Graduate-Level Statistics in Psychology

by Peter Ji

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By: Dr. Peter Ji, Adler University. Image: "Graduate-Level Statistics in Psychology Wordcloud" by D. Banks. CC-BY 4.0

Description from LibreTexts.

Publisher
Adler University
Subjects
Statistics
Licence
CC BY 4.0, as LibreTexts states it
Who can read
Tweens profiles and older, judged from its title and subjects

Contents

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  1. Front Matter
  2. TitlePage
  3. Acknowledgements
  4. LibreTexts Information
  5. Licensing
  6. Table of Contents
  7. 0: Introduction
  8. 0.1: A Perspective on Statistics
  9. 0.2: Psychology Profession
  10. 0.3: Evidence-Based Practice
  11. 0.4: Scientist-Practitioner Model; Scholar-Practitioner Model
  12. 0.5: Social Justice
  13. 0.6: Description of Intended Audience
  14. 0.7: Approach to Learning Statistics
  15. 1: What is a Number?
  16. 1.1: How to Start with Statistics
  17. 1.2: A "Geek Out" Explanation of Variation
  18. 1.3: Variations Lead to Numbers
  19. 1.4: The Battle of Words Versus Numbers
  20. 1.5: "Quantifying" a Construct, or Why Numbers Are Better Than Words
  21. 1.6: Now You Love Numbers as Much as You Love the White Sox and Taylor Swift
  22. 1.7: Statistics Do Something with Numbers
  23. 1.8: Numbers Don't Lie, but Numbers Don't Tell the Full Story
  24. 1.9: Seeing Numbers – What I Want You to Learn
  25. 1.10: Connection Between Understanding How Things Vary and What You are Trying to Do
  26. 1.11: Discussion Questions
  27. 2: What are Statistics?
  28. 2.1: Statistics Involves Comparison and Context
  29. 2.2: Statistics is About Finding Patterns
  30. 2.3: Statistics as Estimates, or Making Guesses
  31. 2.4: Two Broad Categories of the Process of Using Statistics
  32. 2.5: Discussion Questions
  33. 3: Variable Types
  34. 3.1: Variables - Not Always Obvious
  35. 3.2: How to Organize Variation With Variables
  36. 3.3: Categorical Variables- Variables That Vary by Type
  37. 3.4: Continuous Variables- Variables That Vary by Level
  38. 3.5: Nuances in Determining if a Variable Should be a Certain Type
  39. 3.6: Making Decisions About the Variables in Your Research Study
  40. 3.7: Lessons Learned About Variables
  41. 3.8: Discussion Questions
  42. 4: Distributions
  43. 4.1: Distributions – A Picture of Variation
  44. 4.2: Categorical Distributions
  45. 4.2.3: Evaluating the Plot
  46. 4.3: Reviewing Journal Articles for Frequency Distributions for Categorical Variables
  47. 4.4: Continuous Distributions
  48. 4.5: Evaluating the Quality of Frequency Distributions
  49. 4.6: Normal Distribution for Continuous Variables
  50. 4.7: Skewed Distribution- The Opposite of Normal Distribution
  51. 4.8: Evaluating a Continuous Distribution
  52. 4.9: Discussion Questions
  53. 5: Measures of Central Tendency
  54. 5.1: What is Central Tendency? Why Do We Use It?
  55. 5.2: Three Types of Central Tendency and Why We Need Them
  56. 5.3: The Mean, Median, and Mode in Normal and Skewed Distributions
  57. 5.4: Discussion Questions
  58. 6: Measures of Variability
  59. 6.1: What is Variance?
  60. 6.2: How Do You Determine Variance?
  61. 6.3: Partitioning of the Variance
  62. 6.4: Standardized Values of Reporting Scores
  63. 6.5: Discussion Questions
  64. 7: Sampling, Standard Error, Central Limit Theorem
  65. 7.1: The Purpose of Sampling
  66. 7.2: Sampling from the Population
  67. 7.3: Getting a Good Sample - Get a Random Sample
  68. 7.4: Sampling Bias or Error
  69. 7.5: Central Limit Theorem (CLT) and Its Implications
  70. 7.6: Summary of Reasons Why You Need a Large Sample Size
  71. 7.7: Discussion Questions
  72. 8: Probability, Hypothesis Testing, Type I and Type II Errors
  73. 8.1: The Role of Probability
  74. 8.2: Statistical Decision - Is a Result Significant or Not Significant?
  75. 8.3: Hypotheses for Statistical Inference
  76. 8.4: How to Determine if a Result is Significant
  77. 8.5: Interpreting a Statistical Test Result
  78. 8.6: Testing a Hypothesis
  79. 8.7: How Do We Get the P Value for the Statistical Test?
  80. 8.8: Confidence Intervals
  81. 8.9: Type I and Type II Errors
  82. 8.10: The Definition of Type I and Type II Errors
  83. 8.11: Discussion Questions
  84. 9: Effect Sizes and Power Analysis
  85. 9.1: Significant (P Value) Versus Meaningful (Effect Size) Results
  86. 9.2: What Are Effect Sizes?
  87. 9.3: Statistical Power
  88. 9.4: How to Conduct a Power Analysis
  89. 9.5: Pause and Process Check, Pulling P Values and Effect Sizes Together
  90. 9.6: Discussion Questions
  91. 10: Introduction to Inferential Statistics
  92. 10.1: The Meaning of Inferential
  93. 10.2: Patterns
  94. 10.3: General Linear Model
  95. 10.4: Discussion Questions
  96. 11: T-tests
  97. 11.1: When to Use the Independent T-test
  98. 11.2: Design Set Up
  99. 11.3: Commentary
  100. 11.4: T-test Assumptions
  101. 11.5: Reading T-tests
  102. 11.6: T-Tests You Need to Know for the Examination for Professional Practice in Psychology (EPPP) Licensing Exam
  103. 11.7: Reading Articles That Involve a T-test
  104. 11.8: Reading T-tests from Journal Articles
  105. 11.9: Summary
  106. 11.10: Discussion Questions
  107. 12: Analysis of Variance – ANOVA
  108. 12.1: When to Use the ANOVA
  109. 12.2: The Concept Behind Analysis of Variance
  110. 12.3: - Interpreting the F-test and Post-Hoc Comparisons
  111. 12.4: Commentary
  112. 12.5: Reading Articles – ANOVA
  113. 12.6: Summary
  114. 12.7: Discussion Questions
  115. Back Matter
  116. Index
  117. Appendices
  118. Detailed Licensing