Drukuj sylabusStructural Equation Modeling - Continuation
Informacje ogólne
| Kod przedmiotu: | 2500-PL-PS-SP15-18 |
| Kod Erasmus / ISCED: |
14.4
|
| Nazwa przedmiotu: | Structural Equation Modeling - Continuation |
| Jednostka: | Wydział Psychologii |
| Grupy: | |
| Punkty ECTS i inne: |
2.00
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| Język prowadzenia: | angielski |
| Założenia (opisowo): | Completed introductory course on Structural Equation Modeling |
| Skrócony opis: |
This course is a continuation of the introductory structural equation modeling (SEM) course. Throughout the consecutive classes participants will be presented with advanced structural models and methods of handling difficulties (e.g., missing data) that may arise in SEM. The classes will involve the combination of lectures and lab sessions focusing on the specification, estimation, and interpretation of complex structural models. |
| Efekty uczenia się: |
First, students will acquire knowledge about the possibilities posed by the advanced applications of SEM. Second, participants will gain familiarity, experience, and confidence in estimating and interpreting complex structural models. |
Zajęcia w cyklu "Semestr letni 2023/24" (zakończony)
| Okres: | 2024-02-19 - 2024-06-16 |
 
PN WT ŚR CZ SEM
PT |
| Typ zajęć: |
Seminarium, 15 godzin
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| Koordynatorzy: | (brak danych) | |
| Prowadzący grup: | Paulina Górska | |
| Lista studentów: | (nie masz dostępu) | |
| Zaliczenie: |
Przedmiot -
Zaliczenie na ocenę
Seminarium - Zaliczenie na ocenę |
|
| Pełny opis: |
Structural equation modeling (SEM) is a powerful and still developing statistical technique that allows for addressing simple as well as complex research questions. While researchers are relatively familiar with the basic applications of SEM (e.g., Confirmatory Factor Analysis), their knowledge of more advanced models (e.g., SEM-based meta-analysis) is relatively limited. Consequently, potentially valuable insights have no chance to emerge and extend our knowledge of the world. This course is a continuation of the introductory SEM course. Throughout the consecutive classes, participants will be presented with a range of advanced applications of SEM. We will discuss measurement invariance, multigroup analysis, SEM-based meta-analysis, latent variable interactions, power analysis for structural models, and methods of handling missing data. The classes will involve the combination of lectures and lab sessions focusing on the specification, estimation, and interpretation of structural equation models. All analyses will be performed in R lavaan and related packages. Upon completing the course, students will be equipped for studying SEM independently in accordance with their professional needs. |
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| Literatura: |
Main texts: Brown, T. A. (2006). Confirmatory Factor Analysis for applied research. New York: Guilford Press. Enders, C. K. (2023). Fitting Structural Equation Models with Missing Data (pp. 224-240). In R. H. Hoyle (Ed.), Handbook of structural equation modeling. Second Edition. Guilford Press. Gana, K., & Broc, G. (2019). Structural Equation Modeling with lavaan. Hoboken, NJ: Wiley. Jak, S. (2015). Meta-analytic structural equation modelling (pp. 1-88). Dordrecht, Neth: Springer. Jobst, L. J., Bader, M., & Moshagen, M. (2021). A tutorial on assessing statistical power and determining sample size for structural equation models. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000423 Kline, R. B. (2015). Principles and practice of Structural Equation Modeling. New York: Guilford Press. Schoemann AM, Jorgensen TD. Testing and Interpreting Latent Variable Interactions Using the semTools Package. Psych. 2021; 3(3):322-335. https://doi.org/10.3390/psych3030024 Wang, Y. A., & Rhemtulla, M. (2021). Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial. Advances in Methods and Practices in Psychological Science, 4(1). https://doi.org/10.1177/2515245920918253. |
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Właścicielem praw autorskich jest Uniwersytet Warszawski.
