图书简介

1. Evaluating Numeric 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 \\ Non-experimental Research Design \\ Quasi- Experimental 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 2 A: More About Levels of Measurement \\ Appendix 2 B: Justification for 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 3 A: Getting Started in IBM SPSS ® version 25 \\ Appendix 3 B: Missing Values in Frequency Tables \\ Appendix 3 C: 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: 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 4 A Order of Arithmetic Operations \\ Appendix 4 B Rounding \\ 5. Graphs: Bar Charts, Histograms, and Box Plots \\ 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 \\ Box Plot (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 Original Units of X \\ Understanding Values of z \\ Qualitative Description of Normal Distribution Shape \\ More Precise Description of Normal Distribution Shape \\ 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 \\ Reporting Information About Distribution Shape, Missing Values, Outliers, and Descriptive Statistics for Quantitative Variables \\ Summary \\ Appendix 6 A: The Mathematics of the Normal Distribution \\ Appendix 6 B: How to Select and Remove Outliers in SPSS \\ Appendix 6 C: Quantitative Assessments of Departure from Normality \\ Appendix 6 D: Why Are Some Real-World Variables Approximately Normally Distributed? \\ 7. Sampling Error and Confidence Intervals \\ Descriptive Versus Inferential Uses of Statistics \\ Notations 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 \\ Effect of N on Value of the Population Standard Error \\ Describing the Location of a Single Outcome for M Relative to a Population Sampling Distribution (Setting Up a z Ratio) \\ What We Do When ?? 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 CIs \\ 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 (?) Level \\ Specifying Reject Regions Based on ?, 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 Example: Mean Driving Speed (Nondirectional Test) \\ SPSS Analysis: One Sample t Test for Mean Driving Speed \\ “Exact” p Values \\ Reporting Results for a Two-tailed One-Sample t Test \\ The Driving Speed Data Reconsidered Using a One-Tailed Test \\ Reporting Results for a One-tailed One-Sample t Test: \\ Advantages/ 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 of Type I Error From Multiple Tests Interpretation of Null Outcomes \\ 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 9 A Further Explanation of Statistical Power \\ 10. Bivariate Pearson Correlation \\ Research Situations Where Pearson r Is Used \\ Correlation and Causal Inference \\ How Sign and Magnitude of r Describe an X, Y Relationship \\ Setting Up Scatter Plots With Examples of Perfect Linearity \\ Most Associations Are Not Perfect \\ Different Situations In Which r = 0 \\ Assumptions for Use of Pearson r \\ Preliminary Data Screening for Pearson r \\ Effect of Extreme Bivariate Outliers \\ Research Example \\ Data Screening for Research Example \\ Computation of Pearson r \\ How Computation for Correlation Is Related to Pattern of Data Points in the Scatter Plot \\ Testing the Hypothesis That ?0 = 0 \\ Reporting Many Correlations and Inflated Risk of Type I Error \\ Obtaining CIs for Correlations \\ Pearson’s r and r2 as Effect-Size Indexes and Partition of Variance \\ Statistical Power and Sample Size for Correlation Studies \\ Interpretation of Outcomes for Pearson’s r \\ SPSS Example \\ Results Sections for One and Several Pearson r Values \\ Reasons to Be Skeptical of Correlations \\ Summary \\ Appendix 10 A: Nonparametric Alternatives to Pearson r \\ Appendix 10 B: Setting Up a 95% CI for Pearson r \\ Appendix 10 C: Testing Significance of Differences Between Correlations \\ Appendix 10 D: Factors That Artifactually Influence the Magnitude of Pearson’s r \\ Appendix 10 E: Analysis of Non Linear Relationships \\ 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 \\ Plotting the Regression Line: Salary Data \\ Results Section: Salary Data \\ Using Regression Equation to Predict Score for Individual: Joe’s Hr Data \\ Partition of SS in Bivariate Regression: Joe’s Hr Data \\ Issues in Planning a Bivariate Regression Study \\ Plotting Residuals \\ Standard Error of the Estimate, sy.x \\ Summary \\ Appendix 11 A OLS Derivation of Equation for Regression Coefficients \\ Appendix 11 B Fully Worked Example for SS values: Joe’s HR Data \\ 12. The Independent Samples t Test \\ Research Situations Where the Independent Samples t Test is Used \\ Hypothetical Research Example \\ Assumptions for Use of the 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 12 A: A Nonparametric Alternative to the Independent Samples t Test \\ 13. One-Way Between-S Analysis of Variance \\ Research Situations Where Between-S 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 (CIs) 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 Procedure 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 A ANOVA Model and Division of Scores Into Components \\ Appendix B Expected Value of F When H0 is True \\ Appendix C Comparison of ANOVA to t Test \\ Appendix D Nonparametric Alternative to One Way Between S ANOVA \\ 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 of Results For Independent Samples t and Paired Samples t Tests \\ Effect Size and Power \\ Some Design Problems in Repeated Measures Designs \\ Results for Paired Samples t-Test: Stress and HR \\ Further Evaluation of Assumptions for Larger Dataset \\ Summary \\ Appendix A Nonparametric Alternative to Paired Samples t: Wilcoxon Signed Rank Test \\ 15. One Way Repeated Measures ANOVA \\ 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 for 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 15 A Test for Person by Treatment Interaction \\ 16. Factorial Analysis of Variance (Between – S) \\ 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, df, and MS in Two Way Factorial \\ 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 16 A: Unequal Cell ns in Factorial ANOVA \\ Appendix 16 B: Weighted Versus Unweighted Means \\ Appendix 16 C: Model for Factorial ANOVA \\ Appendix 16 D: Fixed Versus Random Factors \\ 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 True \\ Computation of Chi Squared Significance Test \\ Evaluation of Statistical Significance of ?2. \\ 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 17 A: Margin of Error For Percentages in Surveys \\ Appendix 17 B: Contingency Tables With Repeated Measures: McNemar Test \\ Appendix 17 C: Fisher Exact Test \\ Appendix 17 D: How Marginal Distributions for X and Y Constrain Maximum Value of ?? \\ Appendix 17 E: Other Uses of ?2 \\ 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 \\ Degrees of Belief

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