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The Secondary Analysis of Survey Data (Sage Benchmarks in Social Research Methods)

The Secondary Analysis of Survey Data (Sage Benchmarks in Social Research Methods)

By: Martin I. A. Bulmer (editor), Nick Allum (editor), Patrick Sturgis (editor)Hardback

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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.

About Author

Nick Allum is Professor of Sociology at the University of Essex, with expertise in survey design and analysis, public understanding of science, social and political trust and risk perception. He has published widely in these areas and is a regular advisor to government and 3rd sector partners on large-scale research projects in the area of public understanding of science and survey research more generally. He teaches statistical methods, directs the MSc in Survey Methods for Social Research at Essex and specialises in latent variable and structural equation modelling. Nick has been academic advisor for several major UK surveys, including the Wellcome Trust Monitor and the Dept of Business Industry and Skills (BIS) Public Attitudes to Science Survey as well as advising the US National Science Board on several science surveys. Nick is currently General Secretary of the European Survey Research Association.


Volume 1: Issues in the Analysis of Survey DataIntroduction to the four volumes - Martin Bulmer, Patrick Sturgis and Nick Allum Introduction to Volume One of the set - Martin BulmerSecondary Analysis and Sharing DataUsing social science data archives - Morris RosenbergAn introduction to secondary analysis - Angela Dale, Sara Arber and Mike ProcterSharing research data in the social sciences - Jerome M Clubb et al Toward cumulative knowledge: theoretical and methodological issues - Stephen E Fienberg et al Issues in Research DesignSome observations on study design - S A StoufferDurkheim's SUICIDE and the problems of empirical research - Hannan C SelvinLongitudinal v cross-sectional methods for behavioural research: a first round knock-out - R B Davies and A R PicklesCausality and Causal OrderSome statistical aspects of causality - D R Coxand and N WermuthThe quantitative analysis of large-scale data-sets and rational action theory: for a sociological alliance - J H GoldthorpeRethinking Causality - S LiebersonCausality: production and propagation - Wesley C Salmon Causal order - T Hirschi and H C Selvin ElaborationTest factor standardization as a method of interpretation - Morris RosenbergAttitudes, behavior and the intervening variables - Howard EhrlichThe logical structure of suppressor variables - Morris Rosenberg, MorrisElaborating the association between variables - Mervin SusserAnalytic IssuesEcological correlations and the behavior of individuals. - W S Robinson Replication, replication - Gary KingQuality issues with survey research - Angela DaleDivorce effects' and causality in the social sciences - Maire NiBhrolchainVolume 2: Measurement and InferenceIssues in Survey MeasurementOn the theory of scales of measurement - S S StevensFactor scaling, external consistency and the measurement of theoretical constructs - R A Zeller and E G CarminesA simple theory of the survey response: Answering questions versus revealing preferences. - J Zaller and S FeldmanSamples, Inference and ErrorHistory and development of the theoretical foundations of survey based estimation and analysis - J N K Rao and D R BellhouseVariance estimation for complex estimators in sample surveys - K RustA 'super-population viewpoint' for finite population sampling - H O Hartley R L Sielken Jr.Statistics and causal inference - P Holland Weighting methods - G Kalton and I Flores-CervantesSampling weights and regression analysis - C Winship and L RadbillInference under Complex Sample DesignsInference with survey weights - R J A LittleInference from complex samples - L Kish and M R FrankelAnalysing complex survey data: Clustering, stratification and weights - P SturgisMissing data in large surveys - R LittleAnalyzing incomplete political science data: An alternative algorithm for multiple imputation - G King, et al Volume 3: Summarizing and Modelling Survey DataExploratory Data AnalysisSummarizing distributions - Melissa HardyHow to display data badly - Howard WainerCluster analysis - D Bartholomew et al Correspondence Analysis: Graphical Representation of Categorical Data in Marketing Research - Donna Hoffman and George FrankeLinear and Non-linear RegressionThe moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations - R M Baron and D A KennyIn defense of multiplicative terms in multiple regression equations - R J FriedrichHow Not to Lie with Statistics: Avoiding Common Mistakes in Quantitative Political Science - G KingA Tutorial in Logistic Regression - Alfred DeMarisLoglinear Models: A Way to Study Main Effects and Interactions for Multidimensional Contingency Tables With Categorical Data - Leonard Marascuilo and Patricia Busk Latent Variable ModelsLatent variables in psychology and the social sciences - Kenneth BollenComponent analysis versus common factor analysis: Some issues in selecting an appropriate procedure - W F Velicer and D N JacksonConfirmatory factor analysis - D L BandalosMeasurement invariance, factor analysis and factorial invariance - W MeredithVolume 4: Simultaneous Equations, Hierarchical and Longitudinal ModelsStructural Equation ModelsThe decomposition of effects in path analysis - Duane F Alwin and Robert M HauserA general method for estimating a linear structural equation system - Karl G JoereskogPrinciples and practice in reporting structural equation analyses - R P McDonald and M H Ring HoHierarchical Data Structures: Multilevel and Longitudinal AnalysisMultilevel modelling of survey data - Harvey GoldsteinModeling multilevel data structures - M R Steenbergen and B S JonesContext, composition and heterogeneity: using multilevel models in health research - C Duncan and G MoonMultilevel models for repeated binary outcomes: attitudes and voting over the electoral cycle - M Yang, H Goldstein and A HeathA didactic example of multilevel structural equation modelling applicable to the study of organisations - D Kaplan and P R ElliotUsing panel data to estimate the effect of events - Paul AllisonPanel Models in Sociological Research: Theory into Practice - Charles Halaby, CharlesMyths and methods: "Myths about longitudinal research" plus supplemental questions. - D R RogosaCohort analysts' futile quest: statistical attempts to separate age, period and cohort effects - David GlennChanging attitudes towards pre-marital sex: cohort, period and ageing effects - D Harding and C JencksLatent curve analysis - W Meredith and J Tisak General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation - Bengt Muthen and Patrick CurranApplication of hierarchical linear models to assessing change - A S Bryk and S W RaudenbushUsing covariance structure analysis to detect correlates and predictors of change - J B Willett and A Sayer

Product Details

  • ISBN13: 9781412903844
  • Format: Hardback
  • Number Of Pages: 1664
  • ID: 9781412903844
  • ISBN10: 141290384X

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