Title:
The secondary analysis of survey data
Series:
Sage benchmarks in social research methods
Publication Information:
London, UK : Sage Publications Ltd., 2009
Physical Description:
4 v. ; ill. , 23 cm.
ISBN:
9781412903844
Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010191983 | HA29 S42 2009 vol..1 | Open Access Book | Great Book | Searching... |
Searching... | 30000010191982 | HA29 S42 2009 vol..2 | Open Access Book | Great Book | Searching... |
Searching... | 30000010191981 | HA29 S42 2009 vol..3 | Open Access Book | Great Book | Searching... |
Searching... | 30000010191980 | HA29 S42 2009 vol..4 | Open Access Book | Great Book | Searching... |
On Order
Summary
Summary
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.
Table of Contents
| Volume 1 Issues in the Analysis of Survey Data |
| Introduction to the four volumesMartin Bulmer and Patrick Sturgis and Nick Allum |
| Introduction to Volume One of the setMartin Bulmer |
| Secondary Analysis and Sharing Data |
| Using social science data archivesMorris Rosenberg |
| An introduction to secondary analysisAngela Dale and Sara Arber and Mike Procter |
| Sharing research data in the social sciencesJerome M Clubb et al |
| Toward cumulative knowledge: theoretical and methodological issuesStephen E Fienberg et al |
| Issues in Research Design |
| Some observations on study designS A Stouffer |
| Durkheim's SUICIDE and the problems of empirical researchHannan C Selvin |
| Longitudinal v cross-sectional methods for behavioural research: a first round knock-outR B Davies and A R Pickles |
| Causality and Causal Order |
| Some statistical aspects of causalityD R Coxand and N Wermuth |
| The quantitative analysis of large-scale data-sets and rational action theory: for a sociological allianceJ H Goldthorpe |
| Rethinking CausalityS Lieberson |
| Causality: production and propagationWesley C Salmon |
| Causal orderT Hirschi and H C Selvin |
| Elaboration |
| Test factor standardization as a method of interpretationMorris Rosenberg |
| Attitudes, behavior and the intervening variablesHoward Ehrlich |
| The logical structure of suppressor variablesMorris Rosenberg and Morris |
| Elaborating the association between variablesMervin Susser |
| Analytic Issues |
| Ecological correlations and the behavior of individuals.W S Robinson |
| Replication, replicationGary King |
| Quality issues with survey researchAngela Dale |
| Divorce effects' and causality in the social sciencesMaire NiBhrolchain |
| Volume 2 Measurement and Inference |
| Issues in Survey Measurement |
| On the theory of scales of measurementS S Stevens |
| Factor scaling, external consistency and the measurement of theoretical constructsR A Zeller and E G Carmines |
| A simple theory of the survey response: Answering questions versus revealing preferences.J Zaller and S Feldman |
| Samples, Inference and Error |
| History and development of the theoretical foundations of survey based estimation and analysisJ N K Rao and D R Bellhouse |
| Variance estimation for complex estimators in sample surveysK Rust |
| A 'super-population viewpoint' for finite population samplingH O Hartley and R L Sielken Jr. |
| Statistics and causal inferenceP Holland |
| Weighting methodsG Kalton and I Flores-Cervantes |
| Sampling weights and regression analysisC Winship and L Radbill |
| Inference under Complex Sample Designs |
| Inference with survey weightsR J A Little |
| Inference from complex samplesL Kish and M R Frankel |
| Analysing complex survey data: Clustering, stratification and weightsP Sturgis |
| Missing data in large surveysR Little |
| Analyzing incomplete political science data: An alternative algorithm for multiple imputationG King, et al |
| Volume 3 Summarizing and Modelling Survey Data |
| Exploratory Data Analysis |
| Summarizing distributionsMelissa Hardy |
| How to display data badlyHoward Wainer |
| Cluster analysisD Bartholomew et al |
| Correspondence Analysis: Graphical Representation of Categorical Data in Marketing ResearchDonna Hoffman and George Franke |
| Linear and Non-linear Regression |
| The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerationsR M Baron and D A Kenny |
| In defense of multiplicative terms in multiple regression equationsR J Friedrich |
| How Not to Lie with Statistics: Avoiding Common Mistakes in Quantitative Political ScienceG King |
| A Tutorial in Logistic RegressionAlfred DeMaris |
| Loglinear Models: A Way to Study Main Effects and Interactions for Multidimensional Contingency Tables With Categorical DataLeonard Marascuilo and Patricia Busk |
| Latent Variable Models |
| Latent variables in psychology and the social sciencesKenneth Bollen |
| Component analysis versus common factor analysis: Some issues in selecting an appropriate procedureW F Velicer and D N Jackson |
| Confirmatory factor analysisD L Bandalos |
| Measurement invariance, factor analysis and factorial invarianceW Meredith |
| Volume 4 Simultaneous Equations, Hierarchical and Longitudinal Models |
| Structural Equation Models |
| The decomposition of effects in path analysisDuane F Alwin and Robert M Hauser |
| A general method for estimating a linear structural equation systemKarl G Jöreskog |
| Principles and practice in reporting structural equation analysesR P McDonald and M H Ring Ho |
| Hierarchical Data Structures: Multilevel and Longitudinal Analysis |
| Multilevel modelling of survey dataHarvey Goldstein |
| Modeling multilevel data structuresM R Steenbergen and B S Jones |
| Context, composition and heterogeneity: using multilevel models in health researchC Duncan and G Moon |
| Multilevel models for repeated binary outcomes: attitudes and voting over the electoral cycleM Yang and H Goldstein and A Heath |
| A didactic example of multilevel structural equation modelling applicable to the study of organisationsD Kaplan and P R Elliot |
| Using panel data to estimate the effect of eventsPaul Allison |
| Panel Models in Sociological Research: Theory into PracticeCharles Halaby and Charles |
| Myths and methods: "Myths about longitudinal research" plus supplemental questions.D R Rogosa |
| Cohort analysts' futile quest: statistical attempts to separate age, period and cohort effectsDavid Glenn |
| Changing attitudes towards pre-marital sex: cohort, period and ageing effectsD Harding and C Jencks |
| Latent curve analysisW Meredith and J Tisak |
| General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimationBengt Muthén and Patrick Curran |
| Application of hierarchical linear models to assessing changeA S Bryk and S W Raudenbush |
| Using covariance structure analysis to detect correlates and predictors of changeJ B Willett and A Sayer |
