Most core statistics texts cover subjects like analysis of variance and regression, but not in much detail. This book provides clear and comprehensive coverage of the concepts behind ANOVA as well as its technical implementation. It emphasizes facilitating students' intuitive and common sense understanding of the concepts before delving into computation.
This engaging text shows how statistics and methods work together, demonstrating a variety of techniques for evaluating statistical results against the specifics of the methodological design. Richard Gonzalez elucidates the fundamental concepts involved in analysis of variance (ANOVA), focusing on single degree-of-freedom tests, or comparisons, wherever possible. Potential threats to making a causal inference from an experimental design are highlighted. With an emphasis on basic between-subjects and within-subjects designs, Gonzalez resists presenting the countless "exceptions to the rule" that make many statistics textbooks so unwieldy and confusing for students and beginning researchers. Ideal for graduate courses in experimental design or data analysis, the text may also be used by advanced undergraduates preparing to do senior theses. Useful pedagogical features include: Discussions of the assumptions that underlie each statistical test Sequential, step-by-step presentations of statistical procedures End-of-chapter questions and exercises Accessible writing style with scenarios and examples This book is intended for graduate students in psychology and education, practicing researchers seeking a readable refresher on analysis of experimental designs, and advanced undergraduates preparing senior theses. It serves as a text for graduate level experimental design, data analysis, and experimental methods courses taught in departments of psychology and education. It is also useful as a supplemental text for advanced undergraduate honors courses.
A complete course in data collection and analysis for students who need to go beyond the basics. A true course companion, the engaging writing style takes readers through challenging topics, blending examples and exercises with careful explanations and custom-drawn figures ensuring the most daunting concepts can be fully understood.
This book offers a step-by-step guide to the experimental planning process and the ensuing analysis of normally distributed data, emphasizing the practical considerations governing the design of an experiment. Data sets are taken from real experiments and sample SAS programs are included with each chapter. Experimental design is an essential part of investigation and discovery in science; this book will serve as a modern and comprehensive reference to the subject.
This bestselling professional reference has helped over 100,000 engineers and scientists with the success of their experiments. The new edition includes more software examples taken from the three most dominant programs in the field: Minitab, JMP, and SAS. Additional material has also been added in several chapters, including new developments in robust design and factorial designs. New examples and exercises are also presented to illustrate the use of designed experiments in service and transactional organizations. Engineers will be able to apply this information to improve the quality and efficiency of working systems.
Provides timely applications, modifications, and extensions ofexperimental designs for a variety of disciplines Design and Analysis of Experiments, Volume 3: Special Designsand Applications continues building upon the philosophicalfoundations of experimental design by providing important, modernapplications of experimental design to the many fields that utilizethem. The book also presents optimal and efficient designs forpractice and covers key topics in current statistical research. Featuring contributions from leading researchers and academics,the book demonstrates how the presented concepts are used acrossvarious fields from genetics and medicinal and pharmaceuticalresearch to manufacturing, engineering, and national security. Eachchapter includes an introduction followed by the historicalbackground as well as in-depth procedures that aid in theconstruction and analysis of the discussed designs. Topicalcoverage includes: Genetic cross experiments, microarray experiments, and varietytrials Clinical trials, group-sequential designs, and adaptivedesigns Fractional factorial and search, choice, and optimal designs forgeneralized linear models Computer experiments with applications to homeland security Robust parameter designs and split-plot type response surfacedesigns Analysis of directional data experiments Throughout the book, illustrative and numerical examples utilizeSAS®, JMP®, and R software programs to demonstrate thediscussed techniques. Related data sets and software applicationsare available on the book's related FTP site. Design and Analysis of Experiments, Volume 3 is an idealtextbook for graduate courses in experimental design and alsoserves as a practical, hands-on reference for statisticians andresearchers across a wide array of subject areas, includingbiological sciences, engineering, medicine, and business.
