This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including ...
Author: Bradley Huitema
Publisher: John Wiley & Sons
A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments Power analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs Measurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials Thoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering.
Randomized - block ANOVA , repeated - measurement ANOVA , and two - factor
ANOVA were referred to as alternatives to the randomized - group ANCOVA . In
this chapter we present an overview of the essentials of these alternatives and ...
This new edition now explains specific and multiple comparisons of experimental conditions before and after the Omnibus ANOVA, and describes the estimation of effect sizes and power analyses leading to the determination of appropriate ...
Author: Andrew Rutherford
Publisher: John Wiley & Sons
Provides an in-depth treatment of ANOVA and ANCOVA techniquesfrom a linear model perspective ANOVA and ANCOVA: A GLM Approach provides a contemporary look atthe general linear model (GLM) approach to the analysis of variance(ANOVA) of one- and two-factor psychological experiments. With itsorganized and comprehensive presentation, the book successfullyguides readers through conventional statistical concepts and how tointerpret them in GLM terms, treating the main single- andmulti-factor designs as they relate to ANOVA and ANCOVA. The book begins with a brief history of the separate developmentof ANOVA and regression analyses, and then goes on to demonstratehow both analyses are incorporated into the understanding of GLMs.This new edition now explains specific and multiple comparisons ofexperimental conditions before and after the Omnibus ANOVA, anddescribes the estimation of effect sizes and power analyses leadingto the determination of appropriate sample sizes for experiments tobe conducted. Topics that have been expanded upon and addedinclude: Discussion of optimal experimental designs Different approaches to carrying out the simple effect analysesand pairwise comparisons with a focus on related and repeatedmeasure analyses The issue of inflated Type 1 error due to multiple hypothesestesting Worked examples of Shaffer's R test, which accommodates logicalrelations amongst hypotheses ANOVA and ANCOVA: A GLM Approach, Second Edition is an excellentbook for courses on linear modeling at the graduate level. It isalso a suitable reference for researchers and practitioners in thefields of psychology and the biomedical and social sciences.
This book begins with a brief history of the separate development of ANOVA and regression analyses and demonstrates how both analysis forms are subsumed by the General Linear Model.
Author: Andrew Rutherford
Category: Social Science
Traditional approaches to ANOVA and ANCOVA are now being replaced by a General Linear Modeling (GLM) approach. This book begins with a brief history of the separate development of ANOVA and regression analyses and demonstrates how both analysis forms are subsumed by the General Linear Model. A simple single independent factor ANOVA is analysed first in conventional terms and then again in GLM terms to illustrate the two approaches. The text then goes on to cover the main designs, both independent and related ANOVA and ANCOVA, single and multi-factor designs. The conventional statistical assumptions underlying ANOVA and ANCOVA are detailed and given expression in GLM terms. Alternatives to traditional ANCO
In ANCOVA, these interaction terms are differences between slopes for different
factor levels (recall that in multi-way ANOVA, the interaction ... Huitema, B.E. (
1980) The Analysis of Covariance and Alternatives, John Wiley & Sons, New
Author: Michael J. Crawley
Publisher: John Wiley & Sons
"...I know of no better book of its kind..." (Journal of the Royal Statistical Society, Vol 169 (1), January 2006) A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t--tests and chi--squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. Includes numerous worked examples and exercises within each chapter.
The Approach Based on Influence Functions HARTUNG, KNAPP, and SINHA -
Statistical Meta-Analysis with ... INIK - Applied MANOVA and Discriminant
Analysis, Second Edition HUITEMA - The Analysis of Covariance and Alternatives: ...
Author: Myles Hollander
Publisher: John Wiley & Sons
Praise for the Second Edition “This book should be an essential part of the personallibrary of every practicingstatistician.”—Technometrics Thoroughly revised and updated, the new edition of NonparametricStatistical Methods includes additional modern topics andprocedures, more practical data sets, and new problems fromreal-life situations. The book continues to emphasize theimportance of nonparametric methods as a significant branch ofmodern statistics and equips readers with the conceptual andtechnical skills necessary to select and apply the appropriateprocedures for any given situation. Written by leading statisticians, Nonparametric StatisticalMethods, Third Edition provides readers with crucialnonparametric techniques in a variety of settings, emphasizing theassumptions underlying the methods. The book provides an extensivearray of examples that clearly illustrate how to use nonparametricapproaches for handling one- or two-sample location and dispersionproblems, dichotomous data, and one-way and two-way layoutproblems. In addition, the Third Edition features: The use of the freely available R software to aid incomputation and simulation, including many new R programs writtenexplicitly for this new edition New chapters that address density estimation, wavelets,smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science,astronomy, biology, criminology, education, engineering,environmental science, geology, home economics, medicine,oceanography, physics, psychology, sociology, and spacescience Nonparametric Statistical Methods, Third Edition is anexcellent reference for applied statisticians and practitioners whoseek a review of nonparametric methods and their relevantapplications. The book is also an ideal textbook forupper-undergraduate and first-year graduate courses in appliednonparametric statistics.
