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home) and community-based care (R. A. Kane 1995). To the extent that personal care and hous- ing-related services in nursing homes could be separated, residents and families could gain greater control of their lives. With the support of flexible home care options, many frail older persons could remain in innovative community residential options, such as assisted living arrangements, for the remainder of their lives. Such potential developments are attractive, but assume major changes in the financing of long-term care and the building of private accommodations, which include baths and kitchenettes, in housing for nursing home residents. Such new trends in financing and delivery of care would have to be based on dramatic expansion of personal long-term care insurance and/or availability of savings to finance long-term care. Alternatively, they could be implemented based on major new universal services furnished through social insurance programs (Kingson 1996). However, there are no clear indications that any of these financing options is likely to materialize in the near future. Furthermore it is likely that in the future most new and even existing long-term care services will be implemented by individual states. Increasing involvement in delivery of long-term care by states is likely to lead to increasing variability in the quality of care delivered (R. L. Kane et al. 1998).

In spite of expected increases in alternative long-term care facilities, the population of old-old adults residing in nursing homes is expected to increase dramatically, due largely to the ever-in- creasing age structure of the U.S. population. It is notable in this regard that research projecting future nursing home use demonstrates that better health in future cohorts of the old-old will only slightly decrease the proportion of time older adults will spend in nursing homes, or the proportion of this cohort who will enter nursing homes (Laditka 1998). Thus the nursing home as an organization is here to stay for the foreseeable future.

Furthermore, it is important to recognize that all the numerous alternatives proposed to address the shortcomings in the current system involve potential problems and tradeoffs (R. A. Kane et al. 1998). Ultimately, the planning, financing, delivery, and oversight of long-term care challenges our values and ingenuity as a society. Accordingly, sociologists, who have generally opted out of the

study of long-term care, are very much needed to get involved in this area, if we are to gain deeper understandings and develop systematic research based guidelines for improving services in this field.

CONCLUSION

The foregoing discussion has highlighted a series of counterpoints in consideration of nursing home care on the macro and micro levels. On the macro level, distinctions between medical and social models of care, quality-of-care, and quality-of-life issues have been discussed. Societal needs for cost containment have been juxtaposed with the need to invest greater resources in long-term care to ensure provision of high-quality care. Regulation and free market competition have been presented as alternative strategies to improve standards of care. Uncertainty about service models, along with a great concern about costs of care, have resulted in a stalemate in the field of long-term care (Vladeck 1995).

On the micro level, we have noted evidence for depersonalizing aspects of institutional living, along with data about protective features and benefits of long-term care environments. Furthermore, residents of nursing homes have been described as frail and vulnerable on the one hand, and as adaptable and resilient on the other. The ultimate well-being of nursing home residents is seen as a function of the environment, of the person, or of transactions between the two. These dualities are useful to propel dialogue and to permit a thorough examination of nursing home care. At the same time they hold the danger of oversimplification of issues that may be approached from a unidimensional framework as proponents advocate one pole of the duality or the other. In fact, a sociological understanding of nursing homes underscores the complexity and multidimensionality of the social context and the social world of the nursing home. Thus, there is great benefit in attempting to integrate insights gained from both poles of the dualities discussed.

The nursing home resident of the future is likely to be ever more frail, especially if we succeed in developing more home-based alternatives to care. Hence, we cannot reject the medical model in favor of a social model of care, or focus exclusively on quality of life rather than on quality of

care. Instead, we need to complement concerns of high-quality health care with those of high standards for social care. Similarly, just as proprietary care is likely to remain a part of health care in the United States, so regulation is here to stay. Although prospective nursing home residents are likely to be ever more frail, they are also likely to be more highly educated and more conscious of their rights as consumers. Financing mechanisms that enhance the ability of the consumers of long-term care to exercise control over their lives can complement regulatory efforts to upgrade quality of care in nursing home settings. On the broadest societal level, decisions about both commitment of resources and development of creative alternatives to institution-based, long-term care are likely to shape the parameters and qualities of nursing homes of the future.

