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Statistics A Gentle Introduction

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

27.01.2020

Verlag

Sage Publications

Seitenzahl

536

Maße (L/B/H)

23,5/19,1/2,9 cm

Gewicht

890 g

Auflage

4. überarbeitete Auflage

Sprache

Englisch

ISBN

978-1-5063-6843-6

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

27.01.2020

Verlag

Sage Publications

Seitenzahl

536

Maße (L/B/H)

23,5/19,1/2,9 cm

Gewicht

890 g

Auflage

4. überarbeitete Auflage

Sprache

Englisch

ISBN

978-1-5063-6843-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Statistics
  • Produktbild: Statistics
  • Preface
    Acknowledgments
    About the Author
    Chapter 1: A Gentle Introduction
    How Much Math Do I Need to Do Statistics?
    The General Purpose of Statistics: Understanding the World
    What Is a Statistician?
    Liberal and Conservative Statisticians
    Descriptive and Inferential Statistics
    Experiments Are Designed to Test Theories and Hypotheses
    Oddball Theories
    Bad Science and Myths
    Eight Essential Questions of Any Survey or Study
    On Making Samples Representative of the Population
    Experimental Design and Statistical Analysis as Controls
    The Language of Statistics
    On Conducting Scientific Experiments
    The Dependent Variable and Measurement
    Operational Definitions
    Measurement Error
    Measurement Scales: The Difference Between Continuous and Discrete Variables
    Types of Measurement Scales
    Rounding Numbers and Rounding Error
    Statistical Symbols
    Summary
    History Trivia: Achenwall to Nightingale
    Key Terms
    Chapter 1 Practice Problems
    Chapter 1 Test Yourself Questions
    SPSS Lesson 1
    Chapter 2: Descriptive Statistics: Understanding Distributions of Numbers
    The Purpose of Graphs and Tables: Making Arguments and Decisions
    A Summary of the Purpose of Graphs and Tables
    Graphical Cautions
    Frequency Distributions
    Shapes of Frequency Distributions
    Grouping Data Into Intervals
    Advice on Grouping Data Into Intervals
    The Cumulative Frequency Distribution
    Cumulative Percentages, Percentiles, and Quartiles
    Stem-and-Leaf Plot
    Non-normal Frequency Distributions
    On the Importance of the Shapes of Distributions
    Additional Thoughts About Good Graphs Versus Bad Graphs
    History Trivia: De Moivre to Tukey
    Key Terms
    Chapter 2 Practice Problems
    Chapter 2 Test Yourself Questions
    SPSS Lesson 2
    Chapter 3: Statistical Parameters: Measures of Central Tendency and Variation
    Measures of Central Tendency
    Choosing Among Measures of Central Tendency
    Klinkers and Outliers
    Uncertain or Equivocal Results
    Measures of Variation
    Correcting for Bias in the Sample Standard Deviation
    How the Square Root of x2 Is Almost Equivalent to Taking the Absolute Value of x
    The Computational Formula for Standard Deviation
    The Variance
    The Sampling Distribution of Means, the Central Limit Theorem, and the Standard Error of the Mean
    The Use of the Standard Deviation for Prediction
    Practical Uses of the Empirical Rule: As a Definition of an Outlier
    Practical Uses of the Empirical Rule: Prediction and IQ Tests
    Some Further Comments
    History Trivia: Fisher to Eels
    Key Terms
    Chapter 3 Practice Problems
    Chapter 3 Test Yourself Questions
    SPSS Lesson 3
    Chapter 4: Standard Scores, the z Distribution, and Hypothesis Testing
    Standard Scores
    The Classic Standard Score: The z Score and the z Distribution
    Calculating z Scores
    More Practice on Converting Raw Data Into z Scores
    Converting z Scores to Other Types of Standard Scores
    The z Distribution
    Interpreting Negative z Scores
    Testing the Predictions of the Empirical Rule With the z Distribution
    Why Is the z Distribution So Important?
    How We Use the z Distribution to Test Experimental Hypotheses
    More Practice With the z Distribution and T Scores
    Summarizing Scores Through Percentiles
    History Trivia: Karl Pearson to Egon Pearson
    Key Terms
    Chapter 4 Practice Problems
    Chapter 4 Test Yourself Questions
    SPSS Lesson 4
    Chapter 5: Inferential Statistics: The Controlled Experiment, Hypothesis Testing, and the z Distribution
    Hypothesis Testing in the Controlled Experiment
    Hypothesis Testing: The Big Decision
    How the Big Decision Is Made: Back to the z Distribution