... and MICTDRIIA=FESER - Robust Methods in Diostatistics HllilILELMAliti and
REMPTHDRDE ~ Design and Analysis of ... Discriminant Analysis, Second
Edition HUTTEMA »- The Analysis of Covariance and Alternatives: Statistical
Author: Carl Graham
Publisher: John Wiley & Sons
Markov Chains: Analytic and Monte Carlo Computations introduces the main notions related to Markov chains and provides explanations on how to characterize, simulate, and recognize them. Starting with basic notions, this book leads progressively to advanced and recent topics in the field, allowing the reader to master the main aspects of the classical theory. This book also features: Numerous exercises with solutions as well as extended case studies. A detailed and rigorous presentation of Markov chains with discrete time and state space. An appendix presenting probabilistic notions that are necessary to the reader, as well as giving more advanced measure-theoretic notions.
... and VICTORIA-FESER · Robust Methods in Biostatistics HINKELMANN and
KEMPTHORNE · Design and Analysis of ... Applied MANOVA and Discriminant
Analysis, Second Edition HUITEMA · The Analysis of Covariance and Alternatives: ...
Author: John I. McCool
Publisher: John Wiley & Sons
Category: Technology & Engineering
Understand and utilize the latest developments in Weibull inferential methods While the Weibull distribution is widely used in science and engineering, most engineers do not have the necessary statistical training to implement the methodology effectively. Using the Weibull Distribution: Reliability, Modeling, and Inference fills a gap in the current literature on the topic, introducing a self-contained presentation of the probabilistic basis for the methodology while providing powerful techniques for extracting information from data. The author explains the use of the Weibull distribution and its statistical and probabilistic basis, providing a wealth of material that is not available in the current literature. The book begins by outlining the fundamental probability and statistical concepts that serve as a foundation for subsequent topics of coverage, including: • Optimum burn-in, age and block replacement, warranties and renewal theory • Exact inference in Weibull regression • Goodness of fit testing and distinguishing the Weibull from the lognormal • Inference for the Three Parameter Weibull Throughout the book, a wealth of real-world examples showcases the discussed topics and each chapter concludes with a set of exercises, allowing readers to test their understanding of the presented material. In addition, a related website features the author's own software for implementing the discussed analyses along with a set of modules written in Mathcad®, and additional graphical interface software for performing simulations. With its numerous hands-on examples, exercises, and software applications, Using the Weibull Distribution is an excellent book for courses on quality control and reliability engineering at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for engineers, scientists, and business analysts who gather and interpret data that follows the Weibull distribution
... Understanding Robust and Exploratory Data Analysis HOCHBERG and
TAMHANE · Multiple Comparison Procedures ... Analysis, Second Edition
HUITEMA · The Analysis of Covariance and Alternatives: Statistical Methods for
Author: Dennis V. Lindley
Publisher: John Wiley & Sons
Praise for the First Edition "...a reference for everyone who is interested in knowing and handling uncertainty." —Journal of Applied Statistics The critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty to be handled sensibly in everyday life. These ideas were extended to embrace the scientific method and to show how decisions, containing an uncertain element, could be rationally made. Featuring new material, the Revised Edition remains the go-to guide for uncertainty and decision making, providing further applications at an accessible level including: A critical study of transitivity, a basic concept in probability A discussion of how the failure of the financial sector to use the proper approach to uncertainty may have contributed to the recent recession A consideration of betting, showing that a bookmaker's odds are not expressions of probability Applications of the book’s thesis to statistics A demonstration that some techniques currently popular in statistics, like significance tests, may be unsound, even seriously misleading, because they violate the rules of probability Understanding Uncertainty, Revised Edition is ideal for students studying probability or statistics and for anyone interested in one of the most fascinating and vibrant fields of study in contemporary science and mathematics.
The analysis of covariance and alternatives. New York: Wiley. Huberty, C. J. (
2002). A history of effect size indices. Educational and Psychological
Measurement, 62(2), 227–240. Huynh, H., & Feldt, L. S. (1970). Conditions under
which mean ...
