Multilevel Modeling of Categorical Outcomes Using IBM SPSS

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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.

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About the author

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.

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Additional Information

Publisher
Routledge
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Published on
May 7, 2013
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Pages
456
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ISBN
9781136672347
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Language
English
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Genres
Business & Economics / Statistics
Education / Statistics
Mathematics / Probability & Statistics / Multivariate Analysis
Medical / Biostatistics
Psychology / Statistics
Social Science / Statistics
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Content Protection
This content is DRM protected.
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Available on Android devices
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Now in its 6th edition, the authoritative textbook Applied Multivariate Statistics for the Social Sciences, continues to provide advanced students with a practical and conceptual understanding of statistical procedures through examples and data-sets from actual research studies. With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power. In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally.

Features new to this edition include:

NEW chapter on Logistic Regression (Ch. 11) that helps readers understand and use this very flexible and widely used procedure

NEW chapter on Multivariate Multilevel Modeling (Ch. 14) that helps readers understand the benefits of this "newer" procedure and how it can be used in conventional and multilevel settings

NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles

NEW coverage of missing data (Ch. 1) to help students understand and address problems associated with incomplete data

Completely re-written chapters on Exploratory Factor Analysis (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. 16) with increased focus on understanding models and interpreting results

NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach

Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3)

A free online resources site at www.routledge.com/9780415836661 with data sets and syntax from the text, additional data sets, and instructor’s resources (including PowerPoint lecture slides for select chapters, a conversion guide for 5th edition adopters, and answers to exercises).

Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference. Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed.

Featuring a practical approach with numerous examples, this book focuses on helping the reader develop a conceptual, rather than technical, understanding of categorical methods, making it a much more accessible text than others on the market. The authors cover common categorical analyses and emphasize specific research questions that can be addressed by each analytic procedure so that readers are able to address the research questions they wish to answer. To achieve this goal, the authors:

Review the theoretical implications and assumptions underlying each of the procedures Present each concept in general terms and illustrate each with a practical example Demonstrate the analyses using SPSS and SAS and show the interpretation of the results provided by these programs.

A "Look Ahead" section at the beginning of each chapter provides an overview of the material covered so that the reader knows what to expect. This is followed by one or more research questions that can be addressed using the procedure(s) covered in the chapter. A theoretical presentation of the material is provided and illustrated using realistic examples from the behavioral and social sciences. To further enhance accessibility, the new procedures introduced in the book are explicitly related to analytic procedures covered in earlier statistics courses, such as ANOVA and linear regression. Throughout each chapter the authors use practical examples to demonstrate how to obtain and interpret statistical output in both SPSS and SAS. Their emphasis on the relationship between the initial research question, the use of the software to carry out the analysis, and the interpretation of the output as it relates to the initial research question, allows readers to easily apply the material to their own research. The data sets for executing chapter examples using SAS Version 9.1.3 and/or IBM SPSS Version 18 are available on a book specific web site. These data sets and syntax allow readers to quickly run the programs and obtain the appropriate output. The book also includes both conceptual and analytic end-of-chapter exercises to assist instructors and students in evaluating the understanding of the material covered in each chapter.

This book covers the most commonly used categorical data analysis procedures. It is written for those without an extensive mathematical background, and is ideal for graduate courses in categorical data analysis or cross-classified data analysis taught in departments of psychology, human development & family studies, sociology, education, and business. Researchers in these disciplines interested in applying these procedures to their own research will appreciate this book’s accessible approach.

This book demonstrates how to use multilevel and longitudinal modeling techniques available in the IBM SPSS mixed-effects program (MIXED). Annotated screen shots provide readers with a step-by-step understanding of each technique and navigating the program. Readers learn how to set up, run, and interpret a variety of models. Diagnostic tools, data management issues, and related graphics are introduced throughout. Annotated syntax is also available for those who prefer this approach. Extended examples illustrate the logic of model development to show readers the rationale of the research questions and the steps around which the analyses are structured. The data used in the text and syntax examples are available at www.routledge.com/9780415817110.

Highlights of the new edition include:

Updated throughout to reflect IBM SPSS Version 21.

Further coverage of growth trajectories, coding time-related variables, covariance structures, individual change and longitudinal experimental designs (Ch.5).

Extended discussion of other types of research designs for examining change (e.g., regression discontinuity, quasi-experimental) over time (Ch.6).

New examples specifying multiple latent constructs and parallel growth processes (Ch. 7).

Discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures (Ch.1).

The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques which facilitate working with multilevel, longitudinal, and cross-classified data sets. Chapters 3 and 4 introduce the basics of multilevel modeling: developing a multilevel model, interpreting output, and trouble-shooting common programming and modeling problems. Models for investigating individual and organizational change are presented in chapters 5 and 6, followed by models with multivariate outcomes in chapter 7. Chapter 8 provides an illustration of multilevel models with cross-classified data structures. The book concludes with ways to expand on the various multilevel and longitudinal modeling techniques and issues when conducting multilevel analyses.

Ideal as a supplementary text for graduate courses on multilevel and longitudinal modeling, multivariate statistics, and research design taught in education, psychology, business, and sociology, this book’s practical approach also appeals to researchers in these fields. The book provides an excellent supplement to Heck & Thomas’s An Introduction to Multilevel Modeling Techniques, 2nd Edition; however, it can also be used with any multilevel and/or longitudinal modeling book or as a stand-alone text.

Univariate and multivariate multilevel models are used to understand how to design studies and analyze data in this comprehensive text distinguished by its variety of applications from the educational, behavioral, and social sciences. Basic and advanced models are developed from the multilevel regression (MLM) and latent variable (SEM) traditions within one unified analytic framework for investigating hierarchical data. The authors provide examples using each modeling approach and also explore situations where alternative approaches may be more appropriate, given the research goals. Numerous examples and exercises allow readers to test their understanding of the techniques presented.

Changes to the new edition include:

-The use of Mplus 7.2 for running the analyses including the input and data files at www.routledge.com/9781848725522.

-Expanded discussion of MLM and SEM model-building that outlines the steps taken in the process, the relevant Mplus syntax, and tips on how to evaluate the models.

-Expanded pedagogical program now with chapter objectives, boldfaced key terms, a glossary, and more tables and graphs to help students better understand key concepts and techniques.

-Numerous, varied examples developed throughout which make this book appropriate for use in education, psychology, business, sociology, and the health sciences.

-Expanded coverage of missing data problems in MLM using ML estimation and multiple imputation to provide currently-accepted solutions (Ch. 10).

-New chapter on three-level univariate and multilevel multivariate MLM models provides greater options for investigating more complex theoretical relationships(Ch.4).

-New chapter on MLM and SEM models with categorical outcomes facilitates the specification of multilevel models with observed and latent outcomes (Ch.8).

-New chapter on multilevel and longitudinal mixture models provides readers with options for identifying emergent groups in hierarchical data (Ch.9).

-New chapter on the utilization of sample weights, power analysis, and missing data provides guidance on technical issues of increasing concern for research publication (Ch.10).

Ideal as a text for graduate courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this book’s practical approach also appeals to researchers. Recommended prerequisites are introductory univariate and multivariate statistics.

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