Interpreting and Using Statistics in Psychological Research

Andrew (Drew) N. Christopher grew up in Plano, Texas. He received his undergraduate degree from Stetson University in 1992 with a major in economics and finance and a minor in psychology. He holds an MBA from Southern Methodist University and his Ph.D. from the University of Florida. Drew has taught at Albion College since 2001. In addition to teaching courses in research design and analysis, he also teaches "Introductory Psychology," "Industrial/Organizational Psychology," "Senior Research Seminar," and an honor's college course called "Black Swans and Everyday Life," and is developing a new first-year seminar titled "Football and American Society." He has published more than 30 peer-reviewed papers with 28 undergraduate authors since arriving at Albion. Many more undergraduate collaborators have presented their work at venues such as the International Society for the Scientific Study of Individual Differences (ISSID), Association for Psychological Science (APS), Michigan Undergraduate Psychology Research Conference (MUPRC), and Albion College's Elkin Isaac Research Symposium. Drew has twice been named Albion College's Phi Beta Kappa Scholar of the Year. In recognition of his work with students, he was awarded the Robert S. Daniel Excellence in Teaching Award at a 4-year college or university in 2013 and named his College's Teacher of the Year in 2014. He has been editor-in-chief of the Society for the Teaching of Psychology's journal, Teaching of Psychology, since 2009. Away from academic responsibilities, Drew works out regularly, not because he enjoys doing so (in fact, he hates it) but because it allows him to eat foods that he probably otherwise should not eat so much of. Toward that end, he enjoys cooking and is particularly adept at making various types of pizza and a wide range of unhealthy desserts. Any leftovers from his creations are gladly consumed by his two beagles, Sybil and Hans. Drew enjoys almost all sports, particularly college football and professional hockey. As a University of Florida graduate, Drew maintains his loyalty to the Southeastern Conference despite living in Big Ten territory. As a Tampa Bay Lightning fan living in south central Michigan, he is a regular recipient of dirty looks from Detroit Red Wings and Chicago Blackhawks fans who populate the area.

Preface Acknowledgments About the Author Chapter 1- Why Do I Have to Learn Statistics? The Value of Statistical Thinking in Life Statistical Thinking and Everyday Life Failing to Use Information About Probability Availability heuristic Representativeness heuristic Misunderstanding Connections Between Events Illusory correlations Gambler's fallacy Goals of Research Goal: To Describe Goal: To Predict Goal: To Explain Goal: To Apply Statistical Thinking: Some Basic Concepts Parameters Versus Statistics Descriptive Statistics Versus Inferential Statistics Sampling Error Chapter Application Questions Questions for Class Discussion Chapter 2- Basics of Quantitative Research: Variables, Scales of Measurement, and an Introduction to the Statistical Package for the Social Sciences (SPSS) The Study Variables Operational Definitions Measurement Reliability and Validity Scales of Measurement: How We Measure Variables Nominal Data Ordinal Data Interval and Ratio (Scale) Data Discrete Versus Continuous Variables The Basics of SPSS Variable View Data View Chapter Application Questions Questions for Class Discussion Chapter 3- Describing Data With Frequency Distributions and Visual Displays The Study Frequency Distributions Frequency Distribution Tables Frequency Distribution Graphs Common Visual Displays of Data in Research Bar Graphs Scatterplots Line Graphs Using SPSS to Make Visual Displays of Data Making a Bar Graph Making a Scatterplot Making a Line Graph Chapter Application Questions Questions for Class Discussion Chapter 4- Making Sense of Data: Measures of Central Tendency and Variability Measures of Central Tendency Three Measures of Central Tendency Mean Median Mode Reporting the measures of central tendency in research Choosing a Measure of Central Tendency Consideration 1: Outliers in the data Consideration 2: Skewed data distributions Consideration 3: A variable's scale of measurement Consideration 4: Open-ended response ranges Measures of Central Tendency and SPSS Measures of Variability What Is Variability? Why Should We Care About Variability? Three Measures of Variability Range Variance Standard deviation Reporting variability in research Measures of Variability and SPSS Chapter Application Questions Questions for Class Discussion Chapter 5- Determining "High" and "Low" Scores: The Normal Curve, z Scores, and Probability Types of Distributions Normal Distributions Skewed Distributions Standardized Scores (z Scores) z Scores, the Normal Distribution, and Percentile Ranks Locating Scores Under the Normal Distribution Percentile Ranks z Scores and SPSS Chapter Application Questions Questions for Class Discussion Chapter 6- Drawing Conclusions From Data: Descriptive Statistics, Inferential Statistics, and Hypothesis Testing Basics of Null Hypothesis Testing Null Hypotheses and Research Hypotheses Alpha Level and the Region of Null Hypothesis Rejection Gathering Data and Testing the Null Hypothesis Making a Decision About the Null Hypothesis Type I Errors, Type II Errors, and Uncertainty in Hypothesis Testing The z Test A Real-World Example of the z Test Ingredients for the z Test Using the z Test for a Directional (One-Tailed) Hypothesis Using the z Test for a Nondirectional (Two-Tailed) Hypothesis One-Sample t Test A Real-Word Example of the One-Sample t Test Ingredients for the One-Sample t Test Using the One-Sample t Test for a Directional (One-Tailed) Hypothesis Using the One-Sample t Test for a Nondirectional (Two-Tailed) Hypothesis One-Sample t Test and SPSS Statistical Power and Hypothesis Testing Chapter Application Questions Questions for Class Discussion Chapter 7- Comparing Two Group Means: The Independent Samples t Test Conceptual Understanding of the Statistical Tool The Study The Tool Ingredients Hypothesis from Kasser and Sheldon (2000) Interpreting the Tool Assumptions of the tool Testing the null hypothesis Extending our null hypothesis test Using Your New Statistical Tool Hand-Calculating the Independent Samples t Test Step 1: State hypotheses Step 2: Calculate the mean for each of the two groups Step 3: Calculate the standard error of the difference between the means Step 4: Calculate the t test statistic Step 5: Determine degrees of freedom (dfs) Step 6: Locate the critical value Step 7: Make a decision about the null hypothesis Step 8: Calculate an effect size Step 9: Determine the confidence interval Independent Samples t Test and SPSS Establishing your spreadsheet Running your analyses What am I looking at? Interpreting your SPSS output Chapter Application Questions Questions for Class Discussion Chapter 8- Comparing Two Repeated Group Means: The Paired Samples t Test Conceptual Understanding of the Tool The Study The Tool Ingredients Hypothesis from Stirling et al. (2014) Interpreting the Tool Testing the null hypothesis Extending our null hypothesis test Assumptions of the tool Using Your New Statistical Tool Hand-Calculating the Paired Samples t Test Step 1: State hypotheses Step 2: Calculate the mean difference score Step 3: Calculate the standard error of the difference scores Step 4: Calculate the t test statistic Step 5: Determine degrees of freedom (dfs) Step 6: Locate the critical value Step 7: Make a decision about the null hypothesis Step 8: Calculate an effect size Step 9: Determine the confidence interval Paired Samples t Test and SPSS Establishing your spreadsheet Running your analyses What am I looking at? Interpreting your SPSS output Chapter Application Questions Questions for Class Discussion Chapter 9- Comparing Three or More Group Means: The One-Way, Between-Subjects Analysis of Variance (ANOVA) Conceptual Understanding of the Tool The Study The Tool Ingredients Assumptions of the tool Hypothesis from Eskine (2012) Interpreting the Tool Testing the null hypothesis Extending our null hypothesis test Going beyond the F ratio: Post hoc tests Using Your New Statistical Tool Hand-Calculating the One-Way, Between-Subjects ANOVA Step 1: State hypotheses Step 2: Calculate the mean for each group Step 3: Calculate the sums of squares (SSs) Total Sums of Squares (SStotal) Within-Groups Sums of Squares (SSwithin-groups) Between-Groups Sums of Squares (SSbetween-groups) Step 4: Determine degrees of freedom (dfs) Total Degrees of Freedom (dftotal) Within-Groups Degrees of Freedom (dfwithin-groups) Between-Groups Degrees of Freedom (dfbetween-groups) Step 5: Calculate the mean squares (MSs) Step 6: Calculate your F ratio test statistic Step 7: Locate the critical value Step 8: Make a decision about the null hypothesis Step 9: Calculate an effect size Step 10: Perform post hoc tests One-Way Between-Subjects ANOVA and SPSS Establishing your spreadsheet Running your analysis What am I looking at? Interpreting your SPSS output Chapter Application Questions Questions for Class Discussion Chapter 10- Comparing Three or More Repeated Group Means: The One-Way, Repeated-Measures Analysis of Variance (ANOVA) Conceptual Understanding of the Tool The Study The Tool Between-subjects versus repeated-measures ANOVAs Assumptions of the tool Hypothesis from Bernard et al. (2014) Interpreting the Tool Testing the null hypothesis Extending our null hypothesis test Going beyond the F ratio: Post hoc tests Using Your New Statistical Tool Hand-Calculating the One-Way, Repeated-Measures ANOVA Step 1: State the hypothesis Step 2: Calculate the mean for each group Step 3: Calculate the sums of squares (SSs) Total Sums of Squares (SStotal) Between Sums of Squares (SSbetween) Error Sums of Squares (SSerror) Step 4: Determine degrees of freedom (dfs) Total Degrees of Freedom (dftotal) Between Degrees of Freedom (dfbetween) Error Degrees of Freedom (dferror) Step 5: Calculate the mean squares (MSs) Step 6: Calculate your F ratio test statistic Step 7: Locate the critical value Step 8: Make a decision about the null hypothesis Step 9: Calculate an effect size Step 10: Perform post hoc tests One-Way, Repeated-Measures ANOVA and SPSS Establishing your spreadsheet Running your analysis What am I looking at? Interpreting your SPSS output Chapter Application Questions Questions for Class Discussion Chapter 11- Analyzing Two or More Influences on Behavior: Factorial Designs for Two Between-Subjects Factors Conceptual Understanding of the Tool The Study The Tool Factorial notation Main effects and interactions Hypothesis from Troisi and Gabriel (2011) Interpreting the Tool Testing the null hypothesis Extending the null hypothesis tests Dissecting a statistically significant interaction Using Your New Statistical Tool Hand-Calculating the Two-Way, Between-Subjects ANOVA Step 1: State the hypotheses Step 2: Calculate the mean for each group and the marginal means Step 3: Calculate the sums of squares (SSs) Total Sums of Squares (SStotal) Within-Groups Sums of Squares (SSwithin-groups) Between-Groups Sums of Squares (SSbetween-groups) Step 4: Determine degrees of freedom (dfs) Total Degrees of Freedom (dftotal) Within-Groups Degrees of Freedom (dfwithin-groups) Between-Groups Degrees of Freedom (dfbetween-groups) Step 5: Calculate the mean squares (MSs) Step 6: Calculate your F ratio test statistics Step 7: Locate the critical values Step 8: Make a decision about each null hypothesis Step 9: Calculate the effect sizes Step 10: Perform follow-up tests Two-Way, Between-Subjects ANOVA and SPSS Establishing your spreadsheet Running your analysis What am I looking at? Interpreting your SPSS output Dissecting interactions in SPSS Chapter Application Questions Questions for Class Discussion Chapter 12- Determining Patterns in Data: Correlations Conceptual Understanding of the Tool The Study The Tool Types (directions) of correlations Strength of correlations Assumptions of the Pearson correlation Uses for correlations Use 1: Studying naturally occurring relationships Use 2: Basis for predictions Use 3: Establishing measurement reliability and validity Hypotheses from Clayton et al. (2013) Interpreting the Tool Testing the null hypothesis Cautions in interpreting correlations Caution 1: Don't confuse type (direction) and strength of a correlation Caution 2: Range restriction Caution 3: "Person-who" thinking Caution 4: Curvilinear relationships Caution 5: Spurious correlations Using Your New Statistical Tool Hand-Calculating the Person Correlation Coefficient (r) Step 1: State hypotheses Step 2: For both variables, find each participant's