number of suggestions for changing the image of engineering among high school students and potential college majors, particularly women. Given what we know of actual practices in different kinds of engineering workplaces, whatever negative views students have ought to be reexamined. There is just as much complexity and difference, joy and difficulty, in the engineering workplace as there is in other occupations. Engineers are not a monolithic gang of boys "tinkering" in a technological "sandbox," and telling bad jokes about incompetence. Foremost among the suggestions is that neither women nor men will choose engineering for the right reasons unless the profession can reach out to a broad population with a full portrait of the richness of its culture and practice, and with a clear map of its intersections with and divergences from bench science.

The study also concludes with suggestions to other disciplines for undertaking similar tracking studies, particularly in fields such as psychology or nursing, where men have been a distinct minority.

One of the most persistent questions in higher education concerns the rate and fate of student progress toward credentials. Provosts, deans, and state legislators naturally seek evidence that students entering college progress toward and complete degrees--and within a reasonable amount of time. The terms of measuring progress include "retention," "persistence," and "attainment." But department chairs and the directorates of professional and learned societies have another question, one that we might label "field attrition" or, better, "field migration." Simply put, do students who begin to specialize in a given field in college complete a degree in the same field, and if not, to what fields do they migrate and why? The question is also important to provosts, deans, and state legislators. It bears on academic planning and staffing, on what is known in the trade as "enrollment mix," and on the allocation of non-instructional resources. If we know the comparative shares of total undergraduate enrollments by major, and the way these shares are likely to shift as students move through college, we can budget, make capital investments, and staff with greater efficiency. A reflexive information system within an institution can also provide guidance both by analyzing the characteristics of students who major in different fields, and by seeking feedback from students themselves concerning initial and (if applicable) subsequent selection of major field.

It's not that the choices of entering college students are set in stone (Astin, 1977; Pascarella and Terenzini, 1991; Astin, 1993; Astin, Tsui, and Avalos, 1996). And a modest proportion of students enter college undecided as to their major, though this feature of student choice seems to be modestly correlated with institutional selectivity.⁽¹⁾ Academic administrators are of two minds about this: they prefer certainty in planning, but they must respect the process of learning and growth within which students discover their own preferences. Rare is the high school that can introduce a student to linguistics, for example, and very few high school students understand how the study of chemical engineering differs from the study of chemistry. We expect students to explore such possibilities and distinctions in the course of their college careers, and we expect them to develop an academic identity.

As one college dean hyperbolized, "if that [developing an academic identity] means that I process more change-of-major forms a year than we have students, then that's what it means!" The dean was swift to add, though, that "I wish our pre-college radar screen had better electronics and that our academic advisers had--and used--three-dimensional information." When inadequately-informed advisement and the search for academic identity intersect, the results both drag down degree completion rates and stretch time-to-degree, the two basic outcomes of higher education that are of most concern to provosts, deans, and state legislators, let alone students themselves. Some 8 percent of students entering higher education directly from high school wind up, at age 30, with more than 60 credits but no degree whatsoever, partly as a consequence of mediocre performance, but also because they wander from major to major, and with each change of major, backtrack to pick up a course or two to meet requirements, and postpone--perhaps forever--the completion of an academic identity.

Student "traffic" among the disciplines (Astin and Astin, 1993) moves at an even higher rate. In the national sample we will use in this monograph, 42 percent of those students who indicated an intended major for their college careers and who earned bachelor's degrees by age 30 actually earned the degree in their intended field. Table 1 indicates the comparative extent of this phenomenon by general major field and for men and women:

Table 1.--Percent of 4-year college students who completed bachelor's degrees by age 30, by sex and intended field as indicated in grade 12

NOTES: 1)"Applied Social Science" includes communications, public administration, social work, home economics (though nutrition/dietetics degrees are included with health sciences/services). 2) N.A.=Not Applicable. 3) *Differences between men and women are significant at p<.05. Universe: All students who indicated an intended major in grade 12, for whom a 4-year college was the true institution of first attendance, and who subsequently earned more than 10 credits from a 4-year college. Weighted N=1.08 million. SOURCE: National Center for Education Statistics: High School & Beyond/Sophomores.

The reader will immediately observe that the overall bachelor's degree completion rate for this national cohort seems high (two out of three) in light of what one usually reads in the newspapers, but that is a function of the way the universe is defined and the long-term (11 years from high school graduation: 1982-1993) nature of the study. This degree completion rate has not changed in 20 years (Smith et al, 1996, page 25).

Intended major, of course, is not equivalent to the actual first declaration of major (and the "pre-professionals" and undecideds eventually tell the deans what they are going to do). But it is important here to acknowledge that the overall proportion of 42 percent completing degrees in their intended field is a condition of our existence in higher education. This rate may vary from one entering class to another, and vary, too, on the basis of when one asks the question about intended major (see Part 5 below). For students entering higher education at the traditional age, as this study hopes to demonstrate, the migration rate is a by-product of factors in student choice of field including (but not limited to) curricular momentum and quality of academic performance carried forward from high school and/or established early in college careers, the growing trend toward multi-institutional attendance, the nature of community college curricula for transfer students, credit loads and stop-out behavior, classroom experiences, changing student perceptions of the labor market, and student misconceptions of what given fields of study and occupations are all about.

Engineering is a discipline that can illustrate the features of student choice that affect field migration and attrition in very clear terms, and was chosen for this study because, however complex its story, all the variables affecting choice, persistence, and migration come into play. Because undergraduate engineering programs are offered in a limited number of institutions, we can offer a sharper primary story line about student history and choice. This sharper story will provide deans in search of "three-dimensional information" with better radar-screen electronics.

