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The Secondary Analysis of Survey Data

The Secondary Analysis of Survey Data

Four Volume Set
Edited by:

February 2009 | 1 664 pages | SAGE Publications Ltd
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.
Volume 1: Issues in the Analysis of Survey Data
Martin Bulmer, Patrick Sturgis and Nick Allum
Introduction to the four volumes
Martin Bulmer
Introduction to Volume One of the set
Secondary Analysis and Sharing Data
Morris Rosenberg
Using social science data archives
Angela Dale, Sara Arber and Mike Procter
An introduction to secondary analysis
Jerome M Clubb et al
Sharing research data in the social sciences
Stephen E Fienberg et al
Toward cumulative knowledge: theoretical and methodological issues
Issues in Research Design
S A Stouffer
Some observations on study design
Hannan C Selvin
Durkheim's SUICIDE and the problems of empirical research
R B Davies and A R Pickles
Longitudinal v cross-sectional methods for behavioural research: a first round knock-out
Causality and Causal Order
D R Coxand and N Wermuth
Some statistical aspects of causality
J H Goldthorpe
The quantitative analysis of large-scale data-sets and rational action theory: for a sociological alliance
S Lieberson
Rethinking Causality
Wesley C Salmon
Causality: production and propagation
T Hirschi and H C Selvin
Causal order
Morris Rosenberg
Test factor standardization as a method of interpretation
Howard Ehrlich
Attitudes, behavior and the intervening variables
Morris Rosenberg, Morris
The logical structure of suppressor variables
Mervin Susser
Elaborating the association between variables
Analytic Issues
W S Robinson
Ecological correlations and the behavior of individuals.
Gary King
Replication, replication
Angela Dale
Quality issues with survey research
Maire NiBhrolchain
Divorce effects' and causality in the social sciences
Volume 2: Measurement and Inference
Issues in Survey Measurement
S S Stevens
On the theory of scales of measurement
R A Zeller and E G Carmines
Factor scaling, external consistency and the measurement of theoretical constructs
J Zaller and S Feldman
A simple theory of the survey response: Answering questions versus revealing preferences.
Samples, Inference and Error
J N K Rao and D R Bellhouse
History and development of the theoretical foundations of survey based estimation and analysis
K Rust
Variance estimation for complex estimators in sample surveys
H O Hartley R L Sielken Jr.
A 'super-population viewpoint' for finite population sampling
P Holland
Statistics and causal inference
G Kalton and I Flores-Cervantes
Weighting methods
C Winship and L Radbill
Sampling weights and regression analysis
Inference under Complex Sample Designs
R J A Little
Inference with survey weights
L Kish and M R Frankel
Inference from complex samples
P Sturgis
Analysing complex survey data: Clustering, stratification and weights
R Little
Missing data in large surveys
G King, et al
Analyzing incomplete political science data: An alternative algorithm for multiple imputation
Volume 3: Summarizing and Modelling Survey Data
Exploratory Data Analysis
Melissa Hardy
Summarizing distributions
Howard Wainer
How to display data badly
D Bartholomew et al
Cluster analysis
Donna Hoffman and George Franke
Correspondence Analysis: Graphical Representation of Categorical Data in Marketing Research
Linear and Non-linear Regression
R M Baron and D A Kenny
The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations
R J Friedrich
In defense of multiplicative terms in multiple regression equations
G King
How Not to Lie with Statistics: Avoiding Common Mistakes in Quantitative Political Science
Alfred DeMaris
A Tutorial in Logistic Regression
Leonard Marascuilo and Patricia Busk
Loglinear Models: A Way to Study Main Effects and Interactions for Multidimensional Contingency Tables With Categorical Data
Latent Variable Models
Kenneth Bollen
Latent variables in psychology and the social sciences
W F Velicer and D N Jackson
Component analysis versus common factor analysis: Some issues in selecting an appropriate procedure
D L Bandalos
Confirmatory factor analysis
W Meredith
Measurement invariance, factor analysis and factorial invariance
Volume 4: Simultaneous Equations, Hierarchical and Longitudinal Models
Structural Equation Models
Duane F Alwin and Robert M Hauser
The decomposition of effects in path analysis
Karl G Jöreskog
A general method for estimating a linear structural equation system
R P McDonald and M H Ring Ho
Principles and practice in reporting structural equation analyses
Hierarchical Data Structures: Multilevel and Longitudinal Analysis
Harvey Goldstein
Multilevel modelling of survey data
M R Steenbergen and B S Jones
Modeling multilevel data structures
C Duncan and G Moon
Context, composition and heterogeneity: using multilevel models in health research
M Yang, H Goldstein and A Heath
Multilevel models for repeated binary outcomes: attitudes and voting over the electoral cycle
D Kaplan and P R Elliot
A didactic example of multilevel structural equation modelling applicable to the study of organisations
Paul Allison
Using panel data to estimate the effect of events
Charles Halaby, Charles
Panel Models in Sociological Research: Theory into Practice
D R Rogosa
Myths and methods: "Myths about longitudinal research" plus supplemental questions.
David Glenn
Cohort analysts' futile quest: statistical attempts to separate age, period and cohort effects
D Harding and C Jencks
Changing attitudes towards pre-marital sex: cohort, period and ageing effects
W Meredith and J Tisak
Latent curve analysis
Bengt Muthén and Patrick Curran
General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation
A S Bryk and S W Raudenbush
Application of hierarchical linear models to assessing change
J B Willett and A Sayer
Using covariance structure analysis to detect correlates and predictors of change

Martin I A Bulmer

Patrick Sturgis

Nick Allum

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... More About Author

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ISBN: 9781412903844
£ 675.00