Encyclopedia of Sociology Vol

status by tabulating scale scores of health-specific indices and/or calculating frequency distributions of individual health status questions. In the case of the latter, the investigator might determine presence/absence of a specific health problem (e.g., Do you have arthritis?) and measure central tendencies and variance in the data.

Functional ability is considered to an important indicator of health status in a sociological context. While the level of functioning can be measured as physical, mental, or social impairment, most of the interest is in physical functioning. This approach conceptualizes functional ability as ability to conduct activities of daily living (ADLs) or instrumental activities of daily living (IADLs) without assistance from others. Instruments used include measures suitable for older people (e.g., the OARS instrument of Duke University 1978), disease-specific measures (e.g., the arthritis functioning measure of Patrick and Deyo 1989), and instruments that assess cognitive functioning as a component of physical functioning (e.g., Keller et al. 1993).

The individual’s perception of his or her health is often measured. In these investigations, the concern is with how the person assesses general health status at the present time or in reference to other people and other times. Thus, they may be asked ‘‘Is your health generally excellent, good, fair, poor, bad?’’ or ‘‘How does your health compare with that of people your own age?’’ or ‘‘Is your health better or worse than one year ago?’’ They may also be asked about perceived functional ability (Duke University 1978; Lawton et al. 1982).

Emotional and psychological health are also measured. Well-validated instruments such as the CES-D of the Center for Epidemiological Studies or the Zung Depression Measure are used for depression (DeForge and Sobal 1988). Investigators also seek to determine mood (Profile of Mood States of McNair et al. 1971), positive and negative effect (Bradburn 1969), morale (Lawton et al. 1982), and subjective well-being (Dupuy 1984).

Quality of life indicators are among the least well validated instruments used to assess health status. There are many different approaches to quality of life that represent medical, psychological, and social models of illness. Since sociologists

prefer a multidimensional view, Levine and Croog (1984) presented five components of quality of life: social-role performance, physiologic state, emotional state, intellectual function, and general satisfaction or feeling of well-being. Also considered to measure quality of life are some subscales of the Sickness Impact Profile (Bergner 1984). The full 134-item instrument assesses physical, social, psychological, and interactional aspects of illness, but some scales are specific to pain or impairment level and tend to reflect a medical, rather than psychosocial, view of quality of life. Indeed, the medical approach may concentrate exclusively on disease-specific or treatment-specific variables. It may measure, for example inability to eat or excessive fatigue among cancer patients undergoing chemotherapy, or frequency of urination among individuals with hypertension who are prescribed medications to expel fluids from the body. Still other medically focused approaches to quality of life may focus on the experience of pain, as in the previously mentioned Sickness Impact Profile.

Health status assessment is clearly a broad field. It includes measurement of disease patterns in a population, self-reports by individuals of generalized health status, and middle-level health measurements. These latter measurements are neither macro level, like population-based mortality and morbidity statistics, nor micro level, like individual reports. Rather, they include validated scales, indices, or series of questions that may be widely used among general or specific populations. Many, if not most, measures have been found to be reliable and valid indicators of health status. The medical sociologist or related researcher thus has a wide range of instruments to assess health status.

CONCLUSION

Health promotion includes a wide-ranging set of activities that (1) enhance health status, (2) prevent disease, (3) seek to control the spread of chronic or infectious disease, and (4) attempt to arrest or delay deterioration that occurs as the result of these conditions. Health-promoting activities occur at the societal and individual levels and include a long list of agents. Essentially, health promotion represents the principle that maintaining health, preventing disease, and avoiding decline or complications of progressive illness are all

achievable goals. For any society to have a healthy, vital citizenry, it must reduce the financial, social, and medical burdens of illness. All these are accomplished with health-promoting practices.

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Dupuy, H. J. 1984 ‘‘The Psychological General WellBeing (PGWB) Index.’’ In N. Wenger, M. Mattson, C. Furberg, and J. Elinson, eds., Assessment of Quality of Life in Clinical Trials of Cardiovascular Therapies. New York: Le-Jacq.

