Experimental Design
Objectives
The course of Experimental Design (XD) aims to develop on students an improved understanding and practice in key topics related to experimental design and causal research. It also provides a hands-on approach using a diversity of challenges and paths in which students can craft their own learning journey.
General characterization
Code
200163
Credits
4.0
Responsible teacher
Diego Costa Pinto
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
There are no enrollment requirements. However, students should be familiar with statistical analysis (ANOVA, T-test, Regression, etc)
Bibliography
Teaching method
The curricular unit is based on theoretical-practical classes. Several teaching strategies are applied, including
workshops, project-based learning and the development of papers.
Evaluation method
1st call:
Exam (individual): 50%
Final Project* (group): 50%
2nd call:
Exam (individual): 80%
Final Project* (group): 20%
Minimum grade of 8.0 (in 20) for the Exam
Minimum grade of 10.0 (in 20) for the Project
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* Final Project includes the development and collection of at least 1 experimental study in a group.
The experimental study is reported in an extended abstract (ACR Format).
Subject matter
The curricular unit is organized in the following Learning Units:
1. Introduction: Experiments and behavioral research
2. Theoretical Assumptions of Experimental Design (validity, causality)
3. How to develop a contribution and hypotheses using Experimental Design
4. Randomization and Design of Experiments (between and within subjects)
5. Types of Studies: Field, Laboratory, and Survey settings
6. Power and Sample Size
7. Measuring, Priming, and Manipulating Variables
8. Manipulation Checks, control variables, covariates, and confounds
9. Internal and External Validity
10. Developing an Experimental Plan
11. Working with Series of Experiments
12. Types of Analysis: Main Effects and Interaction Effects
13. Contrasts and Multiple Comparisons
14. Advanced Experimental Design: Moderation and Mediation
15. Writing and publishing an experimental paper
Programs
Programs where the course is taught:
- Specialization in Information Analysis and Management
- Specialization in Risk Analysis and Management
- PhD in Information Management
- Especialização em Data Science for Marketing
- Especialização em Digital Marketing and Analytics
- Specialization in Knowledge Management and Business Intelligence
- Specialization in Information Systems and Technologies Management
- Specialization in Marketing Intelligence
- Especialização em Marketing Intelligence
- Especialização em Marketing Research e CRM
- Specialization in Marketing Research and CRM
- Laboral - Especialização em Digital Marketing and Analytics
- Specialization in Knowledge Management and Business Intelligence – Working Hours Format
- Specialization in Information Systems and Technologies Management - Working Hours Format
- Specialization in Marketing Intelligence - Working Hours Format
- Laboral - Especialização em Marketing Intelligence
- Mestrado em Data-Driven Marketing
- Post-Graduation in Information Analysis and Management
- Post-Graduation Risk Analysis and Management
- PostGraduate in Smart Cities
- PostGraduate in Data Science for Marketing
- PostGraduate in Digital Enterprise Management
- PostGraduate Digital Marketing and Analytics
- PostGraduate in Information Management and Business Intelligence in Healthcare
- Post-Graduation in Knowledge Management and Business Intelligence
- Post-Graduation Information Systems and Technologies Management
- PostGraduate in Marketing Intelligence
- PostGraduate Marketing Research e CRM
- PostGraduate in Enterprise Information Systems
- Tronco comum