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| Additional Physical Format: | Online version: Walsh, Anthony, 1941- Essential statistics for the social and behavioral sciences. Upper Saddle River, NJ : Prentice Hall, 2001 (OCoLC)762682581 |
|---|---|
| Document Type: | Book |
| All Authors / Contributors: |
Anthony Walsh; Jane C Ollenburger |
| ISBN: | 0130193399 9780130193391 |
| OCLC Number: | 44681910 |
| Description: | xii, 305 p. : ill. ; 24 cm. |
| Contents: | Chapter 1. Introduction to Statistical Analysis -- Thinking Statistically -- Descriptive and Inferential Statistics -- Descriptive Statistics -- Inferential Statistics -- Statistics and Error -- Parametric and Nonparametric Statistics -- Operationalization -- Reliability and Validity -- Measurement -- Dependent and Independent Variables -- Nominal Level -- Ordinal Level -- Interval Level -- Ratio Level -- The Role of Statistics in Science -- Practice Application: Variables and Levels of Measurement -- Chapter 2. Presenting and Summarizing Data -- Types of Frequency Distributions -- Interpreting Cumulative Frequencies -- Frequency Distribution of Grouped Data -- Limits, Sizes, and Midpoints of Class Intervals -- Advantages and Disadvantages of Grouping Data -- Bar Graphs and Pie Charts -- Histograms and Frequency Polygons -- Numerical Summation of Data: Percentages, Proportions, and Ratios -- Practice Application: Displaying and Summarizing Data -- Chapter 3. Central Tendency and Dispersion -- Measures of Central Tendency -- Mode -- Median -- Computing the Median with Grouped Data -- The Mean -- Computing the Mean from Grouped Data -- A Research Example -- Choosing a Measure of Central Tendency -- Measures of Dispersion -- Range -- Standard Deviation -- Computational Formula for s -- Variability and Variance -- Computing the Standard Deviation from Grouped Data -- Coefficient of Variation -- Practice Application: Central Tendency and Dispersion -- Chapter 4. Probability and the Normal Curve -- Probability -- The Multiplication Rule -- The Addition Rule -- Theoretical Probability Distributions -- The Normal Curve -- Different Kinds of Curves -- The Standard Normal Curve -- The z Scores -- Finding Area of the Curve Below the Mean -- Practice Application: The Normal Curve and z Scores -- Chapter 5. The Sampling Distribution and Estimation Procedures -- Sampling -- Simple Random Sampling -- Stratified Random Sampling -- The Sampling Distribution -- The Central Limit Theorem -- Standard Error of the Sampling Distribution -- Point and Interval Estimates -- Confidence Intervals and Alpha Levels -- Calculating Confidence Intervals -- Sampling and Confidence Intervals -- Interval Estimates for Proportions -- Estimating Sample Size -- Estimating Sample Size for Proportions -- Practice Application: The Sampling Distribution and Estimation -- Chapter 6. Hypothesis Testing: Interval/Ratio Data -- The Logic of Hypothesis Testing -- The Evidence and Statistical Significance -- Errors in Hypothesis Testing -- One Sample z Test -- Decision Rule -- The t Test -- Degrees of Freedom -- The t Distribution -- Directional Hypotheses: One- and Two-Tailed Tests -- Computing t -- t Test for Correlated (Dependent) Means -- Effects of Sample Variance on H[subscript 0] Decision -- Large Sample t Test: A Computer Example -- Interpreting the Printout -- Calculating t with Unequal Variances -- Testing Hypotheses for Single-Sample Proportions -- Statistical Versus Substantive Significance, and Strength of Association -- Practice Application: t Test -- Chapter 7. Analysis of Variance -- Assumptions of Analysis of Variance -- The Basic Logic of ANOVA -- The Idea of Variance Revisited -- The Advantage of ANOVA over Multiple Tests -- The F Distribution -- An Example of ANOVA -- Determining Statistical Significance: Mean Square and the F Ratio -- ETA Squared -- Multiple Comparisons: The Scheffe Test -- Two-Way Analysis of Variance -- Determining Statistical Significance -- Significance Levels -- Understanding Interaction -- A Research Example of a Significant Interaction Effect -- Practice Application -- Chapter 8. Hypothesis Testing with Categorical Data: Chi-Square Test -- Table Construction -- Putting Percentages in Tables -- Assumptions for the Use of Chi-Square -- The Chi-Square Distribution -- Yates' Correction for Continuity -- Chi-Square Distribution and Goodness of Fit -- Chi-Square-Based Measures of Association -- Sample Size and Chi-Square -- Contingency Coefficient -- Cramer's V -- A Computer Example of Chi-Square -- Kruskal-Wallis One-Way Analysis of Variance -- Practice Application: Chi-Square -- Chapter 9. Nonparametric Measures of Association -- The Idea of Association -- Does an Association Exist? -- What Is the Strength of the Association? -- What Is the Direction of the Association? -- Proportional Reduction in Error -- The Concept of Paired Cases -- A Computer Example -- Gamma -- Lambda -- Somer's d -- Tau-B -- The Odd's Ratio and Yule's Q -- Spearman's Rank Order Correlation -- Which Test of Association Should We Use? -- Practice Application: Nonparametric Measures of Association -- Chapter 10. Elaboration of Tabular Data -- Causal Analysis -- Criteria for Causality -- Association -- Temporal Order -- Spuriousness -- Necessary Cause -- Sufficient Cause -- Necessary and Sufficient Cause -- A Statistical Demonstration of Cause-and-Effect Relationships -- Multivariate Contingency Analysis -- Introducing a Third Variable -- Explanation and Interpretation -- Illustrating Elaboration Outcomes -- Controlling for One Variable -- Further Elaboration: Two Control Variables -- Partial Gamma -- When Not to Compute Partial Gamma -- Problems with Tabular Elaboration -- Practice Application: Bivariate Elaboration -- Chapter 11. Bivariate Correlation and Regression -- Preliminary Investigation: The Scattergram -- The Slope -- The Intercept -- The Pearson Correlation Coefficient -- Covariance and Correlation -- Partitioning r Squared and Sum of Squares -- Standard Error of the Estimate -- Standard Error of r -- Significance Testing for Pearson's r -- The Interrelationship of b, r, and [beta] -- Summarizing Properties of r, b, and [beta] -- Summarizing Prediction Formulas -- A Computer Example of Bivariate Correlation and Regression -- Practice Application: Bivariate Correlation and Regression -- Practice Application: Bivariate Correlation and Regression -- Chapter 12. Multivariate Correlation and Regression -- Partial Correlation -- Computing Partial Correlations -- Computer Example and Interpretation -- Second-Order Partials: Controlling for Two Independent Variables -- The Multiple Correlation Coefficient -- Multiple Regression -- The Unstandardized Partial Slope -- The Standardized Slope ([beta]) -- A Computer Example of Multiple Regression and Interpretation -- Summary Statistics: Multiple R, R[superscript 2], s[subscript Y.X], and ANOVA -- The Predictor Variables: b, [beta], and t -- A Visual Representation of Multiple Regression -- Dummy Variable Regression -- Regression and Interaction -- Practice Application: Partial Correlation -- Chapter 13. Introduction to Logistic Regression -- An Example of Logit Regression -- Interpretation: Probabilities and Odds -- Assessing the Model Fit -- Multiple Logistic Regression -- Practice Application: Logistic Regression -- Appendix A. Statistical Tables -- Appendix B. Answers to Odd Numbered Problems. |
| Responsibility: | Anthony Walsh, Jane C. Ollenburger. |
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