Multilevel Modeling of Categorical Outcomes Using IBM SPSS

Author: Ronald H Heck
Publisher: Routledge
ISBN: 1136672346
Category : Psychology
Languages : en
Pages : 449

Book Description
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.

Multilevel Modeling of Categorical Outcomes Using IBM SPSS

Author: Ronald H Heck
Publisher: Routledge
ISBN: 1136672354
Category : Psychology
Languages : en
Pages : 456

Multilevel and Longitudinal Modeling with IBM SPSS

Author: Ronald H. Heck
Publisher: Routledge
ISBN: 1135074240
Category : Psychology
Languages : en
Pages : 753

Book Description
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. It's ideal for courses on multilevel and longitudinal modeling, multivariate statistics, and research design taught in education, psychology, business, and sociology.

Multilevel and Longitudinal Modeling with IBM SPSS

Author: Ronald H. Heck
Publisher: Routledge
ISBN: 100052227X
Category : Psychology
Languages : en
Pages : 703

Book Description
Multilevel and Longitudinal Modeling with IBM SPSS, Third Edition, demonstrates how to use the multilevel and longitudinal modeling techniques available in IBM SPSS Versions 25-27. Annotated screenshots with all relevant output provide readers with a step-by-step understanding of each technique as they are shown how to navigate the program. Throughout, diagnostic tools, data management issues, and related graphics are introduced. SPSS commands show the flow of the menu structure and how to facilitate model building, while annotated syntax is also available for those who prefer this approach. Extended examples illustrating the logic of model development and evaluation are included throughout the book, demonstrating the context and rationale of the research questions and the steps around which the analyses are structured. The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques that facilitate working with multilevel, longitudinal, or cross-classified data sets. The next few chapters introduce the basics of multilevel modeling, developing a multilevel model, extensions of the basic two-level model (e.g., three-level models, models for binary and ordinal outcomes), and troubleshooting techniques for everyday-use programming and modeling problems along with potential solutions. Models for investigating individual and organizational change are next developed, followed by models with multivariate outcomes and, finally, models with cross-classified and multiple membership data structures. The book concludes with thoughts about ways to expand on the various multilevel and longitudinal modeling techniques introduced and issues (e.g., missing data, sample weights) to keep in mind in conducting multilevel analyses. Key features of the third edition: Thoroughly updated throughout to reflect IBM SPSS Versions 26-27. Introduction to fixed-effects regression for examining change over time where random-effects modeling may not be an optimal choice. Additional treatment of key topics specifically aligned with multilevel modeling (e.g., models with binary and ordinal outcomes). Expanded coverage of models with cross-classified and multiple membership data structures. Added discussion on model checking for improvement (e.g., examining residuals, locating outliers). Further discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures. Supported by online data sets, the book's practical approach makes it an essential text for graduate-level courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, or advanced quantitative techniques taught in departments of business, education, health, psychology, and sociology. The book will also prove appealing to researchers in these fields. The book is designed to provide an excellent supplement to Heck and Thomas's An Introduction to Multilevel Modeling Techniques, Fourth Edition; however, it can also be used with any multilevel or longitudinal modeling book or as a stand-alone text.

Categorical Data Analysis and Multilevel Modeling Using R

Author: Xing Liu
Publisher: SAGE Publications
ISBN: 154432491X
Category : Political Science
Languages : en
Pages : 745

Book Description
Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal, nominal, and count response variables using the R software. Author Xing Liu offers a unified framework for both single-level and multilevel modeling of categorical and count response variables with both frequentist and Bayesian approaches. Each chapter demonstrates how to conduct the analysis using R, how to interpret the models, and how to present the results for publication. A companion website for this book contains datasets and R commands used in the book for students, and solutions for the end-of-chapter exercises on the instructor site.

An Introduction to Multilevel Modeling Techniques

Author: Ronald H. Heck
Publisher: Routledge
ISBN: 1317598504
Category : Psychology
Languages : en
Pages : 461

Book Description
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.