A complete and up-to-date discussion of optimal split plot andsplit block designs Variations on Split Plot and Split Block ExperimentDesigns provides a comprehensive treatment of the design andanalysis of two types of trials that are extremely popular inpractice and play an integral part in the screening of appliedexperimental designs—split plot and split block experiments.Illustrated with numerous examples, this book presents atheoretical background and provides two and three error terms, athorough review of the recent work in the area of split plot andsplit blocked experiments, and a number of significant results. Written by renowned specialists in the field, this bookfeatures: Discussions of non-standard designs in addition to coverage ofsplit block and split plot designs Two chapters on combining split plot and split block designsand missing observations, which are unique to this book and to thefield of study SAS® commands spread throughout the book, which allowreaders to bypass tedious computation and reveal startlingobservations Detailed formulae and thorough remarks at the end of eachchapter Extensive data sets, which are posted on the book's FTPsite The design and analysis approach advocated in Variations onSplit Plot and Split Block Experiment Designs is essential increating tailor-made experiments for applied statisticians fromindustry, medicine, agriculture, chemistry, and other fields ofstudy.
This text reflects the practical approach of the authors. Barbara Tabachnick and Linda Fidell emphasize the use of statistical software in design and analysis of research in addition to conceptual understanding fostered by the presentation and interpretation of fundamental equations. EXPERIMENTAL DESIGN USING ANOVA includes the regression approach to ANOVA alongside the traditional approach, making it clearer and more flexible. The text includes details on how to perform both simple and complicated analyses by hand through traditional means, through regression, and through SPSS and SAS.
A Conceptual and Computational Approach with SPSS and SAS
Author: Glenn Gamst
Publisher: Cambridge University Press
Category: Social Science
ANOVA (Analysis Of Variance) is one of the most fundamental and ubiquitous univariate methodologies employed by psychologists and other behavioural scientists. Analysis of Variance Designs presents the foundations of this experimental design, including assumptions, statistical significance, strength of effect, and the partitioning of the variance. Exploring the effects of one or more independent variables on a single dependent variable as well as two-way and three-way mixed designs, this textbook offers an overview of traditionally advanced topics for advanced undergraduates and graduate students in the behavioural and social sciences. Separate chapters are devoted to multiple comparisons (post hoc and planned/weighted), ANCOVA, and advanced topics. Each of the design chapters contains conceptual discussions, hand calculations, and procedures for the omnibus and simple effects analyses in both SPSS and the new 'click and shoot' SAS Enterprise Guide interface.
Hypothesis testing is a common method of drawing inferences about a population based on statistical evidence from a sample. For example, the z-test (ztest) and the t-test (ttest) both assume that the data areindependently sampled from a normal distribution. Statistics and Machine LearningToolbox functions are available for testing this assumption, such as chi2gof, jbtest, lillietest, and normplot. You can use the Statistics and Machine Learning Toolbox function anova1 to perform one-way analysis of variance (ANOVA). The purpose of one-way ANOVA is to determine whether data from several groups (levels) of a factor have a common mean. That is, oneway ANOVA enables you to find out whether different groups of an independent variable have different effects on the response variable y. You can use the Statistics and Machine Learning Toolbox function anova2 to perform a balanced two-way analysis of variance (ANOVA). To perform two-way ANOVA for an unbalanced design, use anovan. The Statistics and Machine Learning Toolbox function multcompare performs multiple pairwise comparison of the group means, or treatment effects. The options are Tukey's honestly significant difference criterion (default option), the Bonferroni method, Scheffe's procedure, Fisher's least significant differences (lsd) method, and Dunn & Sidak's approach to t-test. You can use the Statistics and Machine Learning Toolbox function anovan to perform Nway ANOVA. Use N-way ANOVA to determine if the means in a set of data differ with respect to groups (levels) of multiple factors. Traditional experimental designs ("Full Factorial Designs," "Fractional Factorial Designs," and "Response Surface Designs") are appropriate for calibrating linear models in experimental settings where factors are relatively unconstrained in the region of interest. In some cases, however, models are necessarily nonlinear. In other cases, certain treatments (combinations of factor levels) may be expensive or infeasible to measure. D-optimal designs are model-specific designs that address these limitations of traditional designs. In practice, you may want to add runs to a completed experiment to learn more about a process and estimate additional model coefficients. The daugment function uses a coordinate-exchange algorithm to augment an existing D-optimal design. MATLAB shows how to improve the performance of an engine cooling fan through a Design for Six Sigma approach using Define, Measure, Analyze, Improve, and Control (DMAIC). Statistical process control (SPC) refers to a number of different methods for monitoring and assessing the quality of manufactured goods. Combined with methods from the design of experiments, SPC is used in programs that define, measure, analyze, improve, and control development and production processes. These programs are often implemented using "Design for Six Sigma" methodologies. This bok develops hypothesis test, ANOVA models, ANCOVA models, MANOVA models and MANCOVA models. It also develops Traditional experimental designs ("Full Factorial Designs," "Fractional Factorial Designs," and "Response Surface Designs") and D-Optimal designs. Also improve Design for Six Sigma approach using Define, Measure, Analyze, Improve, and Control (DMAIC). Finaly, he book develops Statistical process control (SPC) implemented using "Design for Six Sigma" methodologies.