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HEALTH STATUS

MEASUREMENT

See Health and Illness Behavior; Health Promotion and Health Status; Quality of Life; Medical Sociology.

HETEROSEXUAL BEHAVIOR PATTERNS

See Courtship; Sexual Behavior in Marriage and Close Relationships; Sexual Behavior Patterns; Sexual Orientation; Sexual Violence and Exploitation.

HIERARCHICAL LINEAR MODELS

Hierarchical linear models are applicable in situations where data have been collected from two (or more) different levels. Sociology’s initial interest in such multilevel relationships can be traced back to Durkheim’s research into the impact of community on suicide (Durkheim [1898] 1951). More recently, these models have been related to the topic of contextual analysis (Boyd and Iversen 1979), where researchers are interested in investigating linkages between micro-level and macrolevel variables. Sociological theories have been classified into three groups according to the degree to which they incorporate multilevel variables (Coleman 1986). In one group, variation in a dependent variable is explained through independent variables obtained from the same social level (e.g., country, community, individual). In a second group, attempts are made to account for differences in a dependent variable at one level by examining variation in an independent variable at a higher level; and in a third group, variations in a dependent variable are explained by variations in an independent variable at a lower level. Theories

that fall into either the second or third group are multilevel theories and can be explored using hierarchical linear models.

SPECIFICATION OF THE HIERARCHICAL

LINEAR MODEL

A wide variety of hierarchical models can be specified. However, in order to outline the basic features of such models, a simple example will be developed. Assume that a researcher is interested in modeling the length of hospital stay (LOS) for a specific individual (Yi) as a function of the severity of that individual’s illness (Xi) and the bed occupancy rate for the institution in which that individual is hospitalized (Gj). In this hypothetical model we have one criterion (or dependent) variable, Yi, at the micro level, one micro-level predictor (or independent) variable, Xi, and one macro predictor (or independent) variable, Gj. This produces a two-level hierarchical model. The technique is quite flexible and can be expanded to include multiple predictor variables at either (or both) the microand macro-levels and additional levels. In the given example, an index of individual comorbidity could be included as an additional micro-level predictor, type of hospital (e.g., public vs. private) could be included as an additional macro-level predictor, and an additional level of the gross national product (GNP) of the country in which the hospital is located could be added to create a three-level model.

The first step in developing hierarchical models is to specify a model for the micro-level variables that is identical for all contexts. In the present example a linear model relating LOS as a function of severity of illness is specified for each of the hospitals.

Where j=1, 2, . . . . , j denotes the macro-level contexts (e.g., the hospitals) and i=1, 2, . . . . , nj denotes micro-level observations within contexts (e.g., individuals within hospitals). The intercepts from Equation 1 (ß0j) provide estimates of the expected LOS for individual i in hospital j whose severity of illness is zero, whereas the slopes (ß1j) provide estimates for the effect of a unit change in the severity of the illness for individual i in hospital

j. Finally, the ßij’s represent random errors or residuals. It is assumed that these errors are normally distributed within each context with a mean of zero and a constant variance σ2. This is a standard linear model with the exception that the coefficients (i.e., the ßj’s) are allowed to vary across contexts (hospitals).

In situations where separate regression equations are estimated for various contexts, four different patterns can emerge. These patterns are depicted in Figures 1a, 1b, 1c, and 1d. In Figure 1a, the functional relationship between the microlevel variables is identical for all the contexts, and thus the intercepts and slopes are the same for all contexts. In Figure 1b, the degree of linear relationship between the micro-level variables is equivalent across contexts; however, the initial ‘‘location’’ (i.e., the intercept) of this relationship varies across contexts. In Figure 1c, the degree of linear relationship between the micro-level variables varies as a function of context, although the initial ‘‘location’’ is consistent across contexts. Finally, in Figure 1d, both the initial location and the relationship between the micro-level variables vary significantly across contexts.