Engineering was selected for another reason, one that structures the second story line in this study. For some fields, student choice is particularly important for reasons of what one might call "equity policy." That is, where, historically, there has been a severe imbalance in participation by gender and/or race, the affected disciplines have made special efforts over the past two decades to recruit and retain students from underrepresented population groups. Since success is measured in proportions of students majoring in a field, this competition among disciplines takes place in a finite glass: the proportions always add to 100 percent, and where there are disciplinary "winners" there must be disciplinary "losers." Initial success in recruitment, too, does not automatically translate into success in retention (Moller-Wong and Eide, 1997; Grandy, 1994; LeBold and Ward, 1988).

Field attrition in a discipline with historical equity problems is not a happy situation. It is particularly unhappy in professional fields of study that lead to licensure. The profession itself, considered as a labor force, comes to exhibit what some economists call "segmentation" (England, 1984). That is, individuals from certain demographic groups are found both concentrated and dominant in the occupation. While a degree of segmentation in the professions may be beyond the control of higher education, excessive concentrations such as those found among nurses, elementary school teachers, and engineers is worrisome. For any human service economy to work efficiently, the specialization of labor should be based not only on learned skills, acquired knowledge, and developed talent, but ability to communicate effectively with a demographically diverse group of clients. There is, in fact, an economic utility of more demographically balanced work forces.

When one compares the proportion of men and women who earned bachelor's in their intended field (table 1), one finds four general fields that evidence a combination of low intention to major and low field completion. Two of these fields, education and health sciences/services, affect men. The other two, physical sciences and engineering/architecture, affect women.

The reasons for choosing engineering in examining the gender issues in field migration are evident in table 2. Taking account of the changing gender mix and growth rates of undergraduate education over the past 15 years, table 2 displays what Alsalam and Rogers (1991) termed a "female field concentration ratio"* that uses three temporal reference points:

• the year prior to the modal year of college entry for the cohort that provides the data for our story, 1980-81;

• the modal year of bachelor's degree attainment for that cohort, 1986-87; and

• the most recent year for which these data are available, 1993-94.

During the period 1980-1994, the number of bachelor's degrees awarded annually increased by 200,000; women's share of those degrees increased by five percent to a solid majority; and, most importantly, the field distribution of those degrees changed. The proportion of all degrees that were awarded in computer science, for example, declined dramatically from 1986-87 to 1993-94; the proportion of business degrees rose substantially between 1980-81 and 1986-87; architecture's share of degrees shrank slowly during the period while that of engineering rose. These swirling currents of choice and academic fashion make definitive statements concerning trends difficult.

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* The proportion of all women who earned bachelor's degrees who majored in a specific field divided by the proportion of all men who earned bachelor's degrees who majored in the same field. Changes in the ratio indicate whether field differences between men and women are widening or contracting, and thus provide some hints of who will be entering the labor market in different occupational areas. This analytic tool is different from "segregation indices" and the segregation curves that are independent of the fraction of women in the analysis (Ransom, 1990).

Table 2.--Female field concentration ratios at the bachelor's degree level, 1980-1994

SOURCES: Digest of Education Statistics, 1987 (table 159); Digest of Education 
Statistics, 1989 (table 215); Digest of Education Statistics, 1996 (table 260).

We can feel confident, though, that since the mid 1980s--and with the exception of psychology--there has been a moderation in the gender segmentation of traditional female disciplines. This trend is accounted for, in part, by the numbers of women moving into business fields, particularly accounting (Flynn, Leeth, and Levy, 1996); and business has become the most gender-neutral of undergraduate majors. We can also say that engineering has remained consistently at the bottom rank of female representation, not only in the United States (Dorato and Abdallah, 1993; Organization for Economic Cooperation and Development [OECD], 1997⁽²⁾), and that there has been only modest improvement in the relative representation of women in the field.

This monograph is, foremost, about the paths taken by college students who reach the "curricular threshold" of engineering. It asks who crosses that threshold, what they brought to the threshold from secondary school, how they subsequently perform, and what happens to them. While I will note and offer some observations about the occupational destinations of our subjects, by "what happens to them," is meant, principally, educational experiences and attainment by age 30. These experiences include details on the content of their study, number and types of institutions attended, degrees earned and ultimate major field. The model can be used in undergraduate majors where curricular thresholds--that is, common patterns of coursework across many institutions--can be identified from student records. In the process, and with the assistance of a remarkable national data source, I hope to help redefine what we mean by "field attrition," and to turn some conventional wisdoms concerning who engineering students are and what happens to them into mythologies. The model of engineering should throw into bold relief the types of information that might be gathered at the institutional level to facilitate a more realistic assessment of student choice and progress.

Secondly and critically, the monograph is about the differential paths traversed by women and men as they moved toward the engineering path from their high school days, some backing off before arriving in college, some testing the terrain, some crossing the threshold but then migrating to other fields, and some actually completing degrees in engineering. The emphases of this story are those that emerge from the generic tracking of engineering students, hence they do not cover all the variations from the literature on women's education.

The analysis treats the generic story first. The gender story does not begin until part 4, then becomes a stronger line as the analysis progresses. Both stories are driven by historical method and objectives: to come as close to a true tale as the evidence allows. The remarkable evidence, in this case, has never been used before for this purpose.

