Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data. The book’s goal is to provide a strong conceptual foundation to enable readers to generalize concepts to new research situations. Emphasis is placed on the underlying logic and assumptions of the analysis and what it tells the researcher, the limitations of the analysis, and the consequences of violating assumptions. Sampling, design efficiency, and statistical models are emphasized throughout. As per APA recommendations, emphasis is also placed on data exploration, effect size measures, confidence intervals, and using power analyses to determine sample size. "Real-world" data sets are used to illustrate data exploration, analysis, and interpretation. The book offers a rare blend of the underlying statistical assumptions, the consequences of their violations, and practical advice on dealing with them. Changes in the New Edition: Each section of the book concludes with a chapter that provides an integrated example of how to apply the concepts and procedures covered in the chapters of the section. In addition, the advantages and disadvantages of alternative designs are discussed. A new chapter (1) reviews the major steps in planning and executing a study, and the implications of those decisions for subsequent analyses and interpretations. A new chapter (13) compares experimental designs to reinforce the connection between design and analysis and to help readers achieve the most efficient research study. A new chapter (27) on common errors in data analysis and interpretation. Increased emphasis on power analyses to determine sample size using the G*Power 3 program. Many new data sets and problems. More examples of the use of SPSS (PASW) Version 17, although the analyses exemplified are readily carried out by any of the major statistical software packages. A companion website with the data used in the text and the exercises in SPSS and Excel formats; SPSS syntax files for performing analyses; extra material on logistic and multiple regression; technical notes that develop some of the formulas; and a solutions manual and the text figures and tables for instructors only. Part 1 reviews research planning, data exploration, and basic concepts in statistics including sampling, hypothesis testing, measures of effect size, estimators, and confidence intervals. Part 2 presents between-subject designs. The statistical models underlying the analysis of variance for these designs are emphasized, along with the role of expected mean squares in estimating effects of variables, the interpretation of nteractions, and procedures for testing contrasts and controlling error rates. Part 3 focuses on repeated-measures designs and considers the advantages and disadvantages of different mixed designs. Part 4 presents detailed coverage of correlation and bivariate and multiple regression with emphasis on interpretation and common errors, and discusses the usefulness and limitations of these procedures as tools for prediction and for developing theory. This is one of the few books with coverage sufficient for a 2-semester course sequence in experimental design and statistics as taught in psychology, education, and other behavioral, social, and health sciences. Incorporating the analyses of both experimental and observational data provides continuity of concepts and notation. Prerequisites include courses on basic research methods and statistics. The book is also an excellent resource for practicing researchers.
18.5 OTHER MEASURES OF CORRELATION In this section, we introduce several classes of correlation measures other than the usual Pearson product-moment correlation coefficient. We first discuss four measures used when one or both variables ...
Roy's largest root only uses the variance from the dimension that separates the groups most (the largest “root” or difference). Appropriate and very powerful when the DVs are strongly interrelated on a single dimension.
This book takes the reader through the entire research process: choosing a question, designing a study, collecting the data, using univariate, bivariate and multivariable analysis, and publishing the results.
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From Hypothesis to Results William E. Martin, Krista D. Bridgmon. Copyright © 2012 by John Wiley & Sons, Inc. All rights reserved. Published by JosseyBass A Wiley Imprint One Montgomery Street, Suite 1200, San Francisco, CA 941044594 ...
This volume is intended as a “quick fix”, allowing readers to look up information rapidly about various design types and statistical methods to see what the pros, cons, and indications for each are.
For a standard two sample t-test, the signal to noise ratio is called Cohen's d, which is estimated from data as (see Chap. 3): d = . Cohen's d tells you how easily you can discriminate different means. The mean difference is in the ...
125. Moore , D. W. , & Readence , J. E. ( 1984 ) . A quantitative and qualitative review of graphic organizer research . Journal of Educational Research , 78 , 11–17 . 126. Mumford , E. , Schlesinger , H. J. , & Glass , G. V. ( 1982 ) .
The text opens with introductory discussions of why psychologists conduct and analyze research before digging into the process of designing an experiment and performing statistical analyses.
This text takes a broader, more general and philosophical view of the statistics for the more fundamental aspects of design than do the standard treatments of experimental design.