Packed with exercises, checklists, and how-to sections, the robust Lab Manual for Statistical Analysis by Dawn M. McBride and J. Cooper Cutting gives students hands-on guidance and practice for analyzing their own psychological research. The lab manual’s four sections include activities that correspond directly with the chapters of McBride’s The Process of Statistical Analysis in Psychology; activities related to data analysis projects (including data sets) that students can manipulate and analyze; activities designed to help students choose the correct test for different types of data; and exercises designed to help students write up results from analyses in APA style.
|Edition description:||Lab Manual|
|Product dimensions:||8.30(w) x 10.80(h) x 0.70(d)|
About the Author
Dawn M. McBride is a professor of psychology at Illinois State University. Her research interests include automatic forms of memory, false memory, prospective memory, and forgetting. She has taught courses in introductory psychology, statistics, research methods, cognition and learning, human memory, and a graduate course in experimental design. She is a recipient of the Illinois State University Teaching Initiative Award. Her out-of-work interests include spending time with her family, traveling, watching Philadelphia (her place of birth) sports teams, learning new languages (currently, Japanese) 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, 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 teaches courses in research methods, statistics, cognitive psychology, computer applications in psychology, human memory, psycholinguistics, and sensation and perception.
Table of ContentsPrefaceIntroduction for StudentsSection 1. Topic Activities1. Statistics in the Media2. The Purpose of Statistics3. Understanding Your Data4. Research Concepts: Designs, Validity, and Scales of Measurement5. Measurement Group Activity6. Designing an Experiment Group Activity7. Experimental Variables8. Distributions and Probability9. Displaying Distributions10. Setting Up Your Data in SPSS: Creating a Data File11. Displaying Distributions in SPSS12. Basic Probability13. Sampling14. Central Tendency 115. Central Tendency 216. Central Tendency in SPSS17. Describing a Distribution by Hand18. More Describing Distributions19. Descriptive Statistics Exercise20. Descriptive Statistics With Excel21. Measures of Variability in SPSS22. Calculating z Scores Using SPSS23. The Normal Distribution24. z Scores and the Normal Distribution25. Hypothesis Testing With Normal Populations26. Stating Hypotheses and Choosing Tests27. Hypothesis Testing With z Tests28. Hypothesis Testing With a Single Sample29. One-Sample t Test in SPSS30. One-Sample t Tests by Hand31. Related Samples t Tests32. Related Samples t Test in SPSS33. Independent Samples t Tests34. Hypothesis Testing: Multiple Tests35. More Hypothesis Tests With Multiple Tests36. t Tests Summary Worksheet37. Choose the Correct t Test38. One-Way Between-Subjects ANOVA by Hand39. One-Way Between-Subjects ANOVA in SPSS40. Factorial ANOVA41. One-Way Within-Subjects ANOVA42. One-Way Within-Subjects ANOVA in SPSS43. ANOVA Review44. Which Test Should I Use?45. Correlations and Scatterplots Exercise46. Correlations and Scatterplots 247. Correlations and Scatterplots in SPSS48. Computing Correlations by Hand49. Computing Correlations by Hand 250. Hypothesis Testing With Correlation51. Hypothesis Testing With Correlation Using SPSS52. Regression53. Chi-Square Test for IndependenceSection 2. Meet the Formulae54. Meet the Formula: Standard Deviation55. Meet the Formula: z Score Transformation56. Meet the Formula: Single-Sample z Tests and t Tests57. Meet the Formula: Comparing Independent Samples and Related Samples t Tests58. Meet the Formula: One-Factor Between-Subjects ANOVA59. Meet the Formula: Two-Factor ANOVA60. Meet the Formula: One-Factor Within-Subjects ANOVA61. Meet the Formula: Correlation62. Meet the Formula: Bivariate RegressionSection 3. Data Analysis Projects63. Data Analysis Exercise: von Hippel, Ronay, Baker, Kjelsaas, and Murphy (2016)64. Data Analysis Exercise: Nairne, Pandeirada, and Thompson (2008)65. Data Analysis Project 1: Crammed Versus Distributed Study66. Data Analysis Project 2: Teaching Techniques Study67. Data Analysis Project 3: Distracted Driving Study68. Data Analysis Project 4: Temperature and Air Quality Study69. Data Analysis Project 5: Job Type and Satisfaction Study70. Data Analysis Project 6: Attractive Face Recognition Study71. Data Analysis Project 7: Discrimination in the Workplace StudySection 4. How to Choose a Statistical Test72. Using the Flowchart to Find the Correct Statistical Test73. More Using the Flowchart to Find the Correct Statistical Test74. Research Design Exercise75. Design and Data Collection Exercise76. Designs and AnalysesSection 5. Describing and Interpreting Results in APA Style77. Writing a Results Section From SPSS Output: t Tests78. Writing a Results Section From SPSS Output: ANOVA79. Interpreting Results Exercise: Sproesser, Schupp, and Renner (2014)80. Interpreting Results Exercise: Ravizza, Uitvlugt, and Fenn (2017)Appendix: Summary of FormulaeReferences