Research Design and Statistical AnalysisHarperCollins, 1991 - 713 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. |
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A₁ A₂ analysis of variance ANOVA assume assumption average B₁ B₂ between-subjects design box plots C₁ calculate cell means Chapter column components confidence interval consider contrasts covariance data of Table data set difference scores equal error rate error term error variance estimate example expected mean squares experiment experimental F ratio F statistic F test factors grand mean group means H₁ independent variable interaction effects levels linear combination main effect matrix measure MSSA nonadditive normally distributed Note null hypothesis obtained orthogonal panel plot population means population of scores population variance predicted predictor presented pretest probability procedure quadratic random regression coefficients regression equation reject repeated-measures design residual result sample mean sampling distribution Section SSAB standard error subjects sum of squares SYSTAT test statistic tion treatment population Type 1 error weights within-subjects X₁ Y₁ zero