Systematic differences across contexts are reflected in three of the figures (viz., Figures 1b, 1c, and 1d). The presence of these differences leads to questions of whether there are contextual or mac- ro-level variables that could be associated with the varying micro-level coefficients (i.e., the slopes and/or intercepts). Questions of this type are addressed by specifying a second-level model. For example, if there is significant variation among the micro-level coefficients, then this variation could be modeled as a function of contextual or macrolevel variables as follows:

where Gj is a contextual (or macro-level) variable, γ00 and γ10 are the intercepts from the second-level models, γ01 and γ11 are the slopes from the secondlevel model, and U0j and U1j are the second-level residuals. It is assumed that the residuals are distributed multivariate normal with mean vector 0 and variance-covariance matrix T. In the present example, Equation 2 would be used to model differences across hospitals among the intercepts

of the micro-level equations (cf. Figures 1b and 1d), whereas Equation 3 would be used to model differences across hospitals in the slopes of the micro-level equations (cf. Figures 1c and 1d).

Depending on the actual variability of the micro-level coefficients (i.e., the ßj’s), different second-level models would be justified. For example, in situations where there is no variation in the slopes across contexts (see Figure 1b), the inclusion of Gj in Equation 3 would not be meaningful given that ß1j is the same across all contexts. Similarly, in situations where there is no variation in the intercepts across contexts (see Figure 1c), the inclusion of Gj in Equation 2 would not be meaningful given that ß0j is the same across all contexts.

By substituting Equations 2 and 3 into Equation 1, we can obtain a single equation form of the hierarchical model as follows:

Y ij = γ00 + γ01 G j + γ10 X 1ij + γ11 G j X 1ij +

(4)

(U0j + U1j X 1ij + εij)

The model represented by Equation 4 is a mixed model with both fixed coefficients (viz., the γ’s) and random coefficients (viz., the U’s and the ε’s). Further, since the random coefficients are allowed to covary across contexts, it can be called a variance component model.

The approach to investigating relationships occurring across hierarchical levels represented by the equations above is not new. Burstein and colleagues (1978) discussed a similar approach under the conceptualization of ‘‘slopes as outcomes.’’ Conceptually, this is an accurate description, given that the regression coefficients estimated within each context at the micro level are used

as criterion (or dependent) measures in the mac- ro-level (or second-level) model (cf. Equations 2 and 3). However, while this conceptualization of the relationship between microand macro-level variables has been understood for a number of years, concerns about the adequacy of estimating such models using traditional statistical techniques (viz., ordinary least squares, OLS) have been expressed. However, separate statistical advances throughout the 1980s improved the estimation procedures for these models (for reviews see Burstein and colleagues 1989; Raudenbush 1988), with the advances resulting in several different software packages being developed specifically for the estimation of hierarchical linear models (e.g., GENMOD, HLM, ML3, and VARCL).

ESTIMATION OF HIERARCHICAL

LINEAR MODELS

In estimating the various components of the hierarchical linear model, a distinction is made among fixed effects, random effects, and variance components. Specifically, fixed effects are those parameter estimates that are assumed to be constant across contexts (e.g., the γ’s from Equations 2 and 3), whereas random effects are parameter estimates that are free to vary across contexts (e.g., ß0j and ß1j from Equation 1). Hierarchical linear models also allow for the estimation of the variance components of the model. These include (1) the variance of the residuals from the micro-level model (i.e., the variance of the εij’s identified as σ2 above);

(2) the variance of the second-level residuals (i.e., U0j and U1j); and (3) the covariance of the secondlevel residuals (i.e., the covariance of U0j and U1j). The variance–covariance matrix of the secondlevel residuals was previously defined as T.

Estimation of Fixed Effects. One approach that could be used to estimate the γ’s from Equations 2 and 3 is traditional OLS regression. However, because the precision of estimation of these parameters will vary as a function of contexts, the usual OLS assumption of equal error variances (i.e., homoscedasticity) will be violated. In order to deal with this violation the second-level regression coefficients (the γ’s) are estimated using a more sophisticated procedure, generalized least squares (GLS). GLS techniques provide weighted estimates

of the second-level regression coefficients such that the contexts that have more precise estimation of the micro-level parameters receive more weight in the estimation. That is, those contexts in which there is greater precision in estimating the parameters (the slopes and the intercepts) receive more weight in estimating the second-level regressions.