Our data come from a national age-cohort longitudinal study, and rely heavily on the college transcripts of participants in that study. The study, conducted over 13 years by the National Center for Education Statistics (hereafter referred to as NCES), followed the high school graduating class of 1982, known as the High School & Beyond/Sophomore Cohort (hereafter referred to as HS&B/So). The college transcripts were gathered between February and September of 1993, when the members of this cohort were 29/30 years old. Labor market histories are complete through mid-1992, when the last of the surveys of this cohort were conducted. The HS&B/So, like its predecessor ten years earlier (the National Longitudinal Study of the High School Class of 1972) and its still-in-progress successor (the National Education Longitudinal Study of 1988), is an incredibly rich data archive, and includes high school records, test scores, and data on family background, family formation, changing attitudes and opinions, financial aid files, and military service, for example. The sample is robust: 14,825, of whom we have high school records for 13,020, test scores for 12,969, postsecondary transcripts for 8,395, labor market histories for 12,640. No other national longitudinal studies data sets have all this information--and at such high response rates⁽³⁾--particularly the information from unobtrusive sources such as high school and college transcripts, and covering such a long period of generational history (from the earliest high school transcript entry to the most recent graduate school transcript entry can be a span of 15 years). Its power as an analytical tool will become apparent in the course of this analysis.

However robust the sample as assembled in the tenth grade, the subsequent life-courses of students have a winnowing effect on the range of analyses. When it comes to describing the careers of students who crossed the curricular threshold of engineering studies in college, the size of the sample is more modest. The N for this group is insufficient for analyses by either race or engineering sub-field, and often yields large standard errors of estimates (see Technical Appendix). And because of the low participation rates of women in engineering, we must aggregate the categories of academic career histories in order to produce statistically significant comparisons⁽⁴⁾.

How good is the HS&B/So sample compared to the actual national census? In the matter of engineering degree completers, the average annual census for 1987-1991 was 64,800 (Heckel, 1995); the weighted HS&B/So engineering degree completers group for the over-lapping period, 1986-1993, was 63,736. That is a stunningly close match!

National data sets that allow tracking of student careers have a serious limitation: they do not provide the kind of information that allows direct judgment of the quality of student learning. Thus this monograph cannot ask whether a generation of engineering students mastered design problems or theoretical constructs, or how much the non-engineering portions of their curricula influenced their ability to set engineering problems in larger contexts. We do not know, for example, how well they understand viscous flow or how well they can apply that theoretical knowledge to a practical problem involving ferroliquids on the sealant rings used in space shuttles. We can--and will--make some estimates of the general contours of undergraduate engineering curricula as experienced by students and of the relative strengths of engineering sub-fields in the valises of knowledge with which engineering graduates leave college. But that is as far as one can reach toward evaluating the quality of student learning with a transcript sample from students who attended over 2,500 institutions.

Historically, engineering has been a very self-conscious discipline, one in which education "has been one of the most studied activities" (California Postsecondary Education Commission, 1981, page x i). As is the case in other licensure fields, an enormous amount of literature is devoted to instruction, curriculum and assessment. The teaching interests of the field are not tucked away in the back sections of scholarly journals: there are separate journals, and electronic bulletin boards on which engineering faculty exchange notes on experiments, assignments, classroom processes, and test items. These bulletin boards existed long before the Internet (Warren, 1989).

In many institutions, students are admitted to the engineering program and the college simultaneously, and the literature on engineering education evidences considerable concern with field attrition, particularly among women and minorities. One body of the literature focuses on psychological profiles of students in relation to academic performance, degree of commitment to the field, learning styles, and such cognitive traits as spatial ability (for example, Greenfield, Holloway and Remus, 1982; Tobias, 1990; Hackett, Betz, Casas and Rocha-Singh, 1992; Peters, Chisholm, and Laeng, 1995; Seymour and Hewitt, 1997). Another important research line carries a more "economic" tone, in that it takes the engineering profession (or engineering as a sub-species of the practice of science and technology) as its setting, and considers organizational culture, modes of work, and occupational dynamics in relation to the experience of underrepresented groups (Saigal, 1987; Brush, 1991; Ellis and Eng, 1991; McIlwee and Robinson, 1992; Etzkowitz et al, 1994; Traunter, Chou, Yates, and Stalnaker, 1996). While this literature is not always explicit in the matter, there is no doubt that messages of the experience of women, in particular, filter back through the educational system to influence student perceptions, behaviors and decisions (Morgan, 1992; Didion, 1993; Henes, Bland, Darby and McDonald, 1995).

With few exceptions (Astin and Astin, 1993; California Postsecondary Education Commission, 1986; Grandy, 1995; Seymour and Hewitt, 1997), the studies that relate student psychological profiles and teaching modalities to retention and completion in the field are individual institutional research efforts and the institutions at issue are usually four-year colleges. A cohort of students who declare engineering as a major upon entrance to college is identified and tracked from the introductory engineering design course onward (e.g. Schonberger, 1990; Epstein, 1991; Humphreys and Freeland, 1992; Ginorio, Brown, Henderson and Cook, 1993). Given the restricted nature of some of the populations, the conclusions of such studies may be more suggestive than generalizable.

