Table Of Contents:

• Preface
• Acknowledgments
• About the Author
• 1. Evaluating Numerical Information
• Introduction
• Guidelines for Numeracy
• Source Credibility
• Message Content
• Evaluating Generalizability
• Making Causal Claims
• Quality Control Mechanisms in Science
• Biases of Information Consumers
• Ethical Issues in Data Collection and Analysis
• Lying with Graphs and Statistics
• Degrees of Belief
• Summary
• 2. Basic Research Concepts
• Introduction
• Types of Variables
• Independent and Dependent Variables
• Typical Research Questions
• Conditions for Causal Inference
• Experimental Research Design
• Nonexperimental Research Design
• Quasi-Experimental Research Designs
• Other Issues in Design and Analysis
• Choice of Statistical Analysis (Preview)
• Populations and Samples: Ideal Versus Actual Situations
• Common Problems in Interpretation of Results
• Appendix 2A: More About Levels of Measurement
• Appendix 2B: Justification for the Use of Likert and Other Rating Scales as Quantitative Variables (in Some Situations)
• 3. Frequency Distribution Tables
• Introduction
• Use of Frequency Tables for Data Screening
• Frequency Tables for Categorical Variables
• Elements of Frequency Tables
• Using SPSS to Obtain a Frequency Table
• Mode, Impossible Score Values, and Missing Values
• Reporting Data Screening for Categorical Variables
• Frequency Tables for Quantitative Variables
• Frequency Tables for Categorical Versus Quantitative Variables
• Reporting Data Screening for Quantitative Variables
• What We Hope to See in Frequency Tables for Categorical Variables
• What We Hope to See in Frequency Tables for Quantitative Variables
• Summary
• Appendix 3A: Getting Started in IBM SPSS® Version 25
• Appendix 3B: Missing Values in Frequency Tables
• Appendix 3C: Dividing Scores Into Groups or Bins
• 4. Descriptive Statistics
• Introduction
• Questions about Quantitative Variables
• Notation
• Sample Median
• Sample Mean (M)
• An Important Characteristic of M: The Sum of Deviations From M = 0
• Disadvantage of M: It is Not Robust Against Influence of Extreme Scores
• Behavior of Mean, Median, and Mode in Common Real-World Situations
• Choosing Among Mean, Median, and Mode
• Using SPSS to Obtain Descriptive Statistics for a Quantitative Variable
• Minimum, Maximum, and Range: Variation among Scores
• The Sample Variance s2
• Sample Standard Deviation (s or SD)
• How a Standard Deviation Describes Variation Among Scores in a Frequency Table
• Why Is There Variance?
• Reports of Descriptive Statistics in Journal Articles
• Additional Issues in Reporting Descriptive Statistics
• Summary
• Appendix 4A: Order of Arithmetic Operations
• Appendix 4B: Rounding
• 5. Graphs: Bar Charts, Histograms, and Boxplots
• Introduction
• Pie Charts for Categorical Variables
• Bar Charts for Frequencies of Categorical Variables
• Good Practice for Construction of Bar Charts
• Deceptive Bar Graphs
• Histograms for Quantitative Variables
• Obtaining a Histogram Using SPSS
• Describing and Sketching Bell-Shaped Distributions
• Good Practices in Setting up Histograms
• Boxplot (Box and Whiskers Plot)
• Telling Stories About Distributions
• Uses of Graphs in Actual Research
• Data Screening: Separate Bar Charts or Histograms for Groups
• Use of Bar Charts to Represent Group Means
• Other Examples
• Summary
• 6. The Normal Distribution and z Scores
• Introduction
• Locations of Individual Scores in Normal Distributions
• Standardized or z Scores
• Converting z Scores Back Into X Units
• Understanding Values of z
• Qualitative Description of Normal Distribution Shape
• More Precise Description of Normal Distribution Shape
• Areas Under the Normal Distribution Curve Can Be Interpreted as Probabilities
• Reading Tables of Areas for the Standard Normal Distribution
• Dividing the Normal Distribution Into Three Regions: Lower Tail, Middle, Upper Tail
• Outliers Relative to a Normal Distribution
• Summary of First Part of Chapter
• Why We Assess Distribution Shape
• Departure from Normality: Skewness
• Another Departure from Normality: Kurtosis
• Overall Normality
• Practical Recommendations for Preliminary Data Screening and Descriptions of Scores for Quantitative Variables
• Reporting Information About Distribution Shape, Missing Values, Outliers, and Descriptive Statistics for Quantitative Variables
• Summary
• Appendix 6A: The Mathematics of the Normal Distribution
• Appendix 6B: How to Select and Remove Outliers in SPSS
• Appendix 6C: Quantitative Assessments of Departure From Normality
• Appendix 6D: Why Are Some Real-World Variables Approximately
• 7. Sampling Error and Confidence Intervals
• Descriptive Versus Inferential Uses of Statistics
