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independence holds, then P(A) (i.e., .54) must equal P(A|B) for all B (i.e., the proportion enrolled in each of the columns). Hence, if statistical independence held, we would have 378 whites in college (i.e., 54 percent of the 700 whites in the table), 108 blacks in college (i.e., 54 percent of the 200 blacks in the table), and 54 Asian-Americans in college (i.e., 54 percent of the 100 Asian-Ameri- cans in the table.) Evidently, the number not enrolled in each racial grouping could be obtained in a similar way (i.e., 46 percent of each column total), or by subtraction (e.g., 700 whites minus 378 whites enrolled leaves 322 whites not attending college). Hence, the frequencies that would be ‘‘expected’’ for each cell if statistical independence held can be calculated, not just for Table 1 but for any cross-classification table.

If the ‘‘expected’’ frequencies for each cell are very similar to the ‘‘observed’’ frequencies, then the departure from statistical independence is slight. But if the ‘‘expected’’ frequencies differ greatly from the corresponding ‘‘observed’’ frequencies, then the table displays a large departure from statistical independence. When the departure from statistical independence reaches its maximum, an ideally normed measure of association should then indicate an association of 1.0.

A quantity called chi square is conventionally used to reflect the degree of departure from statistical independence in a cross-classification table. Chi square was originally devised as a statistic to be used in tests against the null hypothesis; it was not designed to serve as a measure of association for a cross-classification table and hence it does not range between 0 and 1.0. Furthermore, it is not well suited to serve as a measure of association because it is heavily influenced by the total number of cases and by the number of rows and columns in the cross-classification table. Even so, calculating chi square constitutes the first step in calculating measures of association based on departure from statistical independence. For a crossclassification table, this statistic will be zero when the observed frequencies are identical to the frequencies that would be expected if statistical independence held, and chi square will be progressively larger as the discrepancy between observed and expected frequencies increases. As indicated below, in the calculation of chi square, the differences between the frequencies observed and the

frequencies expected if statistical independence held are squared and weighted by the reciprocal of the expected frequency. This means, for example, that a discrepancy of 3 will be more heavily weighted when the expected frequency is 5 than when the expected frequency is 50. These operations are succinctly represented in the following formula for chi square:

χ2 = Σ Ei where χ2 = chi square

Σ is the instruction to sum the quantity that follows over all cells

Oi = the frequency observed in the i th cell

Ei = the frequency expected in the i th cell if statistical independence holds

To illustrate equation (2), consider Table 2, which shows (1) the observed frequency (O) for each cell as previously shown in Table 1; (2) the frequency expected (E) for each cell if statistical independence held (in italics), and, (3) for each cell, the squared difference between observed and expected frequencies, divided by the expected frequency (in bold type). When the quantities in bold type are summed over the six cells—in accord with the instruction in equation 2—we obtain a chi square of 76.3. There are various ways to norm chi square to create a measure of association, although no way of norming chi square is ideal since the maximum possible value will not be 1.0 under some commonly occurring conditions.

The first measure of association based on chi square was Pearson’s Coefficient of Contingency (C), which is defined as follows:

(3)

C = ! χ2 χ+2 N

This measure can never reach 1.0, although its maximum possible value approaches 1.0 as the number of rows and columns in the table increases. For Table 1, C = .27.

An alternative measure of association based on chi square is Cramer’s V, which was developed in an attempt to achieve a more appropriately normed measure of association. V is defined as follows:

(4)

χ2

V = ! (N )[Min (r –1, c –1]

The instruction in the denominator is to multiply the table total (N) by whichever is smaller: the number of rows minus 1 or the number of columns minus 1 (i.e., the ‘‘Min,’’ or minimum, of the two quantities in parentheses). In Table 1, the minimum of the two quantities is r − 1 = 1, and the denominator thus becomes N. Hence, for Table 1, V = .28.

