Introductory Statistics for the Behavioral Sciences

PrefaceAcknowledgmentsGlossary of SymbolsPart I. Introduction 1. Introduction Descriptive and Inferential Statistics Populations, Samples, Parameters, and Statistics Summation Notation SummaryPart II. Descriptive Statistics 2. Frequency Distributions and Graphs The Purpose of Descriptive Statistics Regular Frequency Distributions Cumulative Frequency Distributions Grouped Frequency Distributions Graphic Representations Shapes of Frequency Distributions Summary 3. Transformed Scores I: Percentiles Definition of Percentiles Deciles, Quartiles, and the Median Summary 4. Measures of Central Tendency The Mean The Median The Mode Summary 5. Measures of Variability The Concept of Variability The Range The Standard Deviation and Variance Summary 6. Transformed Scores II: Z and T Scores Rules for Changing X and σ Standard Scores (Z Scores) T Scores SAT Scores Summary Appendix to Chapter 6: Proofs of Rules for Changing X and σPart III. Inferential Statistics 7. The General Strategy of Inferential Statistics The Goals of Inferential Statistics The Strategy of Inferential Statistics Statistical Models Summary 8. The Normal Curve Model Score Distributions Characteristics of the Normal Curve Illustrative Examples Summary 9. Inferences About the Mean of a Single Population The Standard Error of the Mean Hypothesis Testing The Statistical Test for the Mean of a Single Population when σ Is Known The Statistical Test for the Mean of a Single Population when σ Is Not Known: The t Distributions Interval Estimation The Standard Error of a Proportion One-Tailed Tests of Significance Summary 10. Testing Hypotheses About the Difference Between the Means of Two Populations The Standard Error of the Difference Estimating the Standard Error of the Difference The t Test for Two Sample Means Measures of the Strength of the Relationship Between the Two Variables Confidence Intervals for μ1—μ2 Using the t Test for Two Sample Means: Some General Considerations The t Test for Matched Samples Summary 11. Linear Correlation and Prediction Describing the Linear Relationship Between Two Variables Testing the Significance of the Correlation Coefficient Prediction and Linear Regression Measuring Prediction Error: The Standard Error of Estimate Summary Appendix to Chapter 11 : Equivalence of the Various Formulas for r 12. Other Correlational Techniques The Relationship Between Ranked Variables: The Spearman Rank-Order Correlation Coefficient The Relationship Between One Dichotomous and One Continuous Variable The Relationship Between Two Dichotomous Variables Summary 13. Introduction to Power Analysis Concepts of Power Analysis The Test of the Mean of a Single Population The Significance Test of the Proportion of a Single Population The Significance Test of a Pearson r Testing the Significance of the Difference Between Independent Means Summary 14. One-Way Analysis of Variance The General Logic of ANOVA Computational Procedures One-Way ANOVA with Unequal Sample Sizes Some Comments on the Use of ANOVA Summary Appendix to Chapter 14: Proof That Total Variance Is Equal to the Sum of Between-Group and within-Group Variance 15. Introduction to Factorial Design: Two-Way Analysis of Variance Computational Procedures The Meaning of Interaction Summary 16. Chi Square Chi Square and Goodness of Fit: One-Variable Problems Chi Square as a Test of Independence: Two-Variable Problems Measures of Strength of Association in Two-Variable Tables SummaryAppendixIndex