Straightforward Statistics: Understanding the Tools of Research

Paperback | October 16, 2015

byGlenn Geher, Sara Hall

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Straightforward Statistics: Understanding the Tools of Research is a clear and direct introduction to statistics for the social, behavioral, and life sciences. Based on Glenn Geher's extensive experience teaching undergraduate statistics, this book provides a narrative presentation of the coreprinciples that provide the foundation for modern-day statistics. With step-by-step guidance on the nuts and bolts of computing these statistics, the book includes detailed tutorials how to use state-of-the-art software, SPSS, to compute the basic statistics employed in modern academic and appliedresearch. Across 13 succinct chapters, this text presents statistics using a conceptual approach along with information on the relevance of the different tools in different contexts and summaries of current research examples.Students should find this book easy useful and engaging in its presentation while instructors should find it detailed, comprehensive, accessible, and helpful in complementing a basic course in statistics.

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Straightforward Statistics: Understanding the Tools of Research is a clear and direct introduction to statistics for the social, behavioral, and life sciences. Based on Glenn Geher's extensive experience teaching undergraduate statistics, this book provides a narrative presentation of the coreprinciples that provide the foundation for ...

Glenn Geher is Professor and Chair of Psychology at the State University of New York at New Paltz, where he has taught Statistics and various other courses related to psychology and evolution since 2000. He also is the founding director of New Paltz's Evolutionary Studies Program, which has been awarded thousands of dollars from the Na...

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Format:PaperbackDimensions:416 pages, 9.88 × 7.01 × 0.79 inPublished:October 16, 2015Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0190276959

ISBN - 13:9780190276959

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Table of Contents

Acknowledgements1. Prelude: Why Do I Need to Learn Statistics?-The Nature of Findings and Facts in the Behavioral Sciences- Statistical Significance and Effect Size- Descriptive and Inferential Statistics- A Conceptual Approach to Teaching and Learning Statistics- The Nature of this Book- How to Approach this Class and What You Should Get Out of It- Key Terms2. Describing a Single Variable- Variables, Values, and Scores- Types of Variables- Describing Scores for a Single Variable- Indices of Central Tendency- Indices of Variability (and the Sheer Beauty of Standard Deviation!)- Rounding- Describing Frequencies of Values for a Single Variable- Representing Frequency Data Graphically- Describing Data for a Categorical Variable- A Real Research Example- Summary- Key Terms3. Standardized Scores- When a Z-Score Equals 0, the Raw Score It Corresponds to Must Equal the Mean- Verbal Scores for the Madupistan Aptitude Measure- Quantitative Scores for the Madupistan Aptitude Measure- Every Raw Score for Any Variable Corresponds to a Particular Z-Score- Computing Z-Scores for All Students for the Madupistan Verbal Test- Computing Raw Scores from Z-Scores- Comparing Your GPA of 3.10 from Solid State University with Pat's GPA of 1.95 from Advanced Technical University- Each Z-Score for Any Variable Corresponds to a Particular Raw Score- Converting Z-Scores to Raw Scores (The Dorm Resident Example)- A Real Research Example- Summary- Key Terms4. Correlation- Correlations Are Summaries- Representing a Correlation Graphically- Representing a Correlation Mathematically- Return to Madupistan- Correlation Does Not Imply Causation- A Real Research Example- Summary- Key Terms5. Statistical Prediction and Regression- Standardized Regression- Predicting Scores on Y with Different Amounts of Information- Beta Weight- Unstandardized Regression Equation- The Regression Line- Quantitatively Estimating the Predictive Power of Your Regression Model- Interpreting r2- A Real Research Example- Conclusion- Key Terms6. The Basic Elements of Hypothesis Testing- The Basic Elements of Inferential Statistics- The Normal Distribution- A Real Research Example- Summary- Key Terms7. Introduction to Hypothesis Testing- The Basic Rationale of Hypothesis Testing- Understanding the Broader Population of Interest- Population versus Sample Parameters- The Five Basic Steps of Hypothesis Testing- A Real Research Example- Summary- Key Terms8. Hypothesis Testing if N 1- The Distribution of Means- Steps in Hypothesis Testing if N 1- Confidence Intervals- Real Research Example- Summary- Key Terms9. Statistical Power- What Is Statistical Power?- An Example of Statistical Power- Factors that Affect Statistical Power- A Real Research Example- Summary- Key Terms10. t-tests (One-Sample and Within-Groups)- One-Sample t-test- Steps for Hypothesis Testing with a One-Sample t-test- Here Are Some Simple Rules to Determine the Sign of tcrit with a One-Sample t-Test- Computing Effect Size with a One-Sample t-Test- How the t-Test Is Biased Against Small Samples- The Within-Group t-Test- Steps in Computing the Within-Group t-Test- Computing Effect Size with a Within-Group t-test- A Real Research Example- Summary- Key Terms11. The Between-Groups t-test- The Elements of the Between-Groups t-test- Effect Size with the Between-Groups t-test- Another Example- Real Research Example- Summary- Key Terms12. Analysis of Variance- ANOVA as a Signal-Detection Statistic- An Example of the One-Way ANOVA- What Can and Cannot Be Inferred from ANOVA (The Importance of Follow-Up Tests)- Estimating Effect Size with the One-Way ANOVA- Real Research Example- Summary- Key Terms13. Chi Square and Hypothesis-Testing with Categorical Variables- Chi Square Test of Goodness of Fit- Steps in Hypothesis Testing with Chi Square Goodness of Fit- What Can and Cannot Be Inferred from a Significant Chi Square- Chi Square Goodness of Fit Testing for Equality across Categories- Chi Square Test of Independence- Real Research Example- Summary- Key TermsAppendix A: Cumulative Standardized Normal DistributionAppendix B: t Distribution: Critical Values of tAppendix C: F Distribution: Critical Values of FAppendix D: Chi Square Distribution: Critical Values of ?2 (Chi Squared) Distribution: Critical Values of ?2Appendix E: Advanced Statistics to Be Aware of- Advanced Forms of ANOVA- Summary- Key TermsAppendix F: Using SPSS- Activity 1: SPSS Data Entry Lab- Activity 2: Working with SPSS Syntax Files- Syntax Files, Recoding Variables, Compute Statements, Out Files, and the Computation of Variables in SPSS- Recoding Variables- Computing New Variables- Output Files- Example: How to Recode Items for the Jealousy Data and Compute Composite Variables- Activity 3: Descriptive Statistics- Frequencies, Descriptives, and Histograms- Frequencies, Descriptives, and Histograms for Data Measured in Class- The Continuous Variable- The Categorical Variable- Activity 4: Correlations- Activity 5: Regression- Activity 6: t-tests- Independent Samples Test- Activity 7: ANOVA with SPSS- Post Hoc Tests- Homogeneous Subsets- Activity 8: Factorial ANOVA- Recomputing Variables so as to Be Able to Conduct a One-Way ANOVA to Examine Specific Differences Between Means- Activity 9: Chi Square- CrosstabsGlossaryReferencesIndex