While this measure can reach an upper limit of 1.0 under certain conditions, it cannot be 1.0 in some tables. In Table 1, for example, if all of the 540 persons attending college were white, and all blacks and Asian-Americans were not in college, chi square would reach a value of approximately 503 (and no other distribution that preserves the marginals would yield a larger chi square). This maximum possible departure from statistical independence (given the marginal frequencies) yields a V of .71.

In the special case of a cross-classification table with two rows and two columns (a ‘‘2 × 2 table’’) V becomes phi (Φ), where

(5)

Φ = ! Nχ2

The maximum possible value of phi in a given table is 1.0 if and only if the distribution over the two row categories is identical to the distribution over the two column categories. For example, if the cases in the two row categories are divided, with 70 percent in one and 30 percent in the other, the maximum value of phi will be 1.0 if and only if the cases in the two column categories are also divided, with 70 percent in one and 30 percent in

College Attendence by Race: Observed Frequencies (from Table 1), Expected Frequencies Assuming Statistical Independence (in Italic), and (O–E)2/E for Each Cell (in Bold Type)

χ2 = 1.3 + 21.3 + 12.5 + 1.5 + 25.0 + 14.7 + 76.3

C = 0.27

V = 0.28

Table 2

the other. Some consider measures of association based on chi square to be flawed because commonly encountered marginals may imply that the association cannot possibly reach 1.0, even if the observed frequencies display the maximum possible departure from statistical independence, given the marginal frequencies. But one may also consider this feature appropriate because, if the degree of statistical association in a cross-classifica- tion table were perfect, the marginal distributions would not be disparate in a way that would limit the maximum value of the measure.

A more nagging concern about measures of association based on the departure from statistical independence is the ambiguity of their meaning. One can, of course, use such measures to say that one association is very weak (i.e., close to zero) and that another is relatively strong (i.e., far from zero and perhaps close to the maximum possible value, given the marginals), but ‘‘weak’’ and ‘‘strong’’ are relatively crude descriptors. The measures of association based on chi square may also be used in making comparisons. Thus, if a researcher wished to compare the degree of association in two populations, C or V could be compared for the two populations to determine whether the association was approximately the same in both and, if not, in which population the association was stronger. But there is no clear interpretation that can be

attached to a coefficient of contingency of precisely .32, or a Cramer’s V of exactly .47.

Relative Reduction in Prediction Error. We shift now to measures of association that reflect the relative reduction in prediction error. Since such measures indicate the proportion by which prediction errors are reduced by shifting from one prediction rule to another, we follow common practice and refer to them as proportional reduction in error (PRE) measures. Every PRE measure has a precise interpretation, and sometimes it is not only precise but also clear and straightforward. On the other hand, the PRE interpretation of some measures may seem strained and rather far removed from the common sense way of thinking about the prediction of one variable from another.

The basic elements of a PRE measure of association are:

1.a specification of what is to be predicted, and a corresponding definition of prediction error. For example, we might say that what is to be predicted is the row category into which each case falls, and a corresponding definition of prediction error would be that a case falls in a row category other than that predicted. Referring again to Table 1, if we are predicting whether a given case is attending college or not, a corresponding definition of prediction error would be that our predicted category (attending or not) is not the same as the observed category for that case.

2.a rule for predicting either the row variable or the column variable in a crossclassification table from knowledge of the marginal distribution of that variable alone. We will refer to the prediction

error when applying this rule as E1. For example, if what is to be predicted is as specified above (i.e., the row category into which each case falls), the rule for predicting the row variable from knowledge of its marginal distribution might be to predict the modal category for every case. This is not the only possible prediction rule but it is a reasonable one (and there is no rule based only on the marginals that would have higher predictive accuracy). Applying this rule to Table

1, we would predict ‘‘attending college’’ (the modal category) for every case. We would then be wrong in 460 cases out of the 1,000 cases in the table, that is, each of the 460 cases not attending college would be a prediction error, since we predicted attending college for every case. Hence, in this illustration E1 = 460.

