Lab Manual for Psychological Research and Statistical Analysis

Dawn M. McBride is professor of psychology at Illinois State University, where she has taught research methods since 1998. Her research interests include automatic forms of memory, false memory, prospective memory, task order choices, and forgetting. In addition to research methods, she teaches courses in introductory psychology, cognition and learning, and human memory; she also teaches a graduate course in experimental design. She is a recipient of the Illinois State University Teaching Initiative Award and the Illinois State University SPA/Psi Chi Jim Johnson Award for commitment to undergraduate mentorship, involvement, and achievement. Her nonacademic interests include spending time with her family, traveling, watching Philadelphia sports teams (it was a good year for Philly sports this year!), and reading British murder mysteries. She earned her PhD in cognitive psychology from the University of California, Irvine, and her BA from the University of California, Los Angeles. J. Cooper Cutting (PhD, cognitive psychology, University of Illinois at Urbana-Champaign) is associate professor of psychology at Illinois State University. Dr. Cutting’s research interests are in psycholinguistics, primarily, with a focus on the production of language. A central theme of his research is how different types of information interact during language use. He has examined this issue in the context of lexical access, within-sentence agreement processes, figurative language production, and pragmatics. He has taught courses in research methods, statistics, cognitive psychology, computer applications in psychology, human memory, psycholinguistics, and sensation and perception. He is also a recipient of the Illinois State University SPA/Psi Chi Jim Johnson Award for commitment to undergraduate mentorship, involvement, and achievement. His non-academic interests include gardening and reading science fiction and fantasy novels.

Introduction for Instructors
CHAPTER 1 • Psychological Research: The Whys and Hows of the Scientific Method and Statistics
1a: The Purpose of Statistics
1b: Science in the Media
1c: Understanding Your Data
1d: Displaying Distributions
1e: Making and Interpreting Graphs
1f: Setting up Your Data in SPSS: Creating a Data File
1g: Displaying Distributions in SPSS
CHAPTER 2 • Developing a Research Question and Understanding Research Reports
2a: How to Read Empirical Journal Articles
2b: Reading Journal Articles—Mueller and Oppenheimer (2014)
2c: Reading Journal Articles—Roediger and Karpicke (2006)
2d: Reviewing the Literature
2e: Creating References
2f: APA Style
2g: APA-Style Manuscript Checklist
CHAPTER 3 • Ethical Guidelines for Psychological Research
3a: Ethics
3b: Ethics in a Published Study
3c: Academic Honesty Guidelines—What Is (and Isn’t) Plagiarism
3d: Examples of Plagiarism
3e: Identifying and Avoiding Plagiarism
CHAPTER 4 • Probability and Sampling
4a: Distributions and Probability
4b: Basic Probability
4c: Subject Sampling
4d: Sampling
CHAPTER 5 • How Psychologists Use the Scientific Method: Data Collection Techniques and Research Designs
5a: Naturalistic Observation Group Activity
5b: Basics of Psychological Research
5c: Designing an Experiment Activity
5d: Research Design Exercise
5e: Design and Data Collection Exercise
CHAPTER 6 • Descriptive Statistics
6a: Central Tendency: Comparing Data Sets
6b: Understanding Central Tendency
6c: Central Tendency in SPSS
6d: Describing a Distribution (Calculations by Hand)
6e: More Describing Distributions
6f: Descriptive Statistics With Excel
6g: Measures of Variability in SPSS
CHAPTER 7 • Independent Variables and Validity in Research
7a: Identifying and Developing Hypotheses About Variables
7b: Independent and Dependent Variables
7c: Identifying Variables From Abstracts
7d: Identifying Variables From Empirical Articles
7e: Research Concepts: Designs, Validity, and Scales of Measurement
7f: Internal and External Validity
