Analyzing Complex Survey Data: Quantitative Applications in the Social Sciences, cartea 71
Autor Eun Sul Lee, Ronald N. Forthoferen Limba Engleză Electronic book text – 29 noi 2005
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Specificații
ISBN-13: 9781452210445
ISBN-10: 1452210446
Pagini: 104
Dimensiuni: 140 x 216 mm
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
Locul publicării:Thousand Oaks, United States
ISBN-10: 1452210446
Pagini: 104
Dimensiuni: 140 x 216 mm
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
Locul publicării:Thousand Oaks, United States
Cuprins
Series
Editor’s
Introduction
Acknowledgments
1. Introduction
2. Sample Design and Survey Data
Types of Sampling
The Nature of Survey Data
A Different View of Survey Data
3. Complexity of Analyzing Survey Data
Adjusting for Differential Representation: The Weight
Developing the Weight by Poststratification
Adjusting the Weight in a Follow-Up Survey
Assessing the Loss or Gain in Precision: The Design Effect
The Use of Sample Weights for Survey Data Analysis
4. Strategies for Variance Estimation
Replicated Sampling: A General Approach
Balanced Repeated Replication
Jackknife Repeated Replication
The Bootstrap Method
The Taylor Series Method (Linearization)
5. Preparing for Survey Data Analysis
Data Requirements for Survey Analysis
Importance of Preliminary Analysis
Choices of Method for Variance Estimation
Available Computing Resources
Creating Replicate Weights
Searching for Appropriate Models for Survey Data Analysis
6. Conducting Survey Data Analysis
A Strategy for Conducting Preliminary Analysis
Conducting Descriptive Analysis
Conducting Linear Regression Analysis
Conducting Contingency Table Analysis
Conducting Logistic Regression Analysis
Other Logistic Regression Models
Design-Based and Model-Based Analyses
7. Concluding Remarks
Notes
References
Index
About the Authors
Acknowledgments
1. Introduction
2. Sample Design and Survey Data
Types of Sampling
The Nature of Survey Data
A Different View of Survey Data
3. Complexity of Analyzing Survey Data
Adjusting for Differential Representation: The Weight
Developing the Weight by Poststratification
Adjusting the Weight in a Follow-Up Survey
Assessing the Loss or Gain in Precision: The Design Effect
The Use of Sample Weights for Survey Data Analysis
4. Strategies for Variance Estimation
Replicated Sampling: A General Approach
Balanced Repeated Replication
Jackknife Repeated Replication
The Bootstrap Method
The Taylor Series Method (Linearization)
5. Preparing for Survey Data Analysis
Data Requirements for Survey Analysis
Importance of Preliminary Analysis
Choices of Method for Variance Estimation
Available Computing Resources
Creating Replicate Weights
Searching for Appropriate Models for Survey Data Analysis
6. Conducting Survey Data Analysis
A Strategy for Conducting Preliminary Analysis
Conducting Descriptive Analysis
Conducting Linear Regression Analysis
Conducting Contingency Table Analysis
Conducting Logistic Regression Analysis
Other Logistic Regression Models
Design-Based and Model-Based Analyses
7. Concluding Remarks
Notes
References
Index
About the Authors
Descriere
This
book
examines
ways
to
analyze
complex
surveys,
and
focuses
on
the
problems
of
weights
and
design
effects.
This
new
edition
incorporates
recent
practice
of
analyzing
complex
survey
data,
introduces
the
new
analytic
approach
for
categorical
data
analysis
(logistic
regression),
reviews
new
software
and
provides
an
introduction
to
the
model-based
analysis
that
can
be
useful
analyzing
well-designed,
relatively
small-scale
social
surveys.
Notă biografică
Experience & Interest
Teaching and research in public health (Survey design and analysis in mental health and nutrition, demographic analysis of infant mortality and reproductive health, biostatical and epidemiologic analysis of cancer registry data and environmental surveys, statistical methods in epidemiology)
Teaching and research in public health (Survey design and analysis in mental health and nutrition, demographic analysis of infant mortality and reproductive health, biostatical and epidemiologic analysis of cancer registry data and environmental surveys, statistical methods in epidemiology)