3.A rule for predicting the same variable as in step (2) from knowledge of the joint distribution of both variables. We will refer to the prediction error when apply-

ing this rule as E2. For example, continuing with the specifications in steps (1) and

(2) above, we specify that we will predict the row category for each case by taking the modal category for each column.

Thus, in Table 1, we would predict ‘‘attending college’’ for all whites (the modal category for whites), ‘‘not attending college’’ for all blacks (the modal category for blacks), and ‘‘attending college’’ for Asian-Americans. The prediction errors are then 300 for whites (i.e., the 300 not attending college, since we predicted attending for all whites), 60 for blacks (i.e., the 60 attending college, since we predicted nonattending for all blacks), and 20 for Asian-Americans, for a total of 380 predic-

tion errors. Thus, in this illustration E2 = 380.

4.The calculation of the proportion by which prediction errors are reduced by shifting from the rule in step (2) to the rule in step (3), that is, the calculation of the proportional reduction in error. This is calculated by:

(6)

E1 – Ε2

PRE =

E1

The numerator in this calculation is the amount by which error is reduced. Dividing this amount by the starting error indicates what proportion of the possible reduction in prediction error has actually been achieved. Utilizing the error calculations above, we can compute the proportional reduction in error.

This calculation indicates that we achieve a 17.4 percent reduction in prediction error by shifting from predicting the row category that is the marginal mode for all cases to predicting the columnspecific modal category for all cases in a given column.

The PRE measure of association with prediction rules based on modal categories (illustrated above) is undoubtedly the simplest of the many PRE measures that have been devised. This measure is called lambda (λ), and for a given crossclassification table there are two lambdas. One of these focuses on predicting the row variable (λr), and the other focuses on predicting the variable that is represented in columns (λc). In the illustration above, we computed λr; that is, we were predicting college attendance, which is represented in rows. Shifting to λc (i.e., making the column variable the predicted variable), we find that the proportional reduction in error is 0. This outcome is evident from the fact that the modal column for the table (‘‘White’’) is also the modal column for each row. Thus, the prediction errors based on the marginals sum to 300 (i.e., the total who are not ‘‘white’’) and the prediction errors based on rowspecific modal categories also sum to 300, indicating no reduction in prediction error. Thus, the proportional reduction in prediction error (as measured by lambda) is not necessarily the same for predicting the row variable as for predicting the column variable.

An alternative PRE measure for a cross-classi- fication table is provided by Goodman and Kruskal’s tau measures (τr and τc) (1954). These measures are based on rediction rules that entail distributing predictions so as to recreate the observed distributions instead of concentrating all predictions in the modal category. In doing so, there is an expected number of misclassified cases (prediction errors), and these expected numbers are used in calculating the proportional reduction in error. In Table 1, τr = .25 and τc = .05. Although λc was found to be zero for Table 1 because the modal column is the same in all rows, λc is not zero because the percentage distributions within rows are not identical.

Other PRE measures of association have been developed, and some have been designed specifically for a cross classification of ordered categories (ordinal variables). For example, if people were classified by the highest level of education completed (e.g., into the categories pre–high school, high school graduation, bachelor’s degree, higher degree) we would have cases classified into a set of ordered categories and hence an ordinal variable. If the same cases were also classified into three levels of income (high, medium, and low) the result would be a cross classification of two ordinal variables. Although several measures of association for ordinal variables have been devised, the one now most commonly used is probably Goodman and Kruskal’s gamma (γ). Gamma is a PRE measure, with a focus on the prediction of order within pairs of cases, with order on one variable being predicted with and without knowledge of the order on the other variable, disregarding pairs in which there is a tie on either variable.

