Research Design & Statistical AnalysisPsychology Press, 30 jan 2003 - 736 pagina's This book emphasizes the statistical concepts and assumptions necessary to describe and make inferences about real data. Throughout the book the authors encourage the reader to plot and examine their data, find confidence intervals, use power analyses to determine sample size, and calculate effect sizes. The goal is to ensure the reader understands the underlying logic and assumptions of the analysis and what it tells them, the limitations of the analysis, and the possible consequences of violating assumptions. The simpler, less abstract discussion of analysis of variance is presented prior to developing the more general model. A concern for alternatives to standard analyses allows for the integration of non-parametric techniques into relevant design chapters, rather than in a single, isolated chapter. This organization allows for the comparison of the pros and cons of alternative procedures within the research context to which they apply. Basic concepts, such as sampling distributions, expected mean squares, design efficiency, and statistical models are emphasized throughout. This approach provides a stronger conceptual foundation in order to help the reader generalize the concepts to new situations they will encounter in their research and to better understand the advice of statistical consultants and the content of articles using statistical methodology. The second edition features a greater emphasis on graphics, confidence intervals, measures of effect size, power analysis, tests of contrasts, elementary probability, correlation, and regression. A Free CD that contains several real and artificial data sets used in the book in SPSS, SYSTAT, and ASCII formats, is included in the back of the book. An Instructor's Solutions Manual, containing the intermediate steps to all of the text exercises, is available free to adopters. |
Inhoudsopgave
Chapter 1 Introduction | 1 |
Univariate Distributions | 10 |
Relations Between Quantitative Variables | 39 |
Chapter 4 Probability and the Binomial Distribution | 66 |
The Normal Distribution | 108 |
The t Distribution | 153 |
Chapter 7 The ChiSquare and F Distributions | 190 |
One Factor | 210 |
Chapter 16 Hierarchical Designs | 483 |
Chapter 17 Latin Squares and Related Designs | 506 |
Chapter 18 More About Correlation | 532 |
Chapter 19 More About Bivariate Regression | 576 |
Chapter 20 Multiple Regression | 625 |
The General Linear Model | 683 |
Notation and Summation Operations | 713 |
Expected Values and Their Applications | 724 |
Chapter 9 Contrasts Among Means | 257 |
Chapter 10 Trend Analysis | 294 |
Significance Tests in the TwoWay Case | 314 |
Further Developments | 348 |
Chapter 13 RepeatedMeasures Designs | 378 |
BetweenSubjects and WithinSubjects Factors | 427 |
Blocking and Analysis of Covariance | 457 |
Statistical Tables | 729 |
Answers to Selected Exercises | 765 |
Endnotes | 804 |
814 | |
827 | |
837 | |
Overige edities - Alles bekijken
Research Design and Statistical Analysis, Volume 1 Jerome L. Myers,Arnold Well Geen voorbeeld beschikbaar - 2003 |
Veelvoorkomende woorden en zinsdelen
analysis ANOVA assume assumption average box plots calculated cell means change scores Chapter cholesterol component confidence interval consider contrast correlation coefficient covariate critical value data points data set degrees of freedom depression scores discussed dummy variables effect size equal error rate error term error variance estimate example expected mean squares experiment F ratio F test factors function group means independent variable Latin square linear main effects measures median method multiple regression normally distributed Note null hypothesis obtained outliers panel participants performance plot population means population parameters population variance predicted presented probability procedure random regression coefficients regression equation reject H0 relation repeated-measures design residual sampling distribution scatterplot Seasons significance tests skewed slope sources of variance SPSS standard deviation standard error subjects sum of squares SYSTAT Table test statistic Type 1 error within-subjects zero