    The Parameter of Major Interest in Hypothesis Testing: The Mean
    Nondirectional and Directional Alternative Hypotheses
    A Debate: Retain the Null Hypothesis or Fail to Reject the Null Hypothesis
    The Null Hypothesis as a Nonconservative Beginning
    The Four Possible Outcomes in Hypothesis Testing
    Significance Levels
    Significant and Nonsignificant Findings
    Trends, and Does God Really Love the .05 Level of Significance More Than the .06 Level?
    Directional or Nondirectional Alternative Hypotheses: Advantages and Disadvantages
    Did Nuclear Fusion Occur?
    Baloney Detection
    Conclusions About Science and Pseudoscience
    The Most Critical Elements in the Detection of Baloney in Suspicious Studies and Fraudulent Claims
    Can Statistics Solve Every Problem?
    Probability
    History Trivia: Egon Pearson to Karl Pearson
    Key Terms
    Chapter 5 Practice Problems
    Chapter 5 Test Yourself Questions
    SPSS Lesson 5
    Chapter 6: An Introduction to Correlation and Regression
    Correlation: Use and Abuse
    A Warning: Correlation Does Not Imply Causation
    Another Warning: Chance Is Lumpy
    Correlation and Prediction
    The Four Common Types of Correlation
    The Pearson Product-Moment Correlation Coefficient
    Testing for the Significance of a Correlation Coefficient
    Obtaining the Critical Values of the t Distribution
    If the Null Hypothesis Is Rejected
    Representing the Pearson Correlation Graphically: The Scatterplot
    Fitting the Points With a Straight Line: The Assumption of a Linear Relationship
    Interpretation of the Slope of the Best-Fitting Line
    The Assumption of Homoscedasticity
    The Coefficient of Determination: How Much One Variable Accounts for Variation in Another Variable-The Interpretation of r2
    Quirks in the Interpretation of Significant and Nonsignificant Correlation Coefficients
    Linear Regression
    Reading the Regression Line
    Final Thoughts About Multiple Regression Analyses: A Warning About the Interpretation of the Significant Beta Coefficients
    Spearman's Correlation
    Significance Test for Spearman's r
    Ties in Ranks
    Point-Biserial Correlation
    Testing for the Significance of the Point-Biserial Correlation Coefficient
    Phi (F) Correlation
    Testing for the Significance of Phi
    History Trivia: Galton to Fisher
    Key Terms
    Chapter 6 Practice Problems
    Chapter 6 Test Yourself Questions
    SPSS Lesson 6
    Chapter 7: The t Test for Independent Groups
    The Statistical Analysis of the Controlled Experiment
    One t Test but Two Designs
    Assumptions of the Independent t Test
    The Formula for the Independent t Test
    You Must Remember This! An Overview of Hypothesis Testing With the t Test
    What Does the t Test Do? Components of the t Test Formula
    What If the Two Variances Are Radically Different From One Another?
    A Computational Example
    Marginal Significance
    The Power of a Statistical Test
    Effect Size
    The Correlation Coefficient of Effect Size
    Another Measure of Effect Size: Cohen's d
    Confidence Intervals
    Estimating the Standard Error
    History Trivia: Gosset and Guinness Brewery
    Key Terms
    Chapter 7 Practice Problems
    Chapter 7 Test Yourself Questions
    SPSS Lesson 7
    Chapter 8: The t Test for Dependent Groups
    Variations on the Controlled Experiment
    Assumptions of the Dependent t Test
    Why the Dependent t Test May Be More Powerful Than the Independent t Test
    How to Increase the Power of a t Test
    Drawbacks of the Dependent t Test Designs
    One-Tailed or Two-Tailed Tests of Significance
    Hypothesis Testing and the Dependent t Test: Design 1
    Design 1 (Same Participants or Repeated Measures): A Computational Example
    Design 2 (Matched Pairs): A Computational Example
    Design 3 (Same Participants and Balanced Presentation): A Computational Example
    History Trivia: Fisher to Pearson
    Key Terms
    Chapter 8 Practice Problems
    Chapter 8 Test Yourself Questions
    SPSS Lesson 8
    Chapter 9: Analysis of Variance (ANOVA): One-Factor Completely Randomized Design
    A Limitation of Multiple t Tests and a Solution
    The Equally Unacceptable Bonferroni Solution
    The Acceptable Solution: An Analysis of Variance
    The Null and Alternative Hypotheses in ANOVA
    The Beauty and Elegance of the F Test Statistic
    The F Ratio
    How Can There Be Two Different Estimates of Within-Groups Variance?
    ANOVA Designs
