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 InstructorsCHAPTER 1 • Psychological Research: The Whys and Hows of the Scientific Method and Statistics1a: The Purpose of Statistics1b: Science in the Media1c: Understanding Your Data1d: Displaying Distributions1e: Making and Interpreting Graphs1f: Setting up Your Data in SPSS: Creating a Data File1g: Displaying Distributions in SPSSCHAPTER 2 • Developing a Research Question and Understanding Research Reports2a: How to Read Empirical Journal Articles2b: Reading Journal Articles—Mueller and Oppenheimer (2014)2c: Reading Journal Articles—Roediger and Karpicke (2006)2d: Reviewing the Literature2e: Creating References2f: APA Style2g: APA-Style Manuscript ChecklistCHAPTER 3 • Ethical Guidelines for Psychological Research3a: Ethics3b: Ethics in a Published Study3c: Academic Honesty Guidelines—What Is (and Isn’t) Plagiarism3d: Examples of Plagiarism3e: Identifying and Avoiding PlagiarismCHAPTER 4 • Probability and Sampling4a: Distributions and Probability4b: Basic Probability4c: Subject Sampling4d: SamplingCHAPTER 5 • How Psychologists Use the Scientific Method: Data Collection Techniques and Research Designs5a: Naturalistic Observation Group Activity5b: Basics of Psychological Research5c: Designing an Experiment Activity5d: Research Design Exercise5e: Design and Data Collection ExerciseCHAPTER 6 • Descriptive Statistics6a: Central Tendency: Comparing Data Sets6b: Understanding Central Tendency6c: Central Tendency in SPSS6d: Describing a Distribution (Calculations by Hand)6e: More Describing Distributions6f: Descriptive Statistics With Excel6g: Measures of Variability in SPSSCHAPTER 7 • Independent Variables and Validity in Research7a: Identifying and Developing Hypotheses About Variables7b: Independent and Dependent Variables7c: Identifying Variables From Abstracts7d: Identifying Variables From Empirical Articles7e: Research Concepts: Designs, Validity, and Scales of Measurement7f: Internal and External ValidityCHAPTER 8 • One-Factor Experiments8a: Bias and Control Exercise8b: Experimental Variables8c: Experiments Exercise8d: Experimental DesignsCHAPTER 9 • Hypothesis-Testing Logic9a: Inferential Statistics Exercise9b: Calculating z Scores Using SPSS9c: The Normal Distribution9d: z Scores and the Normal Distribution9e: Hypothesis Testing With Normal Populations9f: Hypothesis Testing With z TestsCHAPTER 10 • t Tests10a: Hypothesis Testing With a Single Sample10b: One-Sample t Test in SPSS10c: One-Sample t Tests by Hand10d: Related-Samples t Tests10e: Related-Samples t Test in SPSS10f: Independent Samples t Tests10g: Hypothesis Testing—Multiple Tests10h: More Hypothesis Tests With Multiple Tests10i: t Tests Summary Worksheet10j: Choose the Correct t Test10k: Writing a Results Section From SPSS Output—t TestsCHAPTER 11 • One-Way Analysis of Variance11a: One-Way Between-Subjects Analysis of Variance (Hand Calculations)11b: One-Way Between-Subjects Analysis of Variance in SPSS11c: Writing a Results Section From SPSS Output—Analysis of Variance11d: Inferential Statistics and AnalysesCHAPTER 12 • Correlation Tests and Simple Linear Regression12a: Creating and Interpreting Scatterplots12b: Understanding Correlations12c: Correlations and Scatterplots in SPSS12d: Computing Correlations by Hand12e: Hypothesis Testing With Correlation Using SPSS12f: RegressionCHAPTER 13 • Chi-Square Tests13a: Chi-Square Crosstabs Tables13b: Chi-Square Hand Calculations From Crosstabs Tables13c: Chi-Square in SPSS—Type in the Data13d: Chi-Square in SPSS From a Data FileCHAPTER 14 • Multifactor Experiments and Two-Way Analysis of Variance (Chapters 14 and 15)14a: Factorial Designs14b: Factorial Designs Article—Sproesser, Schupp, and Renner (2014)14c: Factorial Designs Article—Farmer, McKay, and Tsakiris (2014)14d: Describing Main Effects and Interactions14e: Factorial Analysis of Variance14f: Analysis of Variance Review14g: Main Effects and Interactions in Factorial Analysis of VarianceCHAPTER 15 • One-Way Within-Subjects Analysis of Variance15a: One-Way Within-Subjects Analysis of Variance15b: One-Way Within-Subjects Analysis of Variance in SPSS15c: One-Way Within-Subjects Analysis of Variance ReviewCHAPTER 16 • Meet the Formulae and Practice Computation Problems16a: Meet the Formula and Practice Problems: z Score Transformation16b: Meet the Formula and Practice Problems: Single-Sample z Tests and t Tests16c: Meet the Formula and Practice Problems: Comparing Independent Samples and Related Samples t Tests16d: Meet the Formula and Practice Problems: One-Factor Between-Subjects Analysis of Variance16e: Meet the Formula and Practice Problems: Two-Factor Analysis of Variance16f: Meet the Formula and Practice Problems: One-Factor Within-Subjects Analysis of Variance16g: Meet the Formula and Practice Problems: Correlation16h: Meet the Formula and Practice Problems: Bivariate RegressionAppendix A. Data Sets and ActivitiesA1: 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 StudyA4: Data Analysis Project—Teaching Techniques StudyA5: Data Analysis Project—Distracted Driving StudyA6: Data Analysis Project—Temperature and Air Quality StudyA7: Data Analysis Project—Job Type and Satisfaction StudyA8: Data Analysis Project—Attractive Face Recognition StudyA9: Data Analysis Project—Discrimination in the Workplace StudyAppendix B. Overview and Selection of Statistical TestsB1: Finding the Appropriate Inferential TestB2: Finding the Appropriate Inferential Test From Research DesignsB3: Finding the Appropriate Inferential Test From Research QuestionsB4: Identifying the Design and Finding the Appropriate Inferential Test From AbstractsB5: Identifying Variables and Determining the Inferential Test From AbstractsAppendix C. Summary of FormulaeReferences