deviation score and then multiply them together Step 3: Sum the products in step 2 Step 4: Calculate the sums of squares for both variables Step 5: Multiply the two sums of squares and then take the square root Step 6: Calculate the correlation coefficient (r) test statistic Step 7: Locate the critical value Step 8: Make a decision about the null hypothesis The Pearson Correlation (r) and SPSS Establishing your spreadsheet Running your analysis What am I looking at? Interpreting your SPSS output Chapter Application Questions Questions for Class Discussion Chapter 13- Predicting the Future: Univariate and Multiple Regression Univariate Regression Ingredients Hand-Calculating a Univariate Regression Step 1: Calculate the slope of the line (b) Step 2: Calculate the y-intercept (a) Step 3: Make predictions Univariate Regression and SPSS Running your analysis What am I looking at? Interpreting your SPSS output Multiple Regression Understanding Multiple Regression in Research Multiple Regression and SPSS Establishing your spreadsheet Running your analysis What am I looking at? Interpreting your SPSS output Chapter Application Questions Questions for Class Discussion Chapter 14- When We Have Exceptions to the Rules: Nonparametric Tests Chi-Square (x2) Tests Chi-Square (x2) Goodness-of-Fit Test Hand-calculating the ?2 goodness-of-fit test Step 1: State hypotheses Step 2: Determine degrees of freedom (dfs) Step 3: Calculate the x2 test statistic Step 4: Find the critical value and make a decision about the null hypothesis x2 goodness-of-fit test and SPSS Establishing your spreadsheet Running your analysis What am I looking at? Interpreting your SPSS output Chi-Square (x2) Test of Independence Hand-calculating the x2 test of independence Step 1: State hypotheses Step 2: Determine degrees of freedom (dfs) Step 3: Calculate expected frequencies Step 4: Calculate the x2 test statistic Step 5: Find the critical value and make a decision about the null hypothesis Step 6: Calculate an effect size x2 test for independence and SPSS Establishing your spreadsheet Running your analysis What am I looking at? Interpreting your SPSS output Spearman Rank-Order Correlation Coefficient Hand-Calculating the Spearman Rank-Order Correlation Step 1: State the hypothesis Step 2: Calculate the difference (D) score between each pair of rankings Step 3: Square and sum the difference scores in step 2 Step 4: Calculate the Spearman correlation coefficient (rs) test statistic Step 5: Locate the critical value and make a decision about the null hypothesis Spearman's Rank-Order Correlation and SPSS Establishing your spreadsheet Running your analysis What am I looking at? Interpreting your SPSS output Mann-Whitney U Test Hand-Calculating the Mann-Whitney U Test Step 1: State hypotheses Step 2: Calculate the ranks for categories being compared Step 3: Sum the ranks for each category Step 4: Find the U for each group Step 5: Locate the critical value and make a decision about the null hypothesis Mann-Whitney U Test and SPSS Establishing your spreadsheet Running your analysis What am I looking at? Interpreting your SPSS output Chapter Application Questions Questions for Class Discussion Chapter 15- Bringing It All Together: Using Your Statistical Toolkit Deciding on the Appropriate Tool: Six Examples Study 1: "Waiting for Merlot: Anticipatory Consumption of Experiential and Material Purchases Study 2: "Evaluations of Sexy Women in Low- and High-Status Jobs" Study 3: "Evil Genius? How Dishonesty Can Lead to Greater Creativity" Study 4: "Differential Effects of a Body Image Exposure Session on Smoking Urge Between Physically Active and Sedentary Female Smokers" Study 5: "Texting While Stressed: Implications for Students' Burnout, Sleep, and Well-Being" Study 6: "How Handedness Direction and Consistency Relate to Declarative Memory Task Performance" Using Your Toolkit to Identify Appropriate Statistical Tools Study 7: "Borderline Personality Disorder: Attitudinal Change Following Training" Study 8: "Effects of Gender and Type of Praise on Task Performance Among Undergraduates" Study 9: "Please Respond ASAP: Workplace Telepressure and Employee Recovery" Answers to Studies 7, 8, and 9 Appendices: Statistical Tables Glossary References Index