Archive: Statistics for Psychologists
  • Back to the Current Materials
    • 2023/2024
      • PSYC121
        • Statistics for Psychologists I
          • Statistics for Psychologists
          • 2. Descriptive statistics in RStudio
          • 3. DVs and IVs in RStudio
          • 4. Customisation of graphs, and z-scores
          • 5. Class test
          • 6. Sampling, probability and binomial tests
          • 7. Filtering data and testing means (one-sample t-test)
          • 8. Related-samples t-tests, plotting means and SE bars
          • 9. Unrelated-samples t-test and Power
          • Data
            • Week 6
              • Week_6
            • Week 9
              • PSYC121: Week 9 Lab
      • PSYC122
        • Statistics for Psychologists II
          • 1. Week 11 - Correlation
          • 2. Week 12 - Correlation Part 2
          • 3. Week 13 - The Linear Model
          • 4. Week 14 - Chi-Square
          • 5. Week 16 – Hypotheses, associations
          • 6. Week 17 – Better understanding the linear model
          • 7. Week 18 – Developing the linear model
          • 8. Week 19 – Linear models – critical perspectives
          • Data
            • Week11
              • 122_wk11_labActivities2_3
            • Week12
              • 122_wk12_labActivity2
            • Week13
              • 122_wk13_labActivity2
            • Week14
              • 122_wk13_labActivity2
            • Week16
              • 2023-24-PSYC122-w16-how-to
              • 2023-24-PSYC122-w16-workbook-answers
            • Week17
              • 2023-24-PSYC122-w17-how-to
              • 2023-24-PSYC122-w17-workbook-answers
            • Week18
              • 2023-24-PSYC122-w18-how-to
              • 2023-24-PSYC122-w18-workbook-answers
            • Week19
              • 2023-24-PSYC122-w19-how-to
              • 2023-24-PSYC122-w19-workbook-answers
      • PSYC402
        • Part1
          • 1. Recap of the linear model and practising data-wrangling in R
          • 2. Categorical predictors
          • 3. More on interactions
          • 4. Logistic regression
          • 5. Poisson regression
        • Preface
          • Introduction to multilevel data
          • Introduction to linear mixed-effects models
          • Developing linear mixed-effects models
          • Introduction to Generalized Linear Mixed-effects Models
          • Introduction to Ordinal Models
          • How
          • Introduction: the why
          • R knowledge
          • LICENSE
          • References
          • Summary
          • Data visualization
          • What
    • 2024/2025
      • PSYC121
        • Statistics for Psychologists I
          • Statistics for Psychologists
          • 2. Descriptive statistics in RStudio
          • 3. DVs and IVs in RStudio
          • 4. Customisation of graphs, and z-scores
          • 5. Class test
          • 6. Sampling, probability and binomial tests
          • 7. Filtering data and testing means (one-sample t-test)
          • 8. Related-samples t-tests, plotting means and SE bars
          • 9. Unrelated-samples t-test and Power
          • Data
            • Week 6
              • Week_6
            • Week 9
              • PSYC121: Week 9 Lab
      • PSYC122
        • Statistics for Psychologists II
          • 1. Week 11 - Correlation
          • 2. Week 12 - Correlation Part 2
          • 3. Week 13 - The Linear Model
          • 4. Week 14 - Chi-Square
          • 5. Week 16 – Hypotheses, associations
          • 6. Week 17 – Better understanding the linear model
          • 7. Week 18 – Developing the linear model
          • 8. Week 19 – Linear models – critical perspectives
          • Data
            • Week11
              • 122_wk11_labActivities2_3
              • wk11
            • Week12
              • 122_wk12_labActivity2
            • Week13
              • 122_wk13_labActivity2
            • Week14
              • 122_wk13_labActivity2
            • Week16
              • PSYC122-w16-how-to
              • PSYC122-w16-workbook-answers
            • Week17
              • PSYC122-w17-how-to
              • PSYC122-w17-workbook-answers
            • Week18
              • PSYC122-w18-how-to
              • PSYC122-w18-workbook-answers
            • Week19
              • PSYC122-w19-how-to
              • PSYC122-w19-workbook-answers
      • PSYC214
        • Statistics for Group Comparisons
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
      • PSYC234
        • From association to modelling causality
          • 1. Review of correlation, simple regression and demonstration of multiple regression
          • 2. Multiple Regression Including Categorical Predictors
          • 3. Multiple Regression Models that Include Interactions (Moderated Variables)
          • 4. Mediation
          • 5. Factor Analysis and the Binomial Test
          • 6. Wilcoxon rank-sum test and Wilcoxon signed-rank test
          • 7. Kruskal-Wallis test and Friedman’s ANOVA
          • 8. Binary logistic regression models
          • 9. Expanding on binary logistic regression
          • Data
            • Wk1
              • PSYC234 Week 11
                • Correlation_Review_2025
                • Multiple_Regression
                • Simple Regression Review
            • Wk3
              • Lecture 3 Materials
                • Multiple_Regression_With_Interactions
      • PSYC411
        • Part1
          • Week 1. Introducing Data
          • Week 2. Manipulating data
          • Week 3. Drawing graphs from data
          • Week 4. Testing nominal data
          • Week 5. Testing differences between groups
        • Part2
          • Week 6. The structured research report – Quick start
          • Week 6. How you can do the analysis work
          • Week 6. Why we are asking you to do this
          • Week 7. Hypotheses and associations
          • Week 8. Introduction to the linear model
          • Week 9. Data visualization practices
          • Week 10. Developing the linear model
      • PSYC412
        • Part1