    ANOVA Assumptions
    Pragmatic Overview
    What a Significant ANOVA Indicates
    A Computational Example
    Degrees of Freedom for the Numerator
    Degrees of Freedom for the Denominator
    Determining Effect Size in ANOVA: Omega Squared (w2)
    Another Measure of Effect Size: Eta (h)
    History Trivia: Gosset to Fisher
    Key Terms
    Chapter 9 Practice Problems
    Chapter 9 Test Questions
    Chapter 9 Test Yourself Questions
    SPSS Lesson 9
    Chapter 10: After a Significant ANOVA: Multiple Comparison Tests
    Conceptual Overview of Tukey's Test
    Computation of Tukey's HSD Test
    What to Do If the Number of Error Degrees of Freedom Is Not Listed in the Table of Tukey's q Values
    Determining What It All Means
    Warning!
    On the Importance of Nonsignificant Mean Differences
    Final Results of ANOVA
    Quirks in Interpretation
    Tukey's With Unequal Ns
    Key Terms
    Chapter 10 Practice Problems
    Chapter 10 Test Yourself Questions
    SPSS Lesson 10
    Chapter 11: Analysis of Variance (ANOVA): One-Factor Repeated-Measures Design
    The Repeated-Measures ANOVA
    Assumptions of the One-Factor Repeated-Measures ANOVA
    Computational Example
    Determining Effect Size in ANOVA
    Key Terms
    Chapter 11 Practice Problems
    Chapter 11 Test Yourself Questions
    SPSS Lesson 11
    Chapter 12: Factorial ANOVA: Two-Factor Completely Randomized Design
    Factorial Designs
    The Most Important Feature of a Factorial Design: The Interaction
    Fixed and Random Effects and In Situ Designs
    The Null Hypotheses in a Two-Factor ANOVA
    Assumptions and Unequal Numbers of Participants
    Computational Example
    Key Terms
    Chapter 12 Practice Problems
    Chapter 12 Test Yourself Problems
    SPSS Lesson 12
    Chapter 13: Post Hoc Analysis of Factorial ANOVA
    Main Effect Interpretation: Gender
    Why a Multiple Comparison Test Is Unnecessary for a Two-Level Main Effect, and When Is a Multiple Comparison Test Necessary?
    Main Effect: Age Levels
    Multiple Comparison Test for the Main Effect for Age
    Warning: Limit Your Main Effect Conclusions When the Interaction Is Significant
    Multiple Comparison Tests
    Interpretation of the Interaction Effect
    Final Summary
    Writing Up the Results Journal Style
    Language to Avoid
    Exploring the Possible Outcomes in a Two-Factor ANOVA
    Determining Effect Size in a Two-Factor ANOVA
    History Trivia: Fisher and Smoking
    Key Terms
    Chapter 13 Practice Problems
    Chapter 13 Test Yourself Questions
    SPSS Lesson 13
    Chapter 14: Factorial ANOVA: Additional Designs
    The Split-Plot Design
    Overview of the Split-Plot ANOVA
    Computational Example
    Two-Factor ANOVA: Repeated Measures on Both Factors Design
    Overview of the Repeated-Measures ANOVA
    Computational Example
    Key Terms and Definitions
    Chapter 14 Practice Problems
    Chapter 14 Test Yourself Questions
    SPSS Lesson 14
    Chapter 15: Nonparametric Statistics: The Chi-Square Test and Other Nonparametric Tests
    Overview of the Purpose of Chi-Square
    Overview of Chi-Square Designs
    Chi-Square Test: Two-Cell Design (Equal Probabilities Type)
    The Chi-Square Distribution
    Assumptions of the Chi-Square Test
    Chi-Square Test: Two-Cell Design (Different Probabilities Type)
    Interpreting a Significant Chi-Square Test for a Newspaper
    Chi-Square Test: Three-Cell Experiment (Equal Probabilities Type)
    Chi-Square Test: Two-by-Two Design
    What to Do After a Chi-Square Test Is Significant
    When Cell Frequencies Are Less Than 5 Revisited
    Other Nonparametric Tests
    History Trivia: Pearson and Biometrika
    Key Terms
    Chapter 15 Practice Problems
    Chapter 15 Test Yourself Questions
    SPSS Lesson 15
    Chapter 16: Other Statistical Topics, Parameters, and Tests
    Big Data
    Health Science Statistics
    Additional Statistical Analyses and Multivariate Statistics
    A Summary of Multivariate Statistics
    Coda
    Key Terms
    Chapter 16 Practice Problems
    Chapter 16 Test Yourself Questions
    Appendix A: z Distribution
    Appendix B: t Distribution
    Appendix C: Spearman's Correlation
    Appendix D: Chi-Square ?2 Distribution
    Appendix E: F Distribution
    Appendix F: Tukey's Table
    Appendix G: Mann-Whitney U Critical Values
    Appendix H: Wilcoxon Signed-Rank Test Critical Values
    Appendix I: Answers to Odd-Numbered Test Yourself Questions
    Glossary
    References
    Index