An accessible and practical approach to the design and analysis of experiments in the health sciences Design and Analysis of Experiments in the Health Sciences provides a balanced presentation of design and analysis issues relating to data in the health sciences and emphasizes new research areas, the crucial topic of clinical trials, and state-of-the- art applications. Advancing the idea that design drives analysis and analysis reveals the design, the book clearly explains how to apply design and analysis principles in animal, human, and laboratory experiments while illustrating topics with applications and examples from randomized clinical trials and the modern topic of microarrays. The authors outline the following five types of designs that form the basis of most experimental structures: Completely randomized designs Randomized block designs Factorial designs Multilevel experiments Repeated measures designs A related website features a wealth of data sets that are used throughout the book, allowing readers to work hands-on with the material. In addition, an extensive bibliography outlines additional resources for further study of the presented topics. Requiring only a basic background in statistics, Design and Analysis of Experiments in the Health Sciences is an excellent book for introductory courses on experimental design and analysis at the graduate level. The book also serves as a valuable resource for researchers in medicine, dentistry, nursing, epidemiology, statistical genetics, and public health.
Intercropping is an area of research for which there is a desperate need, both in developing countries where people are rapidly depleting scarce resources and still starving, and in developed countries, where more ecologically and economically sound ways of feeding ourselves must be developed. The only published guidelines for conducting such research and analyzing the data have been scattered about in various journal articles, many of which are hard to find. This book condenses these methods and will be immensely valuable to agricultural researchers and to the statisticians who help them design their experiments and interpret their results.
This book discusses special modifications and extensions of designs that arise in certain fields of application such as genetics, bioinformatics, agriculture, medicine, manufacturing, marketing, etc. Well-known and highly-regarded contributors have written individual chapters that have been extensively reviewed by the Editor to ensure that each individual contribution relates to material found in Volumes 1 and 2 of this book series. The chapters in Volume 3 have an introductory/historical component and proceed to a more advanced technical level to discuss the latest results and future developm.
A revision of this classic statistics text for first-year graduate students in psychology, education and related social sciences. The two new authors are former students of Winer's. They have updated, rewritten and reorganized the text to fit the course as it is now taught.
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.
A complete and well-balanced introduction to modern experimentaldesign Using current research and discussion of the topic along withclear applications, Modern Experimental Design highlightsthe guiding role of statistical principles in experimental designconstruction. This text can serve as both an applied introductionas well as a concise review of the essential types of experimentaldesigns and their applications. Topical coverage includes designs containing one or multiplefactors, designs with at least one blocking factor, split-unitdesigns and their variations as well as supersaturated andPlackett-Burman designs. In addition, the text contains extensivetreatment of: Conditional effects analysis as a proposed general method ofanalysis Multiresponse optimization Space-filling designs, including Latin hypercube and uniformdesigns Restricted regions of operability and debarredobservations Analysis of Means (ANOM) used to analyze data from varioustypes of designs The application of available software, including Design-Expert,JMP, and MINITAB This text provides thorough coverage of the topic while alsointroducing the reader to new approaches. Using a large number ofreferences with detailed analyses of datasets, ModernExperimental Design works as a well-rounded learning tool forbeginners as well as a valuable resource for practitioners.