CHAPTER 8 • One-Factor Experiments
8a: Bias and Control Exercise
8b: Experimental Variables
8c: Experiments Exercise
8d: Experimental Designs
CHAPTER 9 • Hypothesis-Testing Logic
9a: Inferential Statistics Exercise
9b: Calculating z Scores Using SPSS
9c: The Normal Distribution
9d: z Scores and the Normal Distribution
9e: Hypothesis Testing With Normal Populations
9f: Hypothesis Testing With z Tests
CHAPTER 10 • t Tests
10a: Hypothesis Testing With a Single Sample
10b: One-Sample t Test in SPSS
10c: One-Sample t Tests by Hand
10d: Related-Samples t Tests
10e: Related-Samples t Test in SPSS
10f: Independent Samples t Tests
10g: Hypothesis Testing—Multiple Tests
10h: More Hypothesis Tests With Multiple Tests
10i: t Tests Summary Worksheet
10j: Choose the Correct t Test
10k: Writing a Results Section From SPSS Output—t Tests
CHAPTER 11 • One-Way Analysis of Variance
11a: One-Way Between-Subjects Analysis of Variance (Hand Calculations)
11b: One-Way Between-Subjects Analysis of Variance in SPSS
11c: Writing a Results Section From SPSS Output—Analysis of Variance
11d: Inferential Statistics and Analyses
CHAPTER 12 • Correlation Tests and Simple Linear Regression
12a: Creating and Interpreting Scatterplots
12b: Understanding Correlations
12c: Correlations and Scatterplots in SPSS
12d: Computing Correlations by Hand
12e: Hypothesis Testing With Correlation Using SPSS
12f: Regression
CHAPTER 13 • Chi-Square Tests
13a: Chi-Square Crosstabs Tables
13b: Chi-Square Hand Calculations From Crosstabs Tables
13c: Chi-Square in SPSS—Type in the Data
13d: Chi-Square in SPSS From a Data File
CHAPTER 14 • Multifactor Experiments and Two-Way Analysis of Variance (Chapters 14 and 15)
14a: Factorial Designs
14b: Factorial Designs Article—Sproesser, Schupp, and Renner (2014)
14c: Factorial Designs Article—Farmer, McKay, and Tsakiris (2014)
14d: Describing Main Effects and Interactions
14e: Factorial Analysis of Variance
14f: Analysis of Variance Review
14g: Main Effects and Interactions in Factorial Analysis of Variance
CHAPTER 15 • One-Way Within-Subjects Analysis of Variance
15a: One-Way Within-Subjects Analysis of Variance
15b: One-Way Within-Subjects Analysis of Variance in SPSS
15c: One-Way Within-Subjects Analysis of Variance Review
CHAPTER 16 • Meet the Formulae and Practice Computation Problems
16a: Meet the Formula and Practice Problems: z Score Transformation
16b: Meet the Formula and Practice Problems: Single-Sample z Tests and t Tests
16c: Meet the Formula and Practice Problems: Comparing Independent Samples and Related Samples t Tests
16d: Meet the Formula and Practice Problems: One-Factor Between-Subjects Analysis of Variance
16e: Meet the Formula and Practice Problems: Two-Factor Analysis of Variance
16f: Meet the Formula and Practice Problems: One-Factor Within-Subjects Analysis of Variance
16g: Meet the Formula and Practice Problems: Correlation
16h: Meet the Formula and Practice Problems: Bivariate Regression
Appendix A. Data Sets and Activities
A1: Data Analysis Exercise—von Hippel, Ronay, Baker, Kjelsaas, and Murphy (2016)
A2: Data Analysis Exercise—Nairne, Pandeirada, and Thompson (2008)
A3: Data Analysis Project—Crammed vs. Distributed Study
A4: Data Analysis Project—Teaching Techniques Study
A5: Data Analysis Project—Distracted Driving Study
A6: Data Analysis Project—Temperature and Air Quality Study
A7: Data Analysis Project—Job Type and Satisfaction Study
A8: Data Analysis Project—Attractive Face Recognition Study
A9: Data Analysis Project—Discrimination in the Workplace Study
Appendix B. Overview and Selection of Statistical Tests
B1: Finding the Appropriate Inferential Test
B2: Finding the Appropriate Inferential Test From Research Designs
B3: Finding the Appropriate Inferential Test From Research Questions
B4: Identifying the Design and Finding the Appropriate Inferential Test From Abstracts
B5: Identifying Variables and Determining the Inferential Test From Abstracts
Appendix C. Summary of Formulae
References