Author: Richard G Lomax
This comprehensive, flexible text is used in both one- and two-semester courses to review introductory through intermediate statistics. Instructors select the topics that are most appropriate for their course. Its conceptual approach helps students more easily understand the concepts and interpret SPSS and research results. Key concepts are simply stated and occasionally reintroduced and related to one another for reinforcement. Numerous examples demonstrate their relevance. This edition features more explanation to increase understanding of the concepts. Only crucial equations are included. In addition to updating throughout, the new edition features: New co-author, Debbie L. Hahs-Vaughn, the 2007 recipient of the University of Central Florida's College of Education Excellence in Graduate Teaching Award. A new chapter on logistic regression models for today's more complex methodologies. More on computing confidence intervals and conducting power analyses using G*Power. Many more SPSS screenshots to assist with understanding how to navigate SPSS and annotated SPSS output to assist in the interpretation of results. Extended sections on how to write-up statistical results in APA format. New learning tools including chapter-opening vignettes, outlines, and a list of key concepts, many more examples, tables, and figures, boxes, and chapter summaries. More tables of assumptions and the effects of their violation including how to test them in SPSS. 33% new conceptual, computational, and all new interpretative problems. A website that features PowerPoint slides, answers to the even-numbered problems, and test items for instructors, and for students the chapter outlines, key concepts, and datasets that can be used in SPSS and other packages, and more. Each chapter begins with an outline, a list of key concepts, and a vignette related to those concepts. Realistic examples from education and the behavioral sciences illustrate those concepts. Each example examines the procedures and assumptions and provides instructions for how to run SPSS, including annotated output, and tips to develop an APA style write-up. Useful tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. 'Stop and Think' boxes provide helpful tips for better understanding the concepts. Each chapter includes computational, conceptual, and interpretive problems. The data sets used in the examples and problems are provided on the web. Answers to the odd-numbered problems are given in the book. The first five chapters review descriptive statistics including ways of representing data graphically, statistical measures, the normal distribution, and probability and sampling. The remainder of the text covers inferential statistics involving means, proportions, variances, and correlations, basic and advanced analysis of variance and regression models. Topics not dealt with in other texts such as robust methods, multiple comparison and nonparametric procedures, and advanced ANOVA and multiple and logistic regression models are also reviewed. Intended for one- or two-semester courses in statistics taught in education and/or the behavioral sciences at the graduate and/or advanced undergraduate level, knowledge of statistics is not a prerequisite. A rudimentary knowledge of algebra is required.
Applied Survival Analysis: Regression Modeling of Time-to-Event Data, Second
Edition HUBER. ... The Analysis of Covariance and Alternatives: Statistical
Methods for Experiments, Quasi-Experiments, and Single-Case Studies, Second
Author: David W. Scott
Publisher: John Wiley & Sons
Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis. The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition also features: Over 150 updated figures to clarify theoretical results and to show analyses of real data sets An updated presentation of graphic visualization using computer software such as R A clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering More than 130 problems to help readers reinforce the main concepts and ideas presented Boxed theorems and results allowing easy identification of crucial ideas Figures in color in the digital versions of the book A website with related data sets Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.
2 4 6 8 10 12 α 1 α 2 α 3 ζμ FIGURE 8.4 Single subject activation experiment, ANCOVA design. Illustrations for a three-condition experiment with four scans in
each of three conditions, ANCOVA design. ... In SPM, there are two alternatives.
Author: William D. Penny
In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. An essential reference and companion for users of the SPM software Provides a complete description of the concepts and procedures entailed by the analysis of brain images Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data Stands as a compendium of all the advances in neuroimaging data analysis over the past decade Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes Structured treatment of data analysis issues that links different modalities and models Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible
Analysis: of two-way tables by medians, e.g. 547, 871,916, 1320, 1339 of
unbalanced data 789, 1142 of variance, e.g. 83 ... 1288, 1397 ANCOVA: See
also analysis of covariance alternatives to 100,412,459, 762, 1284 estimating the
Author: Lothar Sachs
Publisher: Springer Science & Business Media
Readers of my books, students and scientists, often ask for spe cial references not commonly found in introductory or interme diate books on statistics. From the titles and contents of 1449 key papers and books which are listed and numbered in Sec tion 5, I have selected keywords and subject headings and ar ranged them alphabetically together with the numbers of perti nent references in Section 3. Number 1153, for instance, denotes my book" Applied Statis tics". It contains a bibliographical section on pages 568 to 641. Supplementary material is displayed in this small bibliographi cal guide. It also complements well-known textbooks of Box, Hunter and Hunter (No.121), Dixon and Massey (No.286), Snedecor and Cochran (No. 1238), and many recent competitors. Since the methodology of statistics is expanding rapidly, many methods are not considered at all or only introduced in the basic textbooks of statistics. There is a need for intermediate statistical methods concerned with increasingly complicated ap plications of statistics to actual research situations. Here the specification of terms helps to find some sources. Since the ref erences vary considerably in length and content, the number of culled or extracted terms per referenced page varies even more, as does also their degree of specialization; however in most cases an intermediate statistical level is maintained.