A fine example within the universe of these studies began with 124 students in an introductory (sophomore level) chemical engineering course at North Carolina State University in 1990 (Felder et al, 1993; Felder, Mohr, Dietz, and Baker-Ward, 1994; Felder et al, 1995; and Felder, 1995), and sought to explain their academic fates (completion, retention, GPAs) with multivariate statistics controlling for sequences of background variables, initial experiences in the engineering curriculum, and responses to a five-semester experimental course sequence. N.C. State is the flagship engineering school in the North Carolina higher education system, and its engineering programs are selective. Felder and his colleagues were interested in psychological profiles and correlates of progress in student behaviors ranging from extracurricular activities to test strategies. Grades and expectations about grades/performance play significant roles in the analysis. We emerge with a rich sense of the students, instructional methods, and constructive suggestions for classroom and course-sequence design. The demographics of this group of students, though, seems to limit the generalizability of the study: nearly half of them came from rural high schools, twice the proportion in the national sample used in this monograph⁽⁵⁾, and were not as well prepared as their urban and suburban peers (Felder, Mohr, Dietz and Baker-Ward, 1994).

Another line of retention studies broadens the population to all science, mathematics, engineering and technology (SMET) students, but then restricts the population to elites, thus censoring the behavior of the mass. Seymour and Hewitt (1997) for example, interviewed 335 students with SATQ scores of 650 or higher; Strenta et al (1993) confined their study to highly selective institutions; Tobias' (1990) "second tier" subjects were all graduates of elite schools. Some 95 percent of U.S. undergraduates do not attend elite schools or score above 650 on the SATQ. Lessons from the right tail of a distribution have rarely been generalizable, though they might provide some hints of what to look for elsewhere. For example, if you start with a group of students whose SATQ=>650, common sense says it is hardly likely that they will leave a field because of "personal inadequacy in the face of academic challenge" (Seymour and Hewitt, page 32). If that is true for students with a high general learned ability quotient, it comes to serve as a benchmark for other hypotheses that may apply to a broader population, for example, students' growing understanding of the nature of work in different occupations in relation to their own proclivities.

The mini-longitudinal studies involving institutional cohorts are very helpful. But they would teach us more if they were set against a national tapestry of potential engineering students, including those who transfer from two-year to four-year colleges. National samples of college transcripts from the NCES longitudinal studies (as well as the surveys of the Engineering Manpower Commission⁽⁶⁾) teach us that not all engineering majors declared engineering as a major on entrance to college, that some of those who did declare engineering never followed up on their declaration, and that a better approach to identifying potential engineering majors is to find those students who explore the lower boundaries of the engineering curriculum and its co-requisites to reach a "threshold" of the field. These students have demonstrated sufficient interest and (in some cases) achievement to cross the threshold, and pursue further work in engineering. As we will see, many students who reach the threshold do not cross. In fact, some of them simply happen across the threshold in the course of other lower division undergraduate work, and have no intention of following the path towards an engineering degree. Others may attend technology institutes with core freshman year curricula that cover the threshold course work, or the U.S. military service academies that require a minimum number of engineering courses. For these students, this accidental "threshold" coursework may not be concentrated in the first year of study. They are nonetheless included in the analysis because they share enough of a curricular experience with others on the engineering path that they could return to these byways, with purpose.

The HS&B/So database and the stories it reveals differentiate this exposition from those based on the intermittent longitudinal follow-ups to the national entering freshman classes surveyed by the Cooperative Institutional Research Project (CIRP) of the Higher Education Research Institute at UCLA. CIRP has conducted annual surveys of entering college freshmen since 1966. In 1982, for example, when most of the HS&B/So students entered college, CIRP surveys from nearly 190,000 students from 350 institutions were included in the national norms (Astin, Hemond, and Richardson, 1982). The universe is stratified by institutional control, size and selectivity, and student data are weighted differentially within stratification cells. The result is representative of first-time freshmen, though part-time students and transfers are excluded from the national norms. The survey collects a considerable amount of information from a very large number of students about pre-college activities, secondary school curriculum and performance, educational and occupational aspirations, values and attitudes. As a consistent series of cross-sectional portraits, the CIRP freshman surveys present reliable and intriguing trends.

Our principal interest in the CIRP data, though, lies in three occasions on which longitudinal follow-ups to a given entering class were conducted, as these have formed the basis for an enormous amount of research on the impact of higher education on students. The grandmother of these occasions was a 1972 follow-up to the entering freshman class of 1968, the data from which formed the basis of Astin's seminal Four Critical Years (1977). The occasions of the greatest interest to our study are a 1989 follow-up to the entering freshman class of 1985 (used in Astin, 1993; and Astin and Astin, 1993), and a 1991 follow-up to the entering class of 1987, the unpublished data from which were provided to Seymour and Hewitt (1997). This literature contains a variety of conclusions about engineering students and their experience that I attempt to replicate using a different national database and a different approach to defining the population of "engineering students." It will come as no surprise that, in matters of curriculum, performance and academic attainment, a database grounded in high school and college transcripts (including all institutions attended by the student) will agree but occasionally with a database grounded principally in surveys and with interests confined to the institution in which the student was first surveyed. This contrast also applies to the "National Graduation Rate Study" of 204,000 entering freshmen in 38 public, land grant, research universities in 1988 and 1990 (Kroc, Howard, Hull, and Woodard, 1997) that we will also have occasion to cite.

For decades, the higher education literature has struggled toward a language that reflects student careers, particularly in scientific fields. The problem with the most common of the metaphors, e.g. "pipeline," is that they are driven by frameworks of professions, industries, work forces, and/or institutions, not individual student behavior. What students do, after all, cannot be described very well by "pipelines" with "leaks." The metaphors are children of policy needs, not helpful descriptors. Think about the difference in values inherent in the verbs "leak" and "migrate." In the context of human resource development, the subject of the former is a fluid mechanics model and that of the latter a sentient human being.