These and other PRE measures of association for cross-classification tables are described and discussed in several statistics texts (See, for example, Blalock 1979; Knoke and Bohrnstedt 1991; Loether and McTavish 1980; Mueller et al. 1977). In some instances, the prediction rules specified for a given PRE measure may closely match the specific application for which a measure of association is sought. More commonly, however, the application will not dictate a specific kind of prediction rule. The preferred measure should then be the one that seems likely to be most sensitive to the issues at stake in the research problem. For example, in seeking to identify ‘‘risk factors’’ associated with a relatively rare outcome, or with a very common outcome, one of the tau measures would be more appropriate than one of the lambda measures, because the modal category may be so dominant that lambda is zero in spite of distributional differences that may be of interest.

MEASURES OF ASSOCIATION IN

APPLICATION

When the initial measures of association were devised at the beginning of the twentieth century, some regarded them as part of a new mode of inquiry that would replace speculative reasoning

and improve research into the linkages between events. At the end of the twentieth century, we now recognize that finding a statistical association between two variables raises more questions than it answers. We now want to know more than the degree to which two variables are statistically associated; we want also to know why they are associated: that is, what processes, what conditions, and what additional variables are entailed in generating the association?

To a limited degree, the measures of association discussed above can be adapted to incorporate more than two variables. For example, the association between two variables can be explored separately for cases that fall within each category of a third variable, a procedure commonly referred to as ‘‘elaboration.’’ Alternatively, a new variable consisting of all possible combinations of two predictors can be cross-classified with an outcome variable. But the traditional measures of association are not ideally suited for the task of exploring the reasons for association. Additional variables (e.g., potential sources of spuriousness, variables that mediate the effect of one variable on another, variables that represent the conditions under which an association is weak or strong) need to be incorporated into the analysis to yield an improved understanding of the meaning of an observed association—not just one at a time but several simultaneously. Additional modes of analysis (e.g., loglinear analysis; see Goodman 1970; Knoke and Burke 1980) have been developed to allow an investigator to explore the ‘‘interactions’’ between multiple categorical variables in a way that is roughly analogous to multiple regression analysis for quantitative variables. Computer technology has made such modes of analysis feasible.

The same technology has generated a new use for relatively simple measures of association in exploratory data analysis. It is now possible to describe the association between hundreds or thousands of pairs of variables at very little cost, whereas at an earlier time such exhaustive coverage of possible associations would have been prohibitively expensive. Measures of association provide a quick clue to which of the many associations explored may identify useful ‘‘risk factors’’ or which associations suggest unsuspected linkages worthy of further exploration.

REFERENCES

Blalock, Hubert M., Jr. 1979 Social Statistics, 2nd ed. New York: McGraw-Hill.

Bohrnstedt, George W., and David Knoke 1988 Statistics for Social Data Analysis, 2nd ed. Itasca, Ill.: F. E. Peacock Publishers.

Costner, Herbert L. 1965 ‘‘Criteria for Measures of Association.’’ American Sociological Review 30:341–353.

Fienberg, S. E. 1980 The Analysis of Cross-Classified Categorical Data. Cambridge, Mass.: MIT Press.

Goodman, Leo A. 1970 ‘‘The Multivariate Analysis of Qualitative Data: Interactions among Multiple Classifications.’’ Journal of the American Statistical Association 65:226–257.

——— 1984 The Analysis of Cross-Classified Data Having Ordered Categories. Cambridge, Mass.: Harvard University Press.

Goodman, Leo A., and William H. Kruskal 1954 ‘‘Measures of Association for Cross Classifications.’’ Journal of the American Statistical Association 49:732–764.

———1959 ‘‘Measures of Association for Cross Classifications: II. Further Discussion and References.’’

Journal of the American Statistical Association 54:123–163.

———1963 ‘‘Measures of Association for Cross Classifications: III. Approximate Sampling Theory.’’ Journal of the American Statistical Association 58:310–364.

———1972 ‘‘Measures of Association for Cross Classifications: IV. Simplification of Asymptotic Variances.’’

Journal of the American Statistical Association 67:415–421.

Kim, Jae-on 1984 ‘‘PRU Measures of Association for Contingency Table Analysis.’’ Sociological Methods and Research 13:3–44.

