Search Results: introduction-to-structural-equation-modeling-using-ibm-spss-statistics-and-amos

Introduction to Structural Equation Modelling Using SPSS and Amos

Author: Niels Blunch

Publisher: SAGE

ISBN: 1446204790

Category: Social Science

Page: 280

View: 8146

New software (Lisrel and AMOS) has made the techniques of Structural Equation Modelling (SEM) increasingly available to students and researchers, while the recent adoption of AMOS as part of the SPSS suite has improved access still further. As an alternative to existing books on the subject, which are customarily very long, very high-level and very mathematical, not to mention expensive, Niels Blunch's introduction has been designed for advanced undergraduates and Masters students who are new to SEM and still relatively new to statistics. Illustrated with screenshots, cases and exercises and accompanied by a companion website containing datasets that can be easily uploaded onto SPSS and AMOS, this handy introduction keeps maths to a minimum and contains an appendix covering basic forms of statistical analysis.

Introduction to Structural Equation Modeling Using IBM SPSS Statistics and EQS

Author: Niels J. Blunch

Publisher: SAGE

ISBN: 1473943299

Category: Social Science

Page: 360

View: 375

This student orientated guide to structural equation modeling promotes theoretical understanding and inspires students with the confidence to successfully apply SEM. Assuming no previous experience, and a minimum of mathematical knowledge, this is an invaluable companion for students taking introductory SEM courses in any discipline. Niels Blunch shines a light on each step of the structural equation modeling process, providing a detailed introduction to SPSS and EQS with a focus on EQS' excellent graphical interface. He also sets out best practice for data entry and programming, and uses real life data to show how SEM is applied in research. The book includes: Learning objectives, key concepts and questions for further discussion in each chapter. Helpful diagrams and screenshots to expand on concepts covered in the texts. A wide variety of examples from multiple disciplines and real world contexts. Exercises for each chapter on an accompanying companion website. A detailed glossary. Clear, engaging and built around key software, this is an ideal introduction for anyone new to SEM. Available with Perusall—an eBook that makes it easier to prepare for class Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.

Structural Equation Modeling With AMOS

Basic Concepts, Applications, and Programming, Third Edition

Author: Barbara M. Byrne

Publisher: Routledge

ISBN: 1317633121

Category: Psychology

Page: 460

View: 7415

This bestselling text provides a practical guide to structural equation modeling (SEM) using the Amos Graphical approach. Using clear, everyday language, the text is ideal for those with little to no exposure to either SEM or Amos. The author reviews SEM applications based on actual data taken from her own research. Each chapter "walks" readers through the steps involved (specification, estimation, evaluation, and post hoc modification) in testing a variety of SEM models. Accompanying each application is: an explanation of the issues addressed and a schematic presentation of hypothesized model structure; Amos input and output with interpretations; use of the Amos toolbar icons and pull-down menus; and data upon which the model application was based, together with updated references pertinent to the SEM model tested. Thoroughly updated throughout, the new edition features: All new screen shots featuring Amos Version 23. Descriptions and illustrations of Amos’ new Tables View format which enables the specification of a structural model in spreadsheet form. Key concepts and/or techniques that introduce each chapter. Alternative approaches to model analyses when enabled by Amos thereby allowing users to determine the method best suited to their data. Provides analysis of the same model based on continuous and categorical data (Ch. 5) thereby enabling readers to observe two ways of specifying and testing the same model as well as compare results. All applications based on the Amos graphical mode interface accompanied by more "how to" coverage of graphical techniques unique to Amos. More explanation of key procedures and analyses that address questions posed by readers All application data files are available at The two introductory chapters in Section 1 review the fundamental concepts of SEM methodology and a general overview of the Amos program. Section 2 provides single-group analyses applications including two first-order confirmatory factor analytic (CFA) models, one second-order CFA model, and one full latent variable model. Section 3 presents multiple-group analyses applications with two rooted in the analysis of covariance structures and one in the analysis of mean and covariance structures. Two models that are increasingly popular with SEM practitioners, construct validity and testing change over time using the latent growth curve, are presented in Section 4. The book concludes with a review of the use of bootstrapping to address non-normal data and a review of missing (or incomplete) data in Section 5. An ideal supplement for graduate level courses in psychology, education, business, and social and health sciences that cover the fundamentals of SEM with a focus on Amos, this practical text continues to be a favorite of both researchers and practitioners. A prerequisite of basic statistics through regression analysis is recommended but no exposure to either SEM or Amos is required.

