Anthony P. M. Coxon

Images of social stratification : occupational structures and class

PART ONE: ORIENTATION Class Images and Images of Class Attempts to Measure Status The Conventional Account Refutations and Reconstruction The Project Method and Design PART TWO: CRITICAL SHORTCOMINGS OF THE CONVENTIONAL ACCOUNT The End of Consensus On the Fundamental Unreality of Unidimensionality The Evaluation of Jobs and People The Family Resemblance Interpretation of Occupational Structure PART THREE: THE SEARCH FOR STRUCTURE The Remarkable Case of the Spontaneous Set The Hierarchical Family or the Family Tree Language and Class A Generative Capacity? PART FOUR: FINALE Implications and Consequences

Occupational similarities: Subjective aspects of social stratification

ConclusionsThe main thesis of this paper has been that conceptions of the occupational structure differ in sociologically significant ways. We particularly question the assumption that consensus over occupational cognition is so marked that individual variation may be treated as purely idiosyncratic and hence ignored. On the contrary, we have argued that there are important and systematic differences in the ways in which different occupational groups view the occupational structure, and that any given occupational viewpoint is likely to be restricted and partial.

Occupational Attributes: Constructs and Structure

What aspects of occupations are considered most meaningful by those involved in occupational choice? Which of the attributes are most salient in describing the occupations? And which discriminate best between the occupations? These questions are approached by reference to data obtained from a sample of social science undergraduates. Whilst no direct inferences are made concerning the processes involved in the cognition of the occupations, it is found that an adequate hierarchical and spatial representation can be made of the data, which broadly confirm Rosenbergs types of motivation.

Social Structure and Occupations

Why is it that so many official forms and questionnaires contain questions about one’s occupation, or one’s father’s occupation, or one’s spouse’s occupation? Why do introductions at cocktail parties or in bars so frequently involve the exchange of information about occupations? One plausible explanation is that occupational titles provide socially useful information about people. In the situation of informal social interaction, mere observation allows us to discover such things as the sex, age, accent, physical attractiveness, ethnicity, or whatever, of our co-participants. Being told the occupations pursued by the people one is chatting to seems to be thought of as adding to the information that could be gained solely by observation. When taken in context with age and sex, it gives some indication of a person’s likely income bracket, educational level, housing area, and style of life. It also provides a starting point for further conversation.

Mathematical Applications in Sociology: Measurement and Relations

Summary Mathematical applications in sociology have a history dating back at least to the seventeenth century, but have been hampered in their development by critical problems of measurement and adequate theory. Recent developments in ‘non‐metric’ measurement and representation are summarized, by reference to typical sociological problems. Social relational structures are best mapped on to topological and especially graph‐theoretic structures, and a model for analysis of marriage rules and a stochastic model for the description and explanation of large social systems are presented and examined for their utility for sociological analyses.

Measurement, Meanings and Social Research Based on Interviews

We believe that empirical sociology can usefully be divided into three areas: — variable-centred, structure- or network-centred, and meaning-centred. Variable-centred research is the study of individuals and organisations, treating each one as an isolated unit, which has scores on a number of characteristics (variables). A powerful armoury of modern statistical techniques is available to aid the investigator, but the fiction that each unit of analysis is quite separate from any other unit is sometimes difficult to maintain belief in. Structure- or network-centred analysis is the study of systematic aspects of the relationships between individual persons, or between organisations. Thirdly, there is meaning-centred analysis, where interest is focused upon the varying interpretations people make of their social situations, and on how these interpretations relate to social action.

Data from Unconstrained Sortings: Appendices to Chapter 2 of Class and Hierarchy

The selection of subjects for this sorting task was accomplished in two ways: by sampling professional registers (especially for subjects in quadrants A and B)

Design and Methods of Research in The Images of Occupational Prestige

The methods we have used in the first stage of this research are based on the following five propositions which we have discussed in Images.

Rankings and Ratings Data: Appendices to Chapter 4 of The Images of Occupational Prestige

Rankings and ratings data for the standard set of 16 occupational titles were briefly described in Chapter 4 of Images. When several groups of people judge a number of stimuli in terms of a variety of criteria, a vast proliferation of statistical tables can result. We coped with this problem in Images by transforming rank orders into directional data (see section T.4.7). However this is such a novel procedure in the social sciences that we think it desirable to report more conventional analyses of the data as well.

Sentence-frame Data: Appendices to Chapter 5 of Class and Hierarchy

Parametric mapping is a type of scaling that treats both the data and the solution as being metric. Thus it differs from quasi-non-metric scaling methods such as MINISSA, where only rank order comparisons are made among the data elements. There are other metric scaling methods. For example, INDSCAL is most frequently used in its metric version. Like other metric scaling methods, parametric mapping (PARAMAP) takes a set of distances between stimulus points as data, and seeks to find a corresponding set of distances in a ‘solution space’ of low dimensionality. Most other scaling methods try to find a solution space such that the inter-point distances in the data are as close as possible to a linear or monotonic function of the corresponding distances, or dissimilarities in the solution. PARAMAP is different. It tries to find a solution space such that the function by which distances in the ‘data space’ can be predicted from corresponding distances in the solution space is as continuous, or ‘smooth’ as possible. The function relating distances in the data to distances in the solution need not be monotonic, so long as it is ‘smooth’. As with other scaling methods, an iterative ‘steepest descent’ algorithm finds stimulus coordinates in a solution space of given dimensionality. These stimulus coordinates serve to generate Euclidean distances in the solution space, and these are found so as to minimise an index of departure from continuity or ‘smoothness’, of the function relating distances between pairs of points in the data space to corresponding distances in the solution space.