Statistical power analysis, Myors, Brett (griffith University, Australia) Murphy, Kevin R.
Старое издание
Автор: Myors, Brett Название: Statistical Power Analysis ISBN: 1848725884 ISBN-13(EAN): 9781848725881 Издательство: Taylor&Francis Цена: 53070 T Наличие на складе: Нет в наличии.
Автор: McElreath, Richard Название: Statistical Rethinking ISBN: 036713991X ISBN-13(EAN): 9780367139919 Издательство: Taylor&Francis Рейтинг: Цена: 83690.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowledge/confidence in statistical modeling. Pushes readers to perform step-by-step calculations (usually automated.) Unique, computational approach.
Автор: Fagerland, Morten , Lydersen, Stian , Laake, Pet Название: Statistical Analysis of Contingency Tables ISBN: 0367495260 ISBN-13(EAN): 9780367495268 Издательство: Taylor&Francis Рейтинг: Цена: 59190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is an invaluable tool for statistical inference in contingency tables. It covers effect size estimation, confidence intervals, and hypothesis tests for the binomial and the multinomial distributions, unpaired and paired 2x2 tables, rxc tables, ordered rx2 and 2xc tables, paired cxc tables, and stratified tables.
Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data.
New to the Second Edition
Reorganized to focus on unbalanced data
Reworked balanced analyses using methods for unbalanced data
Introductions to nonparametric and lasso regression
Introductions to general additive and generalized additive models
Examination of homologous factors
Unbalanced split plot analyses
Extensions to generalized linear models
R, Minitab(R), and SAS code on the author's website
The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.
Автор: He, Yulei Название: Multiple Imputation Analysis For Ob ISBN: 1498722067 ISBN-13(EAN): 9781498722063 Издательство: Taylor&Francis Рейтинг: Цена: 91860.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis.
Автор: Davey, Adam Название: Statistical Power Analysis with Missing Data ISBN: 0805863702 ISBN-13(EAN): 9780805863703 Издательство: Taylor&Francis Рейтинг: Цена: 46950.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Liu, Xiaofeng Steven Название: Statistical Power Analysis for the Social and Behavioral Sciences ISBN: 1848729804 ISBN-13(EAN): 9781848729803 Издательство: Taylor&Francis Рейтинг: Цена: 163330.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Liu, Xiaofeng Steven Название: Statistical Power Analysis for the Social and Behavioral Sciences ISBN: 1848729812 ISBN-13(EAN): 9781848729810 Издательство: Taylor&Francis Рейтинг: Цена: 57150.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Davey, Adam Savla, Jyoti Название: Statistical power analysis with missing data ISBN: 0805863699 ISBN-13(EAN): 9780805863697 Издательство: Taylor&Francis Рейтинг: Цена: 137810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as:
How missing data affects the statistical power in a study
How much power is likely with different amounts and types of missing data
How to increase the power of a design in the presence of missing data, and
How to identify the most powerful design in the presence of missing data.
Points of Reflection encourage readers to stop and test their understanding of the material. Try Me sections test one's ability to apply the material. Troubleshooting Tips help to prevent commonly encountered problems. Exercises reinforce content and Additional Readings provide sources for delving more deeply into selected topics. Numerous examples demonstrate the book's application to a variety of disciplines. Each issue is accompanied by its potential strengths and shortcomings and examples using a variety of software packages (SAS, SPSS, Stata, LISREL, AMOS, and MPlus). Syntax is provided using a single software program to promote continuity but in each case, parallel syntax using the other packages is presented in appendixes. Routines, data sets, syntax files, and links to student versions of software packages are found at www.psypress.com/davey. The worked examples in Part 2 also provide results from a wider set of estimated models. These tables, and accompanying syntax, can be used to estimate statistical power or required sample size for similar problems under a wide range of conditions.
Class-tested at Temple, Virginia Tech, and Miami University of Ohio, this brief text is an ideal supplement for graduate courses in applied statistics, statistics II, intermediate or advanced statistics, experimental design, structural equation modeling, power analysis, and research methods taught in departments of psychology, human development, education, sociology, nursing, social work, gerontology and other social and health sciences. The book's applied approach will also appeal to researchers in these areas. Sections covering Fundamentals, Applications, and Extensions are designed to take readers from first steps to mastery.
Автор: Myors, Brett (Griffith University, Australia) Murphy, Kevin R. Название: Statistical Power Analysis ISBN: 1032283017 ISBN-13(EAN): 9781032283012 Издательство: Taylor&Francis Рейтинг: Цена: 132710.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Statistical Power Analysis explains the key concepts in statistical power analysis and illustrates their application in both tests of traditional null hypotheses. It provides readers with the tools to understand and perform power analyses for virtually all the statistical methods used in the social and behavioral sciences.
Автор: Kraemer Helena C., Blasey Christine M. Название: How Many Subjects?: Statistical Power Analysis in Research ISBN: 1483319547 ISBN-13(EAN): 9781483319544 Издательство: Sage Publications Рейтинг: Цена: 39060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Addressing a common question posed by researchers, this book introduces readers to power analysis and sample size determination and clearly illustrates why sample sizes need to be sufficiently large to give good power properties and low error rates.
Автор: Christopher H Schmid Название: Handbook Of Meta-Analysis ISBN: 1498703984 ISBN-13(EAN): 9781498703987 Издательство: Taylor&Francis Рейтинг: Цена: 163330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Meta-analysis is the application of statistics to combine results from multiple studies and draw appropriate inferences. Its use and importance have exploded over the years as the need for a robust evidence base has become clear in many scientific areas like medicine and health, social sciences, education, psychology, ecology and economics.
Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain their assumptions and how tests work in future publications. Numerous examples from psychology, education, and other social sciences demonstrate varied applications of the material. Basic statistics and probability are reviewed for those who need a refresher. Mathematical derivations are placed in optional appendices for those interested in this detailed coverage.
Highlights include the following:
Unique coverage of categorical and nonparametric statistics better prepares readers to select the best technique for their particular research project; however, some chapters can be omitted entirely if preferred.
Step-by-step examples of each test help readers see how the material is applied in a variety of disciplines.
Although the book can be used with any program, examples of how to use the tests in SPSS and Excel foster conceptual understanding.
Exploring the Concept boxes integrated throughout prompt students to review key material and draw links between the concepts to deepen understanding.
Problems in each chapter help readers test their understanding of the material.
Emphasis on selecting tests that maximize power helps readers avoid "marginally" significant results.
Website (www.routledge.com/9781138787827) features datasets for the book's examples and problems, and for the instructor, PowerPoint slides, sample syllabi, answers to the even-numbered problems, and Excel data sets for lecture purposes.
Intended for individual or combined graduate or advanced undergraduate courses in categorical and nonparametric data analysis, cross-classified data analysis, advanced statistics and/or quantitative techniques taught in psychology, education, human development, sociology, political science, and other social and life sciences, the book also appeals to researchers in these disciplines. The nonparametric chapters can be deleted if preferred. Prerequisites include knowledge of t tests and ANOVA.
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