A "path" is a story-line created by a central actor, in this case, a student. It is not a paved roadway with exit ramps at set intervals, rather a trail that one constructs along contours of the terrain. One can wander away from a rough trail marked by the footsteps of predecessors, finding another pathway that may fit one's proclivities and changing values better. One may detour and return, and, in the detour, establish an alternative way to get there from here. And "there" is not necessarily an immutable, fixed place. A path through higher education, after all, is not merely one of curriculum. It is also very much about student growth, the discovery of interest, the sanding down of sharp edges, the construction of refuges, the honing of negotiating skills, and the development of behaviors and stances to serve in the workplace, family formation, and community life.

The word, "pathways" is more common than "path" in the science/mathematics/engineering/ technology (SMET) education literature. But the term is invoked almost reflexively, and its meaning is not always clear. The Howard Hughes Medical Institute's report of its 1996 undergraduate program directors meeting, Assessing Science Pathways (1997), for example, includes such notions as "tracking the test scores and course choices of students who have participated in science education programs and research experiences" and "[assessing] changes in attitudes about science . . ." (page x i).

The "pathways" literature frequently includes accounts of mentorships and research experience as critical to persistence in science, and as setting off chains of experiences that wind up in medical schools, post-docs, or corporate research labs. These stories are frequently--but not always (see, e.g. Zuckerman, Cole, and Bruer, 1991)--uplifting, particularly when they involve women and underrepresented minorities. The student records in NCES longitudinal studies unfortunately do not always show special research experiences, and the surveys have never asked questions about mentorship. These are limitations of databases such as the HS&B/So. On the other side, the "pathways" reports are principally anecdotal and do not detail the "chains of experiences," including high school and college coursework, that are essential to the results. When they do (e.g. Vetter, 1988), they lump all SMET fields together, thus failing to discriminate the critical differences between the culture of bench science and the culture of engineering that emerges from the empirical data of our longitudinal studies and that, we can reasonably hypothesize, plays a role in student choice. Indeed, a major objective of this monograph is to remind people just how much engineering is not science, and how confounding the two hampers our ability to advise college students and track their progress.

In a methodological sense, the concept of "path" used in this monograph is loosely analogous to that used in the structural equations of statistical prediction. That is, while we are examining a temporal sequence over a period of 15 years⁽⁷⁾, we start with a "threshold" behavior that occurred roughly one-third of the way through this history, and work out in both temporal directions from this threshold. The threshold censors the population under consideration. A structural equation would focus on a sequence of experiences from year 1 (see, e.g. Grandy, 1995), and sort effects by temporal criteria. In contrast, this study begins with a set of behaviors and performances in the second phase of that temporal sequence, namely, early college experience: what students studied and how well they performed academically. We then look to both the antecedent and subsequent academic history of the students who have been sorted-in by the threshold criterion.

The inquiry of this monograph is not interested in prediction, rather in relationships of experiences. I take this approach because the most convincing statistical methodologies of prediction reduce the richness of experience to dichotomous variables. There is nothing wrong with those techniques, but if one is interested in the detours and variations of human behavior, dichotomous variables (for example, earned a degree/did not earn a degree) are seriously lacking. Thus, too, no causal chains are posited, though now that the longitudinal study has run its course we can make stronger statistical cases for relationships of experiences than were available to earlier students of the HS&B/So cohort in the matter of the SMET "pipeline" (for example, Hilton and Lee, 1988).

Part 1 performs three tasks critical to the entire story. First, it delineates the empirical "threshold" of the engineering path and the various "destinations" of the HS&B/So cohort on that path by age 29/30; this is the basic analytic framework, derived inductively from transcript records. Second, it indicates the various institutional attendance patterns of this group that render the story somewhat more complex than previous research on engineering field attrition--let alone the curricular history of any group of students--has been able to grasp. Lastly, it describes the role of the community college not only in the preparation of transfer students but also in terms of those whose programs are confined to sub-baccalaureate preparation in engineering technologies. The community college portion of the engineering path is not usually acknowledged in the literature.

Part 2 will show precisely what students on the engineering path studied in college, both in terms of proportions of total credits earned and in terms of participation rates in different course clusters. We will compare the "empirical core curriculum" by both engineering path status and by cohort (the HS&B/So engineering graduates of the years 1986-1993 against that of engineering graduates of the years 1976-1984). This section departs from other national science/engineering "pipeline" studies in the attention it pays to content.

Part 3 is an interlude. It reflects on some of the differences between engineering and science, and the culture of engineering education and practice. The purpose of the interlude is to set the stage for deeper analyses of field attrition and the comparative careers of men and women in scientific and engineering fields, as well as to indicate the analytical conundrums that result from the aggregation of engineering with other scientific fields.

Part 4 sets out the secondary school background characteristics of students who reached the threshold of the engineering path, and compares the high school attainments of men and women in terms of highest level of mathematics studied, participation in core science courses, academic performance, and test scores. These are all traditional measures--and predictors--of college attendance, but the dependent variable here is not mere access, rather degree completion. Therefore, the analysis departs from conventional formulas in the research literature. It also advances the importance of "curricular momentum" in sketching the boundaries of academic choice, and concludes by examining students who had enough momentum to carry them onto the engineering path, but chose not to enter.

Part 5 discusses the factors involved in student choice of engineering as both a career and a major field of study in college. This section also looks backwards from labor market status in 1991 to college backgrounds. It examines the timing of choice, the consistency of students' visions of career and major, and the characteristics of those students who are "lost" to engineering between twelfth grade and the first semester of college. Other disciplines in which major is closely tied to occupation, for example, journalism, education, music, health sciences, computer science, and applied visual arts--can be subject to similar analyses.

