Advanced Statistical Analysis

The classes are designed, according to the curricular plan, to be both theoretical and practical. They are planned and prepared considering
active learning activities, to actively engage all students at various moments or throughout the entire class.
The curricular unit combines theoretical exposition with guided problem-solving. In the theoretical part of the lesson, which introduces
concepts, fundamental results, and methods, the expository method will predominantly be used, interspersed with tasks that encourage
active participation from all students. These tasks include posing questions to and by students, orally, as well as proposing
debates/discussions in small groups on certain exposed aspects/topics.
The practical part will be designed to comprehensively develop the listed skills. This will be achieved through problem-solving, using
software, under the guidance of the teacher. Autonomous work or work in small groups will be encouraged.
The classes emphasize the integration of theory and practice, fostering methodological autonomy and the ability to apply knowledge in
professional and research contexts. Additionally, group work is initiated in class, allowing close support, clarification of questions, and
collaborative knowledge building, while also constituting an active learning approach.

Diverse scientific fields require advanced statistical techniques to represent the complexity of the phenomena they study. The ability to
model dynamic relationships, latent variables, and causal patterns is essential to address this complexity and produce robust scientific
knowledge. Accordingly, this curricular unit focuses on panel data models and structural equation models, aiming to develop students’
competences in these areas. Thus, by the end of the curricular unit, students are expected to be able to:
1) Construct and estimate regression models with panel data.
2) Construct and estimate structural equation models.
3) Critically evaluate statistical models and justify methodological choices.
4) Apply these statistical techniques to real data and communicate results in the form of a report or academic article.
5) Use software as a supporting tool in the implementation of these statistical techniques.

1 – Panel Data Models
1.1. Structure and advantages of panel data
1.2. Fixed-effects and random-effects models
1.3. Specification tests
1.4. Dynamic panel data models
1.5. Empirical applications using software and reporting results in reports/articles
2 – Structural Equation Models
2.1. Fundamental concepts: observed and latent variables; causal relationships
2.2. Exploratory factor analysis vs. confirmatory factor analysis
2.3. Measurement models and structural models
2.4. Estimation and assessment of model fit
2.5. Mediation, moderation, and multigroup models
2.6. Empirical applications using software and reporting results in reports/articles

NAO