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are free to select specific individuals fitting these criteria to interview, to gauge political opinions cheaply. Snowball samples are generated when investigators ask known members of a group to identify other members and subsequently ask those who are so identified to identify still other members. This procedure takes advantage of the fact that the members of certain small and hard to locate groups (for example, biochemists studying a particular enzyme) often know one another. None of these nonprobability sampling procedures ensures that all the cases in the target population have a known and nonzero probability of being included in a sample, however (Kalton 1983, pp. 90–93). As a result, one cannot be confident that these procedures will provide unbiased estimates of population parameters or make statistical inferences from the samples they yield. Unless cost and time constraints are severe, researchers seeking to estimate population parameters therefore nearly always use procedures that yield probability samples.

One should distinguish between the representativeness of a sample and whether it was drawn by using probability sampling procedures. Although probability samples have a decidedly better track record in regard to representativeness, not all probability samples are representative and not all nonprobability samples are unrepresentative. Political polls based on quota samples, for example, often produce results that come very close to the subsequent vote. However, there is usually no reason to believe that a nonprobability sampling procedure that has been successful in the past will continue to yield representative results. In contrast, probability sampling procedures are likely to produce representative samples in the future because they are based on a random selection procedure.

Sampling theory, a branch of statistical theory, covers a variety of techniques for drawing probability samples. Many considerations can influence the choice of a sampling procedure for a given project, including feasibility, time constraints, characteristics of the population to be studied, desired accuracy, and cost. Simple sampling procedures are often sufficient for studying small, accessible, and relatively homogeneous populations, but researchers typically must use more complicated procedures to study large and heterogeneous populations. Using complicated procedures requires

consultation with a sampling specialist at a survey organization (the University of Illinois Survey Research Laboratory provides a list of these organizations on its Web site: www.srl.uic.edu).

Any study in which a probability sample will be drawn must begin by defining the population of interest: the target population. The purpose of the study restricts the definition of the target population but rarely specifies it completely. For example, a study of characteristics of U.S. families obviously will define the population as consisting of families, but it will be necessary to define precisely what counts as a family as well as decide how to treat various cases from which it may be difficult to collect data (such as the families of U.S. citizens who live overseas). Sudman (1976, pp. 11–14) discusses general issues involved in defining target populations.

The next step in probability sampling is to construct a sampling frame that identifies and locates the cases in the target population so that they can be sampled. The most basic type of sampling frame is a list of the cases in the target population. Such lists are often unavailable, however, and so researchers usually must construct an alternative. For example, to draw a sample of U.S. public high schools, a researcher might begin with a list of U.S. census tracts, select a sample of those tracts, and then consult maps that indicate the locations of public high schools in the selected tracts. Here the sampling frame would consist of the list of census tracts and their corresponding maps.

A perfect sampling frame includes all the cases in the target population, no inappropriate cases, and no duplications. Most sampling frames are imperfect, however, with failure to include all the cases in the target population being the most serious type of coverage error. For example, tele- phone-number sampling frames, such as those employed in random-digit dialing procedures, do not cover people without a telephone, and sampling frames that are based on dwelling units do not cover homeless people. Undercoverage errors bias sample statistics, with the extent of the bias being positively related to (1) the proportion of the target population not covered by the sampling frame and (2) the magnitude of the difference between those covered and those not covered. Sampling experts have developed many methods to reduce coverage errors, including the use of

multiple frames, multiplicity techniques, and postsurvey adjustments. Kish (1995, pp. 53–59, 384–439) and Groves (1989, pp. 81–132) provide helpful discussions of sampling-frame problems and possible solutions.

BASIC PROBABILITY SAMPLING

PROCEDURES

Characteristics of one’s sampling frame influence the specific sampling procedure appropriate for producing a probability sample. For example, some sampling procedures require that the sampling frame list all the cases in the population, while others do not. In addition, sampling procedures often are combined in situations where the sampling frame is complex. In all situations, however, the key element required for producing a probability sample is the use of a formally random procedure for selecting cases into the sample.

Simple Random Sampling. Simple random sampling (SRS) is the most elementary probability sampling procedure and serves as a benchmark for the evaluation of other procedures. To use SRS, one’s sampling frame must list all the cases in the population. Usually the researcher assigns a unique identification number to each entry in the list and then generates random numbers by using a random number table or a computer program that produces random numbers. If a random number matches one of the identification numbers in the list, the researcher adds the indicated case to the sample (unless it has already been selected). This procedure is followed until it produces the desired sample size. It is important that only the randomly generated numbers determine the sample’s composition; this condition ensures that the sampling procedure will be unbiased and that the chosen cases will constitute a probability sample.

With SRS, all cases in the sampling frame have an equal chance of being selected into the sample. In addition, for a sample of size n, all possible combinations of n different cases in the sampling frame have an equal chance of constituting the sample. The formulas for standard errors found in nearly all statistics textbooks and those used in statistical programs for computers assume that SRS generated the sample data. Most studies of human populations use sampling procedures that are less efficient than SRS, however, and using SRS

formulas in these instances underestimates the sampling variances of the statistics. As a consequence, researchers frequently conclude that differences or effects are statistically significant when they should not do so, or they may report misleadingly small confidence intervals.