Part 6 demonstrates how our data on field migration and degree completion in the HS&B/So compare to those from other major studies of roughly the same period. This is a necessary exercise in light of the whirlwind of data presented to institutional, state system, and national audiences. We find some remarkable agreements from very different databases, but some serious differences in analyses of the "traffic" and "balance of trade" among the disciplines.

Part 7 examines the differential experiences of men and women engineering students in terms of classroom environments and behaviors, credit load, and grades. It reviews some of the observations on the correlates of field attrition offered by other major studies that involved debriefings of students, and melds these into a summary of this particular investigation, its challenges to engineering as a profession and culture, and its generic suggestions for attaining three-dimensional tracking of college students in any field.

This monograph tells a particular kind of story. It does not pretend to confront the panoply of issues facing the conduct and context of engineering education in the United States. While it covers issues of the "student pipeline," curriculum and retention, its data source cannot access questions about faculty, teaching methods, costs, or equipment, let alone graduate education, cooperative assignments, and the continuing education of practicing engineers.

In fact, this monograph is not designed for engineering educators and engineering professional associations alone. It is also for:

• Science educators, scientific societies, and science policy-making bodies that often include engineering under their analytic and programmatic umbrellas;

• Higher education faculty in other disciplines, including non-scientific fields, who I hope will be prodded to investigate the virtues of empirically-based trackings of threshold-crossings and migration in their own areas;

• Counselors and advisers at both secondary school and college levels, whose understanding of such factors in student choice as the image of engineering as an occupation, and the impact of curricular momentum and optimum levels of mathematics to be studied, I trust will be enhanced, particularly for advising women; and

• The provosts, deans, legislators, and journalists who I hope will learn enough to set the local cases with which they deal against the three-dimensional histories of college students from a national tapestry.

The first task in our story is to delineate the threshold and, for those students who stay on the path and take the next step, the stations, or destinations, at which they arrived by age 30. Appropriate to the analysis of field attrition, each destination can be characterized by highest degree attained and major field (whether or not any degree was attained). The paths to all stations will be described in terms of combinations and types of institutions attended, college academic performance, courses taken, and other features of undergraduate careers. The destinations are thus configured in terms of the empirical characteristics and experiences of the students who arrived at them.

The "engineering path" variable was not determined algorithmically. Instead, it emerged in the course of reading standardized records of 8,215 students in the HS&B/So database, line by line. These records contain the transcripts of all institutions attended by the student through age 29/30⁽⁸⁾, and the reading was initially directed at a variety of other issues (accuracy of coding, completeness of record, continuity of enrollment, true date of first attendance, and others). A set of general decision rules for classification of students' programs was hypothesized on the basis of the first thousand records reviewed, and modified as we progressed through the balance. There were two readers, who judged each case separately and then compared their classifications. Where the classifications differed, the case was discussed, occasionally referred to external authority, and the differences resolved. By this inductive process, the threshold of the engineering curriculum was defined. To be judged as having arrived at the threshold, a student had to have earned more than 10 credits from a bachelor's degree-granting institution (though the student may have also attended a two-year college) and completed three courses at any institution during the first four semesters (or six quarters) of his/her academic career: (1) mathematics at a minimum level of pre-calculus; and either (2) both the introductory engineering design course and engineering graphics or (3) either the introductory engineering design course or engineering graphics and the introductory course in an engineering sub-field (electrical, chemical, etc.). Completion means just that. It says nothing about performance: at this stage of the accounting, a D is worth the same as an A. But the criterion of completion excludes all cases where the record indicates a withdrawal from a course.

The three courses cited were the most common in freshmen engineering curricula (other than physics and chemistry) at the time our cohort entered higher education (Kauffman, 1980), though engineering graphics, in particular, is no longer as common (Hendley, 1997). Despite the appeal of the argument that physics "seems to be an intermediary between theoretical mathematics and practical engineering" (Schonberger, 1990, page 102) and that the first college physics course has greater "filtering" effects on women than men, I did not use physics and/or chemistry among the "threshold" criteria principally because cases of Advanced Placement are not always identifiable on the transcripts, and in those colleges that grant credit for AP, a student's record may not indicate any college-level chemistry and/or physics.

The three courses cited are also the most common across all subfields of engineering. At one time, statics or a course combining statics and dynamics might have been included in this common core, but in computer, electrical and chemical engineering, a significant proportion of departments did not require a formal course in this area during the period our cohort was in college (Heggen, 1988). It should be mentioned, too, that traditional graphics was an almost universal requirement in associate's degree engineering technology programs at the time (Eisenberg, 1987).

A separate threshold was established for students who spent nearly all of their postsecondary careers in community colleges and/or other sub-baccalaureate schools. For this group, the mathematics requirement was lowered to college algebra or an algebra/trigonometry-based technical mathematics course, and engineering technology courses could be substituted for engineering design and the introductory course in a discrete field of engineering. The destinations for these students are categorized only by the level of degree they earned in any technology field: none, certificate, or associate's. While a small percentage of this group eventually earned bachelor's degrees, these degrees were not in engineering, engineering technologies, or architecture. The "2-year program only" students are used but occasionally in this analysis, and primarily for comparison with community college transfer students who become bachelor's degree candidates on the engineering path.