• Notation for Samples Versus Populations
• Sampling Error and the Sampling Distribution for Values of M
• Prediction Error
• Sample Versus Population (Revisited)
• The Central Limit Theorem: Characteristics of the Sampling Distribution of M
• Factors That Influence Population Standard Error (sM)
• Effect of N on Value of the Population Standard Error
• Describing the Location of a Single Outcome for M Relative to Population Sampling Distribution (Setting Up a z Ratio)
• What We Do When s Is Unknown
• The Family of t Distributions
• Tables for t Distributions
• Using Sampling Error to Set Up a Confidence Interval
• How to Interpret a Confidence Interval
• Empirical Example: Confidence Interval for Body Temperature
• Other Applications for Confidence Intervals
• Error Bars in Graphs of Group Means
• Summary
• 8. The One-Sample t test: Introduction to Statistical Significance Tests
• Introduction
• Significance Tests as Yes/No Questions About Proposed Values of Population Means
• Stating a Null Hypothesis
• Selecting an Alternative Hypothesis
• The One-Sample t Test
• Choosing an Alpha (a) Level
• Specifying Reject Regions on the Basis of a, Halt, and df
• Questions for the One-Sample t Test
• Assumptions for the Use of the One-Sample t Test
• Rules for the Use of NHST
• First Analysis of Mean Driving Speed Data (Using a Nondirectional Test)
• SPSS Analysis: One-Sample t Test for Mean Driving Speed (Using a Nondirectional or Two-Tailed Test)
• “Exact” p Values
• Reporting Results for a Two-tailed One-Sample t Test
• Second Analysis of Driving Speed Data Using a One-Tailed or Directional Test
• Reporting Results for a One-tailed One-Sample t Test
• Advantages and Disadvantages of One-Tailed Tests
• Traditional NHST Versus New Statistics Recommendations
• Things You Should Not Say About p Values
• Summary
• 9. Issues in Significance Tests: Effect Size, Statistical Power, and Decision Errors
• Beyond p Values
• Cohen’s d: An Effect Size Index
• Factors that Affect the Size of t Ratios
• Statistical Significance Versus Practical Importance
• Statistical Power
• Type I and Type II Decision Errors
• Meanings of “Error”
• Use of NHST in Exploratory Versus Confirmatory Research
• Inflated Risk for Type I Decision Error for Multiple Tests
• Interpretation of Null Outcomes
• Interpretation of Statistically Significant Outcomes
• Understanding Past Research
• Planning Future Research
• Guidelines for Reporting Results
• What You Cannot Say
• Summary
• Appendix 9A: Further Explanation of Statistical Power
• 10. Bivariate Pearson Correlation
• Research Situations Where Pearson’s r Is Used
• Correlation and Causal Inference
• How Sign and Magnitude of r Describe an X, Y Relationship
• Setting Up Scatterplots
• Most Associations Are Not Perfect
• Different Situations in Which r = .00
• Assumptions for Use of Pearson’s r
• Preliminary Data Screening for Pearson’s r
• Effect of Extreme Bivariate Outliers
• Research Example
• Data Screening for Research Example
• Computation of Pearson’s r
• How Computation of Correlation Is Related to Pattern of Data Points in the Scatterplot
• Testing the Hypothesis That p0 = 0
• Reporting Many Correlations and Inflated Risk for Type I Error
• Obtaining Confidence Intervals for Correlations
• Pearson’s r and r2 as Effect Sizes and Partition of Variance
• Statistical Power and Sample Size for Correlation Studies
• Interpretation of Outcomes for Pearson’s r
• SPSS Example: Relationship Survey
• Results Sections for One and Several Pearson’s r Values
• Reasons to Be Skeptical of Correlations
• Summary
• Appendix 10A: Nonparametric Alternatives to Pearson’s r
• Appendix 10B: Setting Up a 95% CI for Pearson’s r by Hand
• Appendix 10C: Testing Significance of Differences Between Correlations
• Appendix 10D: Some Factors That Artifactually Influence Magnitude of r
• Appendix 10E: Analysis of Nonlinear Relationships
• Appendix 10F: Alternative Formula to Compute Pearson’s r
• 11. Bivariate Regression
• Research Situations Where Bivariate Regression Is Used
• New Information Provided by Regression
• Regression Equations and Lines
• Two Versions of Regression Equations
• Steps in Regression Analysis
• Preliminary Data Screening
• Formulas for Bivariate Regression Coefficients
• Statistical Significance Tests for Bivariate Regression
• Confidence Intervals for Regression Coefficients
• Effect Size and Statistical Power
• Empirical Example Using SPSS: Salary Data
• SPSS Output: Salary Data
• Results Section: Hypothetical Salary Data
• Plotting the Regression Line: Salary Data
• Using a Regression Equation to Predict Score for Individual (Joe’s Heart Rate Data)
• Partition of Sums of Squares in Bivariate Regression
• Why Is There Variance (Revisited)?