Systematic-Simple Random Sampling. When a sampling frame contains many cases or the size of the prospective sample is large, researchers often decide to economize by setting a sampling interval and, after a random start, using that interval to choose the cases for the sample. For example, suppose a researcher wanted to select a sample of n cases from a population of size N and n/N = 1/25. To use systematic simple random sampling (SSRS), the researcher would draw a random number, r, between 1 and 25 and, starting with the rth case, select every twenty-fifth case in the sampling frame (for more complicated examples, see Kalton 1983, p. 17). This procedure gives all the cases in the frame an equal probability of being chosen for the sample but, unlike SRS, does not give all combinations of cases equal probabilities of selection. In the above example there are only 25 possible combinations of cases that could constitute the resulting sample (for example, cases 105 and 106 could never be in the same sample).

When the order of the cases in the sampling frame is random with respect to the variables of interest in a study, this property of SSRS is inconsequential, but when the frame is cyclically ordered, the results of SSRS can differ significantly from those of SRS. For example, suppose one wished to sample starting players on college basketball teams to determine their average height and had a sampling frame ordered by team and, within each team, by position. Since there are five starting players on each team, a sampling interval of any multiple of 5 would yield a sample composed of players who all play the same position. There would be a 1 in 5 chance that these players would all be centers (usually the tallest players) and a 2 in 5 chance that they would all be guards (usually the shortest). Thus, in this instance the sampling variation of the players’ mean height would be substantially greater than the variation that SRS would produce. However, there are also situations in which stratified random sampling SSRS is equivalent to (StRS) (see below) and yields samples that have smaller sampling variances than those from SRS (Kish 1995, pp. 113–23). In prac-

tice, most lists of entire populations have orderings, often alphabetical, that are essentially random with respect to the purposes of a study, and lists with potential problems usually are obvious or are quickly recognized. Thus, in most applications SSRS is essentially equivalent to SRS (Sudman 1976, pp. 56–57).

Stratified Random Sampling. When a sampling frame consists of a list of all the cases in a population and also contains additional information about each case, researchers may use StRS. For example, a list of people also might indicate the sex of each person. A researcher can take advantage of this additional information by grouping individuals of each sex into a sublist (called a stratum) and then sampling, using SRS or SSRS, from each stratum. One can use either the same sampling fraction for each stratum, in which case the procedure is called proportionate StRS, or different fractions for different strata (disproportionate StRS). In either case one usually attempts to use the additional information contained in the sampling frame to produce a sample that will be more efficient than one derived from other sampling procedures (i.e., it will need fewer cases to produce a sample with a given precision for estimating a population parameter).

Efficiency is commonly measured by a sampling procedure’s design effect, the ratio of the sampling variance of a statistic based on that procedure to the sampling variance of the same statistic derived from an SRS with the same number of cases (Kalton 1983, pp. 21–24). The efficiency of proportionate StRS is directly related to the correlation between the variable used to stratify the sampling frame and the variable or variables being studied. Thus, if one wished to determine the mean individual income of a population of Americans, proportionate StRS based on sex would produce a more efficient sample than would SRS and would have a design effect smaller than unity, because sex is correlated with income. In the limiting case in which the stratifying variable is perfectly correlated with the variable or variables being studied—for example, if each woman earned $15,000 per year and each man earned $25,000— proportionate SrRS would always yield a sample mean exactly equal to the population mean. By contrast, if sex were completely uncorrelated with income, proportionate StRS would be no more efficient than SRS, and the design effect of StRS

would equal unity. In practice it is usually difficult to obtain sampling frames that contain information about potential stratifying variables that are substantially correlated with the variables being studied, especially when the cases are individuals. As a result, the gains in efficiency produced by proportionate StRS are often modest.

Proportionate StRS often yields small sample sizes for strata that consist of small proportions of a population. Thus, when researchers want to estimate parameters for the individual strata in a population, they sometimes employ disproportionate StRS to ensure that there will be enough cases from each stratum in the overall sample. A second reason for using disproportionate StRS is to design an optimal sample, one that produces the most precise estimates for a given cost, when there are differences between the strata in terms of (1) the cost of sampling and obtaining data, (2) the variability of the variables under study, or (3) prior knowledge about the variables under study. Sudman (1976, pp. 107–130) discusses and gives examples of each of these situations. The benefits of disproportionate StRS may be hard to attain when one wants to draw a multipurpose sample with observations on many variables, however, because the optimal procedures for the different variables may conflict. In addition, although proportionate StRS cannot have a design effect greater than unity, the design effects for disproportionate StRS can be larger than unity, meaning that disproportionate StRS can produce samples that are less efficient than those derived from SRS (Kalton 1983, pp. 20–26).