A Beginner's Guide to Structural Equation Modeling

Fourth Edition

Author: Randall E. Schumacker,Richard G. Lomax

Publisher: Routledge

ISBN: 1317608097

Category: Psychology

Page: 372

View: 4386

Noted for its crystal clear explanations, this book is considered the most comprehensive introductory text to structural equation modeling (SEM). Noted for its thorough review of basic concepts and a wide variety of models, this book better prepares readers to apply SEM to a variety of research questions. Programming details and the use of algebra are kept to a minimum to help readers easily grasp the concepts so they can conduct their own analysis and critique related research. Featuring a greater emphasis on statistical power and model validation than other texts, each chapter features key concepts, examples from various disciplines, tables and figures, a summary, and exercises. Highlights of the extensively revised 4th edition include: -Uses different SEM software (not just Lisrel) including Amos, EQS, LISREL, Mplus, and R to demonstrate applications. -Detailed introduction to the statistical methods related to SEM including correlation, regression, and factor analysis to maximize understanding (Chs. 1 – 6). -The 5 step approach to modeling data (specification, identification, estimation, testing, and modification) is now covered in more detail and prior to the modeling chapters to provide a more coherent view of how to create models and interpret results (ch. 7). -More discussion of hypothesis testing, power, sampling, effect sizes, and model fit, critical topics for beginning modelers (ch. 7). - Each model chapter now focuses on one technique to enhance understanding by providing more description, assumptions, and interpretation of results, and an exercise related to analysis and output (Chs. 8 -15). -The use of SPSS AMOS diagrams to describe the theoretical models. -The key features of each of the software packages (Ch. 1). -Guidelines for reporting SEM research (Ch. 16). which provides access to data sets that can be used with any program, links to other SEM examples, related readings, and journal articles, and more. Reorganized, the new edition begins with a more detailed introduction to SEM including the various software packages available, followed by chapters on data entry and editing, and correlation which is critical to understanding how missing data, non-normality, measurement, and restriction of range in scores affects SEM analysis. Multiple regression, path, and factor models are then reviewed and exploratory and confirmatory factor analysis is introduced. These chapters demonstrate how observed variables share variance in defining a latent variables and introduce how measurement error can be removed from observed variables. Chapter 7 details the 5 SEM modeling steps including model specification, identification, estimation, testing, and modification along with a discussion of hypothesis testing and the related issues of power, and sample and effect sizes.Chapters 8 to 15 provide comprehensive introductions to different SEM models including Multiple Group, Second-Order CFA, Dynamic Factor, Multiple-Indicator Multiple-Cause, Mixed Variable and Mixture, Multi-Level, Latent Growth, and SEM Interaction Models. Each of the 5 SEM modeling steps is explained for each model along with an application. Chapter exercises provide practice with and enhance understanding of the analysis of each model. The book concludes with a review of SEM guidelines for reporting research. Designed for introductory graduate courses in structural equation modeling, factor analysis, advanced, multivariate, or applied statistics, quantitative techniques, or statistics II taught in psychology, education, business, and the social and healthcare sciences, this practical book also appeals to researchers in these disciplines. Prerequisites include an introduction to intermediate statistics that covers correlation and regression principles.

