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Applied Ordinal Logistic Regression Using Stata: From Single-Level to Multilevel Modeling [Pehme köide]

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  • Formaat: Paperback / softback, 552 pages, kõrgus x laius: 231x187 mm, kaal: 970 g
  • Ilmumisaeg: 16-Dec-2015
  • Kirjastus: SAGE Publications Inc
  • ISBN-10: 148331975X
  • ISBN-13: 9781483319759
Teised raamatud teemal:
Applied Ordinal Logistic Regression Using Stata: From Single-Level to Multilevel ModelingSuurem pilt
  • Formaat: Paperback / softback, 552 pages, kõrgus x laius: 231x187 mm, kaal: 970 g
  • Ilmumisaeg: 16-Dec-2015
  • Kirjastus: SAGE Publications Inc
  • ISBN-10: 148331975X
  • ISBN-13: 9781483319759
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781483319759.html
Märksõnad:
Liu presents a supplementary textbook for graduate quantitative methods courses on logistic regression models, ordinal regression models, categorical data analysis, or multi-level modeling in education or in social or behavioral sciences. He works the examples in the proprietary statistics package Stata. Among his topics are a review of basic statistics, logistic regression for binary data, partial proportional odds models and generalized ordinal logistic regression models, stereotype logistic regression models, multi-level modeling for continuous and binary response variables, and multi-level modeling for ordinal response variables. Annotation ©2016 Ringgold, Inc., Portland, OR (protoview.com)

The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data,Applied Ordinal Logistic Regression Using Stata by Xing Liu helps readers learn how to conduct analyses, interpret the results from Stata output, and present those results in scholarly writing. Using step-by-step instructions, this non-technical, applied book leads students, applied researchers, and practitioners to a deeper understanding of statistical concepts by closely connecting the underlying theories of models with the application of real-world data using statistical software. 

Arvustused

In this book, Xing Liu offers a well-crafted book focused on the application of ordinal response models across fields. Readers will be equipped to competently handle a variety of statistical techniques from basic correlations to more advanced generalized ordered logistic regression models. This is an excellent resource for both new consumers of these statistical applications to seasoned veterans working on more complex issues related to ordinal response models.  -- Jennifer Hayes Clark Logistic regression can be difficult to understand. Without a book explaining the test in a plain and easy-to-understand matter, learners will feel lost and get frustrated. However, Applied Ordinal Logistic Regression Using Stata explains the concept clearly and provides practical codes and output.  Learners will find this book approachable and easy to follow.  -- Lu Liu