Cluster Sampling. All the sampling procedures discussed above require that the researcher have a sampling frame that lists the cases in the target population. Unfortunately, such sampling frames rarely exist, especially for human populations defined by area of residence. One can still draw a probability sample, however, if the population can be organized in terms of a grouping principle and each case can be assigned to one of the groups (called clusters). For example, dwellings in cities are located in blocks defined by streets. Even if a list of dwellings does not exist, it is possible to draw a probability sample by constructing a sampling frame that consists of a listing of the blocks, drawing a random sample of the blocks, and then collecting data on the dwellings in the chosen blocks.

This procedure, which is called cluster sampling (CS), is also advantageous when one wishes to use face-to-face interviewing to survey geographically dispersed populations of individuals. In this case CS is less costly because it allows the survey to concentrate interviewers in a small number of locations, thus lowering traveling costs. However, CS usually produces samples that have larger sampling variances than those drawn from SRS. The efficiency of CS is inversely related to (1) the extent to which clusters are internally homogeneous and differ from each other and (2) the number of cases sampled from each cluster. CS is maximally efficient when a population can be divided into clusters that are identical, because each cluster will then be a microcosm of the population as a whole. When clusters are internally homogeneous and differ sharply from each other, as tends to be true for human populations clustered by area of residence, CS is considerably less efficient than SRS (Kalton 1983, pp. 30–33). In this situation, researchers usually attempt to select only a few cases from each of many clusters, but that strategy eliminates the cost savings of CS.

Multistage Sampling. Researchers who want to collect data through face-to-face interviews with a probability sample of people living in a certain area, such as the United States, a state, or even a city, usually combine elements of the procedures discussed above in a multistage sampling procedure. For example, to draw a probability sample of U.S. adults, one might begin by obtaining a list of counties and parishes in the United States and collecting data on several characteristics of those units (region, average household income, etc.). These variables can be used to group the units, called primary sampling units, into strata so that one can use StRS. In addition, one would obtain estimates of the number of residents in each unit so that they could be sampled with probabilities proportional to their estimated population sizes (Sudman 1976, pp. 134–50). After selecting a sample of counties in this fashion, the researcher might proceed to draw a series of nested cluster samples. For example, one could divide each selected county into subareas (perhaps townships or other areabased governmental divisions) and then select a cluster sample from these units, with probabilities once again proportional to estimated population size. Next the researcher might divide each of the selected units into subareas (perhaps on the order

of the U.S. Bureau of the Census’s ‘‘blocks’’) and draw a cluster sample of them. For each chosen block, the researcher might obtain a list of dwelling units and draw another cluster sample. Finally, from each chosen dwelling unit the researcher would choose, according to a specified procedure (Kish 1995, pp. 396–404), an individual to be interviewed. It is crucial that the selection procedure at each stage of the sampling process be based on a formally random selection procedure. For more detailed discussions and examples of the selection of multistage sampling procedures, see Kish (1995, pp. 301–383), Moser and Kalton (1972, pp. 188–210), and Sudman (1976, pp. 131–170). Multistage sampling usually requires considerable resources and expertise, and those who wish to draw such samples should contact a survey organization. Studies of the design effects of multistage samples, such as those carried out by the University of Michigan’s Survey Research Center, show that they usually vary from 1.0 to 2.0, with values around 1.5 being common (Kish 1995, p. 581). A design effect of 1.5 means that the standard error of a statistic is twenty-two percent larger than estimated by standard statistics programs, which assume simple random sampling. There is also variation across kinds of statistics, with univariate statistics, such as the mean, often having larger design effects than do bivariate statistics, such as regression coefficients (Groves 1989, pp. 291– 292). Unfortunately, estimating standard errors for a multistage sample is usually a complicated task, and this complexity, combined with the fact that popular statistics programs for computers use only SRS formulas, has led most researchers to ignore the problem, producing many spurious ‘‘statistically significant’’ findings.

RECENT ADVANCES

Sampling practitioners have made considerable progress in developing techniques for drawing probability samples of rare or elusive populations for which there are no lists and for which conventional multistage sampling procedures would produce sufficient cases only at an exorbitant cost. Sudman et al. (1988) review procedures for screening clusters to determine those that contain concentrations of a rare population’s members and also discuss how multiplicity sampling procedures and capture-recapture methods can be applied to this problem. Researchers also have begun to use

multiplicity sampling of individuals to draw probability samples of businesses and other social organizations to which individuals belong; Sudman et al. (1988) outline the general strategy involved, and Parcel et al. (1991) provide an informative discussion and an example. This approach also can produce ‘‘linked micro-macro samples’’ that facilitate contextual analyses.

Recent developments in statistical theory and computer software promise to make the calculation of standard errors for statistics based on multistage samples much easier. One approach to overcoming these difficulties is to use a computer program to draw many subsamples from an existing sample and then derive an overall estimate of a standard error from the many estimates given by the subsamples. There are several versions of this general approach, including ‘‘bootstrapping,’’ ‘‘jackknife replication,’’ and ‘‘cross-validation’’ (Hinkley 1983). A second approach is to develop computer statistical packages that incorporate information about the sampling design of a study (Wolter 1985, pp. 393–412, contains a list of such programs). The increased availability of such programs should produce greater recognition of the need to take a study’s sampling procedure into account in analyzing the data the study yields.

