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This paper analyses social interactions between detailed occupational positions as a means of exploring social and occupational inequalities. Two methods are employed: descriptive techniques of social network analysis, and a well-established modelling approach (the 'CAMSIS' method of 'Social Interaction Distance' analysis). New results on occupational connections are presented for four countries - the United States, Romania, the Philippines and Venezuela – illustrative of a range of socio-economic regimes. Our analyses use detailed occupational measures based upon census data from 2000 to 2002, and we also use data on educational attainment, cross-classified by occupational positions. A broad, singular dimension of social stratification is shown to be the principal element of the structure of social interactions between occupations, but the methods also reveal the social role of various boundaries in occupational interaction patterns (defined by work location, education, and gender). We argue that such distinctions imply that occupational data at a disaggregated level can provide a more thorough understanding of social structure than is observable using amalgamated occupational schemes.

This article introduces a sociological audience to `quasi-variances' as a solution to the `reference category problem'.The reference category problem is associated with the interpretation of the effects of categorical explanatory variables within statistical models, and is especially relevant to sociological applications, where categorical explanatory variables are very common. This article presents a selection of examples (using multiple and logistic regression) to illustrate and comment on quasi-variance calculations for sociological models. In addition, the article is augmented with online materials provided by the authors, which aim to help social researchers practise and apply this technique using the popular data analysis software packages SPSS and Stata.The authors conclude that quasi-variance methods offer an attractive and practicable solution to the reference category problem that can, and should, be routinely operationalized by sociological researchers.

The application of statistical modelling techniques has become a cornerstone of analyses of large-scale social survey data. Bringing this special section on key variables to a close, this final article discusses several important issues relating to the inclusion of key variables in statistical modelling analyses. We outline two, often neglected, issues that are relevant to a great many applications of statistical models based upon social survey data. The first is known as the reference category problem and is related to the interpretation of categorical explanatory variables. The second is the interpretation and comparison of the effects from models for non-linear outcomes. We then briefly discuss other common complexities in using statistical models for social science research; these include the non-linear transformation of variables, and considerations of intersectionality and interaction effects. We conclude by emphasising the importance of two, often overlooked, elements of the social survey data analysis process, sensitivity analysis and documentation for replication. We argue that more attention should routinely be devoted to these issues.

This article is a review of issues associated with measuring ethnicity and using ethnicity measures in social science research. The review is oriented towards researchers who undertake secondary analyses of large-scale multipurpose social science datasets. The article begins with an outline of two main approaches used in social surveys to measure ethnicity, the 'mutually exclusive category' approach and the 'multiple characteristics' approach. We also describe approaches to the use of ethnicity measures in cross-national comparative research. We emphasise the value of sensitivity analyses. We also encourage researchers to carefully consider the possible relationships between ethnicity and other important variables in order to avoid spurious interpretations of the effects of ethnicity.

Der Verfasser macht auf drei signifikante Probleme bei der Analyse von Ethnizität im Rahmen internationaler Querschnittstudien aufmerksam: (1) Die Analyse von Ethnizität ist bisher im Quer- und Längsschnitt nicht konsistent. (2) Harmonisierte Querschnittstudien leiden unter einem Mangel an Information in Bezug auf Ethnizität. (3) Harmonisierte Kategorisierungen spiegeln die Realität des "ethnischen Mosaiks" weder quantitativ noch qualitativ adäquat wider. Der Verfasser diskutiert zwei Lösungsmöglichkeiten für diese Problematik. Ein "relativistischer" Ansatz zur Harmonisierung ethnischer Daten erfordert die Abbildung von Kategorisierungen auf einer Dimension ethnischer Ungleichheit und die Zuordnung quantitativer Werte, die ihre relative Position beschreiben. Ein "absolutistischer" Ansatz der Harmonisierung stellt sicher, dass für alle Ländern die selben Konzepte zur Differenzierung von Kategorien eingesetzt werden. (ICE)

Metadata Creation, Transformation and Discovery for Social Science Data Management: The DAMES Project Infrastructure

PurposeThis paper aims to present reasons why social classifications which use occupations should seek to adopt "specific" approaches which are tailored to the country, time period and gender of the subjects under study.Design/methodology/approachThe relative motivations for adopting a specific approach to social classifications are discussed and theoretical perspectives on specificity and empirical evidence on the contribution of specific approaches are reviewed. Also the practical costs of implementing specific social classifications are evaluated, and the authors' development of the "GEODE" data service (grid‐enabled occupational data environment), which seeks to assist this process, is discussed.FindingsSpecific approaches make a non‐trivial difference to the conclusions drawn from analyses of occupation‐based social classifications. It is argued that the GEODE service has reduced the practical challenges of implementing specific measures.Research limitations/implicationsThere remain conceptual and pragmatic challenges in working with specific occupation‐based social classifications. Non‐specific ("universal") measures are adequate for many purposes.Practical implicationsThe paper argues that there are few excuses for ignoring specific occupation‐based social classifications.Originality/valueThe paper demonstrates that recent technological developments have shifted the balance in the long‐standing debate between universal and specific approaches to occupation‐based social classifications.