The experience of a given individual in being cared for in a nursing home must ultimately be understood in the context of the complex matrix of influences posed by institutional living. Accordingly, it is not fruitful to focus exclusively on either the ill effects or the benefits of institutionalization. Reviews of nursing homes continue to focus on normative understandings, generally highlighting poor quality of care in such facilities. As we have noted in this essay, empirical support for such negative conclusions is generally derived from qualitative research. Quantitative studies generally provide less support for expectations of decline and adverse patient reactions. In an effort to understand conflicting conclusions of different genres of research in this area, it is useful to focus on personal as well as environmental and situational influences that moderate the effects of institutional living. More carefully designed nursing home– based research, utilizing quantitative as well as qualitative approaches, is needed to specify conditions of both person and environment that maximize the well-being of the individual requiring institutional care.

Newer health care options, such as CCRCs, assisted living, and special care units, have been touted as more patient-responsive solutions to care than traditional nursing homes. Nevertheless, true consumer control and resident outcomes are still largely contingent on the resident’s power to demand and advocate for appropriate care options. If an older adult becomes physically frail or

mentally impaired, such options are likely to diminish, or even evaporate. Consumers are once again at the mercy of bureaucratic decision making about their best interests.

Even advocates of alternative forms of longterm care acknowledge that nursing home care will continue to be needed and utilized by the increasing segment of old-old citizens. Sanctions, incentives, and intervention programs have all been shown to be beneficial, at least to a limited extent, in improving the quality of care and the quality of life in nursing homes.

Enhancing the quality of nursing home care creates a challenge for society to commit greater resources generated by currently productive citizens to the care of those who have made previous contributions. The resources society devotes to long-term care ultimately mirror the value placed by society on its frail or dependent citizens. Thus, a devaluing of older people is likely to result in a devaluing of institutions that care for them, along with a devaluing of the providers of their care. Conversely, more positive societal attitudes toward frail elders are likely to be translated into increasing involvement by high-caliber, trained professionals in the care of the institutionalized elderly. In addition, positive societal attitudes should bring family members and community representatives into closer contact with institutions, and should help to break down barriers between the nursing home and community living.

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EVA KAHANA

LONGITUDINAL RESEARCH

NOTE: Although the following article has not been revised for this edition of the Encyclopedia, the substantive coverage is currently appropriate. The editors have provided a list of recent works at the end of the article to facilitate research and exploration of the topic.

According to Heckman and Singer, ‘‘Longitudinal data are widely and uncritically regarded as a panacea . . . The conventional wisdom in social science equates ‘longitudinal’ with ‘good’ and discussion of the issue rarely rises above that level’’ (1985, p. ix).

There is probably no methodological maxim in sociology more often repeated than the call for longitudinal data. From the work of David Hume more than 250 years ago, to the exhortations for a ‘‘radical reformation’’ in the work of Stanley Lieberson (1985, p. xiii), the importance of longitudinal data has been emphasized and reemphasized. Yet it is doubtful that there is an area of sociological method in which more disagreement exists both as to rationale and as to method. Until relatively recently, it has been possible to ignore the problem because longitudinal data have been relatively rare, and methods for their analysis quite sparse. Since 1965, however, there has been a virtual flood of new longitudinal data and a concomitant increase in sophisticated methods of analysis. In large part the computer has made both developments possible, permitting the management of data sets of enormous complexity along with analyses undreamed of only a short while ago.

At the micro level, numerous longitudinal studies have tracked individuals over a good share of their lifetimes. For example, some participants in the Oakland and Berkeley studies of growth and development have been sporadically studied from birth until well into their seventies (Elder, this volume). On a more systematic basis, the Panel Study of Income Dynamics (PSID) has interviewed a panel based on an original sample of five thousand families (households) on an annual basis since the mid 1960s, supplementing the sample with new households formed as split-offs from the original families (Duncan and Morgan 1985). Many other large-scale panel studies, some extending over periods of thirty years and longer, are in progress (Migdal et al. 1981).

At the macro level, extended time series on various social and economic indicators such as GNP, fertility, mortality, and education are gradually becoming available in machine-readable form from virtually all industrialized societies and from many that are less developed. In some cases, data series, particularly for vital statistics, go back for decades. In other cases, such as China and the

Soviet Union, modern-era data are gradually being accumulated and linked to earlier series. Descriptions of many such data sets can be found in the annual guide published by the Inter-University Consortium for Political and Social Research (ICPSR 1991).