There is now greater recognition that sampling error is just one of many types of error to which studies of human populations are subject. Nonsampling errors, including nonresponse error, interviewer error, and measurement error, also affect the accuracy of surveys. Groves (1989) comprehensively discusses both sampling and nonsampling errors and argues that minimizing one type can increase the other. Thus, decisions about sampling procedures need to take into account likely sources and magnitudes of nonsampling errors.

REFERENCES

Choldin, Harvey M. 1994 Looking for the Last Percent: The Controversy over Census Undercounts. New Brunswick, N.J.: Rutgers University Press.

Groves, Robert M. 1989 Survey Errors and Survey Costs. New York: Wiley.

Hinkley, David 1983 ‘‘Jackknife Methods.’’ In Samuel Kotz and Norman L. Johnson, eds., Encyclopedia of Statistical Sciences, vol. 4. New York: Wiley.

Kalton, Graham 1983 Introduction to Survey Sampling. Newbury Park, Calif.: Sage.

Kish, Leslie 1995 Survey Sampling. New York: Wiley.

Moser, Claus A., and Graham Kalton 1972 Survey Methods in Social Investigation, 2nd ed. New York: Basic.

Parcel, Toby L., Robert L. Kaufman, and Leanne Jolly 1991 ‘‘Going Up the Ladder: Multiplicity Sampling to Create Linked Macro-to-Micro Organizational Samples.’’ In Peter V. Marsden, ed., Sociological Methodology, vol. 21. Oxford, UK: Basil Blackwell.

Sudman, Seymour 1976 Applied Sampling. New York:

Academic Press.

———, Monroe G. Sirken, and Charles D. Cowan 1988 ‘‘Sampling Rare and Elusive Populations.’’ Science 240:991–996.

Wolter, Kirk M 1985 Introduction to Variance Estimation.

New York: Springer-Verlag.

LOWELL L. HARGENS

SCALES AND SCORES

See Factor Analysis; Measurement.

SCANDINAVIAN SOCIOLOGY

Scandinavian sociology emerged in its modern form as an academic discipline just after World War II, although its roots go back considerably further. In Helsinki, the sociologist, ethnologist, and philosopher Edvard A Westermarck (1862– 1939) lectured on sociology in 1890; in Göteborg, Gustaf Fredrik Steffen (1864–1929) became a professor of economics and sociology in 1903.

In 1850, the Norwegian clergyman Eilert Sundt (1817–1875) published a study of Norwegian tramps and the lowest stratum of the rural population. Between 1850 and 1869, when he became a vicar, Sundt received state support for his demographic and sociological studies of Norwegian manners and customs, poverty, and living conditions. In demography he is remembered for ‘‘Sundt’s law,’’ which states that irregularities in the age distribution at a given time generate similar irregularities in the next generation (Ramsøy 1998).

Westermarck held chairs in applied philosophy until 1930, and between 1907 and 1930 he also had a chair in sociology in London. He belonged to the small group of leading European sociologists and philosophers in the early part of the century. His best-known works are studies of the

history of marriage (first volume published in 1891) and of the origin and development of moral ideas (1906–1908, 1932).

Between 1907 and 1913 the statistician A Gustav Sundbärg (1857–1914) directed ‘‘Emigrationsutredningen,’’ an official investigation of Swedish emigration, which was considered one of the most serious problems in Sweden at that time (Sundbärg 1913). The final report was presented in 1913, and between 1908 and 1912 twenty appendices by Sundbärg himself; Nils R Wohlin (1881–1948), secretary of the investigation; and others were published. The investigation contains a wealth of statistical information of great interest.

EARLY SOCIOLOGY

In Scandinavia in the 1940s, sociology was confronted with an established discipline of demography, a reliable and accessible population registration system, a positivistic philosophy, and a reformist policy in need of empirical studies of societal problems. Until the late 1960s, Scandinavian sociologists engaged in empirical studies, mostly quantitative, of social inequality, social mobility and the educational system, work conditions, problems of physical planning and social epidemiology, alcohol problems, and delinquency. The works of Allardt in Helsinki (1965), Carlsson in Lund (1958), Rokkan (1921–1979) in Bergen (Rokkan and Lipset 1967), and Segerstedt (1908–1999) in Uppsala (1955) are representative of early mainstream sociology, and those of Aubert (1922–1988) in Oslo (1965) had a qualitative approach. There was a strong American influence from Scandinavians who had studied at American universities, from journals and textbooks, and from visiting Americans; Norway in particular received a series of Fulbright scholars. Scandinavian sociology became strongly empirical, technical, sociopsychological, and survey-oriented. There was less interest in functionalism, Talcott Parsons, and the classics than in survey analysis and social exchange theory.

