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Applied Ordinal Logistic Regression Using Stata: From Single-Level to Multilevel Modeling
Contributor(s): Liu, Xing (Author)
ISBN: 148331975X     ISBN-13: 9781483319759
Publisher: Sage Publications, Inc
OUR PRICE:   $90.25  
Product Type: Paperback - Other Formats
Published: November 2015
Qty:
Temporarily out of stock - Will ship within 2 to 5 weeks
Additional Information
BISAC Categories:
- Social Science | Statistics
- Mathematics | Probability & Statistics - Multivariate Analysis
- Social Science | Research
Dewey: 519.535
LCCN: 2015023615
Physical Information: 0.8" H x 7.3" W x 9.1" (1.80 lbs) 552 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
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.

Contributor Bio(s): Liu, Xing: -

Xing Liu, Ph.D., is an associate 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, and educational assessment. 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.