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This book describes a practical aproach to univariate and multivariate analysis of variance. It starts with a general non-mathematical account of the fundamental theories and this is followed by a discussion of a series of examples using real data sets from the authors' own work in clinical trials, psychology and industry. Included are discussions of factorial and nested designs, structures on the multiple dependent variables measured on each subject, repeated measures analyses, covariates, choice of text statistic and simultaneous test procedures.
Historical origins of MANOVA; Era of multivariate techniques; Sequential trends in application of multivariate techniques; Conceptual theory underlying MANOVA; Parallels between univariate ANOVA and multivariate MANOVA; Factor analysis and MANOVA; MANOVA tests of statistical significance; Differential sensitivity of test criteria related to distribution of trace; Assumptions underlying ANOVA and MANOVA; Decision strategies; Decision errors; ANOVA power analysis; MANOVA power analysis; Bonferroni t; Classic MANOVA procedure; Hummel-sligo procedure; Mixed strategy; Classic research designs; Two preliminary issues; Control checklist; Origin of all classic ANOVA designs; Extension of t test for independent groups; Extension of the t test for matched pairs (subject as his or her own control); Mixed designs; Applications of MANOVA to classic research designs; Preliminary considerations; Classic designs; Application of MANOVA to univariate designs that involve repeated measures; Distinction between MANOVA applied to univariate and multivariate repeated-measures designs; Univariate analysis of repeated measures; A univariate procedure for analyzing repeated-measures designs; Multivariate analysis of variance of repeated-measures designs; Checklist for the investigator conducting MANOVA research; Decision to conduct a study or experiment; Selection of dependent variables; Selection of a MANOVA test criterion; Statement of problem; Research design; Computer program test; Selection of MANOVA strategy; Hierarchy of hypotheses; Reporting multivariate outcomes; Hand-calculated example of one-way (simple randomized) MANOVA.
Repeated measures data arise when the same characteristic is measured on each case or subject at several times or under several conditions. There is a multitude of techniques available for analysing such data and in the past this has led to some confusion. This book describes the whole spectrum of approaches, beginning with very simple and crude methods, working through intermediate techniques commonly used by consultant statisticians, and concluding with more recent and advanced methods. Those covered include multiple testing, response feature analysis, univariate analysis of variance approaches, multivariate analysis of variance approaches, regression models, two-stage line models, approaches to categorical data and techniques for analysing crossover designs. The theory is illustrated with examples, using real data brought to the authors during their work as statistical consultants.
Thesis (M.A.) from the year 1999 in the subject Mathematics - Statistics, grade: Passed, RMIT, course: MAppSc, language: English, abstract: This thesis considers both univariate and multivariate approaches to the analysis of a set of repeated-measures data. Since repeated measures on the same subject are correlated over time, the usual analysis of variance assumption of independence is often violated. The models in this thesis demonstrate different approaches to the analysis of repeated-measures data, and highlight their advantages and disadvantages. Milk from two groups of lactating cows, one group vaccinated, the other not, was analysed every month after calving for eight months in order to measure the amount of bacteria in the milk. The primary goal of the experiment was to determine if a vaccine developed by the Royal Melbourne Institute of Technology's Biology Department led to a significant decrease in mean bacteria production per litre of milk produced compared to the control group. A univariate model suitable for repeated measures data was initially tried, with mean bacteria production in the treatment group not significantly different from the control group (p
Focusing on situations in which analysis of variance (ANOVA) involving the repeated measurement of separate groups of individuals is needed, Girden reveals the advantages, disadvantages, and counterbalancing issues of repeated measures situations. Using additive and nonadditive models to guide the analysis in each chapter, the book covers such topics as the rationale for partitioning the sum of squares, detailed analyses to facilitate the interpretation of computer printouts, the rationale for the F ratios in terms of expected means squares, validity assumptions for sphericity or circularity and approximate tests to perform when sphericity is not met.
A complete guide to powerful and practical statistical modeling using MANOVA Numerous statistical applications are time dependent. Virtually all biomedical, pharmaceutical, and industrial experiments demand repeated measurements over time. The same holds true for market research and analysis. Yet conventional methods, such as the Repeated Measures Analysis of Variance (Rm ANOVA), do not always yield exact solutions, obliging practitioners to settle for asymptotic results and approximate solutions. Generalized inference in Multivariate Analysis of Variance (MANOVA), mixed models, and growth curves offer exact methods of data analysis under milder conditions without deviating from the conventional philosophy of statistical inference. Generalized Inference in Repeated Measures is a concise, self-contained guide to the use of these innovative solutions, presenting them as extensions of–rather than alternatives to–classical methods of statistical evaluation. Requiring minimal prior knowledge of statistical concepts in the evaluation of linear models, the book provides exact parametric methods for each application considered, with solutions presented in terms of generalized p-values. Coverage includes: New concepts in statistical inference, with special focus on generalized p-values and generalized confidence intervals One-way and two-way ANOVA, in cases of equal and unequal variances Basic and higher-way mixed models, including testing and estimation of fixed effects and variance components Multivariate populations, including basic inference, comparison, and analysis of variance Basic, widely used repeated measures models including crossover designs and growth curves With a comprehensive set of formulas, illustrative examples, and exercises in each chapter, Generalized Inference in Repeated Measures is ideal as both a comprehensive reference for research professionals and a text for students.
Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics
"This book focuses on the practical aspects of modern and robust statistical methods. The increased accuracy and power of modern methods, versus conventional approaches to the analysis of variance (ANOVA) and regression, is remarkable. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems with standard methods that seemed insurmountable only a few years ago"--