The Experts below are selected from a list of 683970 Experts worldwide ranked by ideXlab platform
Sarah L Vowler - One of the best experts on this subject based on the ideXlab platform.
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Analysis of Variance variably complex
Advances in Physiology Education, 2012Co-Authors: Gordon B Drummond, Sarah L VowlerAbstract:Key Points to compare two groups, we described how the t test is used ([1][1], [2][2]). To compare more than two groups, we would use a different test, Analysis of Variance (ANOVA). We start with a premise very similar to the logic of the t test: is it possible that these groups could have been
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Analysis of Variance variably complex
Microcirculation, 2012Co-Authors: Gordon B Drummond, Sarah L VowlerAbstract:Please cite this paper as: Drummond GB, Vowler SL. Analysis of Variance: variably complex. Microcirculation 19: 280–283, 2012.
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Analysis of Variance variably complex
The Journal of Physiology, 2012Co-Authors: Gordon B Drummond, Sarah L VowlerAbstract:To compare two groups, we described how the t test is used (Drummond & Tom, 2011a,b). To compare more than two groups, we would use a different test, Analysis of Variance (ANOVA). We start with a premise very similar to the logic of the t test: is it possible that these groups could have been sampled from a single population? A variety of forms of Analysis of Variance exist, and the test can be used (and misused!) in different ways. Some of these variants are very useful in the Analysis of common experimental designs, when more than one intervention is used. To appreciate the different types of test of this sort, we will go back to the jumping frogs that we discussed before (Drummond & Tom, 2011a). Let's suppose that we have a random sample of 30 frogs from California and also have 30 frogs sampled at random from Texas, and 30 from Ohio. We want to know if the means of the jump distance differ, according to the origin of the frogs.
Gordon B Drummond - One of the best experts on this subject based on the ideXlab platform.
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Analysis of Variance variably complex
Advances in Physiology Education, 2012Co-Authors: Gordon B Drummond, Sarah L VowlerAbstract:Key Points to compare two groups, we described how the t test is used ([1][1], [2][2]). To compare more than two groups, we would use a different test, Analysis of Variance (ANOVA). We start with a premise very similar to the logic of the t test: is it possible that these groups could have been
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Analysis of Variance variably complex
Microcirculation, 2012Co-Authors: Gordon B Drummond, Sarah L VowlerAbstract:Please cite this paper as: Drummond GB, Vowler SL. Analysis of Variance: variably complex. Microcirculation 19: 280–283, 2012.
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Analysis of Variance variably complex
The Journal of Physiology, 2012Co-Authors: Gordon B Drummond, Sarah L VowlerAbstract:To compare two groups, we described how the t test is used (Drummond & Tom, 2011a,b). To compare more than two groups, we would use a different test, Analysis of Variance (ANOVA). We start with a premise very similar to the logic of the t test: is it possible that these groups could have been sampled from a single population? A variety of forms of Analysis of Variance exist, and the test can be used (and misused!) in different ways. Some of these variants are very useful in the Analysis of common experimental designs, when more than one intervention is used. To appreciate the different types of test of this sort, we will go back to the jumping frogs that we discussed before (Drummond & Tom, 2011a). Let's suppose that we have a random sample of 30 frogs from California and also have 30 frogs sampled at random from Texas, and 30 from Ohio. We want to know if the means of the jump distance differ, according to the origin of the frogs.
Marti J Anderson - One of the best experts on this subject based on the ideXlab platform.
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permutation tests for multi factorial Analysis of Variance
Journal of Statistical Computation and Simulation, 2003Co-Authors: Marti J Anderson, Cajo J F Ter BraakAbstract:Several permutation strategies are often possible for tests of individual terms in Analysis-of-Variance (ANOVA) designs. These include restricted permutations, permutation of whole groups of units, permutation of some form of residuals or some combination of these. It is unclear, especially for complex designs involving random factors, mixed models or nested hierarchies, just which permutation strategy should be used for any particular test. The purpose of this paper is two-fold: (i) we provide a guideline for constructing an exact permutation strategy, where possible, for any individual term in any ANOVA design; and (ii) we provide results of Monte Carlo simulations to compare the level accuracy and power of different permutation strategies in two-way ANOVA, including random and mixed models, nested hierarchies and tests of interaction terms. Simulation results showed that permutation of residuals under a reduced model generally had greater power than the exact test or alternative approximate permutation...