Knoke, David, and George W. Bohrnstedt 1991 Basic Social Statistics. Itasca, Ill.: F. E. Peacock Publishers.

Knoke, David, and Peter Burke 1980 Loglinear Models. Beverly Hills, Calif.: Sage.

Loether, Herman J., and Donald G. McTavish 1980

Descriptive Statistics for Sociologists: An Introduction. Boston: Allyn and Bacon.

Mueller, John H., Karl F. Schuessler, and Herbert L. Costner 1977 Statistical Reasoning in Sociology. Boston: Houghton Mifflin.

Reynolds, H. T. 1977 Analysis of Nominal Data. Beverly Hills, Calif.: Sage.

HERBERT L. COSTNER

MEDIA

See American Society; Mass Media Research;

Popular Culture.

MEDICAL SOCIOLOGY

Over the past several decades medical sociology has become a major subdiscipline of sociology, at the same time assuming an increasingly conspicuous role in health care disciplines such as public health, health care management, nursing, and clinical medicine. The name medical sociology garners immediate recognition and legitimacy and, thus, continues to be widely used—for instance, to designate the Medical Sociology Section of the American Sociological Association—even though most scholars in the area concede that the term is narrow and misleading. Many courses and texts, rather than using the term ‘‘sociology of medicine,’’ refer instead to the sociology of health, health and health care, health and illness, health and medicine, or health and healing. The study of medicine is only part of the sociological study of health and health care, a broad field ranging from

(1) social epidemiology, the study of socioeconomic, demographic, and behavioral factors in the etiology of disease and mortality; to (2) studies of the development and organizational dynamics of health occupations and professions, hospitals, health maintenance and long-term care organizations, including interorganizational relationships as well as interpersonal behavior, for example, between physician and patient; to (3) the reactions of societies to illness, including cultural meanings and normative expectations and, reciprocally, the reactions of individuals in interpreting, negotiating, managing, and socially constructing illness experience; to

(4) the social policies, social movements, politics, and economic conditions that shape and are shaped by health and disease within single countries, as well as in a comparative, international context.

The rise of contemporary medical sociology can be traced back to the immediate post-World War II period, when science and medicine were dominant cultural forces, fueling a modern optimism that many of society’s ills could be eliminated. Several key contributions during the 1950s gave credibility and spurred scholarly interest in the newly developing subfield. Koos’s The Health of Regionville (1954) and Hollingshead and Redlich’s

Social Class and Mental Illness (1958) addressed the connections between social circumstances and health status, and were instrumental in establishing a strong tradition of sociological research focusing on the social determinants of health. The finding that individuals in the lower socioeconomic levels of society experience greater morbidity and mortality has turned out to be one of the most consistent of these patterns. Also during this time, a number of sociology’s most prominent theorists turned their attention to health and health care. They approached the topic not because their primary interest was in health care or medicine, but out of a generic interest in authority and the maintenance of social order. Robert Merton, Everett Hughes, and Anselm Strauss all studied professional organizations and socialization during the 1950s, focusing primarily on physicians and the process of medical education (Merton et al. 1957; Becker et al. 1961).

The theoretical work of the 1950s most influential for medical sociology was undoubtedly Talcott Parsons’s The Social System (1951). In it, Parsons recognized illness as a major threat to the stability and productivity of societies and introduced the ‘‘sick role’’ concept to describe the social regulation of sickness and explain the mechanism through which individuals are induced to return to productive activity. Parsons argued that because sick persons were unable to perform their expected social roles, they were subject to being negatively sanctioned. On the other hand, if they had not intended to become ill and were motivated to get well, then, according to Parsons’s analysis, they could claim and be granted temporary exemption without blame from normally expected role responsibilities. Rather than being held accountable for failure to perform, they would be excused as sick. Parsons’s work generated enormous sociological interest because of its analysis of illness and medical care in terms of their broad social consequences and because of its focus on the structure and functions of social roles. His work also expanded the theoretical foundations of medical sociology by provoking equally compelling work from contrasting perspectives. Elliot Freidson in Profession of Medicine (1970) analyzed the dominance of the medical profession, suggesting that power relations in health care were fundamentally contentious. He saw physicians as rising to dominate health care through a process of

