Research Methods and Data Analysis

Recommended Prerequisites

NA

Teaching Methods

It is intended that the flexibility of the adopted pedagogical methodologies allow the student to choose the training path that most favors his doctoral research. The expository moments with group discussions and simulations will motivate the active participation of the students, through the critical analysis of scientific publications and other documents (reports, secondary data, etc.) and the performance of practical activities of application of knowledge, through the use of software (e.g. IBM SPSS Statistics, R, RStudio, MaxQDA, WebQda, RQDA) or from documentary sources of different nature.

Learning Outcomes

- Deepen the knowledge regarding design, planning and development of quantitative, qualitative or mixed empirical studies, ethically responsible and relevant to the intervention;

- Develop in-depth skills, at a methodological level, including critical reflection on issues related to the validity and fidelity of the research process, the characteristics of the data collection instruments, the role of those who do research and the demands of science communication.

- Deepen the knowledge of qualitative methodologies and techniques for the collection and analysis of semantic data (or other types), understanding their specificities and challenges.

- Know advanced statistical methodologies in order to answer research questions;

- Check and validate the requirements in statistical modeling;

- Interpret the results produced by multivariate statistical modeling.

- Consolidate formal writing and scientific literacy skills.

Work Placement(s)

No

Syllabus

The program will consist of several independent modules of 1 to 2 ECTS, each. 4 ECTS are required in “Research Methodology” and 4 ECTS in “Data Analysis”. The remaining 7 ECTS, can be chosen from a set of possible modules, in order to allow students to manage their curricular path according to their needs. The proposed modules will address, for example, the following themes:

Research foundations

Participatory Investigation

Grounded Theory

In-depth issues of quantitative and qualitative methodology;

Advanced analysis of qualitative data (e.g. semantic, visual);

Quantitative designs (quasi-experimental studies) or qualitative designs (participatory action research);

Linear and non-linear models in statistical analysis;

Multilevel regression models;

Exploratory and confirmatory factor analysis;

Item response theory.

Assessment Methods

Assessment method
Resolution Problems: 25.0%
Synthesis work: 25.0%
Research work: 50.0%

Bibliography

Amado, J. (Org.). (2017). Manual de investigação qualitativa em educação (3ª ed.). Coimbra: Imprensa da UC

Brown, T. (2006). Confirmatory factor analysis for applied research. NY: The Guilford Press.

Fraenkel, J. R & Wallen, N. E. (2003). How to design and evaluate research in education (5th ed.). Boston: McGraw-Hill.

Long, J. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks: Sage Publications.

Osborne, J. (2008). Best practices in quantitative methods. Thousand Oaks, CA: Sage Publications.

Paulino, C. D., & Singer, J. M. (2006). Análise de dados categorizados. São Paulo: Edgard Blucher.

Silver, Ch. & Lewis, A. (2014). Using software in qualitative research. A step-by-step guide. London: Sage Publications.

Silverman, D. (2013). Doing qualitative research. London: Sage Publications.

Snijders, T. A. B. & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). London: SAGE.