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Informationen zum Autor 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. Klappentext 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. Zusammenfassung Provides a unified framework for both single-level and multilevel modeling of ordinal categorical data Inhaltsverzeichnis 1. Stata Basics Introduction to Stata Data Management Graphs A Summary of Stata Commands in this Chapter Exercises 2. Review of Basic Statistics Understand Your Data Using Descriptive Statistics Descriptive Statistics for Continuous Variables Using Stata Frequency Distribution for Categorical Variables Using Stata Independent Samples t-test Using Stata Paired Samples t-test Analysis of Variance (ANOVA) Correlation Simple Linear Regression Multiple Linear Regression Chi-Square Test Making Publication-Quality Tables Using Stata General Guidelines for Reporting Resutls A Summary of Stata Commands in this Chapter Exercises 3. Logistic Regression for Binary Data Logistic Regression Models: An Introduction Research Example and Description of the Data and Sample Logistic Regression with Stata: Commands and Output Summary of Stata Commands in this Chapter Exercises 4. Proportional Odds Models for Ordinal Response Variables Proportional Odds Models: An Introduction Research Example and Description of the Data and Sample Proportional Odds Models with Stata: Commands and Output Summary of Stata Commands in this Chapter Exercises 5. Partial Proportional Odds Models and Generalized Ordinal Logistic Regression Models Introduction Research Example and Description of the Data and Sample Partial Proportional Odds Models with Stata: Commands and Output Generalized Ordinal Logistic Regression Models with Stata: An Example Making Publication-Quality Tables Presenting the Results Summary of Stata Commands in this Chapter Exercises 6. Continuation Ratio Models Continuation Ratio Models: An Introduction Research Example and Description of the Data and Sample Continuation Ratio Models with Stata: Commands and Output Making Publication-Quality Tables Presenting the Results Summary of Stata Commands in this Chapter Exercises 7. Adjacent Categories Logistic Regression Models Adjacent Categories Models: An Introduction Research Example and Description of the Data and Sample Adjacent Categories Models with Stata: Commands and Output Presenting the Results Summary of Stata Commands in this Chapter 8. Stereotype Logistic Regressi...
