• Summary
  • Overview
  • Key Readings

This collection brings together the key publications on the secondary analysis of data and embraces many aspects of how to analyze quantitative survey data, whether primary or secondary. As secondary analysis, defined as use of data that was collected by individuals other than the investigator, is often a starting point for other social science research methods, this set will be a critical resource for researchers across the social sciences.

Volume 1 introduces secondary analysis and explores the sources and types of survey data available, research design, causality, and different approaches to analysis.

Volume 2 canters on exploring and describing data, measurement in surveys, inference, and other issues that arise in data analysis.

Volume 3 concerns the general linear model, models for categorical data, classification and typology construction, and latent variable models.

Volume 4 presents structural equation modeling, multilevel modeling, and longitudinal analysis.

Editors' Introduction
MartinBulmer, PatrickJ.Sturgis & NickAllum

This set of four volumes is concerned with the secondary analysis of survey data. This short overview will first seek to define secondary analysis, and then indicate the contents of each volume of the set. Each of the four volumes has it own introduction, written by one of the editors. The origins of secondary analysis go back at least to the end of the nineteenth century, when Emile Durkheim's Le Suicide provided an analysis of the phenomenon of suicide in European countries, and of regions within countries, based upon available data. This was not a secondary analysis in the contemporary sense of reanalysing survey data consisting of information about individuals, but was a study based upon aggregate data and ecological ...

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