Bruce L. Brown – författare

An insightful guide to understanding and visualizing multivariate statistics using SAS®, STATA®, and SPSS® Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach outlines the essential multivariate methods for understanding data in the social and biobehavioral sciences. Using real-world data and the latest software applications, the book addresses the topic in a comprehensible and hands-on manner, making complex mathematical concepts accessible to readers.The authors promote the importance of clear, well-designed graphics in the scientific process, with visual representations accompanying the presented classical multivariate statistical methods . The book begins with a preparatory review of univariate statistical methods recast in matrix notation, followed by an accessible introduction to matrix algebra. Subsequent chapters explore fundamental multivariate methods and related key concepts, including: Factor analysis and related methods Multivariate graphics Canonical correlation Hotelling's T-squared Multivariate analysis of variance (MANOVA) Multiple regression and the general linear model (GLM) Each topic is introduced with a research-publication case study that demonstrates its real-world value. Next, the question "how do you do that?" is addressed with a complete, yet simplified, demonstration of the mathematics and concepts of the method. Finally, the authors show how the analysis of the data is performed using Stata®, SAS®, and SPSS®. The discussed approaches are also applicable to a wide variety of modern extensions of multivariate methods as well as modern univariate regression methods. Chapters conclude with conceptual questions about the meaning of each method; computational questions that test the reader's ability to carry out the procedures on simple datasets; and data analysis questions for the use of the discussed software packages.Multivariate Analysis for the Biobehavioral and Social Sciences is an excellent book for behavioral, health, and social science courses on multivariate statistics at the graduate level. The book also serves as a valuable reference for professionals and researchers in the social, behavioral, and health sciences who would like to learn more about multivariate analysis and its relevant applications.

An insightful guide to understanding and visualizing multivariate statistics using SAS®, STATA®, and SPSS®

Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach outlines the essential multivariate methods for understanding data in the social and biobehavioral sciences. Using real-world data and the latest software applications, the book addresses the topic in a comprehensible and hands-on manner, making complex mathematical concepts accessible to readers.

The authors promote the importance of clear, well-designed graphics in the scientific process, with visual representations accompanying the presented classical multivariate statistical methods . The book begins with a preparatory review of univariate statistical methods recast in matrix notation, followed by an accessible introduction to matrix algebra. Subsequent chapters explore fundamental multivariate methods and related key concepts, including:

Factor analysis and related methods

Multivariate graphics

Canonical correlation

Hotelling''s T-squared

Multivariate analysis of variance (MANOVA)

Multiple regression and the general linear model (GLM)

Each topic is introduced with a research-publication case study that demonstrates its real-world value. Next, the question "how do you do that?" is addressed with a complete, yet simplified, demonstration of the mathematics and concepts of the method. Finally, the authors show how the analysis of the data is performed using Stata®, SAS®, and SPSS®. The discussed approaches are also applicable to a wide variety of modern extensions of multivariate methods as well as modern univariate regression methods. Chapters conclude with conceptual questions about the meaning of each method; computational questions that test the reader''s ability to carry out the procedures on simple datasets; and data analysis questions for the use of the discussed software packages.

Multivariate Analysis for the Biobehavioral and Social Sciences is an excellent book for behavioral, health, and social science courses on multivariate statistics at the graduate level. The book also serves as a valuable reference for professionals and researchers in the social, behavioral, and health sciences who would like to learn more about multivariate analysis and its relevant applications.

An insightful guide to understanding and visualizing multivariate statistics using SAS®, STATA®, and SPSS®

Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach outlines the essential multivariate methods for understanding data in the social and biobehavioral sciences. Using real-world data and the latest software applications, the book addresses the topic in a comprehensible and hands-on manner, making complex mathematical concepts accessible to readers.

The authors promote the importance of clear, well-designed graphics in the scientific process, with visual representations accompanying the presented classical multivariate statistical methods . The book begins with a preparatory review of univariate statistical methods recast in matrix notation, followed by an accessible introduction to matrix algebra. Subsequent chapters explore fundamental multivariate methods and related key concepts, including:

Factor analysis and related methods

Multivariate graphics

Canonical correlation

Hotelling''s T-squared

Multivariate analysis of variance (MANOVA)

Multiple regression and the general linear model (GLM)

Each topic is introduced with a research-publication case study that demonstrates its real-world value. Next, the question "how do you do that?" is addressed with a complete, yet simplified, demonstration of the mathematics and concepts of the method. Finally, the authors show how the analysis of the data is performed using Stata®, SAS®, and SPSS®. The discussed approaches are also applicable to a wide variety of modern extensions of multivariate methods as well as modern univariate regression methods. Chapters conclude with conceptual questions about the meaning of each method; computational questions that test the reader''s ability to carry out the procedures on simple datasets; and data analysis questions for the use of the discussed software packages.