Multilevel Modeling Methods with Introductory and Advanced Applications

Author: Ann A. O'Connell
Publisher: IAP
ISBN: 164802873X
Category : Education
Languages : en
Pages : 645

Book Description
Multilevel Modeling Methods with Introductory and Advanced Applications provides a cogent and comprehensive introduction to the area of multilevel modeling for methodological and applied researchers as well as advanced graduate students. The book is designed to be able to serve as a textbook for a one or two semester course in multilevel modeling. The topics of the seventeen chapters range from basic to advanced, yet each chapter is designed to be able to stand alone as an instructional unit on its respective topic, with an emphasis on application and interpretation. In addition to covering foundational topics on the use of multilevel models for organizational and longitudinal research, the book includes chapters on more advanced extensions and applications, such as cross-classified random effects models, non-linear growth models, mixed effects location scale models, logistic, ordinal, and Poisson models, and multilevel mediation. In addition, the volume includes chapters addressing some of the most important design and analytic issues including missing data, power analyses, causal inference, model fit, and measurement issues. Finally, the volume includes chapters addressing special topics such as using large-scale complex sample datasets, and reporting the results of multilevel designs. Each chapter contains a section called Try This!, which poses a structured data problem for the reader. We have linked our book to a website (http://modeling.uconn.edu) containing data for the Try This! section, creating an opportunity for readers to learn by doing. The inclusion of the Try This! problems, data, and sample code eases the burden for instructors, who must continually search for class examples and homework problems. In addition, each chapter provides recommendations for additional methodological and applied readings.

An Introduction to Multilevel Modeling Techniques

Author: Ronald H. Heck
Publisher: Routledge
ISBN: 0429595662
Category : Psychology
Languages : en
Pages : 388

Book Description
Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements; An expanded set of applied examples used throughout the text; Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online. This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.

IBM SPSS for Introductory Statistics

Author: George A. Morgan
Publisher: Routledge
ISBN: 1136461809
Category : Psychology
Languages : en
Pages : 365

Book Description
Designed to help students analyze and interpret research data using IBM SPSS, this user-friendly book, written in easy-to-understand language, shows readers how to choose the appropriate statistic based on the design, and to interpret outputs appropriately. The authors prepare readers for all of the steps in the research process: design, entering and checking data, testing assumptions, assessing reliability and validity, computing descriptive and inferential parametric and nonparametric statistics, and writing about outputs. Dialog windows and SPSS syntax, along with the output, are provided. Three realistic data sets, available on the Internet, are used to solve the chapter problems. The new edition features: Updated to IBM SPSS version 20 but the book can also be used with older and newer versions of SPSS. A new chapter (7) including an introduction to Cronbach’s alpha and factor analysis. Updated Web Resources with PowerPoint slides, additional activities/suggestions, and the answers to even-numbered interpretation questions for the instructors, and chapter study guides and outlines and extra SPSS problems for the students. The web resource is located www.routledge.com/9781848729827 . Students, instructors, and individual purchasers can access the data files to accompany the book at www.routledge.com/9781848729827 . IBM SPSS for Introductory Statistics, Fifth Edition provides helpful teaching tools: All of the key IBM SPSS windows needed to perform the analyses. Complete outputs with call-out boxes to highlight key points. Flowcharts and tables to help select appropriate statistics and interpret effect sizes. Interpretation sections and questions help students better understand and interpret the output. Assignments organized the way students proceed when they conduct a research project. Examples of how to write about outputs and make tables in APA format. Helpful appendices on how to get started with SPSS and write research questions. An ideal supplement for courses in either statistics, research methods, or any course in which SPSS is used, such as in departments of psychology, education, and other social and health sciences. This book is also appreciated by researchers interested in using SPSS for their data analysis.

A Step-by-Step Guide to Applying the Rasch Model Using R

Author: Iasonas Lamprianou
Publisher: Taylor & Francis
ISBN: 1040259162
Category : Psychology
Languages : en
Pages : 321

Book Description
This new edition provides a step-by-step guide to applying the Rasch model in R, a probabilistic model used by researchers across the social sciences to measure unobservable (“latent”) variables. Although the focus is on simple R code, the book provides updated guidance through the point-and-click menus of BlueSky Statistics software. The book covers all Rasch models frequently used in social sciences, from the Simple Rasch model to the Rating Scale, Partial Credit, and Many-Facets Rasch models. Using a pragmatic approach to model-data fit, this book offers helpful practical examples to investigate Rasch model assumptions. In addition to traditional Rasch model approaches, it introduces the Rasch model as a special case of a Generalized Mixed Effects Model. Readers will also benefit from the online support material which includes all the code used in the book in downloadable and useable files. It also provides a comprehensive guide to R programming and practical guidance on using BlueSky Statistics software's point-and-click menus. This dual approach enables readers to experiment with data analysis using the provided data sets, enhancing their understanding and application of statistical concepts. It will be a valuable resource for both students and researchers who want to use Rasch models in their research.