Logistic Regression Models for Ordinal Response Variables: Quantitative Applications in the Social Sciences, cartea 146
Autor Ann Aileen O'Connellen Limba Engleză Electronic book text – 29 apr 2019
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Specificații
ISBN-13: 9781452210834
ISBN-10: 1452210837
Pagini: 120
Dimensiuni: 140 x 216 mm
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
Locul publicării:Thousand Oaks, United States
ISBN-10: 1452210837
Pagini: 120
Dimensiuni: 140 x 216 mm
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
Locul publicării:Thousand Oaks, United States
Cuprins
List
of
Tables
and
Figures
Series Editor’s Introduction
Acknowledgments
1. Introduction
Purpose of This Book
Software and Syntax
Organization of the Chapters
2. Context: Early Childhood Longitudinal Study
Overview of the Early Childhood Longitudinal Study
Practical Relevance of Ordinal Outcomes
Variables in the Models
3. Background: Logistic Regression
Overview of Logistic Regression
Assessing Model Fit
Interpreting the Model
Measures of Association
EXAMPLE 3.1: Logistic Regression
Comparing Results Across Statistical Programs
4. The Cumulative (Proportional) Odds Model for Ordinal Outcomes
Overview of the Cumulative Odds Model
EXAMPLE 4.1: Cumulative Odds Model With a Single Explanatory Variable
EXAMPLE 4.2: Full-Model Analysis of Cumulative Odds
Assumption of Proportional Odds and Linearity in the Logit
Alternatives to the Cumulative Odds Model
EXAMPLE 4.3: Partial Proportional Odds
5. The Continuation Ratio Model
Overview of the Continuation Ratio Model
Link Functions
Probabilities of Interest
Directionality of Responses and Formation of the Continuation Ratios
EXAMPLE 5.1: Continuation Ratio Model With Logit Link and Restructuring the Data
EXAMPLE 5.2: Continuation Ratio Model With Complementary Log-Log Link
Choice of Link and Equivalence of Two Clog-Log Models
Choice of Approach for Continuation Ratio Models
EXAMPLE 5.3: Full-Model Continuation Ratio Analyses for the ECLS-K Data
6. The Adjacent Categories Model
Overview of the Adjacent Categories Model
EXAMPLE 6.1: Gender-Only Model
EXAMPLE 6.2: Adjacent Categories Model With Two Explanatory Variables
EXAMPLE 6.3: Full Adjacent Categories Model Analysis
7. Conclusion
Considerations for Further Study
Notes
Appendix A: Chapter 3
Appendix B: Chapter 4
Appendix C: Chapter 5
Appendix D: Chapter 6
References
Index
About the Author
Series Editor’s Introduction
Acknowledgments
1. Introduction
Purpose of This Book
Software and Syntax
Organization of the Chapters
2. Context: Early Childhood Longitudinal Study
Overview of the Early Childhood Longitudinal Study
Practical Relevance of Ordinal Outcomes
Variables in the Models
3. Background: Logistic Regression
Overview of Logistic Regression
Assessing Model Fit
Interpreting the Model
Measures of Association
EXAMPLE 3.1: Logistic Regression
Comparing Results Across Statistical Programs
4. The Cumulative (Proportional) Odds Model for Ordinal Outcomes
Overview of the Cumulative Odds Model
EXAMPLE 4.1: Cumulative Odds Model With a Single Explanatory Variable
EXAMPLE 4.2: Full-Model Analysis of Cumulative Odds
Assumption of Proportional Odds and Linearity in the Logit
Alternatives to the Cumulative Odds Model
EXAMPLE 4.3: Partial Proportional Odds
5. The Continuation Ratio Model
Overview of the Continuation Ratio Model
Link Functions
Probabilities of Interest
Directionality of Responses and Formation of the Continuation Ratios
EXAMPLE 5.1: Continuation Ratio Model With Logit Link and Restructuring the Data
EXAMPLE 5.2: Continuation Ratio Model With Complementary Log-Log Link
Choice of Link and Equivalence of Two Clog-Log Models
Choice of Approach for Continuation Ratio Models
EXAMPLE 5.3: Full-Model Continuation Ratio Analyses for the ECLS-K Data
6. The Adjacent Categories Model
Overview of the Adjacent Categories Model
EXAMPLE 6.1: Gender-Only Model
EXAMPLE 6.2: Adjacent Categories Model With Two Explanatory Variables
EXAMPLE 6.3: Full Adjacent Categories Model Analysis
7. Conclusion
Considerations for Further Study
Notes
Appendix A: Chapter 3
Appendix B: Chapter 4
Appendix C: Chapter 5
Appendix D: Chapter 6
References
Index
About the Author
Descriere
Logistic
Regression
Models
for
Ordinal
Response
Variablesprovides
applied
researchers
in
the
social,
educational,
and
behavioral
sciences
with
an
accessible
and
comprehensive
coverage
of
analyses
for
ordinal
outcomes.
The
content
builds
on
a
review
of
logistic
regression,
and
extends
to
details
of
the
cumulative
(proportional)
odds,
continuation
ratio,
and
adjacent
category
models
for
ordinal
data.
Description
and
examples
of
partial
proportional
odds
models
are
also
provided.
This
book
is
highly
readable,
with
lots
of
examples
and
in-depth
explanations
and
interpretations
of
model
characteristics.