# Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences

Author: Brian S. Everitt

Publisher: CRC Press

ISBN: 1439807701

Page: 320

View: 410

Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences shows students how to apply statistical methods to behavioral science data in a sensible manner. Assuming some familiarity with introductory statistics, the book analyzes a host of real-world data to provide useful answers to real-life issues. The author begins by exploring the types and design of behavioral studies. He also explains how models are used in the analysis of data. After describing graphical methods, such as scatterplot matrices, the text covers simple linear regression, locally weighted regression, multiple linear regression, regression diagnostics, the equivalence of regression and ANOVA, the generalized linear model, and logistic regression. The author then discusses aspects of survival analysis, linear mixed effects models for longitudinal data, and the analysis of multivariate data. He also shows how to carry out principal components, factor, and cluster analyses. The final chapter presents approaches to analyzing multivariate observations from several different populations. Through real-life applications of statistical methodology, this book elucidates the implications of behavioral science studies for statistical analysis. It equips behavioral science students with enough statistical tools to help them succeed later on in their careers. Solutions to the problems as well as all R code and data sets for the examples are available at www.crcpress.com

# Latent Markov Models for Longitudinal Data

Author: Francesco Bartolucci,Alessio Farcomeni,Fulvia Pennoni

Publisher: CRC Press

ISBN: 1466583711

Category: Mathematics

Page: 252

View: 1446

Drawing on the authors’ extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the practical use of these models. Numerous examples illustrate how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB® routines used for the examples are available on the authors’ website. The book provides you with the essential background on latent variable models, particularly the latent class model. It discusses how the Markov chain model and the latent class model represent a useful paradigm for latent Markov models. The authors illustrate the assumptions of the basic version of the latent Markov model and introduce maximum likelihood estimation through the Expectation-Maximization algorithm. They also cover constrained versions of the basic latent Markov model, describe the inclusion of the individual covariates, and address the random effects and multilevel extensions of the model. After covering advanced topics, the book concludes with a discussion on Bayesian inference as an alternative to maximum likelihood inference. As longitudinal data become increasingly relevant in many fields, researchers must rely on specific statistical and econometric models tailored to their application. A complete overview of latent Markov models, this book demonstrates how to use the models in three types of analysis: transition analysis with measurement errors, analyses that consider unobserved heterogeneity, and finding clusters of units and studying the transition between the clusters.

# The Analysis and Interpretation of Multivariate Data for Social Scientists

Author: J.I. Galbraith,Irini Moustaki,David J. Bartholomew,Fiona Steele

Publisher: CRC Press

ISBN: 9781584882954

Category: Mathematics

Page: 280

View: 4478

Multivariate analysis is an important tool for social researchers, but the subject is broad and can be quite technical for those with limited mathematical and statistical backgrounds. To effectively acquire the tools and techniques they need to interpret multivariate data, social science students need clear explanations, a minimum of mathematical detail, and a wide range of exercises and worked examples. Classroom tested for more than 10 years, The Analysis and Interpretation of Multivariate Data for Social Scientists describes and illustrates methods of multivariate data analysis important to the social sciences. The authors focus on interpreting the pattern of relationships among many variables rather than establishing causal linkages, and rely heavily on numerical examples, visualization, and on verbal , rather than mathematical exposition. They present methods for categorical variables alongside the more familiar method for continuous variables and place particular emphasis on latent variable techniques. Ideal for introductory, senior undergraduate and graduate-level courses in multivariate analysis for social science students, this book combines depth of understanding and insight with the practical details of how to carry out and interpret multivariate analyses on real data. It gives them a solid understanding of the most commonly used multivariate methods and the knowledge and tools to implement them. Datasets, the SPSS syntax and code used in the examples, and software for performing latent variable modelling are available at http://www.mlwin.com/team/aimdss.html>

