Thematic Seminars on Research Methodology and Statistics II

Recommended Prerequisites

Intermediate knowledge (theoretical and methodological) of quantitative and qualitative research

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 for different qualitative data types). This course will also allow students to critically analyze scientific articles of empirical studies in various areas of Educational Sciences.

Learning Outcomes

- To deepen the educational process regarding the analysis of quantitative, qualitative and mixed data when implementing scientific studies in education

- To promote advanced knowledge and skills in methodology, when performing analysis of quantitative and/or qualitative data

- To design, plan and develop autonomously an empirical study of qualitative, quantitative and/or mixed nature

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

- 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

- To answer research questions in Education Sciences and to be able to critically read scientific publications in this area.

Work Placement(s)

No

Syllabus

- Mixed plans in Educational Sciences research: their nature, potential applications and limitations.

- Advanced Analysis of qualitative data: content analysis of different type of data (semantic, visual, etc.) using the software MaxQDA

- Understanding the notion of saturation in qualitative research design.

- Analysis of covariance (ANCOVA and MANCOVA). Conditions for the application of the models.

- 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 educational sciences such as when dealing with paired data.

- 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.

- Calculation of the statistical power in quantitative research designs.

Head Lecturer(s)

José Manuel Tomás Silva

Assessment Methods

Continuous assessment
Resolution Problems: 20.0%
Synthesis work: 20.0%
Development of an individual work, quantitative, qualitative or mixed: 60.0%

Final assessment
Exam: 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.