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

First Edition


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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