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[Contents] Contents Preface Acknowledgments 1. How We Reason Key Concepts Prologue and Introduction Setting the Stage Science The Scientific Method Testing Hypotheses From Hypotheses to Theories Types of Relationships Association and Causation The Unit of Analysis Conclusion Exercises 2. Levels of Measurement and Forms of Data Key Concepts Prologue and Introduction Measurement Nominal Level of Measurement Ordinal Level of Measurement Likert Scales Scores Versus Frequencies Interval and Ratio Levels of Measurement Tables Containing Nominal Level of Measurement Variables Conclusion Exercises 3. Defining Variables Key Concepts Prologue and Introduction Gathering the Data Operational Definitions Index and Scale Construction Validity Reliability Conclusion Exercises 4. Measuring Central Tendency Key Concepts Prologue and Introduction Central Tendency The Mean The Median Using Central Tendency The Mode Interpreting Graphs Central Tendency and Levels of Measurement Skewness Other Graphic Representations Conclusion Summary of Major Formulas Exercises 5. Measuring Dispersion Key Concepts Prologue and Introduction Visualizing Dispersion The Range The Mean Deviation The Variance and Standard Deviation The Computational Formulas for Variance and Standard Deviation Variance and Standard Deviation for Data in Frequency Distributions Conclusion Summary of Major Formulas Exercises 6. Constructing and Interpreting Contingency Tables Key Concepts Prologue and Introduction Contingency Tables Regrouping Variables Generating Percentages Interpreting Controlling for a Third Variable Partial Tables Causal Models Computer Applications SPSS SAS Conclusion Exercises 7. Statistical Inference and Tests of Significance Key Concepts Prologue and Introduction What Is Statistical Inference? Random Samples Comparing Means The Test Statistic Probabilities Decision Making Directional Versus Nondirectional Alternative Hypotheses (One-Tailed Versus Two-Tailed Tests) Conclusion Summary of Major Formulas Exercises 8. Probability Distributions and One-Sample Z and t Tests Key Concepts Prologue and Introduction Normal Distributions The One-Sample z test for Statistical Significance The Central Limit Theorem The Normality Assumption The One-Sample t Test Degrees of Freedom The t Table An Alternative t Formula A z Test for Proportions Interval Estimation Confidence Intervals for Proportions More on Probability Permutations and Combinations Conclusion Summary of Major Formulas Exercises 9. Two-Sample t Tests Key Concepts Prologue and Introduction Independent Samples Versus Dependent Samples The Two-Sample t Test for Independently Drawn Samples Adjustments for Sigma-Hat Squared (? 2) Interpreting a Computer-Generated t Test Computer Applications Independent Sample Tests SPSS SAS Excel Dependent Sample Tests SPSS SAS Excel The Two-Sample t Test for Dependent Samples Statistical Significance Versus Research Significance Statistical Power Conclusion Summary of Major Formulas Exercises 10. One-Way Analysis of Variance Key Concepts Prologue and Introduction How Analysis of Variance Is Used Analysis of Variance in Experimental Situations F: An Intuitive Approach ANOVA Terminology The ANOVA Procedure Comparing F with t Analysis of Variance With Experimental Data Post Hoc Testing Computer Applications SPSS SAS Excel Two-way Analysis of Variance Conclusion Summary of Major Formulas Exercises 11. Measuring Association in Contingency Tables Key Concepts Prologue and Introduction Measures for Two-by-Two Tables Measures Curvilinearity for n-by-n Other Measures of Association Interpreting an Association Matrix Conclusion Summary of Major Formulas Exercises 12. The Chi-Square Test Key Concepts Prologue and Introduction The Context for the Chi-Square Test Observed Versus Expected Frequencies Using the Table of Critical Value of Chi-Square Calculating the Chi-Square Value Yates? Correction Validity of Chi-Square Directional Alternative Hypotheses Testing Significance of Association Measures Chi-square and Phi Computer Applications SPSS SAS Conclusion Summary of Major Formulas Exercises 13. Correlation and Regression Analysis Key Concepts Prologue and Introduction The Setting Cartesian Coordinates The Concept of Linearity Linear Equations Linear Regression Computer Applications SPSS SAS Excel Correlation Measures for Analysis of Variance Conclusion Summary of Major Formulas Exercises 14. Additional Aspects of Correlation and Regression Analysis Key Concepts Prologue and Introduction Statistical Significance for r and b Significance of r Partial Correlations and Causal Models Multiple Correlation and The Coefficient of Multiple Determination Multiple Regression The Standardized Partial Regression Slope Using a Regression Printout Stepwise Multiple Regression Computer Applications Partial Correlations: SPSS Partial Correlations: Other Programs Multiple Regression: SPSS Multiple Regression: SAS Multiple Regression: Excel Stepwise Multiple Regression: SPSS Stepwise Multiple Regression: SAS Conclusion Summary of Major Formulas Exercises Appendix 1: Proportions of Area Under Standard Normal Curve Appendix 2: Distribution of t Appendix 3: Critical Values of F for p = .05 Appendix 4: Critical Values of Chi-Square Appendix 5: Critical Values of the Correlation Coefficient Answers to Selected Exercises Index About the Author

Library of Congress Subject Headings for this publication:

Social sciences -- Statistical methods.

Statistics.