It is advisable to check model assumptions through an analysis of standardized
residuals. Such an analysis might identify ... Analysis of covariance (ANCOVA) is
a combination of ANOVA and linear regression. It requires a researcher to collect
Author: Julius Sim
Publisher: Nelson Thornes
Providing everything the researcher, in a health care setting, needs to know about undertaking and completing a research project, this book provides detailed information about the various types of research projects that might be undertaken.
Examining identification issues in factor analysis. ... Analysis of
multitraitmultioccasion data: Additive versus multiplicative models. ... Cutoff
criteria for fit indexes in covariance structure analysis: Conventional criteria
versus new alternatives.
Author: Barbara M. Byrne
This bestselling text provides a practical guide to the basic concepts of structural equation modeling (SEM) and the AMOS program (Versions 17 & 18). The author reviews SEM applications based on actual data taken from her research. Noted for its non-mathematical language, this book is written for the novice SEM user. With each chapter, the author "walks" the reader through all steps involved in testing the SEM model including: an explanation of the issues addressed an illustration of the hypothesized and posthoc models tested AMOS input and output with accompanying interpretation and explanation The function of the AMOS toolbar icons and their related pull-down menus The data and published reference upon which the model was based. With over 50% new material, highlights of the new edition include: All new screen shots featuring Version 17 of the AMOS program All data files now available at www.routledge.com/9780805863734 Application of a multitrait-mulitimethod model, latent growth curve model, and second-order model based on categorical data All applications based on the most commonly used graphical interface The automated multi-group approach to testing for equivalence The book opens with an introduction to the fundamental concepts of SEM and the basics of the AMOS program. The next 3 sections present applications that focus on single-group, multiple-group, and multitrait-mutimethod and latent growth curve models. The book concludes with a discussion about non-normal and missing (incomplete) data and two applications capable of addressing these issues. Intended for researchers, practitioners, and students who use SEM and AMOS in their work, this book is an ideal resource for graduate level courses on SEM taught in departments of psychology, education, business, and other social and health sciences and/or as a supplement in courses on applied statistics, multivariate statistics, statistics II, intermediate or advanced statistics, and/or research design. Appropriate for those with limited or no previous exposure to SEM, a prerequisite of basic statistics through regression analysis is recommended.
Neither the addition of the covariates nor the addition of the GLM code to the
sequence added appreciably to the processing time required for the analysis . ANCOVA An alternative to the repeated - measures approach is often more
The analysis with the smallest set of CVs is reported , but mention is made in the
Results section of the discarded CV ( s ) and the fact that the pattern of results did
not change when they were eliminated . 8 . 5 . 5 Alternatives to ANCOVA ...
Author: Barbara G. Tabachnick
Publisher: Allyn & Bacon
Eager to learn everything she can about her new abilities as an Immortal, Ever turns to her beloved Damen to show her the way. But just as her powers are increasing, Damen's are waning. In an attempt to save him, Ever travels to the magical dimension of Summerland, where she learns the secrets of Damen's tortured past; a past which he has always kept hidden from her. But in her quest to cure Damen, Ever discovers an ancient text that details the workings of time. Now Ever must choose between turning back the past and saving her family from the accident that claimed their lives--or staying in the present and saving Damen, who grows sicker every day...
ANCOVA. 5.1. Introduction. In this chapter, the R functions in the packages Rfit
and npsm for the computation of fits and ... including the Kruskal–Wallis (Section
5.2.2) and the Jonckheere–Terpstra tests for ordered alternatives (Section 5.6).
Author: John Kloke
Publisher: CRC Press
A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.
7 Alternatives to ANCOVA Because of violated assumptions and difficulty in
interpreting results of ANCOVA , alternative analytical strategies are sometimes
sought . The choice among alternatives depends on the scale of measurement of
Author: Barbara G. Tabachnick
Publisher: Allyn & Bacon
Designed for upper-level undergraduate- and graduate-level courses in research design and analysis in departments of psychology, education, sociology, anthropology, and other social and behavioral sciences. A comprehensive review of analyses of basic and complex ANOVA models through traditional approaches and multiple regression, integrating the most recent releases of MINITAB, SAS, SPSS, and SYSTAT. In all chapters of this comprehensive text, both the basic model and its numerous complexities are presented along with discussions of effect size, relative efficiency and comparisons, illustrated by numerous examples. For each major model, the text provides tests for assumptions, a hand-worked example, and an example with real data including a write-up of the results using APA format. The text also provides data sets, syntax, and output for accomplishing numerous additional analyses through recent releases of MINITAB, SAS, SPSS and SYSTAT, often neglected in software manuals. *TECHNOLOGY ADVANTAGE: Inclusion of syntax and output from MINITAB, SAS, SPSS, and SYSTAT allows students to concentrate on the research question rather than on the specifics of the software program and provides