struggle with competitors in which they prevailed largely because they gained the support of political institutions, limiting the role of competing occupations. In contrast to the fixed roles in structuralfunctional theory, Freidson argued that illness definitions and illness behavior were socially constructed through a process of negotiation. The debate over structure and agency represented in these early contributions laid theoretical pathways for subsequent scholarship and solidified medical sociology’s ties to some of the central issues of the discipline.

Medical sociology became established in only a few sociology departments during its early years, typically in elite universities. It was not until the 1970s that most graduate departments of sociology began to offer medical sociology. Today, sociology courses on health and medicine can be found in nearly every graduate program in the United States as well as in many other nations, notably the United Kingdom and Germany (Bloom 1986). Research funding to support the growth of medical sociology in many countries has come from government sources. In the 1960s and 1970s, U.S. medical sociology expanded in part because social science research was held in favor by the federal government as well as by influential private foundations. Major funding sources at that time included the National Institute of Mental Health (NIMH) and, later, the National Center for Health Services Research (NCHSR).

It has been argued that the fortunes of medical sociology have shifted in relation to the socialmedical environment (Pescosolido and Kronenfeld 1995). Until the1980s, medical sociology experienced relatively fertile conditions due in part to the fact that the health care system was dominated by professional medicine. Access to health care was the primary health policy concern, while research funding priorities focused on the biomedical and psychosocial aspects of disease, disease prevention, and patient care. This environment encouraged medical sociologists to pursue quantitative research, including surveys, national-level studies, and multivariate statistical models that predicted utilization of health services and the effects of risk factors and other variables. Two particular lines of medical sociology research gained prominence as a result of this focus. The first involved researchers studying utilization patterns for health services. There were two groups, each

using a somewhat different explanatory model. Marshall Becker and his colleagues employed the Health Belief Model, a cognitive framework originated by Rosenstock (1966) and eventually applied in research, to explain a wide variety of preventive and health-related behaviors (Becker and Maiman 1975). Ronald Andersen developed the somewhat broader sociobehavioral model (1995), which included health beliefs but also emphasized economic factors and health needs. The second line of research concerned quantitative studies of social stress. David Mechanic, one of the founders of medical sociology, pioneered sociological research on stress and mental health as early as the 1960s (Mechanic and Volkart 1961). The ‘‘stress process’’ group that emerged in the late 1970s, however, was closer to an interface of psychology and sociology. Using multivariate analyses, they examined the relationships among stress (Aneshensel 1992), social support (Turner and Marino 1994), and coping (Pearlin and Schooler 1978). Much of this research was published in the American Sociological Association’s Journal of Health and Social Behavior, beginning in the late 1970s and continuing into the present (Thoits 1995).

The social-medical environment in the United States changed dramatically in the 1980s, threatening the autonomy and authority of physicians (Starr 1982). The federal government’s increasing role in financing health care (through the Medicare and Medicaid programs) combined with rapidly escalating health care costs and the concern expressed by business, leading to a major federal policy shift. Rather than inequality in access and social factors in illness, public policy attention was now placed on cost control and the cost effectiveness of care. NIMH support for medical sociology was weakened, and soon afterward, the NCHSR became the Agency for Health Care Policy and Research with an agenda of research focused on managed care and evidence-based medicine. Research funding priorities gravitated from the behavioral and social sciences to economics and clinical medicine and epidemiology. No doubt these changes contributed to critical claims in the late 1980s and the 1990s, that medical sociology research had become fragmented.