Estimation of Variance–Covariance Components. The components of the variance–covari- ance matrix T include the variance of the microlevel residuals, and the variance and covariance of the second-level residuals. These components are used in the GLS estimation of the fixed effects of the second-level model. However, the values of the components of this matrix are typically not known and must be estimated. The best methods for doing this are iterative methods that alternatively estimate the parameters of the models and then estimate the variance–covariance matrix T until a convergence is reached. Hierarchical linear models adopt the EM algorithm (Dempster et al. 1977) that produces maximum likelihood estimates for the variance–covariance components of T.

Estimation of Random Effects. The simplest way of estimating the coefficients for the microlevel model (i.e., Equation 1) is to compute an OLS regression for a specific context. In the present example, this would involve obtaining a regression equation relating expected LOS to severity of illness for all individuals within a specific hospital. If there are reasonably large sample sizes within each context, this analysis would provide relatively precise estimates of the coefficients of interest. These estimates will not be stable, however, if sample sizes are smaller. Further, inspection of the second-level models reveals that there is a second estimate of the coefficients from the micro-level models. Thus, for any particular observational unit there are two separate estimates of the microlevel regression coefficients: one from the microlevel regressions themselves and the other from the second-level regression model. The question that this leaves is which of these provides a more accurate estimate of the population parameters for the particular observational unit.

Rather than forcing a choice between one of these two estimates, hierarchical linear models use empirical Bayes estimation procedures (Morris

1983) to compute an optimally weighted combination of the two estimates. The empirical Bayes estimates are a weighted composite of the two estimates discussed above. The micro-level regression coefficients (the ßj’s) estimated by OLS are weighted according to the precision with which they are estimated (i.e., their reliability). In cases where the OLS estimates are not very reliable (e.g., due to small sample size), the empirical Bayes procedure allots greater weight to the second-level estimates. Essentially, then, the weighted composite ‘‘shrinks’’ the micro-level estimate toward the second-level estimate, with the level of shrinkage being determined by the reliability of the microlevel estimate. It has been demonstrated that, in general, the empirical Bayes estimates have smaller mean squared errors than OLS estimates.

Statistical Tests. A variety of statistical tests for hypothesis testing are provided by the various computer programs used to estimate hierarchical linear model. For example, HLM (Bryk et al. 1994) computes a t-test to evaluate the hypothesis that the second-level regression parameters depart significantly from zero. In addition, chi-square tests are provided for tests of whether or not there is significant variation in the second-level residuals. These latter tests allow the researcher to determine the model that best fits the observed data. For example, it might be that there is no significant variation in the slopes across contexts; however, there might be significant variation in the intercepts (as in Figure 1b).

FURTHER ISSUES WITH HIERARCHICAL

LINEAR MODELS

Centering. Often, as in the present example, interpretation of the intercepts is not straightforward, since a value of zero for the independent variable (in the present case, severity of illness) is not meaningful. In situations like this, it is possible to ‘‘center’’ the independent variable as a deviation from the mean level of that variable in the sample as follows.

With this specification, the intercepts now represent estimates of the expected length of stay for individuals in a specific hospital whose severity of

illness is at the mean. The interpretation of the other parameters remain unaltered.

Longitudinal Data. Hierarchical linear models can also be used to analyze longitudinal data collected in order to examine questions regarding the assessment of change (Bryk and Raudenbush 1987). Under this approach, there are repeated observations within an observational unit and there is a sample of different units. This allows for a twolevel conceptualization of development such that change in the individual units is modeled as a function of time and differences in the patterns of change across individual units can be modeled as a function of measurable characteristics of the individual units. Under this conceptualization, interest is in between-individual (unit) differences in within-individual (unit) change.