          • Week 11. Recap of the linear model and practising data-wrangling in R
          • Week 12. Categorical predictors
          • Week 13. More on interactions
          • Week 14. Logistic regression
          • Week 15. Poisson regression
        • Part2
          • Week 16. Workbook introduction to multilevel data
          • Week 16. Conceptual introduction to multilevel data
          • Week 17. Workbook introduction to mixed-effects models
          • Week 17. Conceptual introduction to mixed-effects models
          • Week 18. Developing linear mixed-effects models
          • Week 18. Conceptual introduction to developing linear mixed-effects models
          • Week 19. Workbook introduction to Generalized Linear Mixed-effects Models
          • Week 19. Conceptual introduction to Generalized Linear Mixed-effects Models
          • Week 20. Workbook introduction to Ordinal (Mixed-effects) Models
          • Week 20. Conceptual introduction to Ordinal (Mixed-effects) Models
          • Week 00. The structured research report – Requirements
          • Week 00. Writing reproducible reports using Quarto
          • LICENSE
    • 2025/2026
      • PSYC121
        • Statistics for Psychologists I
          • Statistics for Psychologists
          • 2. Descriptive statistics in RStudio
          • 3. DVs and IVs in RStudio
          • 4. Customisation of graphs, and z-scores
          • 5. Class test
          • 6. Sampling, probability and binomial tests
          • 7. Filtering data and testing means (one-sample t-test)
          • 8. Related-samples t-tests, plotting means and SE bars
          • 9. Unrelated-samples t-test and Power
          • Data
            • Week 6
              • Week_6
            • Week 9
              • PSYC121: Week 9 Lab
      • PSYC122
        • Statistics for Psychologists II
          • Introduction to Part 1
          • 1. Week 11 - Correlation
          • 2. Week 12 - Correlation Part 2
          • 3. Week 13 - The Linear Model
          • 4. Week 14 - Chi-Square
          • 5. Week 16 – Hypotheses, associations
          • 6. Week 17 – Better understanding the linear model
          • 7. Week 18 – Developing the linear model
          • 8. Week 19 – Linear models – interactions
          • Data
            • Week11
              • 122_wk11_labActivities2_3
              • wk11
            • Week12
              • 122_wk12_labActivity2
            • Week13
              • 122_wk13_labActivity2
            • Week14
              • 122_wk13_labActivity2
            • Week16
              • PSYC122-w16-how-to
              • PSYC122-w16-workbook-answers
            • Week17
              • PSYC122-w17-how-to
              • PSYC122-w17-workbook-answers
            • Week18
              • PSYC122-w18-how-to
              • PSYC122-w18-workbook-answers
            • Week19
              • PSYC122-w19-how-to
              • PSYC122-w19-workbook-answers
      • PSYC214
        • Statistics for Group Comparisons
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
          • Statistics for Psychologists
      • PSYC234
        • From association to modelling causality
          • 1. Review of correlation, simple regression and demonstration of multiple regression
          • 2. Multiple Regression Including Categorical Predictors
          • 3. Multiple Regression Models that Include Interactions (Moderated Variables)
          • 4. Mediation
          • 5. Factor Analysis and the Binomial Test
          • 6. Wilcoxon rank-sum test and Wilcoxon signed-rank test
          • 7. Kruskal-Wallis test and Friedman’s ANOVA
          • 8. Binary logistic regression models
          • 9. Expanding on binary logistic regression
          • Data
            • Wk1
              • Week 11 234 Materials
                • Correlation_Review
                • Multiple Regression
                • Simple Regression Review
            • Wk3
              • Lab Week 13 Materials
                • Lab_Week_13_Interactions
              • Lecture Week 13 Materials
                • Multiple_Regression_With_Interactions
            • Wk4
              • Wk14 Lecture Lab R Upload Materials
                • Multiple_Regression_Mediation_Student
                • Multiple_Regression_Mediation
      • PSYC411
        • Part1
          • Week 1. Introducing Data
          • Week 2. Manipulating data
          • Week 3. Drawing graphs from data
          • Week 4. Testing nominal data
          • Week 5. Testing differences between groups
        • Part2
          • Week 6. The structured research report – Quick start
          • Week 6. How you can do the analysis work
          • Week 6. Why we are asking you to do this
          • Week 7. Hypotheses and associations
          • Week 8. Introduction to the linear model
          • Week 10. Developing the linear model
      • PSYC412
        • Part1
          • Week 11. Recap of the linear model and practising data-wrangling in R
          • Week 12. Categorical predictors
          • Week 13. More on interactions
          • Week 14. Logistic regression
          • Week 15. Poisson regression
        • Part2
          • Week 16. Workbook introduction to multilevel data
          • Week 16. Conceptual introduction to multilevel data
          • Week 17. Workbook introduction to mixed-effects models
          • Week 17. Conceptual introduction to mixed-effects models
          • Week 18. Developing linear mixed-effects models
          • Week 18. Conceptual introduction to developing linear mixed-effects models
          • Week 19. Workbook introduction to Generalized Linear Mixed-effects Models
          • Week 19. Conceptual introduction to Generalized Linear Mixed-effects Models
          • Week 20. Workbook introduction to Ordinal (Mixed-effects) Models
          • Week 20. Conceptual introduction to Ordinal (Mixed-effects) Models
          • Week 00. The structured research report – Requirements
          • Week 00. Writing reproducible reports using Quarto
          • LICENSE
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