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Using and Interpreting Statistics in the Social, Behavioral, and Health Sciences

Using and Interpreting Statistics in the Social, Behavioral, and Health Sciences

May 2018 | 232 pages | SAGE Publications, Inc

Using and Interpreting Statistics in the Social, Behavioral, and Health Sciences is designed to be paired with any undergraduate introduction to research methods text used by students in a variety of disciplines. It introduces students to statistics at the conceptual level—examining the meaning of statistics, and why researchers use a particular statistical technique, rather than computational skills. Focusing on descriptive statistics, and some more advanced topics such as tests of significance, measures of association, and regression analysis, this brief, inexpensive text is the perfect companion to help students who have not yet taken an introductory statistics course or are confused by the statistics used in the articles they are reading.

Chapter 1. Brief Introduction to Research in Social, Behavioral, & Health Sciences
What is the Purpose of Research?  
How is Research Done?  
Scientific Method & Hypothesis Testing  
Inductive Research  
Deductive Research  
Research Designs  
Chapter 2. Variables and Measurement
Variables and Data  
Levels of Variable Measurement  
Types of Relationships  
Research Design and Measurement Quality  
Chapter 3. How to Sample and Collect Data for Analysis
Why Use a Sample?  
Probability Sampling Methods  
Non-Probability Sampling Methods  
Validating a Sample  
Split Ballot Designs  
How and Where Are Data Collected Today?  
Chapter 4. Data Frequencies and Distributions
Univariate Frequencies and Relative Frequencies  
Cumulative Percentages and Percentiles  
Frequencies for Quantitative Data  
Univariate Distributions  
The Normal Distribution  
Non-Normal Distribution Characteristics  
Data Transformations for Dealing with Non-Normal Distributions  
Bivariate Frequencies: Contingency Tables  
Chapter 5. Using and Interpreting Univariate and Bivariate Visualizations
Univariate Data Visualization  
Bivariate Data Visualization  
Chapter 6. Central Tendency and Variability
Understanding How to Calculate and Interpret Measures of Central Tendency  
Understanding How Individuals in a Distribution Vary Around a Central Tendency  
Chapter 7. What Are Z-Scores and Why Are They Important?
What is a Z-Score?  
How to Calculate a Z-Score  
The Standard Normal Table  
Working with the Standard Normal Distribution to Calculate Z-Scores, Raw Scores, and Percentiles  
Confidence Intervals  
Chapter 8. Hypothesis Testing and Statistical Significance
Null and Alternative Hypotheses  
Statistical Significance  
Test Statistic Distributions  
Choosing a Test of Statistical Significance  
The Chi-Square Test of Independence  
The Independent Samples t-test  
One-Way Analysis of Variance (ANOVA)  
Chapter 9. How to Measure the Relationship Between Nominal and Ordinal Values
Choosing the Correct Measure of Association  
Trying to Reduce Error (PRE Statistics)  
Calculating and Interpreting Lambda  
Calculating and Interpreting Gamma  
Calculating and Interpreting Somers’ d  
Calculating and Interpreting Kendall’s tau-b  
Interpreting PRE Statistics Overview  
Chapter 10. Effect Size
Effect Size  
Choosing an Effect Size  
Chapter 11. How to Interpret and Report Regression Results
What is a Regression?  
Bivariate Regression  
Coefficient of Determination (r2)  
Multiple Regression  
Logistic Regression  
Chapter 12. Scales, Indices, and Typologies
Indices, Scales, and Typologies Defined and Explained  
Appendix A. Z-Score Table (Standard Normal Table)
Appendix B. Critical Values for T-Statistic
Appendix C. Critical Values for Chi-Square
Appendix D. Critical Values for F-Statistic
Appendix E. Glossary of Terms and Formulas

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