The old models of engineering education are changing. A decade from now it may not be possible to determine the "threshold" of the engineering path by the formula followed in this analysis. Required sequences of courses in mathematics and theoretical science may give way to action-oriented curricula in which the math and scientific theory are acquired on an ad hoc basis. Some engineering schools already use freshman "foundations" courses that integrate calculus, physics, engineering mechanics, and design (see, e.g. Carr et al, 1995), and we will have to learn how to pick these up from transcripts in the next longitudinal studies cohort (the high school graduating class of 1992). But the history of the 1982-1993 cohort can still be written "old-style," so to speak.

Only 9 percent (weighted N=181k) of all students in this cohort, no matter what combination of postsecondary institutions they attended, reached these "thresholds" of the engineering path. It is worth noting that this cohort entered higher education during a period of an upward swing in intention to major in science, mathematics, engineering, and technology fields, and just prior to the peak of that interest (Grandy, 1989; Dey, Astin and Korn, 1991). In engineering, the intents of this cohort coincided with the peak (Grandy, 1989). Declared interest and intent to major, however, provide but general parameters of what will actually happen as students learn what they do not know upon entrance to college.

Beyond the threshold⁽⁹⁾, performance and withdrawal count. The subsequent destinations along the path account for students who "migrated" from engineering to other fields. That is, they took at least two--and often three or four--courses beyond the threshold before engineering was replaced by another field or the record simply ended. For students who attended four-year colleges, the groups that left engineering were classified in two ways, by performance and by curricular destination: science, mathematics, engineering or technology (SMET) or other fields (non-SMET). The performance variable was simple; if the majority of the student's grades in mathematics, core science, and engineering were "C+" or lower, the student was classified as a mediocre performer; otherwise their performance was judged to be adequate/good. Withdrawals (as distinguished from "drops") and incompletes that were never resolved on the transcript were judged to be poor grades, in the category of "C+ or lower." While one might say the data reflect a self-fulfilling prophecy, the GPAs of the two groups of migrants--low performing and high performing--at the end of their undergraduate careers were 2.23 (SD=.353; s.e.*=.117) and 2.80 (SD=.488; s.e.=.082), respectively.

The timing of "migration," defined by custom as "year of study," is difficult to identify. It involves a drifting away from the major, for which reason I prefer the term to "defectors" (Astin and Astin, 1993) or "switchers" (Seymour and Hewitt, 1997). Events such as "changed major" are usually not recorded on transcripts. When they are recorded, they are found as journal entries made by registrars with date stamps that may bear little relationship to the timing of the actual change. Too, in cases of non-continuous enrollment (one out of six migrants stopped out of college at some point in their careers), enrollment that may embrace many summer terms, part-time enrollment, and histories that include withdrawals and failures, it is difficult to say just where one "year of study" begins and another one ends.

The precise nature of coursework beyond the threshold was not a consideration in the determination of the general destination on the path, migrant or completer. Many students might take "mechanics of materials," for example, since it is common to different specialties; fewer would take advanced mathematics courses such as tensor calculus, which is a rare elective. Beyond the threshold we are dealing with specialties, electives, and departmental strengths, and these express themselves inconsistently in a national sample such as ours. Beyond the threshold, too, we found that, within bachelor's degree-granting institutions, the engineering path is shared, in considerable part, by students from two other disciplines: engineering technologies and architecture. The transcripts of architecture graduates (our definition did not include graduates in city, community, or regional planning) show that 75 percent completed more than three engineering courses; and those of engineering technology graduates show 91 percent completing more than three engineering courses.

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*s.e.=standard error of the estimate, here adjusted for design effects. See Technical Appendix.

Table 3.--Destinations of students along the engineering path, 1982-1993

NOTES: (1) *p<.05; (2) columns may not add to 100.0% due to rounding. 
(3) Weighted Ns: all=181.3k; excluding 2-year only=139.5k. 
SOURCE: National Center for Education Statistics, High School &Beyond/Sophomores

Some, but not all, of these students attained sufficient curricular momentum to convert to an engineering major had they chosen to do so. These two groups are thus included in the engineering path analysis. So is a tiny group that completed interdisciplinary bachelor's degrees involving engineering and communications technology, computer science, and industrial management. While these groups constitute a small proportion of the whole, it is important to note that they are included and that, in this respect, our story line is slightly different from others in the literature on engineering education.

If we take the finest gradations of the engineering path, two distributions of our universe are presented in table 3. The first includes all students; the second excludes students whose careers were spent principally (though not exclusively) in 2-year college engineering technology programs. As is immediately apparent, this detailed delineation yields very few statistically significant estimates. One can imagine how small and shaky some of these percentages would become if we divided each category by gender, or the categories of those who did not complete degrees in engineering by the highest degree they did complete or the fields to which they migrated. Thus, for purposes of subsequent analyses, I will aggregate these 16 "destinations" on the engineering path into 3-5 categories depending on the question. The most common aggregation will be tripartite: threshold, migrants, and completers, with students in 2-year only programs analyzed separately and excluding students who were still enrolled in engineering at age 29/30 (see table 4).

Table 4.--Tripartite distribution of students on the engineering path, excluding students in 2-year engineering tech programs and students still enrolled in undergraduate engineering programs at age 29/30.