Latent Variable Models

An Introduction to Factor, Path, and Structural Equation Analysis, Fifth Edition

Author: John C. Loehlin,A. Alexander Beaujean

Publisher: Taylor & Francis

ISBN: 131728528X

Category: Psychology

Page: 376

View: 7735

Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing path diagrams to explain the relationships in the models. This approach helps less mathematically-inclined readers to grasp the underlying relations among path analysis, factor analysis, and structural equation modeling, and to set up and carry out such analyses. This revised and expanded fifth edition again contains key chapters on path analysis, structural equation models, and exploratory factor analysis. In addition, it contains new material on composite reliability, models with categorical data, the minimum average partial procedure, bi-factor models, and communicating about latent variable models. The informal writing style and the numerous illustrative examples make the book accessible to readers of varying backgrounds. Notes at the end of each chapter expand the discussion and provide additional technical detail and references. Moreover, most chapters contain an extended example in which the authors work through one of the chapter’s examples in detail to aid readers in conducting similar analyses with their own data. The book and accompanying website provide all of the data for the book’s examples as well as syntax from latent variable programs so readers can replicate the analyses. The book can be used with any of a variety of computer programs, but special attention is paid to LISREL and R. An important resource for advanced students and researchers in numerous disciplines in the behavioral sciences, education, business, and health sciences, Latent Variable Models is a practical and readable reference for those seeking to understand or conduct an analysis using latent variables.

An Introduction to MATLAB for Behavioral Researchers

Author: Christopher R. Madan

Publisher: SAGE Publications

ISBN: 1483323242

Category: Psychology

Page: 280

View: 4609

MATLAB is a powerful data analysis program, but many behavioral science researchers find it too daunting to learn and use. An Introduction to MATLAB for Behavioral Researchers by Christopher R. Madan is an easy-to-understand, hands-on guide for behavioral researchers who have no prior programming experience. Written in a conversational and non-intimidating style, the author walks students—step by step—through analyzing real experimental data. Topics covered include the basics of programming, the implementation of simple behavioral analyses, and how to make publication-ready figures. More advanced topics such as pseudo-randomization of trial sequences to meet specified criteria and working with psycholinguistic data are also covered. Interesting behavioral science examples and datasets from published studies, such as visualizing fixation patterns in eye-tracking studies and animal search behavior in two-dimensional space, help develop an intuition for data analysis, which is essential and can only be developed when working with real research problems and real data.

Essentials of Structural Equation Modeling

Author: Mustafa Emre Civelek


ISBN: 1609621298


Page: 118

View: 8059

Structural Equation Modeling is a statistical method increasingly used in scientific studies in the fields of Social Sciences. It is currently a preferred analysis method, especially in doctoral dissertations and academic researches. Many universities do not include this method in the curriculum, so students and scholars try to solve these problems using books and internet resources. This book aims to guide the researcher in a way that is free from math expressions. It teaches the steps of a research program using structured equality modeling practically. For students writing theses and scholars preparing academic articles, this book aims to analyze systematically the methodology of studies conducted using structural equation modeling methods in the social sciences. In as simple language as possible, it conveys basic information. It consists of two parts: the first gives basic concepts of structural equation modeling, and the second gives examples of applications.

Basics of Structural Equation Modeling

Author: Geoffrey M. Maruyama

Publisher: SAGE Publications

ISBN: 150632035X

Category: Social Science

Page: 328

View: 4116

With the availability of software programs, such as LISREL, EQS, and AMOS, modeling (SEM) techniques have become a popular tool for formalized presentation of the hypothesized relationships underlying correlational research and test for the plausibility of hypothesizing for a particular data set. Through the use of careful narrative explanation, Maruyama's text describes the logic underlying SEM approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data. In addition, Maruyama provides carefully constructed exercises both within and

Multilevel Modeling of Categorical Outcomes Using IBM SPSS

Author: Ronald H Heck,Scott Thomas,Lynn Tabata

Publisher: Routledge

ISBN: 1136672346

Category: Psychology

Page: 456

View: 6096

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 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.

Confirmatory Factor Analysis for Applied Research, Second Edition

Author: Timothy A. Brown

Publisher: Guilford Publications

ISBN: 146251779X

Category: Science

Page: 462

View: 1861

This accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA) for its emphasis on practical and conceptual aspects rather than mathematics or formulas. Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities ...

Applied Multivariate Research

Design and Interpretation

Author: Lawrence S. Meyers,Glenn Gamst,A.J. Guarino

Publisher: SAGE

ISBN: 141298811X

Category: Psychology

Page: 1078

View: 2630

This book provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter, using a conceptual, non-mathematical, approach. Addressing correlation, multiple regression, exploratory factor analysis, MANOVA, path analysis, and structural equation modeling, it is geared toward the needs, level of sophistication, and interest in multivariate methodology that serves students in applied programs in the social and behavioral sciences. Readers are encouraged to focus on design and interpretation rather than the intricacies of specific computations.