In the late 1960s and throughout the 1970s, new sociological voices were heard in Scandinavia, perhaps more in Denmark and Sweden than in Finland and Norway. They can be characterized as aggressively political and Marxist, antipositivistic, antifunctionalistic, and antiquantitative. In general, the ‘‘new’’ sociology was more theoretical and sociophilosophical and less empirical than the main-

stream versions. Conflict theories, critical theory, symbolic interactionism, and the labeling perspective came to the fore, as did socioanthropological and hermeneutic methods.

INSTITUTIONAL STRUCTURE

In Denmark, the German refugee Theodor Geiger (1891–1952) was appointed a professor of sociology at Aarhus University in 1938. In 1955, Kaare Svalastoga (1914–1997), a Norwegian historian with a sociological education from the University of Washington in Seattle, became professor of sociology at Copenhagen, where a graduate program in sociology was started in 1958. In the 1960s and 1970s, additional chairs were created at Copenhagen and Aalborg and in sociological subdisciplines at unversities in Copenhagen, Aarhus, Aalborg, and Roskilde as well as at the Handelshøjskolen (School of Economics and Business Administration) in Copenhagen. Mostly as a result of problems at the two institutes at the University of Copenhagen, Danish sociology was in a state of crisis after the late 1960s. In 1986– 1987 the government closed down the Copenhagen institutes, and by 1994 they had been reorganized into a new institute with two chairs. The institute offers (as did Aalborg after 1997) a three-year undergraduate program leading to a B.A., followed by a two-year master’s program. At present there are only eight chairs in sociology in Copenhagen, Aalborg, Roskilde, and Handelshøjskolen. Outside the universities, the most important institute in sociology is the Socialforskningsinstituttet (Institute for Social Research). With Henning Friis as director from its start in 1958 until 1979, the institute has carried out many social investigations using its own field organization, for instance, the 1976 and 1986 Danish Welfare Surveys and the report on the 1992 follow-up of the 1968 youth study (Hansen 1995).

In Finland, two of Westermarck’s ethnosociological students held chairs in sociology in Helsinki and Turku in the 1920s. In the mid-1940s, mod- ern-type sociologists got chairs, such as the criminologist Veli Verkko (1893–1955) in Helsinki in 1946. Heikki Waris (1901–1989), professor of social policy from 1948 in Helsinki, also should be mentioned here. Sociology chairs were first established at Helsinki, Turku, Åbo Academy, and Tampere. At present, there are more than twenty

professors of sociology and its subdisciplines at eleven universities and colleges: eight chairs at Helsinki University, four at Tampere, and two each at Jyväskylä and Turku, plus one at Åbo Akademi, also in Turku. Undergraduate studies lead to a master’s degree after five years. There also are important research institutes outside the universities. The National Research and Development Centre for Welfare and Health (STAKES), under the Ministry of Social and Health Care, was established in 1992. Within STAKES there is a Social Research Unit for Alcohol Studies that conducts research on four principal areas, including narcotics. The research directors are Hannu Uusitalo and Jussi Simpura. There also is the Finnish Foundation for Alcohol Studies, which was established in 1950, with Klaus Mäkelä as its leading researcher.

In Norway, Sverre Holm filled the first sociology chair in 1949 in Oslo, and chairs were added in Bergen, Trondheim, and Tromsø in the 1960s. At present there are some twenty-five professorships. A basic undergraduate program takes four years, after which a two-year program leads to a ‘‘cand.polit.’’ degree. Several institutes are important in sociological research and education: Institutt for Samfunnsforskning (Institute for Social Research [ISF]) since 1950, the newly established NOVA (Norwegian Institute for Research on Childhood and Adolescence, Welfare, and Aging), FAFO (Trade Union Movement’s Research Foundation), and Statistisk Sentralbyrå (Statistical Central Bureau [SSB]). Institutt for Anvendt Sosialforskning (Institute for Applied Social Research [INAS]), which was established in 1968, is now included in NOVA. Natalie R Ramsøy was its research director until 1981.

In Sweden, the educator and sociologist E. H. Thörnberg (1873–1961) never held a university position. Gunnar Myrdal (1898–1987) could claim to be the first modern sociologist, but Torgny T. Segerstedt (1908–1999) became the first professor of sociology by converting his chair in applied philosophy to sociology at Uppsala in 1947. Sociology is now taught at Uppsala, Stockholm, Lund, Göteborg, Umeå, and Linköping as well as in several colleges, of which those at Karlstad, Växjö, and Örebro have become universities. There are about thirty sociology professors at the universities. A bachelor’s program in sociology is scheduled for three years, a master degree for a fourth year, and a ‘‘licentiat’’ degree for a fifth

year. Outside the universities, Arbetslivsinstitutet (the National Institute for Working Life); Folkhälsoinstitutet (the National Institute of Public Health), established in 1992; the National Council for Crime Prevention (BRÅ), starting in 1974; and Statistics Sweden are important in Swedish sociology. The Institute for Social Research ([SOFI] 1972, beginning in with five sociology chairs) and the National Center for Social Research on Alcohol and Drugs (since 1999), with Robin Room as research director, are parts of Stockholm University. Since 1992 BRÅ has published Studies on Crime and Crime Prevention, an international biannual journal.

