Data analysis for experimental design

著者

    • Gonzalez, Richard

書誌事項

Data analysis for experimental design

Richard Gonzalez

Guilford Press, c2009

  • : hardcover

大学図書館所蔵 件 / 全1件

この図書・雑誌をさがす

注記

Includes bibliographical references (p. 427-434) and index

内容説明・目次

内容説明

This engaging text shows how statistics and methods work together, demonstrating a variety of techniques for evaluating statistical results against the specifics of the methodological design. Richard Gonzalez elucidates the fundamental concepts involved in analysis of variance (ANOVA), focusing on single degree-of-freedom tests, or comparisons, wherever possible. Potential threats to making a causal inference from an experimental design are highlighted. With an emphasis on basic between-subjects and within-subjects designs, Gonzalez resists presenting the countless exceptions to the rule that make many statistics textbooks so unwieldy and confusing for students and beginning researchers. Ideal for graduate courses in experimental design or data analysis, the text may also be used by advanced undergraduates preparing to do senior theses. Useful pedagogical features include: *Discussions of the assumptions that underlie each statistical test *Sequential, step-by-step presentations of statistical procedures *End-of-chapter questions and exercises *Accessible writing style with scenarios and examples *A companion Web page (www.umich.edu/~gonzo/daed) offering data and syntax files in R and SPSS for the research examples used in the book, a short guide to SPSS syntax, and detailed course notes on each of the book's topics.

