Base Knowledge
Não aplicável
Teaching Methodologies
The methodology is divided between face-to-face activities and autonomous activities: thematic seminars on the CU contents; autonomous work of the student for bibliographic research, identification of the research/organizational problem, structuring of the theoretical framework and definition of the work plan.
Learning Results
This curricular unit aims to apply the knowledge acquired previously, in the handling of techniques of treatment and analysis of qualitative and quantitative data, appropriate to the research projects that students develop in the context of their master’s dissertations.
The seminar on qualitative data analysis aims to provide students with skills for: a) conceptual elaboration between categories and coding systems; b) application of models of relationships between variables; c) classification and processing of qualitative data and its quantification.
The seminar on structural equation modelling aims to provide students with skills to: a) analysis of the psychometric properties of the measurement scales; b) analysis of the relations that compose the structural model.
The Text Mining seminar aims to equip students with skills in processing unstructured text in a format that identifies patterns and knowledge.
Program
Qualitative data analysis
1. Model of content analysis or categorical analysis
1.1. Selection of documents and constitutioni of a corpus
1.2. Construction of categories and development of coding systems
1.3. Information classification and consistency control
2. Content analysis
2.1. Open, axial or selective coding procedures
2.2. Memos and diagrams
2.3. Analysis of qualitative data using software (Nvivo 12 and VosViewer)
Structural equation modelling
1. Analysis of the measurement model
1.1. Reliability
1.2. Convergent validity
1.3. Discriminant validity
2. Analysis of the structural model
2.1. Direct effects
2.2. Indirect effects
2.3. Overall effects
3. Applications using software (AMOS and SmartPLS)
Text Mining
1. Analytical approaches in Text Mining
1.1. Text classification
1.2. Topic modeling
1.3. Sentiment analysis
1.4. Document embeddings
2. Applications using software (Orange)
Curricular Unit Teachers
Ricardo Filipe Carreira RamosGrading Methods
- - Work - 70.0%
- - Oral presentation - 30.0%
- - Written test - 50.0%
- - Individual work - 50.0%
Internship(s)
NAO
Bibliography
Byrne, B. M. (2016). Structural Equation Modeling With AMOS Basic Concepts Applications and Programming (3rd ed.). Routledge.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage.
Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). The Guilford Press Mozzato, A., Grzybovski, D., & Teixeira, A. (2016). Análises qualitativas nos estudos organizacionais: As vantagens no uso do software NVivo®. Revista Alcance, Eletrónica, 23(4), 578-587. doi: alcance.v23n4.p578-587.
Philimore, J., & Goodson, L. (2009). Qualitative research in tourism: Ontologies, epistemologies and methodologies. London: Routledge.
Saur-Amaral (2012). Curso completo de NVivo 10: Como tirar maior proveito do software para a sua investigação. https://www.bubok.pt/livros/6364/Curso-completo-de-NVivo-10–Como-tirar-maior-proveito-do-software-para-a-sua-investigacao