There is considerable interaction among the Scandinavian sociological communities: Professors hold chairs in neighboring countries, such as the leading Finnish alcohol researcher Kettil Bruun (1924–1985) who had a chair in Stockholm in 1982–1984 and Swedish sociologists from Lund who crossed the sound to Copenhagen; comparative studies of the Scandinavian countries are conducted; and there have been joint comparative projects with one editor from each of four countries.

The Scandinavian Sociological Association has some 2,500 members. Approximate memberships are 550 in Denmark, 50 in Iceland, 600 in Finland (in 1995), 700 in Norway, and 500 in Sweden. Since 1955 the association has published Acta Sociologica, a refereed quarterly journal. For the first twelve years Torben Agersnap of Handelshøjskolen in Copenhagen was the editor, and since then editorship has rotated among the countries, with three-year periods since 1985. Each Scandinavian country has a national sociological association. The Finnish Westermarck Society, founded in 1940, is the oldest and includes social anthropologists. There are also national journals: Sociologia (since 1990) in Denmark, Sosiologia in Finland, Tidskrift for Samfunnsforskning (since 1960) and Sosiologisk Tidskrift in Norway, and Sociologisk forskning (since 1964) in Sweden.

Most foreign contacts are still with the United States, although interaction with European, especially British, French, and German, sociologists tends to be more frequent now. Polish, Hungarian, and Estonian contacts are important to Finnish sociology. Comparative studies including nonScandinavian European or OECD countries (Erikson and Goldthorpe 1992; Korpi and Palme 1998)

are conducted frequently as a heritage from Stein Rokkan in political sociology. Comparisons often have been made with the United States and Canada, as occurred when Norway and Sweden were included in Tim Smeeding’s and Lee Rainwater’s so-called ‘‘Luxembourg Income Study’’ around 1980. Furthermore, several Scandinavian sociologists have been visiting researchers at the European Sociological Institute in Florence, Italy. The Danish welfare researcher Esping-Andersen (1990) has a chair at the University of Trent in Italy. Several Scandinavian sociologists have spent most or parts of their careers abroad, especially in the United States, for example, Aage Bøttger Sørensen from Denmark; the Norwegians Stein Rokkan, Jon Elster, and Trond Pedersen; and the Swedes Bo Andersson and Hans L. Zetterberg.

PRESENT SOCIOLOGY

Essays on Scandinavian sociology can be found in Bertilsson and Therborn (1997). New conceptions in Finnish sociology in the early 1990s and their relation to earlier structural-cultural traditions are discussed by Alapuro (1995), while Martinussen (1993) deals with present-day Norwegian sociology. Allardt et al. (1988) offer an official evaluation of Swedish sociology. Gunnlaugsson and Bjarnason (1994) describe Icelandic sociology.

Even after the wave of New Left sociology subsided toward the end of the 1970s, Scandinavian sociology remained diversified, with a continued interest in Marxism and the classics as well as in social exchange theory and a broad spectrum of data analysis. On balance, the focus now is somewhat more on theory and on macrosociology. Interest in theories of organizations (Ahrne 1994), economic sociology (Swedberg 1990), and, especially after Coleman’s book (1990), rational-choice approaches (Hedströem and Swedberg 1998) has been increasing. The statistical analytic orientation has held its own, at least partly because of the rapid development of personal computers and their programs, which permit more adequate analyses. At the same time, the old interest in qualitative methods has remained, especially in connection with the growing field of gender studies.

In Norway, Østerberg (1986, 1988) writes in the hermeneutic and humanistic essay tradition, Yngvar Løchen (1931–1998) carried out action research in medical sociology with relevance to the

lives of sociologists, and Elster is a well-known philosopher and social scientist (1989), favorably disposed to Marxism (1985) but closer to the rational-choice approach. In Sweden, Göran Therborn is an internationally respected Marxist and the social psychologist and essayist Johan Asplund has a prestigious position in the discipline; Antti Eskola is Finland’s best known social psychologist.

