Drukuj sylabusLinear mixed effects models in SPSS
Informacje ogólne
| Kod przedmiotu: | 3201-LST-OC-RM5-OG |
| Kod Erasmus / ISCED: |
14.0
|
| Nazwa przedmiotu: | Linear mixed effects models in SPSS |
| Jednostka: | Instytut Lingwistyki Stosowanej |
| Grupy: |
Przedmioty ogólnouniwersyteckie Instytutu Lingwistyki Stosowanej Przedmioty ogólnouniwersyteckie na Uniwersytecie Warszawskim Przedmioty ogólnouniwersyteckie społeczne |
| Punkty ECTS i inne: |
3.00
|
| Język prowadzenia: | angielski |
| Rodzaj przedmiotu: | ogólnouniwersyteckie |
| Założenia (opisowo): | (tylko po angielsku) Expectations of the participants (skills, applications, equipment): 1. English proficiency at B2 (upper-intermediate) or higher 2. Software IBM SPSS statistics installed and ready to use (the license is available for staff and students at the University of Warsaw), and practical knowledge of it's functions 3. Knowledge of the following statistical concepts will be assumed: - The meaning of p values, standard errors, confidence intervals - The importance of effects sizes and how to obtain them. - The assumptions of parametric tests: how to check the assumptions and potentially solve problems when the assumptions are not met. - Test assumptions using residuals, where appropriate (e.g., normality, homogeneity of variance, linearity, collinearity). - Different types of variable transformations (e.g., log, square root) - T-tests (independent and dependent), ANOVAs (including interactions; between-subject and repeated measures). - Linear regression analysis (simple and multiple regression) and their assumptions. - How to normalise, re-scale (standardize), and center continuous predictors. - Using model predicted values to generate graphs to visualise results Note that some of these topics will be reviewed but only briefly. If necessary, the participant is advised to review these topics in more detail at their own time. A working knowledge of these topics will be assumed during the course. |
| Tryb prowadzenia: | w sali |
| Skrócony opis: |
(tylko po angielsku) Learners will be able to deal multilevel data with different types of outcome variables. |
| Pełny opis: |
(tylko po angielsku) We will learn how to organize data in the long format so it is suitable for mixed models 2. We will learn what multi-level data is. 3. We will learn the difference between fixed effects and random effects 4. We will learn how to conduct mixed models using different types of dependent variables. 5. We will discuss different modelling techniques (and how to steer clear of p hacking). 6. We will discuss model assumptions, e.g., eBLUPS (Empirical Best Linear Unbiased Predictions) and Pearson-residual plots. 7. We will visualize the predicted results. |
| Efekty uczenia się: |
(tylko po angielsku) After completing the course, a participant will: Have basic knowledge of linear mixed-effects models: theory Be able to run simple mixed-effects models Be able to diagnose the models Be able to visualize results |
| Metody i kryteria oceniania: |
(tylko po angielsku) Work methods (underline the relevant points, or suggest others): individual work, pairwork, teamwork, audiovisual material, work on case studies, presentations, brainstorming, conceptual exercises, group discussions, other (specify) …………….. |
Zajęcia w cyklu "Semestr zimowy 2024/25" (w trakcie)
| Okres: | 2024-10-01 - 2025-01-26 |
 
PN WT ŚR CZ PT |
| Typ zajęć: |
Konwersatorium, 30 godzin, 12 miejsc
|
|
| Koordynatorzy: | Breno Barreto Silva | |
| Prowadzący grup: | Breno Barreto Silva | |
| Lista studentów: | (nie masz dostępu) | |
| Zaliczenie: |
Przedmiot -
Egzamin
Konwersatorium - Egzamin |
Właścicielem praw autorskich jest Uniwersytet Warszawski, Wydział Chemii.