目次

_x000D_ _x000D_ 1. The Nature of Research _x000D_ 1.1 Introduction _x000D_ 1.2 Observations and Variables _x000D_ 1.3 Behavioral Variables _x000D_ 1.4 Stimulus Variables _x000D_ 1.5 Individual Difference Variables _x000D_ 1.6 Discrete and Continuous Variables _x000D_ 1.7 Levels of Measurement _x000D_ 1.8 Summarizing Observations in Research _x000D_ 1.9 Questions and Problems _x000D_ 2. Principles of Experimental Design _x000D_ 2.1 The Farmer from Whidbey Island _x000D_ 2.2 The Experiment _x000D_ 2.3 The Question of Interest _x000D_ 2.4 Sample Space and Probability _x000D_ 2.5 Simulation of the Experiment _x000D_ 2.6 Permutations _x000D_ 2.7 Combinations _x000D_ 2.8 Probabilities of Possible Outcomes _x000D_ 2.9 A Sample Space for the Experiment _x000D_ 2.10 Testing a Null Hypothesis _x000D_ 2.11 Type I and Type II Errors _x000D_ 2.12 Experimental Controls _x000D_ 2.13 The Importance of Randomization _x000D_ 2.14 A Variation in Design _x000D_ 2.15 Summary _x000D_ 2.16 Questions and Problems _x000D_ 3. The Standard Normal Distribution: An Amazing Approximation _x000D_ 3.1 Introduction _x000D_ 3.2 Binomial Populations and Binomial Variables _x000D_ 3.3 Mean of a Population _x000D_ 3.4 Variance and Standard Deviation of a Population _x000D_ 3.5 The Average of a Sum and the Variance of a Sum _x000D_ 3.6 The Average and Variance of Repeated Samples _x000D_ 3.7 The Second Experiment with the Farmer: T and sT _x000D_ 3.8 Representing Probabilities by Areas _x000D_ 3.9 The Standard Normal Distribution _x000D_ 3.10 The Second Experiment with the Farmer: A Normal Distribution Test _x000D_ 3.11 The First Experiment with the Farmer: A Normal Distribution Test _x000D_ 3.12 Examples of Binomial Models _x000D_ 3.13 Populations That Have Several Possible Values _x000D_ 3.14 The Distribution of the Sum from a Uniform Distribution _x000D_ 3.15 The Distribution of the Sum T from a U-Shaped Population _x000D_ 3.16 The Distribution of the Sum T from a Skewed Population _x000D_ 3.17 Summary and Sermon _x000D_ 3.18 Questions and Problems _x000D_ 4. Tests for Means from Random Samples _x000D_ 4.1 Transforming a Sample Mean into a Standard Normal Variable _x000D_ 4.2 The Variance and Standard Error of the Mean When the Population Variance s2 Is Known _x000D_ 4.3 The Variance and Standard Error of the Mean When Population s2 Is Unknown _x000D_ 4.4 The t Distribution and the One-Sample t Test _x000D_ 4.5 Confidence Interval for a Mean _x000D_ 4.6 Standard Error of the Difference between Two Means _x000D_ 4.7 Confidence Interval for a Difference between Two Means _x000D_ 4.8 Test of Significance for a Difference between Two Means: The Two-Sample t Test _x000D_ 4.9 Using a Computer Program _x000D_ 4.10 Returning to the Farmer Example in Chapter 2 _x000D_ 4.11 Effect Size for a Difference between Two Independent Means _x000D_ 4.12 The Null Hypothesis and Alternatives _x000D_ 4.13 The Power of the t Test against a Specified Alternative _x000D_ 4.14 Estimating the Number of Observations Needed in Comparing Two Treatment Means _x000D_ 4.15 Random Assignments of Participants _x000D_ 4.16 Attrition in Behavioral Science Experiments _x000D_ 4.17 Summary _x000D_ 4.18 Questions and Problems _x000D_ 5. Homogeneity and Normality Assumptions _x000D_ 5.1 Introduction _x000D_ 5.2 Testing Two Variances: The F Distribution _x000D_ 5.3 An Example of Testing the Homogeneity of Two Variances _x000D_ 5.4 Caveats _x000D_ 5.5 Boxplots _x000D_ 5.6 A t Test for Two Independent Means When the Population Variances Are Not Equal _x000D_ 5.7 Nonrandom Assignment of Subjects _x000D_ 5.8 Treatments That Operate Differentially on Individual Difference Variables _x000D_ 5.9 Nonadditivity of a Treatment Effect _x000D_ 5.10 Transformations of Raw Data _x000D_ 5.11 Normality _x000D_ 5.12 Summary _x000D_ 5.13 Questions and Problems _x000D_ 6. The Analysis of Variance: One Between-Subjects Factor _x000D_ 6.1 Introduction _x000D_ 6.2 Notation for a One-Way Between-Subjects Design _x000D_ 6.3 Sums of Squares for the One-Way Between-Subjects Design _x000D_ 6.4 One-Way Between-Subjects Design: An Example _x000D_ 6.5 Test of Significance for a One-Way Between-Subjects Design _x000D_ 6.6 Weighted Means Analysis with Unequal n's _x000D_ 6.7 Summary _x000D_ 6.8 Questions and Problems _x000D_ 7. Pairwise Comparisons _x000D_ 7.1 Introduction _x000D_ 7.2 A One-Way Between-Subjects Experiment with 4 Treatments _x000D_ 7.3 Protection Levels and the Bonferroni Significant Difference (BSD) Test _x000D_ 7.4 Fisher's Significant Difference (FSD) Test _x000D_ 7.5 The Tukey Significant Difference (TSD) Test _x000D_ 7.6 Scheffe's Significant Difference (SSD) Test _x000D_ 7.7 The Four Methods: General Considerations _x000D_ 7.8 Questions and Problems _x000D_ 8. Orthogonal, Planned and Unplanned Comparisons _x000D_ 8.1 Introduction _x000D_ 8.2 Comparisons on Treatment Means _x000D_ 8.3 Standard Error of a Comparison _x000D_ 8.4 The t Test of Significance for a Comparison _x000D_ 8.5 Orthogonal Comparisons _x000D_ 8.6 Choosing a Set of Orthogonal Comparisons _x000D_ 8.7 Protection Levels with Orthogonal Comparisons _x000D_ 8.8 Treatments as Values of an Ordered Variable _x000D_ 8.9 Coefficients for Orthogonal Polynomials _x000D_ 8.10 Tests of Significance for Trend Comparisons _x000D_ 8.11 The Relation between a Set of Orthogonal Comparisons and the Treatment Sum of Squares _x000D_ 8.12 Tests of Significance for Planned Comparisons _x000D_ 8.13 Effect Size for Comparisons _x000D_ 8.14 The Equality of Variance Assumption _x000D_ 8.15 Unequal

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