Relations and Attributes
The first task must be to define the kind of data for which social network analysis is most appropriate. Those who are interested in its applications will, undoubtedly, have some ideas about this already: it is useful for investigations of kinship patterns, community structure, interlocking directorships and so forth. But it is essential that the common features of these types. of data are understood more clearly. It is my contention that social network analysis is appropriate for 'relational data', and that techniques developed for the analysis of other types of data are likely to be of limited value for research which generates data of this kind.
The most general characteristic of social science data is that they are rooted in cultural values and symbols. Unlike the physical data of the natural sciences, social science data are constituted through meanings, motives, definitions and typifications. As is well known, this means that the production of social science data involves a process of interpretation. On the basis of such processes of interpretation, social scientists have formulated distinct types of data, to each of which distinct methods of analysis are appropriate.
The principal types of data are 'attribute data' and 'relational data'. 1 Attribute data relate to the attitudes, opinions and behaviour of agents, in so far as these are regarded as the properties, qualities or characteristics which belong to them as individuals or groups. The items collected through surveys and interviews, for example, are often regarded simply as the attributes of particular individuals which can be quantified and analysed through the many available statistical procedures. The methods appropriate to attribute data are those of variable analysis, whereby attributes are measured as values of particular variables (income, occupation, education etc.).
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Relational data, on the other hand, are the contacts, ties and connections, the group attachments and meetings, which relate one agent to another and so cannot be reduced to the properties of the individual agents themselves. Relations are not the properties of agents, but of systems of agents; these relations connect pairs of agents into larger relational systems. The methods appropriate to relational data are those of network analysis, whereby the relations are treated as expressing the linkages which run between agents. While it is, of course, possible to undertake quantitative and statistical counts of relations, network analysis consists of a body of qualitative measures of network structure.
Attribute @nd relational data are not the only types of social science data, although they are the most widely discussed in methods texts. A third type comprises ideational data, which describe the meanings, motives, definitions and typifications themselves. Techniques for the analysis of ideational data are less well developed than those for attribute and relational data, despite their centrality to the social sciences. Typological analysis of the kind outlined by Weber (1920-21) is the most fruitful approach here, but these methods are in need of further development (see Layder, 1992).
Although there are distinct types of data (as set out in Figure 1. 1) each with their own appropriate methods of analysis, there is nothing specific about the methods of data collection which can be used to produce them. There is, for example, nothing which distinguishes methods for the collection of attribute data from those for the collection of relational data. The three types of data are often collected alongside one another as integral aspects of the same investigation. A study of political attitudes, for example, may seek to link these to group memberships and community attachments; or an investigation of interlocking directorships may seek to link these to the size and profitability of the companies involved. In either case, questionnaires, interviews, participant observation or docu-
Style of research Source of evidence Type of data Type of analysis
4 Social network analysis
mentary sources can be consulted in order to generate the data.
Studies of friendship, for example, have tended to follow Moreno's (1934) lead in using questionnaires to investigate friendship choices. In such studies, researchers simply ask respondents to identify their friends, using such questions as 'Please name your four closest friends'. Methodological problems do, of course, arise with this kind of research. An unlimited choice question has sometimes been found to be difficult for respondents to answer. Some do not feel that they have four friends to name, and many find the open question both time-consuming and tedious. An alternative approach is to use the roster choice method, in which respondents are asked 'Which of the following would you regard as a friend?' This requires considerable knowledge and preparation on the part of the researcher, who must compile the list, the roster, with which respondents are presented, but it has the advantage that it can be adapted by asking respondents to rank or to rate their affiliations, so indicating their 'intensity' or significance. In both cases, however, these methodological problems of knowledge and respondent cooperation are exactly the same as those which arise in collecting information on attitudes and opinions.
Relational data are central to the principal concerns of the sociological tradition, with its emphasis upon the investigation of the structure of social action. Structures are built from relations, and the structural concerns of sociology can be pursued through the collection and analysis of relational data. Paradoxically, most of the existing texts on research methods and methods of data collection give little attention to this type of data, concentrating instead on the use of variable analysis for the investigation of attribute data. The formal, mathematical techniques of social network analysis, the methods which are specifically geared to relational data, have developed and have been discussed outside the mainstream of research methods. Whilst they have made possible a number of spectacular breakthroughs in structural analysis, they have been largely inaccessible to many of those who would most wish to use them.
Social network analysis developed, initially, in a relatively nontechnical form from the structural concerns of the great anthropologist Radcliffe-Brown. From the 1930s to the 1970s, an increasing number of social anthropologists and sociologists began to build on Radcliffe-Brown's concept of 'social structure' and, in doing so, began to take seriously the metaphors of the 'fabric' and 'web' of social life. From these textile metaphors, aimed at understanding the 'interweaving' and 'interlocking' relations through which social actions were organized, the metaphor of the social 'network' came
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to the fore, and researchers began to investigate the 'density' and 'texture' of the social networks which they studied. From the 1950s, however, a small group of specialists began to concern themselves with devising more formal translations of the metaphor and, from the early 1970s, an avalanche of technical work and specialist applications appeared. From these writings have emerged the key concepts of social network analysis, and it is time that the techniques returned to the mainstream of data analysis and a wider sphere of applications.