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In the investigation of human behaviour, statistical techniques are employed widely in the social sciences. Whilst introductory statistics courses cover essential techniques, the complexities of behaviour demand that more flexible and comprehensive methods are also employed. Analysis of Variance (ANOVA) has become one of the most common of these and it is therefore essential for both student and researcher to have a thorough understanding of it. A Student's Guide to Analysis of Variance covers a range of statistical techniques associated with ANOVA, including single and multiple factor designs, various follow-up procedures such as post-hoc tests, and how to make sense of interactions. Suggestions on the best use of techniques and advice on how to avoid the pitfalls are included, along with guidelines on the writing of formal reports. Introductory level topics such as standard deviation, standard error and t-tests are revised, making this book an invaluable aid to all students for whom ANOVA is a compulsory topic. It will also serve as a useful refresher for the more advanced student and practising researcher.
In the investigation of human behaviour, statistical techniques are employed widely in the social sciences. Whilst introductory statistics courses cover essential techniques, the complexities of behaviour demand that more flexible and comprehensive methods are also employed. Analysis of Variance (ANOVA) has become one of the most common of these and it is therefore essential for both student and researcher to have a thorough understanding of it. A Student's Guide to Analysis of Variance covers a range of statistical techniques associated with ANOVA, including single and multiple factor designs, various follow-up procedures such as post-hoc tests, and how to make sense of interactions. Suggestions on the best use of techniques and advice on how to avoid the pitfalls are included, along with guidelines on the writing of formal reports. Introductory level topics such as standard deviation, standard error and t-tests are revised, making this book an invaluable aid to all students for whom ANOVA is a compulsory topic. It will also serve as a useful refresher for the more advanced student and practising researcher.
This book is a clear and straightforward guide to analysis of variance, the backbone of experimental research. It will show you how to interpret statistical results and translate them into prose that will clearly tell your audience what your data is saying. To help you become familiar with the techniques used in analysis of variance, there are plenty of end-of-chapter practice problems with suggested answers. As life in the laboratory doesnt always follow a script, there are both new and established techniques for coping with situations that deviate from the norm. Data analysis is not a closed subject, so there are pros and cons for the varied situations you will encounter. The final chapter gives the first elementary presentation of functional measurement, or information integration theory, a methodology built upon analysis of variance that is a powerful technique for studying cognitive processes. The accompanying CD contains CALSTAT, analysis of variance software that is easy to use (really!). In addition to programs for standard analysis, the software includes several specialized routines that have heretofore been presented only in journals. Analysis of Variance is an important resource for students and professionals in the social, behavioral, and neurosciences.
Accompanying CD-ROM contains ... "all of the book's data sets as well as exercises for each chapter."--Page 4 of cover.
Having trouble finding a book that shows you not only how to analyze data but also how to collect the data appropriately and fully interpret the analysis, too? Here′s a new book that does all this in a particularly readable fashion. Turner and Thayer′s text: Shows how to design an experiment in the best possible way to investigate the topic of interest Explains which associated analysis will best answer your research question Demonstrates how to conduct the analysis and then fully interpret the results in the context of your research question Organized so that the reader moves from the simplest type of design to more complex ones, the authors introduce five different kinds of ANOVA techniques and explain which design/analysis is appropriate to answer specific questions. They show how to perform each analysis using only a calculator to provide the reader with a better "feel" for the analyses than simply seeing the mathematical answers on a computer print-out. The book concludes with tips for tests on ANOVA, and descriptions of how to use the knowledge gained from the text to determine the credibility of claims made and "statistics" presented in various types of reports.
Statistical Analysis for Business Using JMP: A Student's Guide by Willbann D. Terpening is a complete and thorough introduction to business statistics using JMP. While designed for introductory business statistics courses at the undergraduate or MBA level, industry professionals wanting to brush up on their knowledge of statistics and those wanting an introduction to using JMP for statistical analysis will also find the book useful. The book starts with an introduction to using JMP in statistical analysis, basic descriptive statistics and graphical analysis, and the fundamentals of inferential statistics. The book then covers more advanced topics in inferential statistics organized around the analysis platforms of JMP. Topics include the effects of a qualitative variable on a quantitative variable (two group tests and analysis of variance), the effects of a qualitative variable on a qualitative variable (chi-square and contingency tables), the effects of a quantitative variable on a quantitative variable (simple regression and correlation), and the effects of a quantitative variable on a qualitative variable (logistic and multinomial regression). The final chapter provides an introduction to multivariate statistics and multiple regression.
This text presents a comprehensive treatment of basic statistical methods and their applications. It focuses on the analysis of variance and regression, but also addressing basic ideas in experimental design and count data. The book has four connecting themes: similarity of inferential procedures, balanced one-way analysis of variance, comparison of models, and checking assumptions. Most inferential procedures are based on identifying a scalar parameter of interest, estimating that parameter, obtaining the standard error of the estimate, and identifying the appropriate reference distribution. Given these items, the inferential procedures are identical for various parameters. Balanced one-way analysis of variance has a simple, intuitive interpretation in terms of comparing the sample variance of the group means with the mean of the sample variance for each group. All balanced analysis of variance problems are considered in terms of computing sample variances for various group means. Comparing different models provides a structure for examining both balanced and unbalanced analysis of variance problems and regression problems. Checking assumptions is presented as a crucial part of every statistical analysis. Examples using real data from a wide variety of fields are used to motivate theory. Christensen consistently examines residual plots and presents alternative analyses using different transformation and case deletions. Detailed examination of interactions, three factor analysis of variance, and a split-plot design with four factors are included. The numerous exercises emphasize analysis of real data. Senior undergraduate and graduate students in statistics and graduate students in other disciplines using analysis of variance, design of experiments, or regression analysis will find this book useful.
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.
This book helps students develop a conceptual understanding of a variety of statistical tests by linking the statistics with the computational steps and output from SPSS. Learning how statistical ideas map onto computation in SPSS will help students build a better understanding of both. For example, seeing exactly how the concept of variance is used in SPSS-how it is converted into a number based on real data, which other concepts it is associated with, and where it appears in various statistical tests-will not only help students understand how to use statistical tests in SPSS and how to interpret their output, but will also teach them about the concept of variance itself. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, pointing out wherever possible how the SPSS procedure and output connects back to the conceptual underpinnings of the test. Each of the steps is accompanied by annotated screen shots from SPSS, and relevant components of output are highlighted in both the text and in the figures. Sections explain the conceptual machinery underlying the statistical tests. In contrast to merely presenting the equations for computing the statistic, these sections describe the idea behind each test in plain language and help students make the connection between the ideas and SPSS procedures. These include extensive treatment of custom hypothesis testing in ANOVA, MANOVA, ANCOVA, and regression, and an entire chapter on the advanced matrix algebra functions available only through syntax in SPSS. The book will be appropriate for both advanced undergraduate and graduate level courses in statistics.
This textbook explains ANOVA designs for advanced undergraduates and graduate students in the behavioural sciences.