smobsc
latest
1. Philosophical Foundations
2. Computer stuff
3. Conducting Experiments
4. Analysing Experimental Data
5. Reporting the Results of Scientific Experiments
6. Advanced Methods
6.1. Data wrangling
6.2. Linear and generalized linear mixed-models
6.2.1. Introduction to mixed-effects modeling
6.2.2. Fixed and random effects
6.2.3. Linear vs. generalized linear
6.2.4. Contrast coding
6.2.5. Model comparisons
6.2.6. Determining the random effects structure
6.2.7. Facing a real data set
6.2.8. Power analysis for mixed models
6.3. Multivariate Analysis and Machine Learning
6.4. Special methods
6.5. Neuroscience
6.6. Eye tracking
6.7. Online Experiments
6.8. Formatting - saving data (ext.)
6.9. Data protection - security (ext.)
7. help
8. How to contribute
smobsc
Docs
»
6. Advanced Methods
»
6.2. Linear and generalized linear mixed-models
Edit on GitHub
6.2. Linear and generalized linear mixed-models
ΒΆ
Subchapters
6.2.1. Introduction to mixed-effects modeling
A hands-on crash course in reproducible mixed-effects modeling
6.2.2. Fixed and random effects
6.2.3. Linear vs. generalized linear
hello Aylin is the best <3
6.2.4. Contrast coding
6.2.5. Model comparisons
6.2.6. Determining the random effects structure
6.2.7. Facing a real data set
6.2.8. Power analysis for mixed models