Universität Wien

220050 SE SE Advanced Data Analysis 2 (2022S)

5.00 ECTS (2.00 SWS), SPL 22 - Publizistik- und Kommunikationswissenschaft
Continuous assessment of course work
Moodle

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).

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

Course Objectives and Summary:

This is a data analysis seminar focused on the application of linear regression analysis to explore questions about mediated and moderated effects.

Computer applications will focus on R statistical language and the Rstudio environment (https://www.rstudio.com) and the PROCESS software by Andrew F. Hayes (http://processmacro.org).

The course is subdivided in the following learning units:

• UNIT 1: The introductory part of the course will be dedicated to installing the data science tools R and Rstudio (https://www.rstudio.com) and to learning the basic principles necessary to work on statistical analysis with this software. Students will be required to acquire a basic familiarity with the Rstudio interface and R programming in a short period of time, in order to be able to use it for performing statistical analysis. At home study and exercise will be fundamental to reaching the objective.
• 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.

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