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permutation tests for univariate or multivariate Analysis of Variance and regression
Canadian Journal of Fisheries and Aquatic Sciences, 2001Co-Authors: Marti J AndersonAbstract:The most appropriate strategy to be used to create a permutation distribution for tests of individual terms in complex experimental designs is currently unclear. There are often many possibilities, including restricted permutation or permutation of some form of residuals. This paper provides a summary of recent empirical and theoretical results concerning available methods and gives recommendations for their use in univariate and multivariate applications. The focus of the paper is on complex designs in Analysis of Variance and multiple regression (i.e., linear models). The assumption of exchangeability required for a permutation test is assured by random allocation of treatments to units in experimental work. For observational data, exchangeability is tantamount to the assumption of independent and identically distributed errors under a null hypothesis. For partial regression, the method of permutation of residuals under a reduced model has been shown to provide the best test. For Analysis of Variance, o...
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a new method for non parametric multivariate Analysis of Variance
Austral Ecology, 2001Co-Authors: Marti J AndersonAbstract:Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of Variance is particularly powerful for the Analysis of univariate data. The traditional multivariate analogues, however, are too stringent in their assumptions for most ecological multivariate data sets. Non-parametric methods, based on permutation tests, are preferable. This paper describes a new non-parametric method for multivariate Analysis of Variance, after McArdle and Anderson (in press). It is given here, with several applications in ecology, to provide an alternative and perhaps more intuitive formulation for ANOVA (based on sums of squared distances) to complement the description pro- vided by McArdle and Anderson (in press) for the Analysis of any linear model. It is an improvement on previous non-parametric methods because it allows a direct additive partitioning of variation for complex models. It does this while maintaining the flexibility and lack of formal assumptions of other non-parametric methods. The test- statistic is a multivariate analogue to Fisher's F-ratio and is calculated directly from any symmetric distance or dissimilarity matrix. P-values are then obtained using permutations. Some examples of the method are given for tests involving several factors, including factorial and hierarchical (nested) designs and tests of interactions.
Christopher C. Cheatham - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical Linear Model: Thinking Outside the Traditional Repeated-Measures Analysis-of-Variance Box
Journal of athletic training, 2015Co-Authors: Monica R. Lininger, Jessaca Spybrook, Christopher C. CheathamAbstract:Longitudinal designs are common in the field of athletic training. For example, in the Journal of Athletic Training from 2005 through 2010, authors of 52 of the 218 original research articles used longitudinal designs. In 50 of the 52 studies, a repeated-measures Analysis of Variance was used to analyze the data. A possible alternative to this approach is the hierarchical linear model, which has been readily accepted in other medical fields. In this short report, we demonstrate the use of the hierarchical linear model for analyzing data from a longitudinal study in athletic training. We discuss the relevant hypotheses, model assumptions, Analysis procedures, and output from the HLM 7.0 software. We also examine the advantages and disadvantages of using the hierarchical linear model with repeated measures and repeated-measures Analysis of Variance for longitudinal data.
Richard A Armstrong - One of the best experts on this subject based on the ideXlab platform.
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the application of Analysis of Variance anova to different experimental designs in optometry
Ophthalmic and Physiological Optics, 2002Co-Authors: Richard A Armstrong, Frank Eperjesi, Bernard GilmartinAbstract:Analysis of Variance (ANOVA) is the most efficient method available for the Analysis of experimental data. Analysis of Variance is a method of considerable complexity and subtlety, with many different variations, each of which applies in a particular experimental context. Hence, it is possible to apply the wrong type of ANOVA to data and, therefore, to draw an erroneous conclusion from an experiment. This article reviews the types of ANOVA most likely to arise in clinical experiments in optometry including the one-way ANOVA ('fixed' and 'random effect' models), two-way ANOVA in randomised blocks, three-way ANOVA, and factorial experimental designs (including the varieties known as 'split-plot' and 'repeated measures'). For each ANOVA, the appropriate experimental design is described, a statistical model is formulated, and the advantages and limitations of each type of design discussed. In addition, the problems of non-conformity to the statistical model and determination of the number of replications are considered. © 2002 The College of Optometrists.
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an introduction to Analysis of Variance anova with special reference to data from clinical experiments in optometry
Ophthalmic and Physiological Optics, 2000Co-Authors: Richard A Armstrong, S V Slade, Frank EperjesiAbstract:This article is aimed primarily at eye care practitioners who are undertaking advanced clinical research, and who wish to apply Analysis of Variance (ANOVA) to their data. ANOVA is a data Analysis method of great utility and flexibility. This article describes why and how ANOVA was developed, the basic logic which underlies the method and the assumptions that the method makes for it to be validly applied to data from clinical experiments in optometry. The application of the method to the Analysis of a simple data set is then described. In addition, the methods available for making planned comparisons between treatment means and for making post hoc tests are evaluated. The problem of determining the number of replicates or patients required in a given experimental situation is also discussed. Copyright (C) 2000 The College of Optometrists.
