400018 SE Hierarchical Modelin for Social Scientists' (2017W)
SE Methods for Doctoral Candidates
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Fr 01.09.2017 08:00 to We 27.09.2017 17:00
- Registration is open from We 10.01.2018 08:00 to Mo 15.01.2018 13:00
- Deregistration possible until Su 21.01.2018 13:00
Details
max. 15 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Monday
22.01.
09:00 - 12:00
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Monday
22.01.
14:00 - 16:30
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Tuesday
23.01.
09:00 - 12:00
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Tuesday
23.01.
13:00 - 15:00
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Wednesday
24.01.
09:00 - 12:00
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Wednesday
24.01.
14:00 - 16:30
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Thursday
25.01.
09:00 - 12:00
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Thursday
25.01.
15:15 - 17:45
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Friday
26.01.
09:00 - 12:00
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Information
Aims, contents and method of the course
Assessment and permitted materials
Minimum requirements and assessment criteria
Final test (70%) and participation during course (30%)
Examination topics
Reading list
Gelman and Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models
Association in the course directory
Last modified: Mo 07.09.2020 15:47
- Interactions with dichotomous variables.
- Binary models.• Lab I- Introduction to RStudio.
- Reading in data and manipulating it.
- Estimating linear models.• 2.. Linear Hierarchical Modeling- What ate random effects?
- Varying intercept models.
- Models with systematically varying intercepts.
- Measures of model quality.• Lab II
- Implementing all learned concepts from part 2 in RStudio
• 3. Hierarchical Modeling with Cross-Level Interactions- Hierarchical modeling with cross-level interactions.
- Hierarchical modeling with binary models.
- Non-nested models.• Lab III- Implementing all learned concepts from part 3 in R• 4. Advanced Topics - Multilevel Regression and Post-Stratification (MrP)- Survey methods for sample selection.
- How hierarchical modeling can help (--+ MrP).
- Further developments: Deep interactions, synthetic post-stratification (MrsP).• Lab IV- Estimating response rriodels.- Weighting predicted probabilities for ideal types.
- Generating small sample measurements.• 5. Advanced Topics - Bayesian Hierarchical Modeling- Quick theoretical introduction to Bayesian statistics.
- How and when can a Bayesian model outperform frequentist apporaches?• Lab V- Setting up a model in Stan.
- Re-estimating prior models with convergence problems.