A cluster of overlapping core areas concerning forms of inequality has remained central to Scandinavian sociology. Studies concern gender inequality as well as inequality among social classes and other groupings in regard to political, economic, educational, and social resources, which can be seen as constituting inequality of welfare in a broad sense; these studies present indicators of various welfare dimensions. Research institutes such as the Institute for Social Research in Copenhagen, INAS (now in NOVA) and ISF in Oslo, and SOFI in Stockholm have been important, as STAKES in Helsinki will be. One of the tasks of SOFI has been to follow up an officially commissioned 1968 study of low-income categories. The study was carried out by Sten Johansson, then at Uppsala, and his team, which published several reports in 1970–1971, creating considerable political commotion. SOFI has continued this study as a panel, Levnadsnivåundersökningen (Level of Living Study [LNU]), with new surveys in 1974, 1981, and 1991. A volume edited by Erikson and Åberg (1987) provided a partial summary. Comprehensive welfare studies (level-of-living studies) have been carried out in all Scandinavian countries since the 1970s (Erikson et al. 1987; Hansen et al. 1993). Statistics Sweden has conducted annual level-of- living surveys since 1974 (Vogel 1988). Studies by Gudmund Hernes in Norway have been influential both politically and sociologically. Hernes was the leading researcher in the first Norwegian level- of-living survey and in an official Norwegian 1972– 1982 project on power in society (Hernes 1975, 1978, also involving exchange theory). The task force gave its final report in 1982 (Hernes 1982). The official Swedish study of societal power, ordered after the success of the Norwegian study, was run mainly by political scientists, historians, and economists, although Åberg (1990) was asked to repeat in modern form a community study from the 1950s (Segerstedt and Lundquist 1952, 1955). Here the different roles of Norwegian and Swed-

ish sociology may reflect differences in public opinion: Norwegian sociologists have easy access to the mass media and the political elite. The criminologist Nils Christie has become a wellknown participant in public discussion of social policy issues, and Hernes was the Labor Party’s minister of education and research.

Social stratification and social mobility have long been an area of strong interest in Scandinavian sociology (Carlsson 1958; Svalastoga 1959, 1965). Later works in the field include those of Alestalo (1986), Erikson (1987), Knudsen (1988), and Erikson and Goldthorpe (1992). More recently Erikson and Jonsson reported an officially commissioned investigation of social selection to higher education (Erikson and Jonsson 1993, 1996).

In political sociology Stein Rokkan’s (1921– 1979) far-reaching project (Rokkan and Lipset 1967; Rokkan et al. 1970; Rokkan 1987) was left unfinished. Other important works in this field include Korpi (1989), Therborn (1986, 1995), Martinussen (1988), Alapuro (1988), and Mjøset (1992).

Gender studies are an exceptionally strong field in Norway and fairly strong in Scandinavia generally. In the late 1960s, Holter (1970) analyzed gender roles and their impact on work life, political behavior, and education. Several Norwegian gender studies, among others those by Helga Hernes and Kari Waerness, were published in the series ‘‘Kvinners levekår og livsløp’’ (Women’s Level of Living and Life Course). In Sweden Edmund Dahlström and Rita Liljeström and in Finland Elina Haavio-Mannila pioneered the field.

Outside the sociological core area of inequality there are neighboring fields and sociological specialties. The Finnish demographer Tapani Valkonen is known for skillful context analyses and epidemiological studies. To revitalize Swedish demography, which had stagnated in the 1970s, the Norwegian demometrician Jan M. Hoem was called in from Copenhagen in 1983.

In the lively sociological subfield of literature and mass media, Karl-Erik Rosengren is the leading Swedish researcher. To some extent through east European contacts, the life-course narrative as a research instrument has been developed into a Finnish specialty, mostly by J.P. Roos.

Deviance is another subfield. Usually it is not considered a sociological core area, although it has long-term credentials as a central field both from the classics and from early Scandinavian sociology. The study of deviance has remained an important part of Scandinavian sociology in terms of both applied and basic research. Since the Finnish State Alcohol Monopoly established its research institute in 1950, alcohol studies have been a strong Finnish field. Bruun (Bruun and Frånberg 1985) was its longtime director, and Mäkelä (1996) was his successor. Also Skog (1985) in Oslo and Kühlhorn (Kühlhorn and Björ 1998) and Norström (1988) in Stockholm are well known in the field of alcohol and drug studies. Mathiesen’s penology (1987) is a part of Norwegian sociology, just as studies of crime and crime prevention have been a part of Swedish sociology since the official 1956 juvenile delinquency project (Carlsson 1972) and early BRÅ projects.

Gunnlaugsson and Bjarnason (1994) claim that the four fields of welfare research, stratification research, women’s studies, and cultural studies do not capture the structure of Icelandic sociology, which centers on two broad themes: social conditions and social and cultural problems associated with the development of Icelandic society. These problems essentially concern crime, in particular incidental violence by strangers, alcohol abuse, and the perceived threat of drug abuse, that is, deviance.

Finally, access to an extensive and reliable population registration system has made good sampling frames available to surveys, and governmental microdata have been helpful in longitudinal data sets in Scandinavian behavioral projects. However, since the mid-1970s, statistically oriented behavioral researchers, especially in Sweden, have had problems with privacy-protecting data legislation. According to the pioneering Swedish Data Act of 1973, running data on identified persons by computer requires a permit from a Data Inspection Board (DI). The DI expanded the informedconsent condition for the permit to include the computer use of identified governmental microdata accessed according to the century-old right-of-ac- cess principle. Although this condition has been waived in some cases, it has led to frequent and well-publicized controversies between the DI and social researchers, with the media usually supporting the DI. In Sweden, the media debated privacy

issues mostly in connection with statistical data sets, whereas administrative files largely went unnoticed. Since the conflict came to a head in 1986 over the deidentification of the data set of Project Metropolitan, a longitudinal study of a cohort of Stockholmers, the tensions have smoothed out and access to register data has been made easier. The rich governmental microfiles, including the censuses, remain assets to Scandinavian sociology. In 1998, a new Swedish Personal Data Protection Act in line with EU regulations and modern Internet use was substituted for the 1973 Data Act.

