Multilevel Modeling of Categorical Outcomes Using IBM SPSS

Regular price €62.99
A01=Lynn Tabata
A01=Ronald H Heck
A01=Scott Thomas
advanced categorical data modeling techniques
Author_Lynn Tabata
Author_Ronald H Heck
Author_Scott Thomas
Category=GPS
Category=JHBC
Category=JMB
Category=UFM
Command Tab
count data modeling
Data Set
dichotomous data analysis
eect
eects
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Expected Event Rate
functions
Generalize Linear Model
generalized
Generalized Estimating Equations
generalized linear modeling
GENLINMIXED
GLM
IBM SPSS
intercept
linear
Linear Mixed Models Dialog Box
link
Log Odds
Log Odds Coefficient
Lower Upper
Main Dialog Box
Missing Data
mixed
Multilevel Models
Multinomial Logistic Regression
NB
organizational research methods
Population Average Model
population-average models
random
Random Eect
Random Effect Block
Random Effects Item
repeated measures statistics
Student GPA
Student SES
Ti Ti
xed
Xed Eects

Product details

  • ISBN 9781848729568
  • Weight: 840g
  • Dimensions: 210 x 280mm
  • Publication Date: 16 Apr 2012
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568.

The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced.

Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors’ highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.

Ronald Heck is professor of education at the University of Hawai‘i at Mānoa. His areas of interest include organizational theory, leadership, policy, and quantitative research methods. Scott L. Thomas is professor in the School of Educational Studies at Claremont Graduate University. His specialties include sociology of education, policy, and quantitative research methods. Lynn Tabata is an affiliate graduate faculty member and research consultant at the University of Hawai‘i at Mānoa. Her research interests focus on faculty, distance learning, and technology issues in higher education.