Perhaps the most exciting developments are at the nexus of macroand micro-level analysis. In the United States, for example, the General Social Survey (GSS) has obtained data on repeated crosssectional samples of the population (that is, the population is followed longitudinally but specific individuals are not) on an annual basis (with two exceptions) since 1972. More recently, annual surveys modeled on the GSS have been started in a number of other countries (Smith 1986). Because of the careful replication, these surveys permit one to track aggregate responses of the changing population on a wide variety of issues (such as on attitudes toward abortion or capital punishment) over time. As the time series becomes ever longer, it is possible to carry out a multilevel analysis, linking micro and macro variables. For example, using the GSS, DiPrete and Grusky (1990) attempt to link shortterm changes in rates of social mobility to macrolevel changes in the U.S. economy.

Although the size and complexity of the longitudinal data base has expanded rapidly, so have the statistical tools with which to analyze it. Perhaps the most exciting development is in the area of ‘‘event history models,’’ which permit one to relate an underlying rate of transition in continuous time to a set of ‘‘covariates’’ or independent variables. However, event models are by no means the only development. New and powerful approaches to the analysis of means over time, to structural equation models, and to various forms of categorical data analysis have given the researchers unprecedented power. Standard computer packages now make routine what was impossible in the 1980s.

THE RATIONALE FOR LONGITUDINAL

RESEARCH

Longitudinal studies are carried out in virtually every area of the social sciences. Although studies of infant development and national development share certain similarities, it is not likely that a single rationale, design, or approach to analysis will simultaneously apply to every area in which longitudinal data might be collected. At the most

abstract level, there are three basic reasons for conducting a longitudinal study.

First, in any area in which development and change over time are at issue, there is, almost by definition, a necessity to obtain time-ordered data. Questions pertaining to rate and sequence of change, and to variability in rates and sequences are at the heart of longitudinal research. At one level, these questions are essentially descriptive, and getting an adequate descriptive handle on time-ordered data is an essential first step in coming to any understanding of change.

A second reason involves the role of temporal priority in causal analysis. There are few things on which philosophers of science agree when it comes to causation (see Marini and Singer 1988 for a superb review), but one is that A must precede B in time if A is to be taken as a cause of B. It is natural to assume that observing A before B means that A precedes B. Unfortunately, designs that actually allow one to establish temporal, let alone causal, priority are not as easily arrived at as one might think.

Related to the issue of temporal priority is the cross-sectional fallacy. Lieberson (1985, pp. 179– 183) argues that assertions of causality based on cross-sectional data must necessarily imply a longitudinal relationship. To show that city size ‘‘leads to crime’’ based on cross-sectional data implies a dynamic relationship that may or may not be true. The problem is particularly acute in cross-section- al age comparisons that are necessarily confounded with cohort differences. All cross-sectional attempts to ascertain the ‘‘effect’’ of age on income confound cohort differences in average income with age differences.

A third reason, particularly relevant to sociologists, is the necessity to distinguish gross change from net change. A census taken at two points in time shows changes in the distribution of a variable, say occupation, at the macro level. We might find that the proportion of the population in service-oriented occupations increases over the period of a decade. That indicator of net change conceals myriad patterns of gross change at the individual level. The count of persons in service occupations at two points in time consists of persons who were occupationally stable over the interval and persons who changed in various ways. Of course the population itself changed over the interval due to age-related changes in the labor

force, migration, and differing levels of laborforce participation. All of this is masked by repeated cross-sectional samples.

Finally, although not really a ‘‘rationale’’ for longitudinal research, observation plans with repeated measures on the same individuals can offer certain statistical advantages. Cook and Ware (1983) discuss these in detail.

TYPES OF LONGITUDINAL DATA

For many sociologists, the term longitudinal connotes a particular design, usually referred to as a panel study, in which individual subjects are measured repeatedly over time. A more general definition is desirable. Longitudinal data consist of information that can be ordered in time. Such data can be obtained in a variety of ways: by measuring the subject prospectively at repeated intervals, by obtaining a retrospective history in one or more interviews, from institutional records, or various combinations of these approaches. ‘‘Strong’’ longitudinal data preserve exact time intervals, while ‘‘weak’’ data provide sequence and order but lose interval. The distinction is parallel to that between interval and ordinal measurement.