Preface xv
About the Author xxiv
Chapter 1 Stata Basics 1(42)
1.1 Introduction to Stata
2(17)
1.1.1 Do You Still Need to Use Commands?
3(1)
1.1.2 Stata at First Sight: Interface, Menus, and Toolbar
4(3)
1.1.3 Creating a File and Entering Data
7(2)
1.1.4 How to Open an Existing Data File
9(1)
1.1.5 The Structure of Stata Commands
10(2)
1.1.6 Do-Files
12(3)
1.1.7 How to Save Stata Results
15(1)
1.1.8 What If I Have a Question? How Do I Get Help?
16(3)
1.2 Data Management
19(9)
1.2.1 Creating a New Variable
19(3)
1.2.2 Recoding a Variable
22(1)
1.2.3 Labeling a Variable
23(1)
1.2.4 Labeling Values
24(1)
1.2.5 The egen Command
25(1)
1.2.6 How to Deal With Missing Values When Recoding Variables
26(1)
1.2.7 Other Useful Data Management Commands
27(1)
1.3 Graphs
28(12)
1.3.1 Histograms
28(1)
1.3.2 Bar Charts
29(3)
1.3.3 Box Plots
32(1)
1.3.4 Scatter Plots
33(1)
1.3.5 How to Save Graphs
34(1)
1.3.6 Stata Graph Editor
35(5)
1.4 Summary of Stata Commands in This
Chapter
40(2)
1.5 Exercises
42(1)
Chapter 2 Review of Basic Statistics 43(50)
2.1 Understand Your Data Using Descriptive Statistics
44(1)
2.2 Descriptive Statistics for Continuous Variables Using Stata
44(7)
2.3 Frequency Distribution for Categorical Variables Using Stata
51(5)
2.4 Independent Samples t Test Using Stata
56(4)
2.5 Paired-Samples t Test
60(2)
2.6 Analysis of Variance (ANOVA)
62(4)
2.7 Correlation
66(4)
2.8 Simple Linear Regression
70(4)
2.9 Multiple Linear Regression
74(6)
2.10 Chi-Square Test
80(4)
2.11 Making Publication-Quality Tables Using Stata
84(3)
2.12 General Guidelines for Reporting Results
87(2)
2.13 Summary of Stata Commands in This
Chapter
89(1)
2.14 Exercises
90(3)
Chapter 3 Logistic Regression for Binary Data 93(46)
3.1 Logistic Regression Models: An Introduction
94(13)
3.1.1 Why Do We Need a Logistic Transformation?
95(2)
3.1.2 Probabilities, Odds, and Odds Ratios
97(2)
3.1.3 Transformation Among Probabilities, Odds, and Log Odds in Logistic Regression
99(1)
3.1.4 Goodness-of-Fit Statistics
100(4)
3.1.5 Testing Significance of Predictors
104(1)
3.1.6 Interpretation of Model Parameter Estimates in Logistic Regression
105(2)
3.2 Research Example and Description of the Data and Sample
107(1)
3.3 Logistic Regression With Stata: Commands and Output
107(23)
3.3.1 Simple Logistic Regression Using Stata
107(6)
3.3.2 Multiple Logistic Regression
113(17)
3.4 Making Publication-Quality Tables
130(4)
3.5 Reporting the Results
134(2)
3.6 Summary of Stata Commands in This
Chapter
136(1)
3.7 Exercises
137(2)
Chapter 4 Proportional Odds Models for Ordinal Response Variables 139(186)
4.1 Proportional Odds Models: An Introduction
140(185)
4.1.1 Odds and Odds Ratios in PO Models
143(2)
4.1.2 Brant Test of the PO Assumption
145(1)
4.1.3 Goodness of Fit
145(3)
4.1.4 Interpretation of Model Parameter Estimates
148(177)
Chapter 9 Ordinal Logistic Regression With Complex Survey Sampling Designs 325(24)
9.1 Proportional Odds Models With Complex Survey Sampling Designs: An Introduction
326(3)
9.1.1 Features of Complex Surveys
326(2)
9.1.2 Variance Estimation in Complex Survey Sampling
328(1)
9.2 Research Example and Description of the Data and Sample
329(1)
9.3 Data Analysis With Stata: Commands and Output
329(14)
9.3.1 Proportional Odds Model With Four Explanatory Variables Without Weights
329(2)
9.3.2 Proportional Odds Model With Weights
331(3)
9.3.3 Proportional Odds Model for Complex Survey Data Using the Stata svy Command
334(4)
9.3.4 How to Deal With Singleton Strata
338(5)
9.4 Making Publication-Quality Tables
343(2)
9.5 Reporting the Results
345(2)
9.6 Summary of Stata Commands in This
Chapter
347(1)
9.7 Exercises
348(1)
Chapter 10 Multilevel Modeling for Continuous and Binary Response Variables 349(54)
10.1 Multilevel Modeling: An Introduction
350(6)
10.1.1 Multilevel Data Structure
350(1)
10.1.2 Intraclass Correlation
351(1)
10.1.3 Overview of a Basic Two-Level Model
351(2)
10.1.4 Model-Building Strategies
353(1)
10.1.5 Model Fit Statistics
353(1)
10.1.6 Centering
354(1)
10.1.7 Data Structure for Model Fitting
355(1)
10.2 Multilevel Modeling for Continuous Outcome Variables
356(17)
10.2.1 Research Example and Research Questions
356(1)
10.2.2 Description of the Data and Sample
356(1)
10.2.3 Multilevel Modeling for Continuous Outcome Variables With Stata: Commands and Output
357(11)
10.2.4 Making Publication-Quality Tables
368(1)
10.2.5 Reporting the Results
369(4)
10.3 Multilevel Modeling for Binary Outcome Variables
