Regression models for categorical, count, and related variables: an applied approach
"Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists...
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| Format: | Buch |
| Sprache: | English |
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Oakland, California
University of California Press
[2016]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Zusammenfassung: | "Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes...all presented under the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis. Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data. A companion website includes downloadable versions of all the data sets used in the book"...Provided by publisher |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xv, 411 Seiten Diagramme |
| ISBN: | 9780520289291 0520289293 |
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Datensatz im Suchindex
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| adam_text | REGRESSION MODELS FOR CATEGORICAL, COUNT, AND RELATED VARIABLES
/ HOFFMANN, JOHN P.YYQ(JOHN PATRICK)YYD1962-YYEAUTHOR
: 2016
TABLE OF CONTENTS / INHALTSVERZEICHNIS
REVIEW OF LINEAR REGRESSION MODELS
CATEGORICAL DATA AND GENERALIZED LINEAR MODELS
LOGISTIC AND PROBIT REGRESSION MODELS
ORDERED LOGISTIC AND PROBIT REGRESSION MODELS
MULTINOMIAL LOGISTIC AND PROBIT REGRESSION MODELS
POISSON AND NEGATIVE BINOMIAL REGRESSION MODELS
EVENT HISTORY MODELS
REGRESSION MODELS FOR LONGITUDINAL DATA
MULTILEVEL REGRESSION MODELS
PRINCIPAL COMPONENTS AND FACTOR ANALYSIS
APPENDIX A : SAS, SPSS, AND R CODE FOR EXAMPLES IN CHAPTERS
APPENDIX B : USING SIMULATIONS TO EXAMINE ASSUMPTIONS OF OLS REGRESSION
APPENDIX C : WORKING WITH MISSING DATA
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
Titel: Regression models for categorical, count, and related variables
Autor: Hoffmann, John P
Jahr: 2016
CONTENTS
Preface xi
Acknowledgments xv
Review of Linear Regression Models i
A Brief Introduction to Stata 4
An OLS Regression Model in Stata 5
Checking the Assumptions of the OLS Regression Model 10
Modifying the OLS Regression Model 24
Examining Effect Modification with Interaction Terms 26
Assessing Another OLS Regression Model 30
Final Words 36
Categorical Data and Generalized Linear Models 39
A Brief Review of Categorical Variables 40
Generalized Linear Models 42
Link Functions 43
Probability Distributions as Family Members 44
How Are GLMs Estimated? 50
Hypothesis Tests with ML Regression Coefficients 53
Testing the Overall Fit of ML Regression Models 54
Example of an ML Linear Regression Model 56
Final Words 60
Logistic and Probit Regression Models 63
What Is an Alternative? Logistic Regression 64
The Multiple Logistic Regression Model 70
Model Fit and Testing Assumptions of the Model 73
Probit Regression 77
Comparison of Marginal Effects: dprobit and Margins Using dy/dx 81
Model Fit and Diagnostics with the Probit Model 82
Limitations and Modifications 84
Final Words 85
4 Ordered Logistic and Probit Regression Models 87
The Ordered Logistic Regression Model 90
The Ordered Probit Model 96
Multiple Ordered Regression Models too
Model Fit and Diagnostics with Ordered Models 105
Final Words 108
5 Multinomial Logistic and Probit Regression Models m
The Multinomial Logistic Regression Model 112
The Multiple Multinomial Logistic Regression Model 118
The Multinomial Probit Regression Model 123
Examining the Assumptions of Multinomial Regression Models 124
Final Words 128
6 Poisson and Negative Binomial Regression Models 131
The Multiple Poisson Regression Model 138
Examining Assumptions of the Poisson Model 141
The Extradispersed Poisson Regression Model 143
The Negative Binomial Regression Model 145
Checking Assumptions of the Negative Binomial Model 148
Zero-Inflated Count Models 149
Testing Assumptions of Zero-Inflated Models 155
Final Words 158
7 Event History Models 159
Event History Models 160
Example of Survivor and Hazard Functions 163
Continuous-Time Event History Models with Censored Data 168
The Cox Proportional Hazards Model 176
Discrete-Time Event History Models 180
Final Words 187
Regression Models for Longitudinal Data 189
Fixed- and Random-Effects Regression Models 191
Generalized Estimating Equations for Longitudinal Data 197
Final Words 205
Multilevel Regression Models 207
The Basic Approach of Multilevel Models 210
The Multilevel Linear Regression Model 215
Checking Model Assumptions 219
Group-Level Variables and Cross-Level Interactions 221
Multilevel Generalized Linear Models 224
Multilevel Models for Longitudinal Data 226
Cross-Level Interactions and Correlational Structures in Multilevel Models
for Longitudinal Data 233
Checking Model Assumptions 236
A Multilevel Poisson Regression Model for Longitudinal Data 238
Final Words 240
Principal Components and Factor Analysis 243
Principal Components Analysis 247
Factor Analysis 251
Creating Latent Variables 256
Factor Analysis for Categorical Variables 256
Confirmatory Factor Analysis Using Structural Equation Modeling (SEM) 260
Generalized SEM 262
A Brief Note on Regression Analyses Using Structural Equation Models 265
Final Words 267
Appendix A: SAS, SPSS, and R Code for Examples in Chapters 269
Section 1: SAS Code 269
Section 2: SPSS Syntax 306
Section 3: R Code 354
Appendix B: Using Simulations to Examine Assumptions of OLS Regression 383
Appendix C: Working with Missing Data 389
References 397
Index 403
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| spelling | Hoffmann, John P. 1962- Verfasser (DE-588)1033144061 aut Regression models for categorical, count, and related variables an applied approach John P. Hoffmann Oakland, California University of California Press [2016] © 2016 xv, 411 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index "Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes...all presented under the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis. Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data. A companion website includes downloadable versions of all the data sets used in the book"...Provided by publisher Mathematisches Modell Sozialwissenschaften Regression analysis Mathematical models Regression analysis Computer programs Social sciences Statistical methods Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 s Regressionsanalyse (DE-588)4129903-6 s DE-604 Erscheint auch als Online-Ausgabe Regression models for categorical, count, and related variables LoC Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029157818&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029157818&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hoffmann, John P. 1962- Regression models for categorical, count, and related variables an applied approach Mathematisches Modell Sozialwissenschaften Regression analysis Mathematical models Regression analysis Computer programs Social sciences Statistical methods Sozialwissenschaften (DE-588)4055916-6 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
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| title | Regression models for categorical, count, and related variables an applied approach |
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| title_exact_search | Regression models for categorical, count, and related variables an applied approach |
| title_full | Regression models for categorical, count, and related variables an applied approach John P. Hoffmann |
| title_fullStr | Regression models for categorical, count, and related variables an applied approach John P. Hoffmann |
| title_full_unstemmed | Regression models for categorical, count, and related variables an applied approach John P. Hoffmann |
| title_short | Regression models for categorical, count, and related variables |
| title_sort | regression models for categorical count and related variables an applied approach |
| title_sub | an applied approach |
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