# Applied Regression Modeling

Author: Iain Pardoe

Publisher: John Wiley & Sons

ISBN: 1118345045

Category: Mathematics

Page: 346

View: 9955

Praise for the First Edition "The attention to detail is impressive. The book is very well written and the author is extremely careful with his descriptions . . . the examples are wonderful." —The American Statistician Fully revised to reflect the latest methodologies and emerging applications, Applied Regression Modeling, Second Edition continues to highlight the benefits of statistical methods, specifically regression analysis and modeling, for understanding, analyzing, and interpreting multivariate data in business, science, and social science applications. The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling. In addition, the Second Edition features clarification and expansion of challenging topics, such as: Transformations, indicator variables, and interaction Testing model assumptions Nonconstant variance Autocorrelation Variable selection methods Model building and graphical interpretation Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos. With an intuitive approach that is not heavy on mathematical detail, Applied Regression Modeling, Second Edition is an excellent book for courses on statistical regression analysis at the upper-undergraduate and graduate level. The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work.

# Applied Survey Data Analysis

Author: Steven G. Heeringa,Brady T. West,Patricia A. Berglund

Publisher: CRC Press

ISBN: 9781420080674

Category: Mathematics

Page: 487

View: 5551

Taking a practical approach that draws on the authors’ extensive teaching, consulting, and research experiences, Applied Survey Data Analysis provides an intermediate-level statistical overview of the analysis of complex sample survey data. It emphasizes methods and worked examples using available software procedures while reinforcing the principles and theory that underlie those methods. After introducing a step-by-step process for approaching a survey analysis problem, the book presents the fundamental features of complex sample designs and shows how to integrate design characteristics into the statistical methods and software for survey estimation and inference. The authors then focus on the methods and models used in analyzing continuous, categorical, and count-dependent variables; event history; and missing data problems. Some of the techniques discussed include univariate descriptive and simple bivariate analyses, the linear regression model, generalized linear regression modeling methods, the Cox proportional hazards model, discrete time models, and the multiple imputation analysis method. The final chapter covers new developments in survey applications of advanced statistical techniques, including model-based analysis approaches. Designed for readers working in a wide array of disciplines who use survey data in their work, this book also provides a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. A guide to the applied statistical analysis and interpretation of survey data, it contains many examples and practical exercises based on major real-world survey data sets. Although the authors use Stata for most examples in the text, they offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s website: http://www.isr.umich.edu/src/smp/asda/

# Informative Hypotheses

Theory and Practice for Behavioral and Social Scientists

Author: Herbert Hoijtink

Publisher: CRC Press

ISBN: 1439880514

Category: Mathematics

Page: 241

View: 402

When scientists formulate their theories, expectations, and hypotheses, they often use statements like: ``I expect mean A to be bigger than means B and C"; ``I expect that the relation between Y and both X1 and X2 is positive"; and ``I expect the relation between Y and X1 to be stronger than the relation between Y and X2". Stated otherwise, they formulate their expectations in terms of inequality constraints among the parameters in which they are interested, that is, they formulate Informative Hypotheses. There is currently a sound theoretical foundation for the evaluation of informative hypotheses using Bayes factors, p-values and the generalized order restricted information criterion. Furthermore, software that is often free is available to enable researchers to evaluate the informative hypotheses using their own data. The road is open to challenge the dominance of the null hypothesis for contemporary research in behavioral, social, and other sciences.

# Regression Analysis by Example

Publisher: John Wiley & Sons

ISBN: 0470055456

Category: Mathematics

Page: 416

View: 4624

The essentials of regression analysis through practical applications Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Fourth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. This new edition features the following enhancements: Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis A new chapter entitled Further Topics discusses advanced areas of regression analysis Reorganized, expanded, and upgraded exercises appear at the end of each chapter A fully integrated Web page provides data sets Numerous graphical displays highlight the significance of visual appeal Regression Analysis by Example, Fourth Edition is suitable for anyone with an understanding of elementary statistics. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

# Generalized Linear Models

An Applied Approach

Author: John Patrick Hoffmann

Publisher: Pearson College Division

ISBN: N.A

Category: Mathematics

Page: 204

View: 3268

This brief and economical text shows students with relatively little mathematical background how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and medical sciences, as well as marketing, and business. Less theoretical than competing texts, Hoffman includes numerous exercises and worked-out examples and sample programs and data sets for three popular statistical software programs: SPSS, SAS, and Stata.