The significance of health system changes for the profession of medicine became a hotly debated topic among medical sociologists during the

1980s. The controversy was sparked in 1985 with the publication of McKinley and Arch’s ‘‘Toward the Proletarianization of Physicians,’’ in which the authors argued that historical processes of bureaucratic rationalization were finally reaching medicine, irreversibly eroding the functional autonomy of physicians. This directly challenged Freidson’s medical dominance perspective (1970). Also part of the debate was the hypothesis, introduced by Marie Haug (1976), that physicians had lost authority due to the increasing knowledge and medical sophistication of patients. A plethora of articles appeared, identifying and discussing at length various hypothesized changes in medical dominance and authority and culminating, though by no means ending, with a special issue of the Milbank Quarterly in 1988.

Much of the early growth of medical sociology can be attributed to scholars located outside sociology departments in medical schools, nursing schools, schools of public health, and health administration programs. These individuals addressed research concerns and questions that were of paramount importance in their respective settings, such as the reasons people engage in health-pro- moting behavior, define themselves as sick, use health services, and comply with medical treatment. They contributed to medical and health care disciplines by bringing attention to the significance of culture and human interaction in producing the meaning of illness and shaping illnessrelated behavior (Zola 1966; Mechanic 1995). They dispelled the image of the physician as a purely rational scientist. Sociologists also contributed to the development of social epidemiology, mapping the social patterns of disease, and adding social factors to the causal understanding of mortality and chronic diseases (Berkman and Syme 1979). A third group studied hospitals and health care organizations, bringing an organizational sociology perspective into the field of health services research (Flood and Fennell 1995).

Robert Straus, a medical school sociologist, introduced in 1957 what became for many years a popular way of dividing the subfield. Sociologists such as those described above were designated ‘‘sociologists in medicine’’ in contrast to sociologists of medicine who were typically based in sociology departments. According to Straus, sociologists of medicine used medical settings to address

questions of sociology while sociologists in medicine used sociological knowledge to address questions of medicine. Today, the boundaries between those working in health care settings and those in academic departments of sociology are blurred; sociologists in both venues conduct applied research as well as research that contributes to basic sociological theorizing. In fact, it is quite common for medical sociologists to have multiple academic appointments. On the other hand, the distinction remains valid in the pressure to conduct research that reflects the priorities of the dominant group. Medical sociologists in medicine often engage in research shaped by medical issues and a biomedical approach, whereas those in sociology have an easier time posing sociological questions grounded in sociological theory. In its early years, medical sociology was sometimes dismissed by other academic sociologists as ‘‘applied’’ sociology, based on the rather elitist assumption that its research did not contribute to the basic body of knowledge of the discipline and that it lacked a theoretical body of its own. Today, there is greater understanding of the links between basic sociological theory and medical sociology (Gerhardt 1989). Medical sociology concepts such as ‘‘medicalization’’ have added to the broader understanding of social order and social control (Conrad 1992). Medicine and the other health care disciplines recognize sociology as a valuable discipline that can contribute much to the understanding and application of health care. Academic sociology has come to regard the sociology of medicine as a fruitful area of specialization.

It is in their role of social critic that medical sociologists encounter the greatest resistance from mainstream medicine and health care. Critical medical sociology emerged from both Marxist and social constructionist traditions within the discipline (Waitzkin 1989; Brown 1995). Symbolic interactionists and labeling theorists in the 1960s saw that, despite the Parsonian notion of the sick role, many types of illness and disability were responded to socially as forms of deviance. Goffman’s concept of stigma (1963) explored the relationship between labeling and identity as a process of managing spoiled identity. One of the most powerful explanatory concepts in medical sociology, stigma has been used for decades to capture the experience of mental illness, alcoholism, physical disability, and many types of chronic illness. Goffman

(1961) and Zola (1972), among others, turned the standard notion of medical care as a service on its head by arguing that medicine functions as an institution of social control. Despite strong microsociological interest in the social construction and social consequences of medical labels (i.e., diagnoses), the professional power of physicians made it exceedingly difficult for sociologists to study these processes until the 1980s. What could be studied, however, using the broader, cultural meaning of social construction, were processes of medicalization. Building on the social control perspective of Zola and others, a number of studies examined the processes through which nonmedical phenomena—such as childbirth, excessive drinking, children’s active behavior, and menstrual distress—became medical phenomena, with diagnostic criteria and specific medical treatments.

