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- How Many Subjects? by Helena Chmura Kraemer; Christine M. BlaseyISBN: 9781483319544Publication Date: 2015-02-05With increased emphasis on helping readers understand the context in which power calculations are done, this Second Edition introduces a simple technique of statistical power analysis that allows researchers to compute approximate sample sizes and power for a wide range of research designs. Because the same technique is used with only slight modifications for different statistical tests, researchers can then easily compare the sample sizes required by different designs and tests to make cost-effective decisions in planning a study. These comparisons demonstrate important principles of design, measurement, and analysis that are rarely discussed in courses or textbooks, making this book a valuable instructional resource as well as a must-have guide for frequent reference. New to this edition: Power computations are now placed in the proper context as one small but crucial step in applying the scientific method. The number of tests to which the methods can be applied has been extended. The book now incorporates the authors' experience where errors in design and interpretation of statistical hypothesis testing occur. Recent emphasis on effect sizes rather than p-values is endorsed and emphasized throughout. Recent developments in consideration of moderators and mediators are acknowledged.
- Regression and Linear Modeling by Jason W. OsborneISBN: 9781506302768Publication Date: 2016-05-05In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. Author Jason W. Osborne returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.
- Analysis of Nominal Data by H. T. ReynoldsISBN: 9780803906532Publication Date: 1984-07-01The updated second edition offers expanded discussions of the chi square test of significance and the potential measures of association available for use with categoric data. Reviewing basic techniques in analysis of nominal data, this paper employs survey research data on party identification and ideologies to indicate which measures and tests are most appropriate for particular theoretical concerns. This book serves as an ideal primer for Volume 20, Log-Linear Models.Learn more about "The Little Green Book" - QASS Series! Click Here
- Introduction to Analysis of Variance by J. Rick Turner; Julian F. ThayerISBN: 9780803970755Publication Date: 2001-04-13Having 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.
- Logistic Regression by Scott MenardISBN: 9781412974837Publication Date: 2009-04-29In this text, author Scott Menard provides coverage of not only the basic logistic regression model but also advanced topics found in no other logistic regression text. The book keeps mathematical notation to a minimum, making it accessible to those with more limited statistics backgrounds, while including advanced topics of interest to more statistically sophisticated readers. Not dependent on any one software package, the book discusses limitations to existing software packages and ways to overcome them. Key Features Examines the logistic regression model in detailIllustrates concepts with applied examples to help readers understand how concepts are translated into the logistic regression model Helps readers make decisions about the criteria for evaluating logistic regression models through detailed coverage of how to assess overall models and individual predictors for categorical dependent variables Offers unique coverage of path analysis with logistic regression that shows readers how to examine both direct and indirect effects using logistic regression analysis Applies logistic regression analysis to longitudinal panel data, helping students understand the issues in measuring change with dichotomous, nominal, and ordinal dependent variablesShows readers how multilevel change models with logistic regression are different from multilevel growth curve models for continuous interval or ratio-scaled dependent variablesLogistic Regression is intended for courses such as Regression and Correlation, Intermediate/Advanced Statistics, and Quantitative Methods taught in departments throughout the behavioral, health, mathematical, and social sciences, including applied mathematics/statistics, biostatistics, criminology/criminal justice, education, political science, public health/epidemiology, psychology, and sociology.
- Best Practices in Logistic Regression by Jason W. OsborneISBN: 9781452244792Publication Date: 2014-03-11Throughout the book the author uses simple, intuitive examples from a range of disciplines to demonstrate important aspects of logistic regression and its many uses. The author explains these concepts clearly and seeks to further developed students′ understanding with the use of cases, vignettes and a multitude of figures and tables.
- Experimental Design and the Analysis of Variance by Robert K. LeikISBN: 9780803990067Publication Date: 1997-04-19Why is this Book a Useful Supplement for Your Statistics Course? Most core statistics texts cover subjects like analysis of variance and regression, but not in much detail. This book, as part of our Series in Research Methods and Statistics, provides you with the flexibility to cover ANOVA more thoroughly, but without financially overburdening your students.

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