Description
With humor, extraordinary clarity, and carefully paced explanations and examples, Bruce Thompson shows readers how to use the latest techniques for interpreting research outcomes as well as how to make statistical decisions that result in better research. Utilizing the general linear model to demonstrate how different statistical methods are related to each other, Thompson integrates a broad array of methods involving only a single dependent variable, ranging from classical and robust location descriptive statistics, through effect sizes, and on through ANOVA, multiple regression, loglinear analysis and logistic regression. Special features include SPSS and Excel demonstrations that offer opportunities, in the book’s datasets and on Thompson’s website, for further exploration of statistical dynamics.
Chapter
Some Experimental Design Considerations
Reasonable Expectations for Statistics
Three Classical Location Descriptive Statistics
Four Criteria for Evaluating Statistics
Two Robust Location Statistics
Quality of Location Descriptive Statistics
Important in Its Own Right
Situation-Specific Maximum Dispersion
Robust Dispersion Descriptive Statistics
Two Shape Descriptive Statistics
Two Additional Univariate Graphics
Chapter 5. Bivariate Relationships
Three Interpretation Contextual Factors
Psychometrics of the Pearson r
Two Other r-Equivalent Correlation Coefficients
Chapter 6. Statistical Significance
Properties of Sampling Distributions
Standard Error/Sampling Error
Statistical Precision and Power
Chapter 7. Practical Significance
Confidence Intervals for Effect Sizes
Chapter 8. Multiple Regression Analysis: Basic GLM Concepts
Case #1: Perfectly Uncorrelated Predictors
Case #2: Correlated Predictors, No Suppressor Effects
Case #3: Correlated Predictors, Suppressor Effects Present
β Weights versus Structure Coefficients
A Final Comment on Collinearity
Chapter 9. A GLM Interpretation Rubric
Where Does My Something Originate?
Invoking Some Alternative Models
Chapter 10. One-Way Analysis of Variance (ANOVA)
Experimentwise Type I Error
The Logic of Analysis of Variance
Practical and Statistical Significance
The “Homogeneity of Variance” Assumption
Chapter 11. Multiway and Other Alternative ANOVA Models
Factorial versus Nonfactorial Analyses
Fixed-, Random-, and Mixed-Effects Models
Chapter 12. The General Linear Model (GLM): ANOVA via Regression
Trend/Polynomial Planned Contrasts
Repeated-Measures ANOVA via Regression
Chapter 13. Some Logistic Models: Model Fitting in a Logistic Context
Appendix: Scores (n = 100) with Near Normal Distributions