Advanced Statistical Analysis

Attendance at Statistical Data Analysis classes is recommended.

Classes are theoretical-practical, planned and prepared considering active learning methodologies to foster the active engagement of all students throughout various moments or the entirety of each session. This course unit combines theoretical exposition with guided problem-solving, drawing upon the following teaching methodologies:

ME1. Interactive Lectures: the theoretical component, dedicated to introducing concepts, fundamental results, and methods, is delivered through an expository approach interspersed with tasks that encourage active participation from all students. These tasks include questions posed by both the instructor and students, as well as short group debates/discussions on specific topics presented.

TM2. Guided Problem-Solving: The practical component focuses on the full development of the targeted competencies through problem-solving and software implementation under instructor’s supervision, encouraging both autonomous and small-group work.

TM3. Tutorial Guidance and Continuous Support: Ongoing support dedicated to consolidating course contents and clarifying doubts. It relies on the active engagement and regular attendance of students, ensuring the methodological guidance required for the success of practical exercises and group assignment.

All supporting materials are made available through the InforEstudante platform.

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:

OA1) Construct and estimate regression models with panel data.

OA2) Construct and estimate structural equation models.

OA3) Analyse the validity and quality of statistical models through assumption analysis and goodness-of-fit tests.

OA4) Apply these statistical techniques to real data and correctly interpret the results.

OA5) Use software as a supporting tool in the implementation of these statistical techniques.

Associated competencies:

CD1) Advanced Statistical Literacy: understand and explain the theoretical foundations underlying panel data regression models and structural equation models.

CD2) Proficiency in Statistical Software: independently use statistical software for data preparation, model estimation, and validation.

CD3) Critical Thinking: critically evaluate the adequacy and quality of statistical models and justify methodological choices.

CD4) Communication: effectively communicate results in a structured and rigorous manner, both orally and through written reports.

P1. 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
P2. 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

In this course unit, students are evaluated through a periodic assessment scheme comprising two mandatory components:

MA1. Individual Written Test

- Graded out of 10 points, with a minimum required mark of 5 points.

- Conducted during the teaching period, on a date to be announced by the instructor on the first day of class.

- The specific topics to be evaluated in this test will be communicated to students at least one week in advance.

- Students may consult two sheets of paper (double-sided) containing a summary of the subject matter prepared by themselves.

MA2. Group Assignment and Oral Defence

- Graded out of 10 points.

- If submitted during the teaching period, it will be conducted as a group project (maximum of 4 members), with submission deadline scheduled during that period. The oral defence will take place on the day of the regular examination period.

- If submitted for evaluation during the supplementary or special examination periods, it will be an individual assignment, to be submitted no later than two working days before the date of the examination for which the student is registered, with the oral defence taking place on the examination date.

The final grade (CF) is calculated using the following formula, with the result rounded to the nearest whole number: CF = MA1+MA2, where MA1 is the grade obtained in the written test, on a scale of 0 to 10 points (without rounding) and MA2 is the grade obtained in the assignment and its defence, on a scale of 0 to 10 points (without rounding).

Students must achieve a minimum final grade of 10 points to pass this course.

Notes:

1) A student who obtains the minimum required grade in only one of the two assessment components during an examination period will retain that component's grade across subsequent examination periods within the same academic year until passing the course.

2) For grade improvement purposes, students may choose one of the following options:

a) take a new written test in the assessment period in which they are registered for grade improvement, retaining the grade previously obtained in the group assignment and defence;

b) take a new written test and complete a new individual assignment with its defence, both in the assessment period in which the student is registered for grade improvement.

3) Use of Artificial Intelligence (AI): For MA1, the use of AI tools is not permitted. For MA2, generative AI tools may be used as support for research, conceptual clarification, text revision, and assistance with software code/commands. The assignment must include an AI use declaration specifying the tools used, their respective purposes, and the most relevant prompts.

NAO