RESEARCH METHODOLOGIES AND DATA ANALYSIS

The curricular unit combines expository methods with active learning strategies, promoting the integration of theory and practice. Theoretical classes present fundamental concepts and frame the statistical techniques, while practical sessions use data analysis software to solve exercises and interpret results. Activities such as bibliographic research and the preparation of summary tables are encouraged, reinforcing autonomy and critical thinking. The discussion of empirical studies and collaborative case analysis allows for the consolidation of knowledge and the development of applied skills. The pedagogical model prioritizes active learning, student participation, and the practical application of content, ensuring coherence with the objectives defined for the curricular unit.

Develop skills to conduct rigorous bibliographic research, evaluating the quality of sources and synthesizing relevant information. Understand the structure of the data matrix, the types of variables, and their implications for the choice of analytical methods. Apply fundamental statistical techniques, including ANOVA, simple and multiple linear regression, and principal component analysis, and critically interpret the results. Foster autonomy in research and the ability to select appropriate methods for real-world problems. The teaching method combines theoretical lectures, practical exercises using data analysis software, and critical discussion of empirical studies, ensuring coherence between the stated objectives and the adopted pedagogical practices.

1. Bibliographic Research and Statistical Techniques

– Use of digital tools and AI to identify relevant articles in the field of study

– Basic evaluation of the quality and reliability of the sources

– Review of statistical data analysis from research papers

2. Data Matrix Structure and Core Concepts

– Structure of the data matrix

– Types of variables and their implications for method selection

– Distinction between univariate and multivariate analyses

3. Analysis of Variance (ANOVA)

– One-way and two-way ANOVA

– Critical interpretation of the results

4. Simple and Multiple Linear Regression

– The role of regression in modeling and prediction

– Simple and multiple regression techniques

– Critical interpretation of the results

5. Principal Component Analysis (PCA)

– Concept, objectives, and structure of PCA

– Critical interpretation of results

– Examples of other multivariate techniques and encouragement of autonomous exploration

NAO