Bias in medicine and social inequality in health care have been concerns of critical medical sociology as well as of corresponding social movements initiated to improve health care. Gender analyses, especially those from a feminist perspective, offered a critical, alternative perspective on the medical profession (Lorber 1984) and the health care system (Zimmerman and Hill 1999), as did research on the women’s health movement (Ruzek 1978; Weisman 1998). This work examined the relationship between cultural ideas about gender, medical knowledge, and gender stratification systems; pointed out that the division of labor in medicine is also a gendered division of labor; and observed that the factors that often make women sick are linked to their social roles and disadvantaged social circumstances. Other critical perspectives were offered by disability researchers (Zola 1982) and by researchers focusing on the health and health care of racial and ethnic minorities (Hill 1992; Williams and Collins 1995).

The critical perspective in medical sociology was fortified by Mishler’s (1981) critique of the biomedical model, in which he argued that medicine was itself a culture, based as much on customs, social norms, and values as on scientific fact. Mishler’s view of medicine as socially constructed led to a concern with medical discourse analysis (1984) and, for some researchers, to the study of illness narratives. Departing from the political and critical concerns of the 1980s and 1990s, these

scholars have conducted in-depth, qualitative studies of illness experience, incorporating aspects ignored by their predecessors, such as emotions and the body (Charmaz 1991; Weitz 1991). The ‘‘postmodern turn’’ that swept over academic humanities and social science departments in the latter decades of the twentieth century influenced a number of symbolic interactionist and social constructionist medical sociologists. Working at the interface of constructionism and postmodernism, these scholars created new ways to explore the relationship between illness and identity (Frank 1995; Hall 1998).

Reviewing the literature of medical sociology reveals an unusually broad range of topics, theoretical perspectives, and research methodologies. Beyond the contributions reviewed above, medical sociologists are also active in international comparative research studying health systems or specific health care sectors within them. They are involved in health policy research both at the federal and at the local community level; they are studying alternative health care providers and their clients as well as various forms of folk medicine and lay care; and they are doing research on informal caregivers and the process of care work. Even these additions do not exhaust the parameters of the field. Medical sociology has enriched and continues to enrich the discipline of sociology, as well as making unique and valuable contributions to important policy issues and to the needs of health care professionals, managers, and patients.

REFERENCES

Andersen, Ronald 1995 ‘‘Revisiting the Behavioral Model and Access to Care: Does It Matter?’’ Journal of Health and Social Behavior 36:1–10.

Aneshensel, Carol S. 1992 ‘‘Social Stress: Theory and Research.’’ Annual Review of Sociology 18:15–38.

Avison, William R., and R. Jay Turner 1988 ‘‘Stressful Life Events and Depressive Symptoms: Disaggregating the Effects of Acute Stressors and Chronic Strains.’’

Journal of Health and Social Behavior 29:253–264.

Becker, Howard S., Blanche Geer, Everett C. Hughes, and Anselm Strauss 1961 Boys in White: Student Culture in Medical School. Chicago: University of Chicago Press.

Becker, Marshall, and Lois Maiman 1975 ‘‘Sociobehavioral Determinants of Compliance with Health and Medical Care Recommendations.’’ Medical Care 13:10–24.

Berkman, Lisa, and Leonard Syme 1979 ‘‘Social Network, Host Resistance and Mortality: A Nine Year Follow-up Study of Alameda County Residents.’’ American Journal of Epidemiology 190:186–204.

Bloom, Samuel W. 1986 ‘‘Institutional Trends in Medical Sociology.’’ Journal of Health and Social Behavior

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