# The R Book

Author: Michael J. Crawley

Publisher: John Wiley & Sons

ISBN: 1118448960

Category: Mathematics

Page: 1080

View: 9985

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)

# Multivariable Model - Building

A Pragmatic Approach to Regression Anaylsis based on Fractional Polynomials for Modelling Continuous Variables

Author: Patrick Royston,Willi Sauerbrei

Publisher: John Wiley & Sons

ISBN: 9780470770788

Category: Mathematics

Page: 322

View: 319

Multivariable regression models are of fundamental importance in all areas of science in which empirical data must be analyzed. This book proposes a systematic approach to building such models based on standard principles of statistical modeling. The main emphasis is on the fractional polynomial method for modeling the influence of continuous variables in a multivariable context, a topic for which there is no standard approach. Existing options range from very simple step functions to highly complex adaptive methods such as multivariate splines with many knots and penalisation. This new approach, developed in part by the authors over the last decade, is a compromise which promotes interpretable, comprehensible and transportable models.

# Applied Longitudinal Analysis

Author: Garrett M. Fitzmaurice,Nan M. Laird,James H. Ware

Publisher: John Wiley & Sons

ISBN: 1118551796

Category: Mathematics

Page: 740

View: 3334

# Foundations of factor analysis

Author: Stanley A. Mulaik

Publisher: Chapman & Hall/CRC

ISBN: 9781420099614

Page: 524

View: 5021

Providing a practical, thorough understanding of how factor analysis works,Foundations of Factor Analysis, Second Editiondiscusses the assumptions underlying the equations and procedures of this method. It also explains the options in commercial computer programs for performing factor analysis and structural equation modeling. This long-awaited edition takes into account the various developments that have occurred since the publication of the original edition. New to the Second Edition A new chapter on the multivariate normal distribution, its general properties, and the concept of maximum-likelihood estimation More complete coverage of descriptive factor analysis and doublet factor analysis A rewritten chapter on analytic oblique rotation that focuses on the gradient projection algorithm and its applications Discussions on the developments of factor score indeterminacy A revised chapter on confirmatory factor analysis that addresses philosophy of science issues, model specification and identification, parameter estimation, and algorithm derivation Presenting the mathematics only as needed to understand the derivation of an equation or procedure, this textbook prepares students for later courses on structural equation modeling. It enables them to choose the proper factor analytic procedure, make modifications to the procedure, and produce new results.

# An R Companion to Applied Regression

Author: John Fox,Sanford Weisberg

Publisher: SAGE Publications

ISBN: 141297514X

Category: Social Science

Page: 449

View: 8147

This book aims to provide a broad introduction to the R statistical environment in the context of applied regression analysis, which is typically studied by social scientists and others in a second course in applied statistics.

# R for Everyone

Author: Jared P. Lander

ISBN: 0134546997

Category: Computers

Page: 560

View: 5359

# The British National Bibliography

Author: Arthur James Wells

Publisher: N.A

ISBN: N.A

Category: English literature

Page: N.A

View: 1263

# Introduction to Multivariate Analysis

Author: Chris Chatfield,A. Collins

Publisher: CRC Press

ISBN: 9780412160400

Category: Mathematics

Page: 248

View: 8408

# Solutions Manual to Accompany Introduction to Linear Regression Analysis

Author: Ann G. Ryan,Douglas C. Montgomery,Elizabeth A. Peck,G. Geoffrey Vining

Publisher: John Wiley & Sons

ISBN: 1118548507

Category: Mathematics

Page: 164

View: 4683

As the Solutions Manual, this book is meant to accompany the main title, Introduction to Linear Regression Analysis, Fifth Edition. Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation.

# Applied Linear Regression

Author: Sanford Weisberg

Publisher: John Wiley & Sons

ISBN: 1118625951

Category: Mathematics

Page: N.A

View: 6890

# An Introduction to Applied Multivariate Analysis with R

Author: Brian Everitt,Torsten Hothorn

Publisher: Springer Science & Business Media

ISBN: 9781441996503

Category: Mathematics

Page: 274

View: 1510

The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.

# Multivariate Analysis of Ecological Data

Author: Michael Greenacre,Raul Primicerio

Publisher: Fundacion BBVA

ISBN: 8492937505

Category: Ecology

Page: 331

View: 1960