• Issues in Planning a Bivariate Regression Study
• Plotting Residuals
• Standard Error of the Estimate
• Summary
• Appendix 11A: Review: How to Graph a Line From Two Points Obtained From an Equation
• Appendix 11B: OLS Derivation of Equation for Regression Coefficients
• Appendix 11C: Alternative Formula for Computation of Slope
• Appendix 11D: Fully Worked Example: Deviations and SS
• 12. The Independent-Samples t Test
• Research Situations Where the Independent-Samples t Test Is Used
• Hypothetical Research Example
• Assumptions for Use of Independent-Samples t Test
• Preliminary Data Screening: Evaluating Violations of Assumptions and Getting to Know Your Data
• Computation of Independent-Samples t Test
• Statistical Significance of Independent-Samples t Test
• Confidence Interval Around M1 – M2
• SPSS Commands for Independent-Samples t Test
• SPSS Output for Independent-Samples t Test
• Effect Size Indexes for t
• Factors that Influence the Size of t
• Results Section
• Graphing Results: Means and CIs
• Decisions About Sample Size for the Independent-Samples t Test
• Issues in Designing a Study
• Summary
• Appendix 12A: A Nonparametric Alternative to the Independent-Samples t Test
• 13. One-Way Between-Subjects Analysis of Variance
• Research Situations Where One-Way ANOVA Is Used
• Questions in One-Way Between-S ANOVA
• Hypothetical Research Example
• Assumptions and Data Screening for One-Way ANOVA
• Computations for One-Way Between-S ANOVA
• Patterns of Scores and Magnitudes of SSbetween and SSwithin
• Confidence Intervals for Group Means
• Effect Sizes for One-Way Between-S ANOVA
• Statistical Power Analysis for One-Way Between-S ANOVA
• Planned Contrasts
• Post Hoc or “Protected” Tests
• One-Way Between-S ANOVA in SPSS
• Output From SPSS for One-Way Between-S ANOVA
• Reporting Results From One-Way Between-S ANOVA
• Issues in Planning a Study
• Summary
• Appendix 13A: ANOVA Model and Division of Scores Into Components
• Appendix 13B: Expected Value of F When H0 Is True
• Appendix 13C: Comparison of ANOVA and t Test
• Appendix 13D: Nonparametric Alternative to One-Way Between-S ANOVA: Independent-Samples Kruskal-Wallis Test
• 14. Paired-Samples t Test
• Independent- Versus Paired-Samples Designs
• Between-S and Within-S or Paired-Groups Designs
• Types of Paired Samples
• Hypothetical Study: Effects of Stress on Heart Rate
• Review: Data Organization for Independent Samples
• New: Data Organization for Paired Samples
• A First Look at Repeated-Measures Data
• Calculation of Difference (d) Scores
• Null Hypothesis for Paired-Samples t Test
• Assumptions for Paired-Samples t Test
• Formulas for Paired-Samples t Test
• SPSS Paired-Samples t Test Procedure
• Comparison Between Results for Independent-Samples and Paired-Samples t Tests
• Effect Size and Power
• Some Design Problems in Repeated-Measures Analyses
• Results for Paired-Samples t Test: Stress and Heart Rate
• Further Evaluation of Assumptions
• Summary
• Appendix 14A: Nonparametric Alternative to Paired-Samples t: Wilcoxon Signed Rank Test
• 15. One-Way Repeated-Measures Analysis of Variance
• Introduction
• Null Hypothesis for Repeated-Measures ANOVA
• Preliminary Assessment of Repeated-Measures Data
• Computations for One-Way Repeated-Measures ANOVA
• Use of SPSS Reliability Procedure for One-Way Repeated-Measures ANOVA
• Partition of SS in Between-S Versus Within-S ANOVA
