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Interpreting and Using Statistics in Psychological Research
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Interpreting and Using Statistics in Psychological Research

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September 2016 | 584 pages | SAGE Publications, Inc
This practical, conceptual introduction to statistical analysis by award-winning teacher Andrew N. Christopher uses published research with inherently interesting social sciences content to help students make clear connections between statistics and real life. Using a friendly, easy-to-understand presentation, Christopher walks students through the hand calculations of key statistical tools and provides step-by-step instructions on how to run the appropriate analyses for each type of statistic in SPSS and how to interpret the output. With the premise that a conceptual grasp of statistical techniques is critical for students to truly understand why they are doing what they are doing, the author avoids overly formulaic jargon and instead focuses on when and how to use statistical techniques appropriately.

 
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

Supplements

Student Study Site

Use the Student Study Site to get the most out of your course!
Our Student Study Site at study.sagepub.com/Christopher is completely open-access and offers a wide range of additional features!

  • Mobile-friendly web quizzes allow for independent assessment of progress made in learning course material.
Instructor Resource Site

Calling all instructors!
It’s easy to log on to SAGE’s password-protected Instructor Teaching Site at study.sagepub.com/Christopher for complete and protected access to all text-specific Instructor Resources for Andrew Christopher’s Interpreting and Using Statistics in Psychological Research.  Simply provide your institutional information for verification and within 72 hours you’ll be able to use your login information for any SAGE title! 


Password-protected Instructor Resources include the following:

  • Microsoft® Word® test bank, is available containing multiple choice, true/false, short answer, and essay questions for each chapter. The test bank provides you with a diverse range of pre-written options as well as the opportunity for editing any question and/or inserting your own personalized questions to effectively assess students’ progress and understanding.
  • Editable, chapter-specific Microsoft® PowerPoint® slides offer you complete flexibility in easily creating a multimedia presentation for your course. Highlight essential content and features..
Key features

KEY FEATURES:

  • An applied emphasis throughout the book includes instruction on the process of hand calculating each statistical tool, followed by opportunities to practice.
  • Calculations presented within the larger framework help students understand what they mean and why each statistic is computed the way that it is.
  • Context in the form of a research study as the driving force behind the need for statistical knowledge helps students better understand statistical information.
  • Call-out bubbles highlight what relevant numbers mean on an SPSS printout and how they relate to the statistic under consideration.
  • A distinctive opening chapter on how and why to study statistics highlights its importance, not only in research, but in everyday life.
  • A unique closing chapter provides students with an opportunity to apply skills through the analysis of results from a range of published research studies.
  • Chapter-opening Learning Objectives alert students to what they should be able to do after reading and thinking about that chapter.
  • Technical terminology defined in the margins helps students understand key concepts.
  • Learning Checks allow students to test their knowledge as they move through each chapter.
  • End-of-chapter Application Questions include short-answer and multiple-choice items to help students assess the depth of their understanding of chapter content.

Sample Materials & Chapters

Chapter 1

Chapter 4

Chapter 7


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