Introduction to Mediation, Moderation, and Conditional Process Analysis, Second Edition

A Regression-Based Approach

Author: Andrew F. Hayes

Publisher: Guilford Publications

ISBN: 146253466X

Category: Social Science

Page: 692

View: 4294

Lauded for its easy-to-understand, conversational discussion of the fundamentals of mediation, moderation, and conditional process analysis, this book has been fully revised with 50% new content, including sections on working with multicategorical antecedent variables, the use of PROCESS version 3 for SPSS and SAS for model estimation, and annotated PROCESS v3 outputs. Using the principles of ordinary least squares regression, Andrew F. Hayes carefully explains procedures for testing hypotheses about the conditions under and the mechanisms by which causal effects operate, as well as the moderation of such mechanisms. Hayes shows how to estimate and interpret direct, indirect, and conditional effects; probe and visualize interactions; test questions about moderated mediation; and report different types of analyses. Data for all the examples are available on the companion website (, along with links to download PROCESS. New to This Edition *Chapters on using each type of analysis with multicategorical antecedent variables. *Example analyses using PROCESS v3, with annotated outputs throughout the book. *More tips and advice, including new or revised discussions of formally testing moderation of a mechanism using the index of moderated mediation; effect size in mediation analysis; comparing conditional effects in models with more than one moderator; using R code for visualizing interactions; distinguishing between testing interaction and probing it; and more. *Rewritten Appendix A, which provides the only documentation of PROCESS v3, including 13 new preprogrammed models that combine moderation with serial mediation or parallel and serial mediation. *Appendix B, describing how to create customized models in PROCESS v3 or edit preprogrammed models.

Analysing Quantitative Data

Variable-based and Case-based Approaches to Non-experimental Datasets

Author: Raymond A Kent

Publisher: SAGE

ISBN: 1473917913

Category: Social Science

Page: 376

View: 8709

Lecturers, request your electronic inspection copy This innovative book provides a fresh take on quantitative data analysis within the social sciences. It presents variable-based and case-based approaches side-by-side encouraging you to learn a range of approaches and to understand which is the most appropriate for your research. Using two multidisciplinary non-experimental datasets throughout, the book demonstrates that data analysis is really an active dialogue between ideas and evidence. Each dataset is returned to throughout the chapters enabling you to see the role of the researcher in action; it also showcases the difference between each approach and the significance of researchers’ decisions that must be made as you move through your analysis. The book is divided into four clear sections: Data and their presentation Variable-based analyses Case-based analyses Comparing and combining approaches Clear, original and written for students this book should be compulsory reading for anyone looking to conduct non-experimental quantitative data analysis.

Using Mplus for Structural Equation Modeling

A Researcher's Guide

Author: E. Kevin Kelloway

Publisher: SAGE Publications

ISBN: 1483324893

Category: Social Science

Page: 248

View: 1483

Ideal for researchers and graduate students in the social sciences who require knowledge of structural equation modeling techniques to answer substantive research questions, Using Mplus for Structural Equation Modeling provides a reader-friendly introduction to the major types of structural equation models implemented in the Mplus framework. This practical book, which updates author E. Kevin Kelloway’s 1998 book Using LISREL for Structural Equation Modeling, retains the successful five-step process employed in the earlier book, with a thorough update for use in the Mplus environment. Kelloway provides an overview of structural equation modeling techniques in Mplus, including the estimation of confirmatory factor analysis and observed variable path analysis. He also covers multilevel modeling for hypothesis testing in real life settings and offers an introduction to the extended capabilities of Mplus, such as exploratory structural equation modeling and estimation and testing of mediated relationships. A sample application with the source code, printout, and results is presented for each type of analysis. ”An excellent book on the ins and outs of using Mplus, as well as the practice of structural equation modeling in applied research.” —Kevin J. Grimm, University of California, Davis