Statistical Software. As previously noted, a number of different software programs have been specifically developed in order to estimate hierarchical linear models. Kreft and colleagues (1994) reviewed five of the then-available packages. While they recommended ML3 (Prosser et al. 1991) for the ‘‘serious’’ user, they concluded that HLM’s main advantage is its ease of use. Since that time, both programs have been updated and now versions for Windows ’95 are available (viz., HLM 4 and MlwiN. Information on the latest version of these programs is available from the following Web sites: go to http://www.ssicentral.com/hlm/ mainhlm.htm for information on HLM 4; go to http://www.ioe.ac.uk/mlwin for information on MlwiN.).

CONCLUSION

Hierarchical linear models provide statistically sophisticated ways for dealing with analyses in which data are obtained from multiple levels. Such data are common in sociological research, especially if the investigation deals with contextual effects or longitudinal designs. For more detailed discussions of hierarchical linear models, the interested reader is directed to the following sources that provide more in-depth coverage: Bryk and Raudenbush (1992); Goldstein (1995); or Hox (1995). (At the time of publication, a complete, online version of this text was available from: http:// ioe.ac.uk/multilevel.what-new.html.

REFERENCES

Boyd, L. H., and G. R. Iversen 1979 Contextual Analysis: Concepts and Statistical Techniques. Belmont, Calif.: Wadsworth.

Bryk, A. S., and S. W. Raudenbush 1987 ‘‘Application of Hierarchical Linear Models to Assessing Change.’’

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——— 1992 Hierarchical Linear Models: Applications and Data Analysis Methods. Newbury Park, Calif.: Sage.

———, and R. J. Congdon 1994 Hierarchical Linear Modeling with the HLM/2L and HLM/3L Programs. Chicago: Scientific Software International.

Burstein, L., K. S. Kim, and G. Delandshere 1989 ‘‘Multilevel Investigations of Systematically Varying Slopes: Issues, Alternatives, and Consequences.’’ In R. D. Bock, ed., Multilevel Analysis of Educational Data. New York: Academic Press.

Burstein, L., R. L. Linn, and F. J. Capell 1978 ‘‘Analyzing Multilevel Data in the Presence of Heterogeneous Within-Class Regressions.’’ Journal of Educational Statistics 3:347–383.

Coleman, J. S. 1986 ‘‘Social Theory, Social Research, and a Theory of Action.’’ American Journal of Sociology

91:1309–1336.

Dempster, A. P., N. M. Laird, and D. B. Rubin 1977 ‘‘Maximum Likelihood from Incomplete Data Via the EM Algorithm.’’ Journal of the Royal Statistical Society, Series B 39:1–8.

Durkheim, E. (1898) 1951 Suicide. Glencoe Ill.: Free Press.

Goldstein, H. 1995 Multilevel Statistical Models. New

York: Halstead.

Hox, J. J. 1995 Applied Multilevel Analysis. Amsterdam:

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Kreft, I., J. de Leeuw, and R. van der Leeden 1994 ‘‘Review of Five Multilevel Analysis Programs: BMDP5V, GENMOD, HLM, ML3, VARCL.’’ The American Statistician 48:324–335.

Morris, C. 1983 ‘‘Parametric Empirical Bayes Inference: Theory and Applications.’’ Journal of the American Statistical Association 78:47–65.

Prosser, R., J. Rasbash, and H. Goldstein 1991 ML3: Software for Three-Level Analysis. London: Institute of Education, University of London.

Raudenbush, S. W. 1988 ‘‘Educational Applications of Hierarchical Linear Models: A Review.’’ Journal of Educational Statistics 13:85–116.

GEORGE ALDER

HIGHER EDUCATION

Colleges and universities seem to defy the maxim that only highly rationalized institutions can succeed in the modern world. Only the Catholic Church has a longer continuous existence among Western institutions. Higher education has done more than survive; it is in many ways a pivot of key developments in the social structure and culture. It is central for the generation of research and technological innovations. It is also central in the selection, training, and credentialing of young men and women for higher-level positions in the occupational structure.