NOTES: (1) Standard errors of the estimates are in parentheses. (2) Columns may not add to 100.0% due to rounding. (3) *Row comparisons significant at p<.05. (4) Weighted N=139.5k.
SOURCE: National Center for Education Statistics: High School & Beyond/Sophomores

Presenting descriptive statistics that evidence little statistical significance sounds like an oxymoron and is usually not a good idea. We allow it in table 3 only to provide some hints as to the kind of fine relationships that might be obtained with larger databases, using unweighted Ns in multivariate analyses (for example, in the type of appraisals that Astin and his colleagues have undertaken for many years). For example, the categories of permanent drop-outs and bachelor's degree recipients who continue on to graduate school exhibit, prima facie, very different characteristics and behaviors from the other groups into which they will be aggregated. Long-term non-completers (that is, those who were still enrolled in bachelor's degree programs in engineering as of the last date on their transcript records) are such a small and highly diverse group (including people working on second bachelor's degrees, students who have recently transferred from 2-year to 4-year colleges after a long stop-out period, and students with incomplete records) that they cannot be aggregated with any other group and it is simply best to leave them out altogether.

How well does the gender distribution match data from other sources for the same period? The Engineering Manpower Commission reported that 16.6 percent of entering freshmen engineering majors in 1982 were women. The figure for 1983 was 17 percent (ASEE, 1986, page 43). Assuming that reaching the threshold is equivalent to being an "entering freshman engineering major," then our female proportion of 14.6 percent for students who reached the threshold is a little low, but is understandable since the HS&B/So engineering path universe includes students who started in community colleges, and men tend to dominate community college engineering technology programs even more than they dominate 4-year college engineering programs. The total number of entering engineering majors reported by the EMC for 1982 was 115,303; our weighted N for a matching group (fall, 1982 entrants only) using the threshold criteria is 124,621. These differences are not that great when one considers that the HS&B/So numbers are inflated by "accidental" threshold students.

Table 4 also illustrates some of the wisdom of aggregation for univariate analyses of weighted national samples. There is no statistically significant difference in the proportions of women and men whose destination on the engineering path ended at the threshold. But the differences in the fraction of men and women who became migrants and in the proportion who completed degrees are modestly significant (t=2.06; t=2.59). Without aggregation there is no chance of spotting these relationships.

One of the key issues in studying field attrition in engineering is that of institutional effects (Astin, 1977; Astin, 1993). Why are institutional effects particularly important in engineering? Because institutions with large graduate programs tend to enjoy extensive research support from both industry and federal agencies, and the effects of size seep down into the undergraduate program: the proportion of sections being handled by graduate TAs, the proportion of foreign instructors (Vetter, 1988), the culture of hierarchy (Hacker, 1981), the variety and sophistication of available equipment and facilities, and the nature of problems and topics that move from the "scholarly canon" of research into the "pedagogical canon." The National Research Council has used this particular set of characteristics to divide undergraduate engineering programs into two tiers (National Research Council, 1986). Astin's 1993 analysis of what produces persistence toward a career in engineering reminds us of obvious environmental effects. First, there is a limited number of institutions available to would-be engineering students (in 1986, the modal graduation year for students in the HS&B/So, it was 311; Ellis, 1987), and because of the capital investment and level of faculty expertise required to mount an accreditable engineering program (Accreditation Board for Engineering and Technology, 1991), the vast majority of these schools will not be small colleges with cozy environments. Yes, there are a few small colleges with engineering programs, but these, e.g. Rose-Hulman Institute of Technology with an enrollment of 1,500 or so, are special mission institutions.

Table 5.--Number of colleges attended by HS&B/So students as undergraduates, and number of states in which those colleges were located, by engineering path status

NOTES: (1) Universe includes all students who earned more than 10 credits and for whom engineering path status could be determined. Weighted N=1.958M. (2) Standard errors of the estimates are in parentheses. (3) Rows add to 100.0%
SOURCE: National Center for Education Statistics: High School & Beyond/ Sophomores.

Now if only students stayed in the same college from the time they entered higher education until they left, with or without an undergraduate degree (bachelor's or associate's), the institutional effects analysis would be very compelling. But as table 5 makes abundantly clear, students are highly mobile consumers of higher education. In fact, they have become more so over the past quarter century. For the high school class of 1972 followed for 12 years on college transcripts (to 1984), 32 percent of students who earned more than 10 credits also attended more than one school as undergraduates (Adelman, 1994, page 27). For a more recent (1989-1994) cohort study that did not include transcripts, 45 percent of the participants indicated attendance at more than one institution within five years of first entry to college (McCormick, 1997).

For the HS&B/So sample, 53.5 percent attended more than one college, and 40 percent of this group attended schools in more than one state. With the exceptions of permanent (at age 29/30) drop-outs, 2-year college engineering tech students, and bachelor's degree recipients in engineering who continued on to graduate schools, students on the engineering path had a higher multi-institutional attendance rate than others, and those who earned bachelor's degrees tended to cross state lines at a higher rate than others. It is very difficult to judge institutional effects on students who earned terminal bachelor's degrees in engineering if 71 percent of them attended more than one school along the way, 25 percent attended more than two schools, and 54 percent of those who attended more than one school crossed state lines in the process!

If so many attended more than one institution of higher education, what kinds of schools were involved? There are four questions to be asked here: What was the true first institution of attendance? What combination of institutional types was involved? Do these attendance patterns bear any relation to the elapsed time between true date of first attendance and the date a student either received a bachelor's degree or left higher education? For those who attended both community colleges and 4-year schools, how much of their undergraduate time was spent in community colleges? Tables 6, 7, and 8 (in combination with table 5) answer these questions. A few words on the variables used in these tables might be helpful.

Table 6.--Type of institution of first attendance for HS&B/So students, by engineering path status