Practical Statistics

A Quick and Easy Guide to IBM® SPSS® Statistics, STATA, and Other Statistical Software

Author: David Kremelberg

Publisher: SAGE Publications

ISBN: 150631791X

Category: Social Science

Page: 528

View: 981

Making statistics—and statistical software—accessible and rewarding This book provides readers with step-by-step guidance on running a wide variety of statistical analyses in IBM® SPSS® Statistics, Stata, and other programs. Author David Kremelberg begins his user-friendly text by covering charts and graphs through regression, time-series analysis, and factor analysis. He provides a background of the method, then explains how to run these tests in IBM SPSS and Stata. He then progresses to more advanced kinds of statistics such as HLM and SEM, where he describes the tests and explains how to run these tests in their appropriate software including HLM and AMOS. This is an invaluable guide for upper-level undergraduate and graduate students across the social and behavioral sciences who need assistance in understanding the various statistical packages.

Hierarchical Linear Modeling

Guide and Applications

Author: G. David Garson

Publisher: SAGE

ISBN: 1412998859

Category: Computers

Page: 371

View: 7999

This book provides a brief, easy-to-read guide to implementing hierarchical linear modelling using the three leading software platforms, followed by a set of application articles based on recent work published in leading journals and as part of doctoral dissertations. The "guide" portion consists of three chapters by the editor, covering basic to intermediate use of SPSS, SAS, and HLM for purposes for hierarchical linear modelling, while the "applications" portion consists of a dozen contributions in which the authors emphasize how-to and methodological aspects and show how they have used these techniques in practice.

An Intermediate Guide to SPSS Programming

Using Syntax for Data Management

Author: Sarah Boslaugh

Publisher: SAGE

ISBN: 9780761931850

Category: Computers

Page: 233

View: 2933

An Intermediate Guide to SPSS Programming: Using Syntax for Data Management introduces the major tasks of data management and presents solutions using SPSS syntax. This book fills an important gap in the education of many students and researchers, whose coursework has left them unprepared for the data management issues that confront them when they begin to do independent research. It also serves as an introduction to SPSS programming. All the basic features of SPSS syntax are illustrated, as are many intermediate and advanced topics such as using vectors and loops, reading complex data files, and using the SPSS macro language.

Structural Equation Modeling with Mplus

Basic Concepts, Applications, and Programming

Author: Barbara M. Byrne

Publisher: Routledge

ISBN: 1136663460

Category: Education

Page: 432

View: 6844

"This text aims to provide readers with a nonmathematical introduction to the basic concepts associated with structural equation modeling, and to illustrate its basic applications using the Mplus program"--Provided by publisher.

The Reviewer’s Guide to Quantitative Methods in the Social Sciences

Author: Gregory R. Hancock,Ralph O. Mueller

Publisher: Routledge

ISBN: 1135172994

Category: Education

Page: 448

View: 775

The Reviewer’s Guide is designed for reviewers of research manuscripts and proposals in the social and behavioral sciences, and beyond. Its uniquely structured chapters address traditional and emerging quantitative methods of data analysis.

Handbook of Quantitative Methods for Educational Research

Author: Timothy Teo

Publisher: Springer Science & Business Media

ISBN: 9462094047

Category: Education

Page: 404

View: 4824

As part of their research activities, researchers in all areas of education develop measuring instruments, design and conduct experiments and surveys, and analyze data resulting from these activities. Educational research has a strong tradition of employing state-of-the-art statistical and psychometric (psychological measurement) techniques. Commonly referred to as quantitative methods, these techniques cover a range of statistical tests and tools. Quantitative research is essentially about collecting numerical data to explain a particular phenomenon of interest. Over the years, many methods and models have been developed to address the increasingly complex issues that educational researchers seek to address. This handbook serves to act as a reference for educational researchers and practitioners who desire to acquire knowledge and skills in quantitative methods for data analysis or to obtain deeper insights from published works. Written by experienced researchers and educators, each chapter in this handbook covers a methodological topic with attention paid to the theory, procedures, and the challenges on the use of that particular methodology. It is hoped that readers will come away from each chapter with a greater understanding of the methodology being addressed as well as an understanding of the directions for future developments within that methodological area.

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