Among the most important sociological questions surrounding higher education are the following: (1) To what extent have advanced industrial societies become based on a ‘‘knowledge economy’’ closely related to university research and training? Related to this question is another: To what extent do we see the rise of a ‘‘new class’’ of ‘‘knowledge workers’’ with advanced training— differing in interest and outlook from both business elites and earlier aristocracies of labor? (2) To what extent do institutions of higher education reproduce social inequalities by certifying the cultural advantages of children from the upper classes, or reshuffle the social hierarchy by rewarding intellect and ability independent of students’ so- cial-class background? (3) Do institutions of higher education, with their traditions of collegial control and tenure, represent an alternative model to corporate forms of organization? These issues can be addressed only after examining the historical development, the existing organizational structures, and the contemporary pressures on higher education.

First, it is necessary to define the dimensions of higher education. Formal educational systems are conventionally divided between primary (the first six years), secondary (the next four to six years), and postsecondary education. Some postsecondary schools offer courses of study that are narrowly vocational and very short in duration. These institutions (including secretarial, business, and vocational–technical colleges) are not usually considered to be part of higher education. Institutions must award degrees that are recognized by baccalaureate-granting colleges and universities to be considered part of higher education. By this criterion, community colleges in the United States,

Fachhochschulen in Germany, and the ‘‘further education’’ colleges in the United Kingdom represent bottom tiers of their respective national higher education systems. These are all short-cycle, vocationally oriented institutions, but some of their degrees are transferable to higher-level colleges and universities. Above these lower-tier institutions are a vast array of colleges, universities, and specialized institutions (for example, seminaries and art schools) that constitute the core of the higher education sector in all contemporary societies. Levels in this institutional hierarchy are structured, most fundamentally, by the type of credentials offered. In the United States, for example, levels are marked by movement from the associate to the baccalaureate to the master’s to the doctoral degree.

HISTORICAL DEVELOPMENT

The distant relatives of today’s institutions of higher education go back in the West to the Greek academies of the fourth and fifth centuries B.C.E. In these academies, young men from the governing classes studied rhetoric and philosophy (and lesser subjects) as training for public life (Marrou [1948] 1982). In the East, the roots of higher education go back to the training of future government bureaucrats at the feet of masters of Confucian philosophy, poetry, and calligraphy. In both East and West, a close relationship existed among social class, high culture, and preparation for public life.

However, modern institutions of higher education trace a more direct lineage from the medieval studium generale. In the first European universities of the twelfth and early thirteenth centuries (notably Salerno, Bologna, and Paris), students and masters came together to pore over the new knowledge discovered in ancient texts and developed by the Arab scholars of Spain. These gatherings of students and teachers were a product of the revival of scholarly inquiry in what has been called the ‘‘twelfth-century Renaissance.’’ The medieval universities were similar to modern higher education in that they were permanent institutions of learning with at least a rudimentary formal organization. Courses of study were formally organized, lectures and examinations were given at scheduled times, administrative officials presided, graduation ceremonies were held, and students lived in

lodgings near the university buildings. The studium generale, or leading universities, were recognized as such because they housed at least one of the ‘‘higher faculties’’ in law, medicine, or theology in addition to faculties of the arts. Courses in the arts, typically with an emphasis on logic and philosophy, were common preparation for study in the three learned professions. Thus, from the beginning, a certain vocational emphasis is evident in the university. Degrees awarded on the completion of professional studies certified accomplishments that made their recipients worthy of entry into professional life. Nevertheless, the spirit of inquiry was equally important in the medieval universities; these were places renowned for famous teachers, such as Abelard in Paris and Irnerius in Bologna. Civic competition led to a proliferation of universities. By the end of the Middle Ages, eighty had been founded in different parts of Europe (Rashdall [1985] 1936).