373(25)
10.3.1 Odds and Odds Ratios in Multilevel Logistic Regression Models
375(1)
10.3.2 Research Example and Research Questions
375(1)
10.3.3 Description of the Data and Sample
376(1)
10.3.4 Multilevel Modeling for Binary Outcome Variables With Stata: Commands and Output
376(18)
10.3.5 Making Publication-Quality Tables
394(3)
10.3.6 Reporting the Results
397(1)
10.4 Summary of Stata Commands in This
Chapter
398(3)
10.5 Exercises
401(2)
Chapter 11 Multilevel Modeling for Ordinal Response Variables 403(46)
11.1 Multilevel Modeling for Ordinal Response Variables: An Introduction
404(5)
11.1.1 Model Specification
404(4)
11.1.2 Odds and Odds Ratios in Multilevel PO Models
408(1)
11.1.3 Likelihood Ratio Test
408(1)
11.2 Research Example: Research Problem and Questions
409(1)
11.2.1 Description of the Data and Sample
409(1)
11.3 Multilevel Modeling for Ordinal Response Variables With Stata: Commands and Output
410(29)
11.3.1 Unconditional Model or Null Model (Model 1)
411(3)
11.3.2 Random-Intercept Model (Model 2)
414(3)
11.3.3 Random-Coefficient Model With a Level 1 Variable (Model 3)
417(4)
11.3.4 Contextual Model With Both Level 1 and Level 2 Variables (Model 4)
421(4)
11.3.5 Contextual Model With Cross-Level Interactions (Model 5)
425(3)
11.3.6 Model Comparisons Using the AIC and BIC Statistics
428(1)
11.3.7 Computing the Estimated Probabilities With the margins Command
429(4)
11.3.8 Fitting Multilevel PO Models Using the meg lm Command
433(2)
11.3.9 Fitting Multilevel PO Models Using the gllamm Command
435(4)
11.4 Making Publication-Quality Tables
439(5)
11.5 Reporting the Results
444(2)
11.6 Summary of Stata Commands in This
Chapter
446(1)
11.7 Exercises
447(2)
Chapter 12 Beyond Ordinal Logistic Regression Models: Ordinal Probit Regression Models and Multinomial Logistic Regression Models 449(50)
12.1 Ordinal Probit Regression Models
450(21)
12.1.1 Ordinal Probit Regression Models: An Introduction
450(3)
12.1.2 Description of the Research Example, Data, and Sample
453(1)
12.1.3 Ordinal Probit Models With Stata: Commands and Output
453(1)
12.1.4 Interpreting the Output
454(3)
12.1.5 Interpreting the Marginal Effects With the margins Command
457(3)
12.1.6 Computing the Marginal Effects With the Improved margins Command in Stata 14
460(1)
12.1.7 Interpreting the Estimated Probabilities With the margins Command
461(6)
12.1.8 Model Comparison Using the Log Likelihood Ratio Test
467(1)
12.1.9 Making Publication-Quality Tables Comparing the Probit Model and Proportional Odds Model
468(1)
12.1.10 Reporting the Results
469(2)
12.2 Multinomial Logistic Regression Models
471(23)
12.2.1 Multinomial Logistic Regression Models: An Introduction
471(1)
12.2.2 Odds in Multinomial Logistic Models
472(1)
12.2.3 Odds Ratios or Relative Risk Ratios in Multinomial Logistic Regression Models
473(1)
12.2.4 Description of the Research Example, Data, and Sample
474(1)
12.2.5 Multinomial Logistic Regression Models With Stata: Commands and Output
474(1)
12.2.6 Interpreting the Output
475(3)
12.2.7 Interpreting the Odds Ratios of Being in a Category j Versus the Base Category 1
478(2)
12.2.8 Interpreting the Estimated Probabilities With the margins Command
480(5)
12.2.9 Independence of Irrelevant Alternatives (IIA) Tests
485(3)
12.2.10 Making Publication-Quality Tables
488(4)
12.2.11 Reporting the Results
492(2)
12.3 Summary of Stata Commands in This
Chapter
494(2)
12.4 Exercises
496(3)
Key Formulas for Statistical Models 499(2)
Appendix: List of Stata User-Written Commands 501(2)
References 503(8)
Index 511
Xing Liu Ph.D., is a professor of educational research and assessment at Eastern Connecticut State University. He received his Ph.D. in measurement, evaluation, and assessment in the field of educational psychology from the University of Connecticut, Storrs. His interests include categorical data analysis, multilevel modeling, longitudinal data analysis, structural equation modeling, educational assessment, propensity score methods, data science, and Bayesian methods. He is the author of Applied Ordinal Logistic Regression Using Stata: From Single-Level to Multilevel Modeling (2016). His major publications focus on advanced statistical models. His articles have been recognized among the most popular papers published in the Journal of Modern Applied Statistical Methods (JMASM). Dr. Liu is the recipient of the Excellence Award in Creativity/Scholarship at Eastern Connecticut State University.