As Featherman (1977) notes, under some circumstances, retrospective data collection may have substantial advantages of time and cost. Most surveys collect retrospective data of one kind or another, such as educational attainment, family background, and marital histories. Using structured interviewing methods in which the respondent is provided with a time-oriented matrix, it is possible to collect quite accurate information on many aspects of the life course. For example, as part of an ongoing panel, Freedman et al. (1988) obtained retrospective reports of family structure and other demographic variables that had previously been measured contemporaneously. The results are highly encouraging; when retrospective questions of this kind are asked carefully and interviewers are well trained, respondents can provide accurate and detailed information.

Of course not all variables can be measured retrospectively. Most researchers would argue that any attempt to measure past psychological states is invalid on its face. Reporting of the timing and frequency of events, even those which are quite salient, such as hospitalization, appears to suffer

from serious recall problems. Subjects tend to forget them or to telescope them in time. Reports of exact earnings, hours worked, and other economic variables may suffer from similar problems. The truth is that we don’t have much information on what can and cannot be measured retrospectively. A systematic research program on these issues is becoming more and more necessary as new methods of data analysis that depend on the exact timing of events continue to evolve.

Another serious weakness of the retrospective design is that it represents the population that survives to the point of data collection and not the original population. In some situations this kind of selection bias can be quite serious—for example, in intervention studies that are subject to high levels of attrition.

Prospective studies in which a subject is measured at some baseline and then at repeated intervals are usually referred to as panel studies. Panel designs have a number of strengths along with several significant weaknesses. The primary strength, at least in principle, is accuracy of measurement and correct temporal referents. Depending on the exact design of data collection, subjects are measured at a point close enough in time to the event or status in question to permit reliable and valid assessment. Under certain circumstances temporal priority may be more firmly established.

Second, the prospective design provides greater leverage on attrition. Besides measuring a population defined at baseline, preattrition information can be used to determine the extent to which attrition is ‘‘random’’ and perhaps can be used to correct estimates for selection bias. There is a trade-off, however. Frequent measurement has two potentially undesirable side effects. First, subjects may tire of repeated requests for cooperation and drop out of the study. Second, ‘‘panel conditioning’’ may result in stereotypic responses to repeated questions. Thus, relative to the retrospective design, there may actually be a net decrease in data quality.

On the surface, prospective designs that extend in time are far more costly than retrospective designs. There is a clear cost/quality trade-off, however, that cannot be easily evaluated without consideration of the purposes of the survey. In obtaining population estimates over time, the panel may actually be less expensive to maintain than

resampling the population repeatedly. On the other hand, using a panel for this purpose brings problems of its own in the form of attrition and panel conditioning.

QUASI-EXPERIMENTAL AND DESCRIPTIVE

APPROACHES

The large-scale, multiwave surveys so common now have rather diffuse origins. Paul Lazarsfeld introduced the panel design (Lazarsfeld and Fiske 1938). In his hands, the panel study was basically a quasi-experimental design. A panel of subjects was recruited and measured repeatedly, with the foreknowledge that a particular event would occur at a particular time. The most famous application is to election campaigns. As such, the design is a simple example of an interrupted time series.

A second source of current designs is the child development tradition. Baltes and Nesselroade (1979) cite examples going back to the late eighteenth century, but systematic studies date to the 1920s (Wall and Williams 1970). The best of these studies emphasized cohort-based sampling of newborns and systematic assessment of development at carefully determined intervals. In the tradition of experimental psychology, investigators paid attention to careful control of the measurement process, including the physical environment, the raters and observers, and the measurement instruments. The development of age-specific norms in the form of averages and variation about them was seen as a primary goal of the study. Unanticipated events, such as an illness of either mother or child or factors affecting the family, were seen as undesirable threats to the validity of measurement rather than as opportunities to assess quasi-experimen- tal effects.

Large-scale multiwave panel studies of the kind described above combine aspects of both traditions, often in somewhat inchoate and potentially conflicting ways. On the one hand, investigators are interested in describing a population as it evolves. Often basic descriptive information on rates, variability, and sequence is unknown, calling for frequent measurement at fixed intervals. On the other hand, there is also interest in evaluating the impact of specific transitions and events, such as childbearing, retirement, and loss of spouse. Meeting these two objectives within the constraints of a single design is often difficult.