In the seventeenth and eighteenth centuries, the fortunes of colleges and universities waned. The causes for decline are numerous, including the attractiveness of commercial over scholarly careers, the interference (in some places) of religious and political authorities, and the insularity of faculty who jealously guarded their guild privileges but resisted new currents of thought. During this period, colleges and universities became places concerned with the transmission of ancient texts rather than the further advance of knowledge. Professional training moved out of the universities: into Inns of Court, medical colleges, and seminaries. New elites interested in technical and scientific progress established entirely new institutions rather than allying with the colleges and universities. Napoleon, for example, founded elite professional training institutions, the grandes ecoles, and the early investigators in the natural sciences created separate elite societies to encourage research and discussion.

The revived university is the product of nine- teenth-century European reform movements, led in the beginning by intellectually oriented aristocrats and eminent philosophers and theologians. The University of Berlin, founded in 1810, was the first reformed university, and others shortly followed in its wake. The new university was founded on the ‘‘Humboldtian principles’’ of the unity of teaching and research (meaning that both functions were performed by the professoriate) and

the freedom to teach and learn without fear of outside interference. The development of new academic components, such as the research seminar and the specialized lecture, created an environment in which path-breaking researchers, such as Leopold Ranke in history and Justus von Liebig in chemistry, emerged (McClelland 1980). By midcentury, the German research universities had become a model for reformers throughout Europe and from as far away as the United States and Japan. The first research university in the United States, Johns Hopkins University, founded in 1876, was explicitly modeled on the German research university.

Higher education’s current emphasis on training for a wide range of applied fields has an equally important history. Here the United States, rather than Germany, was the decisive innovator. The Morrill Acts (passed in 1862 and 1890) provided funds for states to establish ‘‘land grant’’ universities to provide both general education and practical training in agricultural and mechanical arts for all qualified applicants. Such institutions encouraged both the democratization of American higher education and a closer connection between universities and emerging markets for educated labor. The American university’s role in society was further enhanced by its willingness to work collaboratively with government, professional associations, and (somewhat later) business and community organizations. The ‘‘Wisconsin Idea’’ encouraged close connection between university experts and government officials during the period before World War I. Universities also cooperated closely with professional associations to raise educational training standards. Connections between university and state were extended, particularly in the sciences, during World War II and the Cold War, when government grants for universitybased scientific research became a very large source of support. These developments encouraged a new view of higher education. In the 1960s, Clark Kerr (1963) coined the term ‘‘multiversity’’ to describe institutions, like his own University of California, as service-based enterprises specializing in training, research, and advice for all major sectors of society. Junior colleges, first established just after the turn of the twentieth century, were by the 1960s even more systematically tied than universities to local and regional markets for semiprofessional and technical labor (Brint and Karabel

1989). In terms of growth, these two-year colleges are the great success story of twentieth-century higher education, and their influence is now evident even in four-year institutions. The utilitarian approach of American educators was resisted for some time in Europe and Asia, where access to higher education was strictly limited to those students who passed rigorous examinations and where higher degrees had long served as important badges of social status linked to cultural refinement. However, by the last quarter of the twentieth century, the entrepreneurial multiversity had become an important model throughout the developed world (Clark 1998).

Institutions of higher education rarely shed their earlier identities completely; instead, they incorporate new emphases through reorganizing and adding new components and new role expectations. Today, all major historical stages of university development remain very much in evidence. Much of the nomenclature, hierarchy, and ritual of the medieval university remains and is in full display at graduation ceremonies. Although the major fields of study have changed dramatically, the underlying liberal arts emphasis of the ancient academies has remained central in the first two years of undergraduate study (the lower division). The nineteenth-century emphasis on specialization is evident in the second two years of undergraduate study (the upper division) and in graduate and professional programs. The nineteenthcentury emphasis on research remains an absorbing occupation of faculty and graduate students. The twentieth-century emphases on ancillary training, service, and advisory activities are organized in separate components (as in the case of university extension programs, agricultural experiment stations, university-based hospitals, and collegiate sports teams) or performed by research faculty in their capacity as consultants and lecturers in the community.

ACADEMIC ORGANIZATION

Contemporary institutions of higher education are organized horizontally by divisions among fields of knowledge and vertically by ranks of authority. The dual hierarchy of professors and administrators is a structural feature of academic organization with particularly important consequences.