Download Free A Monte Carlo Study To Determine Sample Size For Multiple Comparison Procedures In Anova Book in PDF and EPUB Free Download. You can read online A Monte Carlo Study To Determine Sample Size For Multiple Comparison Procedures In Anova and write the review.

A study was conducted on the multiple comparison methods presented by Scheffe, Tukey, Student-Newman-Keuls, and Duncan under the experimental situation in which all populations were normal with equal variances and all means but one were equal. The characteristics of all four test procedures were compared for the case of multiple comparisons of pairs of means. These tests were conducted both with and without the prior performance of an analysis of variance. The Tukey and Scheffe procedures were compared in tests of linear combinations of three means. Estimates were made of the power of the tests and of Type I error rates under both the null and alternate hypotheses. Scheffe's method was found to be too conservative for pairwise comparisons of means, but it was to be preferred over Tukey's method for combinations of more than two means. Duncan's method was the most powerful test of pairwise comparisons, but it maintained little control over one kind of Type I error. The S-N-K procedure showed a good balance between power and control of Type I errors. (Author).
If you conduct research with more than two groups and want to find out if they are significantly different when compared two at a time, then you need Multiple Comparison Procedures. Using examples to illustrate major concepts, this concise volume is your guide to multiple comparisons. Toothaker thoroughly explains such essential issues as planned vs. post-hoc comparisons, stepwise vs. simultaneous test procedures, types of error rate, unequal sample sizes and variances, and interaction tests vs. cell mean tests.
A study was conducted on the multiple comparison methods presented by Scheffe, Tukey, Student-Newman-Keuls, and Duncan under the experimental situation in which all populations were normal with equal variances and all means but one were equal. The characteristics of all four test procedures were compared for the case of multiple comparisons of pairs of means. These tests were conducted both with and without the prior performance of an analysis of variance. The Tukey and Scheffe procedures were compared in tests of linear combinations of three means. Estimates were made of the power of the tests and of Type I error rates under both the null and alternate hypotheses. Scheffe's method was found to be too conservative for pairwise comparisons of means, but it was to be preferred over Tukey's method for combinations of more than two means. Duncan's method was the most powerful test of pairwise comparisons, but it maintained little control over one kind of Type I error. The S-N-K procedure showed a good balance between power and control of Type I errors. (Author).
Praise for the Second Edition "Statistics for Research has other fine qualities besides superior organization. The examples and the statistical methods are laid out with unusual clarity by the simple device of using special formats for each. The book was written with great care and is extremely user-friendly."—The UMAP Journal Although the goals and procedures of statistical research have changed little since the Second Edition of Statistics for Research was published, the almost universal availability of personal computers and statistical computing application packages have made it possible for today's statisticians to do more in less time than ever before. The Third Edition of this bestselling text reflects how the changes in the computing environment have transformed the way statistical analyses are performed today. Based on extensive input from university statistics departments throughout the country, the authors have made several important and timely revisions, including: Additional material on probability appears early in the text New sections on odds ratios, ratio and difference estimations, repeated measure analysis, and logistic regression New examples and exercises, many from the field of the health sciences Printouts of computer analyses on all complex procedures An accompanying Web site illustrating how to use SAS® and JMP® for all procedures The text features the most commonly used statistical techniques for the analysis of research data. As in the earlier editions, emphasis is placed on how to select the proper statistical procedure and how to interpret results. Whenever possible, to avoid using the computer as a "black box" that performs a mysterious process on the data, actual computational procedures are also given. A must for scientists who analyze data, professionals and researchers who need a self-teaching text, and graduate students in statistical methods, Statistics for Research, Third Edition brings the methodology up to date in a very practical and accessible way.
Contents: Statement of the statistical problem S atistical assumptions The experimenter's goal, specification, and requirement Procedure D and the new computing formulae Description of Procedure D Definition of symbols The sampling, stopping, and terminal de cision rules Computation of the stopping statistic Use of Procedure D (method B) with various experimental designs Simplified computing formulae Numerical example Monte Carlo sampling results with Procedure D Description of the sampling procedure Sampling results Discussion of sampling results.
"Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. It covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; it addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; it provides summaries of advantages and disadvantages of often-used strategies; and it uses hundreds of sample tables, figures, and equations based on real-life cases."--Publisher's description.
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.