Foundations of behavioral statistics : an insightbased approach
 Responsibility
 Bruce Thompson.
 Imprint
 New York : Guilford Press, 2008, c2006.
 Physical description
 xii, 457 p. : ill. ; 26 cm.
At the library
Education Library (Cubberley)
Stacks
Call number  Status 

BF39 .T473 2008  Unknown 
More options
Description
Creators/Contributors
 Author/Creator
 Thompson, Bruce, 1951
Contents/Summary
 Bibliography
 Includes bibliographical references (p. 431448) and index.
 Contents

 Part I Introductory Terms and Concepts. Definitions of Some Basic Terms. Levels of Scale. Some Experimental Design Considerations. Some Key Concepts. Reflection Problems. Part II Location. Reasonable Expectations for Statistics. Location Concepts. Three Classical Location Descriptive Statistics. Four Criteria for Evaluating Statistics. Two Robust Location Statistics. Some Key Concepts. Reflection Problems. Part III Dispersion.Quality of Location Descriptive Statistics. Important in Its Own Right. Measures of Score Spread. Variance. SituationSpecific Maximum Dispersion. Robust Dispersion Descriptive Statistics. Standardized Score World. Some Key Concepts. Reflection Problems. Part IV Shape. Two Shape Descriptive Statistics. Normal Distributions. Two Additional Univariate Graphics. Some Key Concepts. Reflection Problems. Part V Bivariate Relationships. Pearson's r. Three Features of r. Three Interpretation Contextual Factors. Psychometrics of the Pearson r. Spearman's rho. Two Other r Equivalent Correlation Coefficients. Bivariate Normality. Some Key Concepts. Reflection Problems. Part VI Statistical Significance. Sampling Distributions. Hypothesis Testing. Properties of Sampling Distributions. Standard Error/Sampling Error. Test Statistics. Statistical Precision and Power. pCALCULATED. Some Key Concepts. Reflection Problems. Part VII Practical Significance. Effect Sizes. Confidence Intervals. Confidence Intervals for Effect Sizes. Some Key Concepts. Reflection Problems. Part VIII Multiple Regression Analysis: Basic GLM Concepts. Purposes of Regression. Simple Linear Prediction. Case #1: Perfectly Uncorrelated Predictors. Case #2: Correlated Predictors, No Suppressor. Effects. Case #3: Correlated Predictors, Suppressor. Effects Present. b Weights versus Structure Coefficients. A Final Comment on Collinearity. Some Key Concepts. Reflection Problems. Part IX A GLM Interpretation Rubric. Do I Have Anything?Where Does My Something Originate? Stepwise Methods. Invoking Some Alternative Models. Some Key Concepts. Reflection Problems. Part X Oneway Analysis of Variance (ANOVA). Experimentwise Type I Error. ANOVA Terminology. The Logic of Analysis of Variance. Practical and Statistical Significance. The "Homogeneity of Variance" Assumption. Post Hoc Tests. Some Key Concepts. Reflection Problems. Part XI Multiway and Alternative ANOVA Models. Multiway Models. Factorial versus Nonfactorial Analyses. Fixed, Random, and MixedEffects Models. Brief Comment on ANCOVA. Some Key Concepts. Reflection Problems. Part XII The General Linear Model (GLM): ANOVA via Regression. Planned Contrasts. Trend/Polynomial Planned Contrasts. Repeated Measures ANOVA via Regression. GLM Lessons. Some Key Concepts. Reflection Problems. Part XIII Some Logistic Models: Model Fitting in a Logistic Context. Logistic Regression. Loglinear Analysis. Some Key Concepts. Reflection Problems. Appendix: Scores (n = 100) with Near Normal Distributions.
 (source: Nielsen Book Data)
 Summary

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.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Reprint/reissue date
 2008
 Original date
 2006
 Note
 Reprint. Originally published: c2006.
 ISBN
 9781593858407 (pbk.)
 159385840X (pbk.)