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