Statistics alive!. (2020)

• Record Type: Book
• Authors: Steinberg, Wendy J; Price, Matthew
• Contents: List of Figures; List of Tables; Preface; Supplemental Material for Use With Statistics Alive!; Acknowledgments; About the Authors; PART I. PRELIMINARY INFORMATION: “FIRST THINGS FIRST”; Module 1. Math Review, Vocabulary, and Symbols; Getting Started; Common Terms and Symbols in Statistics; Fundamental Rules and Procedures for Statistics; More Rules and Procedures; Module 2. Measurement Scales; What Is Measurement?; Scales of Measurement; Continuous Versus Discrete Variables; Real Limits; PART II. TABLES AND GRAPHS: “ON DISPLAY”; Module 3. Frequency and Percentile Tables; Why Use Tables?; Frequency Tables; Relative Frequency or Percentage Tables; Grouped Frequency Tables; Percentile and Percentile Rank Tables; SPSS Connection; Module 4. Graphs and Plots; Why Use Graphs?; Graphing Continuous Data; Symmetry, Skew, and Kurtosis; Graphing Discrete Data; SPSS Connection; PART III. CENTRAL TENDENCY: “BULL’S-EYE”; Module 5. Mode, Median, and Mean; What Is Central Tendency?; Mode; Median; Mean; Skew and Central Tendency; SPSS Connection; PART IV. DISPERSION: “FROM HERE TO ETERNITY”; Module 6. Range, Variance, and Standard Deviation; What Is Dispersion?; Range; Variance; Standard Deviation; Mean Absolute Deviation; Controversy: N Versus n - 1; SPSS Connection; PART V. THE NORMAL CURVE AND STANDARD SCORES: “WHAT’S THE SCORE?”; Module 7. Percent Area and the Normal Curve; What Is a Normal Curve?; History of the Normal Curve; Uses of the Normal Curve; Looking Ahead; Module 8. z Scores; What Is a Standard Score?; Benefits of Standard Scores; Calculating z Scores; Comparing Scores Across Different Tests; SPSS Connection; Module 9. Score Transformations and Their Effects; Why Transform Scores?; Effects on Central Tendency; Effects on Dispersion; A Graphic Look at Transformations; Summary of Transformation Effects; Some Common Transformed Scores; Looking Ahead; PART VI. PROBABILITY: “ODDS ARE”; Module 10. Probability Definitions and Theorems; Why Study Probability?; Probability as a Proportion; Equally Likely Model; Mutually Exclusive Outcomes; Addition Theorem; Independent Outcomes; Multiplication Theorem; A Brief Review; Probability and Inference; Module 11. The Binomial Distribution; What Are Dichotomous Events?; Finding Probabilities by Listing and Counting; Finding Probabilities by the Binomial Formula; Finding Probabilities by the Binomial Table; Probability and Experimentation; Looking Ahead; Nonnormal Data; PART VII. INFERENTIAL THEORY: “OF TRUTH AND RELATIVITY”; Module 12. Sampling, Variables, and Hypotheses; From Description to Inference; Sampling; Variables; Hypotheses; Module 13. Errors and Significance; Random Sampling Revisited; Sampling Error; Significant Difference; The Decision Table; Type I Error; Type II Error; Module 14. The z Score as a Hypothesis Test; Inferential Logic and the z Score; Constructing a Hypothesis Test for a z Score; Looking Ahead; PART VIII. THE ONE-SAMPLE TEST: “ARE THEY FROM OUR PART OF TOWN?”; Module 15. Standard Error of the Mean; Central Limit Theorem; Sampling Distribution of the Mean; Calculating the Standard Error of the Mean; Sample Size and the Standard Error of the Mean; Looking Ahead; Module 16. Normal Deviate Z Test; Prototype Logic and the Z Test; Calculating a Normal Deviate Z Test; Examples of Normal Deviate Z Tests; Decision Making With a Normal Deviate Z Test; Looking Ahead; Module 17. One-Sample t Test; Z Test Versus t Test; Comparison of Z-Test and t-Test Formulas; Degrees of Freedom; Biased and Unbiased Estimates; When Do We Reject the Null Hypothesis?; One-Tailed Versus Two-Tailed Tests; The t Distribution Versus the Normal Distribution; The t Table Versus the Normal Curve Table; Calculating a One-Sample t Test; Interpreting a