This collection brings together the key publications on the secondary analysis of data and embraces many aspects of how to analyse 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 centres 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 and Volume 4 presents structural equation modelling, multilevel modelling and longitudinal analysis.