# 220050 SE SE Advanced Data Analysis 2 (2022S)

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
**Mo 21.02.2022 09:00**to**We 23.02.2022 18:00** - Deregistration possible until
**Th 31.03.2022 23:59**

## Details

max. 30 participants

Language: English

### Lecturers

### Classes (iCal) - next class is marked with N

Monday
07.03.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
14.03.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
21.03.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
28.03.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
04.04.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
25.04.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
02.05.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
09.05.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
16.05.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
23.05.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
30.05.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
13.06.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
20.06.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

Monday
27.06.
09:45 - 11:15
Seminarraum 5, Währinger Straße 29 1.UG

## Information

### Aims, contents and method of the course

### Assessment and permitted materials

The assessment includes online questionnaires distributed at the end of each learning unit to assess the understanding of key concepts. The results of each of the five questionnaires (one for each learning unit) will constitute 60% of the final grade.The evaluation also includes a final data analysis project to be conducted in small groups during the course and presented in class at the end of the course. This constitutes a total of 40% of the final grade.

### Minimum requirements and assessment criteria

Continuous participation in class, reading, home study, and exercise, are fundamental requirements.Elementary knowledge of algebra is assumed, so as to be able to read the regression equations and perform a basic calculation with variables. Moderate proficiency in R and R programming is preferable, but not necessary since the first part of the course will provide a hands-on introduction to this matter. The course will be intensive for everyone, and those with no experience in programming and data analysis should expect the course to be a bit more demanding. Home study and exercise will be required to meet the course objectives.A laptop is also necessary (Windows or Mac indifferently).

### Examination topics

Examination topics consist of the content of the learning units.Required knowledge and practical skills will be conveyed during the lectures.The slides used during the lectures will be shared on Moodle.Additional readings will be also shared on Moodle.

### Reading list

The official handbook of the course is:Andrew F. Hayes. Introduction to Mediation, Moderation, and Conditional Process Analysis. A Regression-Based Approach. 2018. SECOND EDITION. THE GUILFORD PRESS, New York, London.

## Association in the course directory

*Last modified: Th 03.03.2022 15:28*

• UNIT 2: The second part of the course will be dedicated to presenting the principles of regression analysis, and explaining how to fit, visualize, interpret, and evaluate regression models in R.

• UNIT 3: The third unit will be dedicated to mediation analysis, and explaining how to fit, visualize, interpret, and evaluate mediation models using PROCESS in the R environment.

• UNIT 4: The fourth learning unit will be dedicated to moderation analysis, and explaining how to fit, visualize, interpret, and evaluate moderation models using PROCESS in the R environment.

• UNIT 5: The fifth learning unit will be dedicated to conditional process analysis, and explaining how to fit, visualize, interpret, and evaluate conditional process models using PROCESS in the R environment.By the end of this course, participants will be able to:

• Run and interpret the results of linear regression, moderation, mediation, and conditional process models.

• Know how to test competing theories of mechanisms statistically through the comparison of indirect effects in models with multiple mediators.

• Know how to visualize and probe interactions in regression models in order to interpret interaction effects in the appropriate ways.

• Know how to estimate the contingencies of mechanisms through the computation and inference about conditional indirect effects.

• Know how to R language and PROCESS to run, visualize, and understand linear regression, moderation, mediation, and conditional process models.