One-Sample t Test; Looking Ahead; SPSS Connection; Module 18. Interpreting and Reporting One-Sample t: Error, Confidence, and Parameter Estimates; What It Means to Reject the Null; Refining Error; Decision Making With a One-Sample t Test; Dichotomous Decisions Versus Reports of Actual p; Parameter Estimation: Point and Interval; SPSS Connection; PART IX. THE TWO-SAMPLE TEST: “OURS IS BETTER THAN YOURS”; Module 19. Standard Error of the Difference Between the Means; One-Sample Versus Two-Sample Studies; Sampling Distribution of the Difference Between the Means; Calculating the Standard Error of the Difference Between the Means; Importance of the Size of the Standard Error of the Difference Between the Means; Looking Ahead; Module 20. t Test With Independent Samples and Equal Sample Sizes; A Two-Sample Study; Inferential Logic and the Two-Sample t Test; Calculating a Two-Sample t Test; Interpreting a Two-Sample t Test; Looking Ahead; SPSS Connection; Module 21. t Test With Unequal Sample Sizes; What Makes Sample Sizes Unequal?; Comparison of Special-Case and Generalized Formulas; Calculating a t Test With Unequal Sample Sizes; Interpreting a t Test With Unequal Sample Sizes; SPSS Connection; Module 22. t Test With Related Samples; What Makes Samples Related?; Comparison of Special-Case and Related-Samples Formulas; Advantage and Disadvantage of Related Samples; Direct-Difference Formula; Calculating a t Test With Related Samples; Interpreting a t Test With Related Samples; SPSS Connection; Module 23. Interpreting and Reporting Two-Sample t: Error, Confidence, and Parameter Estimates; What Is Confidence?; Refining Error and Confidence; Decision Making With a Two-Sample t Test; Dichotomous Decisions Versus Reports of Actual p; Parameter Estimation: Point and Interval; SPSS Connection; PART X. THE MULTISAMPLE TEST: “OURS IS BETTER THAN YOURS OR THEIRS”; Module 24. ANOVA Logic: Sums of Squares, Partitioning, and Mean Squares; When Do We Use ANOVA?; ANOVA Assumptions; Partitioning of Deviation Scores; From Deviation Scores to Variances; From Variances to Mean Squares; From Mean Squares to F; Looking Ahead; Module 25. One-Way ANOVA: Independent Samples and Equal Sample Sizes; What Is a One-Way ANOVA?; Inferential Logic and ANOVA; Deviation Score Method; Raw Score Method; Remaining Steps for Both Methods: Mean Squares and F; Interpreting a One-Way ANOVA; The ANOVA Summary Table; SPSS Connection; PART XI. POST HOC TESTS: “SO WHO’S RESPONSIBLE?”; Module 26. Tukey HSD Test; Why Do We Need a Post Hoc Test?; Calculating the Tukey HSD; Interpreting the Tukey HSD; SPSS Connection; Module 27. Scheffé Test; Why Do We Need a Post Hoc Test?; Calculating the Scheffé; Interpreting the Scheffé; SPSS Connection; PART XII. MORE THAN ONE INDEPENDENT VARIABLE: “DOUBLE DUTCH JUMP ROPE”; Module 28. Main Effects and Interaction Effects; What Is a Factorial ANOVA?; Factorial ANOVA Designs; Number and Type of Hypotheses; Main Effects; Interaction Effects; Looking Ahead; Module 29. Factorial ANOVA; Review of Factorial ANOVA Designs; Data Setup and Preliminary Expectations; Sums of Squares Formulas; Calculating Factorial ANOVA Sums of Squares: Raw Score Method; Factorial Mean Squares and Fs; Interpreting a Factorial F Test; The Factorial ANOVA Summary Table; SPSS Connection; PART XIII. NONPARAMETRIC STATISTICS: “WITHOUT FORM OR VOID”; Module 30. One-Variable Chi-Square: Goodness of Fit; What Is a Nonparametric Test?; Chi-Square as a Goodness
• Edition: Third edition
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2020
• Extent: 1 online resource, illustrations
• Subjects: 519.5; Statistics; Social sciences -- Statistical methods
• Languages: English
• ISBNs: 9781544328249
• Related ISBNs: 9781544328263
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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