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Applied ordinal logistic regression using Stata : from single-level to multilevel modeling / Xing Liu.

By: Liu, Xing (Education professor).
Material type: materialTypeLabelBookCall no.: QA278.2 .L58 2016Publication: Los Angeles : Sage, c2016Description: xxiv, 523 p. : ill.ISBN: 9781483319759 (pbk.); 148331975X (pbk.).Subject(s): Stata | Logistic regression analysis -- Data processing | Regression analysis -- Data processing
Contents:
Chapter 1 Stata Basics -- Chapter 2 Review of Basic Statistics -- Chapter 3 Logistic Regression for Binary Data -- Chapter 4 Proportional Odds Models for Ordinal Response Variables -- Chapter 5 Partial Proportional Odds Models and Generalized Ordinal Logistic Regression Models -- Chapter 6 Continuation Ratio Models -- Chapter 7 Adjacent Categories Logistic Regression Models -- Chapter 8 Stereotype Logistic Regression Models -- Chapter 9 Ordinal Logistic Regression With Complex Survey Sampling Designs -- Chapter 10 Multilevel Modeling for Continuous and Binary Response Variables -- Chapter 11 Multilevel Modeling for Ordinal Response Variables -- Chapter 12 Beyond Ordinal Logistic Regression Models: Ordinal Probit Regression Models and Multinomial Logistic Regression Models.
List(s) this item appears in: New Books 2017-01 (Foreign) | New Books 2017-04 (Foreign)
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Includes bibliographical references and index.

Chapter 1 Stata Basics -- Chapter 2 Review of Basic Statistics -- Chapter 3 Logistic Regression for Binary Data -- Chapter 4 Proportional Odds Models for Ordinal Response Variables -- Chapter 5 Partial Proportional Odds Models and Generalized Ordinal Logistic Regression Models -- Chapter 6 Continuation Ratio Models -- Chapter 7 Adjacent Categories Logistic Regression Models -- Chapter 8 Stereotype Logistic Regression Models -- Chapter 9 Ordinal Logistic Regression With Complex Survey Sampling Designs -- Chapter 10 Multilevel Modeling for Continuous and Binary Response Variables -- Chapter 11 Multilevel Modeling for Ordinal Response Variables -- Chapter 12 Beyond Ordinal Logistic Regression Models: Ordinal Probit Regression Models and Multinomial Logistic Regression Models.

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