Multivariate Analysis for the Biobehavioral and Social Sciences is an excellent book for behavioral, health, and social science courses on multivariate statistics at the graduate level. The book also serves as a valuable reference for professionals and researchers in the social, behavioral, and health sciences who would like to learn more about multivariate analysis and its relevant applications.

A combination of psychometric theory, history, philosophy, and practice, with recent advances in analytical methods, metrology, and design. Psychometrics, Test Theory, and the Latent Factors Model began as a strong manuscript by Petr Blahuš, based upon his lifelong passion of applying the latent factors model to kinesiology and biomechanics. Before his death he entrusted it to three co-authors who completed the book with in-chapter explanations, accessible mathematical appendices, and computational guides. It was also expanded to include important advances since 2010 in psychometric methods, contemporary developments in metrology, and the science of domain-specific design. Comprehensive in scope, the text contains computational guides for the use of Stata, M-plus, and SPSS in Factor Analysis, Classical Test Theory, and Item-Response Theory. The authors highlight the practicality of software integration in order to successfully produce psychometrically sound research. Written by an international and decades-spanning team of experienced psychometricians, the text anticipates a future psychometric science that could earn its place in the international metrology community. The book is filled with suggestions, tips, and practical guidance about best practices and efficient strategies for modeling and model selection. In addition, the book includes important cautions and warnings about misuse and misinterpretations of common, but limited, analytical techniques. The book is historically informed, philosophically grounded, mathematically justified, and methodologically current. This important text: Applies intuitive reasoning and common examples to aid in the understanding of advanced technical concepts.Includes the conceptual, statistical, and philosophical background of psychometrics.Features recent advances and opposing views in psychometric theory.Contains concrete examples from current research, including cognitive tests and neurological data.Opens the vista for a future of testing with greatly increased use of well-constructed, learner-centered performance scales using computer-adaptive testing with feedback over multiple attempts.Psychometrics, Test Theory, and the Latent Factors Model is intended for forward-looking students and teachers in the behavioral, educational, health, and social sciences.

A combination of psychometric theory, history, philosophy, and practice, with recent advances in analytical methods, metrology, and design. Psychometrics, Test Theory, and the Latent Factors Model began as a strong manuscript by Petr Blahu , based upon his lifelong passion of applying the latent factors model to kinesiology and biomechanics. Before his death he entrusted it to three co-authors who completed the book with in-chapter explanations, accessible mathematical appendices, and computational guides. It was also expanded to include important advances since 2010 in psychometric methods, contemporary developments in metrology, and the science of domain-specific design. Comprehensive in scope, the text contains computational guides for the use of Stata, M-plus, and SPSS in Factor Analysis, Classical Test Theory, and Item-Response Theory. The authors highlight the practicality of software integration in order to successfully produce psychometrically sound research. Written by an international and decades-spanning team of experienced psychometricians, the text anticipates a future psychometric science that could earn its place in the international metrology community. The book is filled with suggestions, tips, and practical guidance about best practices and efficient strategies for modeling and model selection. In addition, the book includes important cautions and warnings about misuse and misinterpretations of common, but limited, analytical techniques. The book is historically informed, philosophically grounded, mathematically justified, and methodologically current. This important text: Applies intuitive reasoning and common examples to aid in the understanding of advanced technical concepts. Includes the conceptual, statistical, and philosophical background of psychometrics. Features recent advances and opposing views in psychometric theory. Contains concrete examples from current research, including cognitive tests and neurological data. Opens the vista for a future of testing with greatly increased use of well-constructed, learner-centered performance scales using computer-adaptive testing with feedback over multiple attempts. Psychometrics, Test Theory, and the Latent Factors Model is intended for forward-looking students and teachers in the behavioral, educational, health, and social sciences.