Socratizer

Online learning platform

Advanced statistical methods and models in experimental design

Bartosz Maćkiewicz

Course page for Cognitive Science students

Course overview

  1. List of topics
  2. R troubleshooting, or how to overcome most common problems
  3. How to Submit Assignments - RMarkdown Basics
  4. How to install R and RStudio? Instructions for Windows/Mac/Linux
  5. Presentation
  6. Policy on Academic Integrity and Use of AI Tools
  7. Excercise datasets
  8. Assignment example
    9. homework_ex.Rmd
    Worksheet
    Due date: 2025-13-13

Review od basic statistical tests

  1. Presentation
    2. adams1996.csv
    Data from Adams et al. (1996) study
    compas1994.csv
    Data from Compas et al. (1994) study
    katz1990.csv
    Data from Katz et al. (1990) study
    unahborger2001.csv
    Data from Unah & Borger (2001) study
    walsh2006.csv
    Data from Walsh et al. (2006) study
  2. Backup way to load files
  3. Assignment (real): Review of basic statistical tests
    4. hoaglin1983.csv
    Data from Hoaglin et al. (1983) study
    hout1987.csv
    Data from Hout et al. (1987) study
    hw_review.Rmd
    Worksheet
    kaufman1962.csv
    Data from Kaufman and Rock (1962) study
    weinberg1986.csv
    Data from Weinberg and Gladen (1986) study
    Due date: 2025-03-12

Covariance and correlation

  1. Presentation
    2. wagner1988.csv
    Data from Wagner et al. (1988) study on stress and mental health
  2. Backup way to load files
  3. Assignment: correlation
    4. hw_correlation.Rmd
    Worksheet
    infmort.xlsx
    Data for all exercises
    Due date: 2025-03-26

Linear regression

  1. Presentation
    2. wagner1988.csv
    Data from Wagner et al. (1988) study on stress and mental health

Linear regression: R2 and diagnostics

  1. Presentation
  2. Assignment: linear regression
    3. hw_regression.Rmd
    Worksheet
    Due date: 2025-04-03

Multiple linear regression

  1. Presentation
    2. guber1999.csv
    Data from Guber (1999) study

Analysis of variance

  1. Presentation
    2. giancola2007.csv
    Data from Giancola and Corman (2007) study
  2. Assignment: one factor ANOVA
    3. eysenck1974.csv
    Data
    hw_anova.Rmd
    Worksheet
    Due date: 2023-04-25

Categorical predictors and interactions in GLM

  1. Presentation
    2. hassles.csv
    Data from the experiment
  2. Assignment: interactions in linear models
    3. hw_interactions.Rmd
    Worksheet
    Due date: 2023-05-09

Linear Mixed Effects Models

  1. Presentation - introduction to mixed-effects models
  2. Presentation - lexical decision experiment
  3. Assignment: linear mixed-effects models
    4. hw_lme.Rmd
    Worksheet
    Due date: 2025-06-05

Meta-analysis

  1. Presentation
    2. p_curve_en.R
    P-curve analysis simulation
    p_hacking_simple_en.R
    P-hacking simulation
    publication_bias_en.R
    Publication bias simulation
  2. Introduction to Meta-Analysis, Borenstein, Hedges, Higgins Rothstein
  3. Recovering Missing or Partial Data from Studies: A Survey of Conversions and Imputationsfor Meta-­analysis, Lajeunesse

Crossed na Nested random effects in Linear Mixed-Effects Models

  1. Presentation
    2. popular.csv
    Excercise dataset
    schools.csv
    Excercise dataset
  2. Multilevel Analysis: Techniques and Applications, J. Hox, M, Moerbeek, R. van de Schoot
  3. A brief introduction to mixed effects modelling and multi-model inference in ecology, Harrison et. al.

Final Project

  1. Final Project
    2. Due date: 29.06.2025

Shark Track

  1. What is the Shark-Track?
  2. Replication of Rochon et al. (2012) study
    3. rochon2012.pdf
    Rochon et al (2012)
    Due date: XXX
  3. Replication of Weber & Sawilowsky (2009) study
    4. weber2009.pdf
    Weber & Sawilowsky (2009)
    Due date: XXX
  4. Replication of Schucany & Ng (2006) study
    5. schucany2006.pdf
    Schucany & Ng (2006)
    Due date: XXX
  5. Shiny version of G-star-Power
    6. Due date: XXX
  6. How To Not Be Confused About Documenting Data and Data Reuse
    7. arslan2019.pdf
    Arslan (2019)
    Due date: XXX
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