Research Methodology and Data Analysis

Recommended Prerequisites

Advanced knowledge (theoretical and methodological) of quantitative and qualitative research and advanced knowledge of statistical data analysis.

Teaching Methods

The methodology allows students to choose the path that better fits their doctoral research. The in-class moments with discussions and scenario simulations will motivate the active participation of students in critical analysis of documents and the performing of practical studies with the use of software (e.g., SPSS, R, for quantitative data; MaxQDA, WebQda or RQDA for different qualitative data types).

Learning Outcomes

- To deepen knowledge in order to help the student to design, plan and develop an empirical study of qualitative, quantitative and/or mixed nature, with implications for research;

- To develop advanced skills in methodology, including critical reflexion about issues concerning validity and reliability of research instruments of data collection and of the process of scientific research.

- To acquire advanced knowledge about qualitative methodologies and techniques of semantic (and other types) data collection and analyses, understanding the specificities of interpretative research.

- To know the advanced statistical methodologies in order to answer research questions;

- To be able to check and validate the assumptions of a statistical model;

- To interpret the results produced by appropriate multivariate statistical analysis in the presence of various types of dependent variables (continuous or categorical) or paired data.

Work Placement(s)

No

Syllabus

The syllabus will be custom designed for the different needs of the enrolled students.The program may be based on a variety of different modules addressing for example the following topics:

- Advanced Analysis of qualitative data (e.g. semantic, visual) using software (e.g. MaxQDA, RQDA).

- Mediation and moderation models. Interpretation and assumptions requirements.

- Non-linear regression models in the treatment of categorical dependent variables: logistic and multinomial regression.

- Multilevel regression models, allowing the analysis of different phenomena in Psychology such as when dealing with paired data or repeated measurements.

- Exploratory and confirmatory factor analysis: the use of path analysis in the multivariate description of the relationships between indicators, constructs and error measurements. Testing and interpreting critical parameters in the model and assessing its fit. 

Head Lecturer(s)

Bruno Cecílio de Sousa

Assessment Methods

Assessment
Other possibilities of assessment can be defined with the students. The assement in thsi teaching unit is on the form Pass/Fail: 100.0%

Bibliography

Amado, J. (Org.). (2014). Manual de investigação qualitativa em educação (2ª ed.). Coimbra: Imprensa da UC

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

Carreira, A., de Sousa, B., & Pinto, G. (2002). Cálculo da probabilidade. Lisboa: Instituto Piaget.

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 Publ.

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.