REFERENCES

Ahrne, Göran 1994 Social Organizations. Newbury Park

CA: Sage.

Alapuro, Risto 1995 ‘‘Structure and Culture: Finnish Sociology, 1990–94’’ Acta Sociologica 38(2):167–180.

Alestalo, Matti 1986 Structural Change, Classes, and the

State. Helsinki: Research Group for Comparative

Sociology.

Åberg, Rune, ed. 1990 Industrisamhälle i omvandling

(Industrial Communities in Transition). Stockholm: Carlssons.

Allardt, Erik 1965 Samhällsstruktur och sociala spänningar

(Societal Structure and Social Tension). Tampere: Söderströms. Also in Finnish.

———, Sverre Lysgaard, and Aage Bøttger Sørensen (1988) Sociologin i Sverige (Sociology in Sweden). Stockholm: HSFR.

Aubert, Vilhelm 1965 The Hidden Society. Totowa, N.J.:

Bedminster.

Bertilsson, Margareta, and Göran Therborn, eds. 1997

From a Doll’s House to the Welfare State. Copenhagen: ISA.

Bruun, Kettil, and Per Frånberg, eds. 1985 Den svenska supen (The Swedish Snaps). Stockholm: Prisma.

Carlsson, N. Gösta 1958 Social Mobility and Class Structure. Lund: Gleerup.

——— 1972 Unga lagöverträdare II (Young Law Violators II). Stockholm: SOU 1972:76

Coleman, James S. 1990 Foundations of Social Theory. Cambridge, Mass.: Belknap.

Elster, Jon 1985 Making Sense of Marx. Cambridge, UK: Cambridge University Press.

——— 1989 Nuts and Bolts for the Social Sciences. Cambridge, UK: Cambridge University Press.

Erikson, Robert 1987 ‘‘The Long Arm of the Origin.’’ In Ulla Bergryd and Carl-Gunnar Janson, eds., Sociologi-

cal Miscellany. Stockholm: Department of Sociology, University of Stockholm.

———, and Rune Åberg, (eds.) 1987 Welfare in Transition. Oxford, UK: Clarendon Press.

———, and John Goldthorpe 1992 The Constant Flux: A Study of Class Mobility in Industrial Society. Oxford, UK: Clarendon.

———, Erik Jørgen Hansen, Stein Ringen, and Hannu Uusitalo, eds. 1987 The Scandinavian Model: Welfare State and Welfare Research. Armonk, N.Y.: Sharpe.

———, and Jan O. Jonsson 1993 Ursprung och utbildning (Social Origin and Education). Stockholm: SOU 1993:85.

———, and ——— (eds.) 1996 Can Education Be Equalized? Oxford, UK: Westview Press.

Esping-Andersen, Gøsta 1990 The Tree Worlds of Welfare Capitalism. Cambridge, UK: Cambridge University Press.

Gunnlaugsson, Helgi, and Thoroddur Bjarnason 1994 ‘‘Establishing a Discipline: The Impact of Society on the Development of Icelandic Sociology.’’ Acta Sociologica 1994(3):303–312.

Hansen, Erik Jørgen 1995 En generation blev voksen (A Generation Grew Up) Copenhagen: Socialforskningsinstituttet, Report 95:8.

———, Stein Ringen, Hannu Uusitalo, and Robert Erikson, eds. 1993 Welfare Trends in the Scandinavian Countries. Armonk, N.Y.: Sharpe.

Hedström, Peter and Richard Swedberg 1998 Social Mechanisms. Cambridge: Cambridge University Press.

Hernes, Germund 1975 Makt og avmakt (Power and Powerlessness). Bergen, Norway: Universitetsforlaget.

———1978 Forhandlingsøkonomi og blandingsadministration

(Transaction Economy and Mixed Administration). Bergen, Norway: Universitetsforlaget.

———1982 Maktutredningen: slutrapport (The Power Study: Final Report). Oslo: NOU 1982:3.

———, and Willy Martinussen 1980 Demokrati og politiske resurser (Democracy and Political Resources). Oslo: NOU 1980:7.

Holter, Harriet 1970 Sex Roles and Social Structure. Oslo: Norwegian University Press.

Knudsen, Knud 1988 ‘‘Class Identification in Norway.’’

Acta Sociologica 31:69–79.

Korpi, Walter 1989 ‘‘Power, Politics, and State Autonomy in the Development of Social Citizenship.’’ American Sociological Review 54:309–328.

———, and Joakim Palme 1998 ‘‘The Paradox of Redistribution and Strategies of Equality.’’ American Sociological Review 63:661–687.