• Assumptions for Repeated-Measures ANOVA
• Choices of Contrasts in GLM Repeated Measures
• SPSS GLM Procedure for Repeated-Measures ANOVA
• Output of GLM Repeated-Measures ANOVA
• Paired-Samples t Tests as Follow-Up
• Results
• Effect Size
• Statistical Power
• Counterbalancing in Repeated-Measures Studies
• More Complex Designs
• Summary
• Appendix 15A: Test for Person-by-Treatment Interaction
• Appendix 15B: Nonparametric Analysis for Repeated Measures (Friedman Test)
• 16. Factorial Analysis of Variance
• Research Situations Where Factorial Design Is Used
• Questions in Factorial ANOVA
• Null Hypotheses in Factorial ANOVA
• Screening for Violations of Assumptions
• Hypothetical Research Situation
• Computations for Between-S Factorial ANOVA
• Computation of SS and df in Two-Way Factorial ANOVA
• Effect Size Estimates for Factorial ANOVA
• Statistical Power
• Follow-Up Tests
• Factorial ANOVA Using the SPSS GLM Procedure
• SPSS Output
• Results
• Design Decisions and Magnitudes of SS Terms
• Summary
• Appendix 16A: Fixed Versus Random Factors
• Appendix 16B: Weighted Versus Unweighted Means
• Appendix 16C: Unequal Cell n’s in Factorial ANOVA: Computing Adjusted Sums of Squares
• Appendix 16D: Model for Factorial ANOVA
• Appendix 16E: Computation of Sums of Squares by Hand
• 17. Chi-Square Analysis of Contingency Tables
• Evaluating Association Between Two Categorical Variables
• First Example: Contingency Tables for Titanic Data
• What Is Contingency?
• Conditional and Unconditional Probabilities
• Null Hypothesis for Contingency Table Analysis
• Second Empirical Example: Dog Ownership Data
• Preliminary Examination of Dog Ownership Data
• Expected Cell Frequencies If H0 Is True
• Computation of Chi Squared Significance Test
• Evaluation of Statistical Significance of x2
• Effect Sizes for Chi Squared
• Chi Squared Example Using SPSS
• Output From Crosstabs Procedure
• Reporting Results
• Assumptions and Data Screening for Contingency Tables
• Other Measures of Association for Contingency Tables
• Summary
• Appendix 17A: Margin of Error for Percentages in Surveys
• Appendix 17B: Contingency Tables With Repeated Measures: McNemar Test
• Appendix 17C: Fisher Exact Test
• Appendix 17D: How Marginal Distributions for X and Y Constrain Maximum Value of f
• Appendix 17E: Other Uses of x2
• 18. Selection of Bivariate Analyses and Review of Key Concepts
• Selecting Appropriate Bivariate Analyses
• Types of Independent and Dependent Variables (Categorical Versus Quantitative
• Parametric Versus Nonparametric Analyses
• Comparisons of Means or Medians Across Groups (Categorical IV and Quantitative DV)
• Problems With Selective Reporting of Evidence and Analyses
• Limitations of Statistical Significance Tests and p Values
• Statistical Versus Practical Significance
• Generalizability Issues
• Causal Inference
• Results Sections
• Beyond Bivariate Analyses: Adding Variables
• Some Multivariable or Multivariate Analyses
• Degree of Belief
• Appendices
• Appendix A: Proportions of Area Under a Standard Normal Curve
• Appendix B: Critical Values for t Distribution
• Appendix C: Critical Values of F
• Appendix D: Critical Values of Chi-Square
• Appendix E: Critical Values of the Pearson Correlation Coefficient
• Appendix F: Critical Values of the Studentized Range Statistic
• Appendix G: Transformation of r (Pearson Correlation) to Fisher’s Z
• Glossary
• References
• Index