2025
Data processing
Name: Data processing
Code: MAT16009D
3 ECTS
Duration: 15 weeks/75 hours
Scientific Area:
Estatística
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Regime de Frequência: Presencial
Sustainable Development Goals
Learning Goals
This curricular unit was designed to provide doctoral students with a robust set of skills in processing statistical data using the IBM SPSS Statistics software. Upon completion of the training hours, students will acquire theoretical-practical knowledge to competently navigate the program structure and its data files, essential for the management and analysis of large volumes of information. They will be able to effectively perform data editing and file modification tasks, apply graphical representation techniques for data visualization, perform descriptive statistics procedures to synthesize complex data sets and, finally, apply correlation and regression methods for analysis predictive and inferential. The practical, interactive and applied teaching methodology complements and consolidates theory, allowing doctoral students not only to understand, but also to apply advanced statistical concepts in their areas of research.
Contents
1: Exploration of the SPSS interface, features, menus, toolbars, data management and definition of variables with understanding of the data window (Data View) and variables (Variable View).
2: Data editing and transformation techniques, including cleaning, recoding, and use of syntaxes as well as data organization.
3: Fundamentals of graphical representation, construction of different types of graphs and visual analysis (boxplot, density, line graphs, etc.).
4: Application of descriptive statistics, analysis of distribution and relationships between variables as well as the representation of categorical and continuous data.
5: Execution of correlation tests, fundamentals of linear regression and construction of statistical models.
2: Data editing and transformation techniques, including cleaning, recoding, and use of syntaxes as well as data organization.
3: Fundamentals of graphical representation, construction of different types of graphs and visual analysis (boxplot, density, line graphs, etc.).
4: Application of descriptive statistics, analysis of distribution and relationships between variables as well as the representation of categorical and continuous data.
5: Execution of correlation tests, fundamentals of linear regression and construction of statistical models.
Teaching Methods
The teaching and learning methodology for the curricular unit "Data Processing" focusing on the IBM SPSS Statistics software is based on principles of the constructivist pedagogical model, encouraging active and meaningful learning. The adopted strategy aims to develop not only knowledge, but also practical and critical skills necessary for research, preparing students for the advanced use of statistical techniques in their doctoral research and future academic and professional projects.
Theoretical-Practical Approach: Each combines theoretical exposure with practical sessions. The theory is presented through minilectures that introduce statistical concepts and foundations, followed by demonstrations in SPSS. Students are then encouraged to apply knowledge through practical exercises on real or simulated data, reflecting common challenges in research.
Problem-Based Learning (PBL): Problems and case studies are used to guide learning by placing students in situations that simulate real research questions. This approach promotes the application of statistical knowledge in practical scenarios, stimulating critical analysis and problem solving.
Use of Technology: SPSS will be used as a learning and teaching tool, allowing students to directly immerse themselves in data processing. Interactive sessions in the computer lab will provide opportunities for supervised practice, with immediate feedback from peers and faculty. Collaborative Work: Group work will be encouraged, promoting the exchange of knowledge and experiences between students. Group discussions on the results of the analyzes carried out in SPSS will foster a collaborative and mutually supportive learning environment. Formative Assessment: Continuous assessment through quizzes, data analysis and presentations will allow students and teachers to monitor progress and understand difficulties encountered, adjusting learning strategies as necessary.
Self-learning: Self-study outside hours will be encouraged, with additional readings and SPSS tutorials, allowing students to deepen the knowledge and skills acquired.
Ongoing Feedback: Regular feedback sessions will provide personalized guidance, encouraging reflection on the learning process and the development of strategies for continuous improvement.
Theoretical-Practical Approach: Each combines theoretical exposure with practical sessions. The theory is presented through minilectures that introduce statistical concepts and foundations, followed by demonstrations in SPSS. Students are then encouraged to apply knowledge through practical exercises on real or simulated data, reflecting common challenges in research.
Problem-Based Learning (PBL): Problems and case studies are used to guide learning by placing students in situations that simulate real research questions. This approach promotes the application of statistical knowledge in practical scenarios, stimulating critical analysis and problem solving.
Use of Technology: SPSS will be used as a learning and teaching tool, allowing students to directly immerse themselves in data processing. Interactive sessions in the computer lab will provide opportunities for supervised practice, with immediate feedback from peers and faculty. Collaborative Work: Group work will be encouraged, promoting the exchange of knowledge and experiences between students. Group discussions on the results of the analyzes carried out in SPSS will foster a collaborative and mutually supportive learning environment. Formative Assessment: Continuous assessment through quizzes, data analysis and presentations will allow students and teachers to monitor progress and understand difficulties encountered, adjusting learning strategies as necessary.
Self-learning: Self-study outside hours will be encouraged, with additional readings and SPSS tutorials, allowing students to deepen the knowledge and skills acquired.
Ongoing Feedback: Regular feedback sessions will provide personalized guidance, encouraging reflection on the learning process and the development of strategies for continuous improvement.
Assessment
Assessment in the curricular unit "Data Processing" will be carried out through an applied project, which will constitute 100% of the student's final grade. This project will consist of analyzing a set of data provided by the teacher, where students will have to employ the techniques and methodologies taught throughout classes.
Rating criteria:
1. Data Understanding and Management: Ability to import, clean and prepare data for analysis in SPSS;
2. Application of Statistical Techniques: Correct use of descriptive statistics tools, graphical representations, correlations and regressions;
3. Interpretation of Results: Ability to interpret SPSS outputs and present valid conclusions;
4. Final Report: Preparation of a written report that documents the analysis process, discussion of results and conclusions, following academic presentation and writing standards.
The work will be evaluated for technical precision, analytical depth, clarity in communicating results and connection to academic standards. This assessment approach is designed to reflect the practical and theoretical skills developed during the course and ensure an integrated understanding of the use of IBM SPSS Statistics software for processing statistical data.
Rating criteria:
1. Data Understanding and Management: Ability to import, clean and prepare data for analysis in SPSS;
2. Application of Statistical Techniques: Correct use of descriptive statistics tools, graphical representations, correlations and regressions;
3. Interpretation of Results: Ability to interpret SPSS outputs and present valid conclusions;
4. Final Report: Preparation of a written report that documents the analysis process, discussion of results and conclusions, following academic presentation and writing standards.
The work will be evaluated for technical precision, analytical depth, clarity in communicating results and connection to academic standards. This assessment approach is designed to reflect the practical and theoretical skills developed during the course and ensure an integrated understanding of the use of IBM SPSS Statistics software for processing statistical data.
Teaching Staff
- Luís Miguel Lindinho da Cunha Mendes Grilo [responsible]
Accreditations and Certifications
