Qualitative Data Analysis

Recommended Prerequisites

Basic notions on research methodology and data collection techniques acquired at the following curricular units: Principles of Research Methodology and Qualitative Data Collection. 

Teaching Methods

This course is taught in practical classes (4 hours per week), combining the presentation of the syllabus topics with its practical application through the resolution of exercises and the use of a specific software.

Learning Outcomes

Overall objectives

The main objective of the course is to acquaint students with qualitative approaches for data processing.

Specific objectives

Familiarize students with deductive and inductive logics of classification and manipulation of qualitative data;

To practice analytical procedures that lead to the quantification of qualitative data;

To practice analytical procedures intended to operate in a strictly qualitative way.

Generic skills

To acquire and train conceptualization skills, as much as to relate concepts to data/information.

Specific skills

It is expected students to acquire the following skills: developing categories and coding systems; organization and classification of unstructured data; evaluation of consistency and reliability of encoding processes; development and application of categories relatedness models and operating hypothesis; formalization of general propositions.

Usage of specific software to apply the referred skills. 

Work Placement(s)

No

Syllabus

1. Introduction to Qualitative Data Analysis: the logic of qualitative research approaches; diversity of perspectives and qualitative methodologies; the qualitative nature of the data and the objectives of the research;

2. Moments of Qualitative Data Analysis: organize, present and interpret data; draw analytical conclusions;

3. The model of Content Analysis or Categorical Analysis: describe to quantify and test; selection of documents and creation of a corpus; problems of relevance and representativeness; construction of categories and development of coding schemes; coding information and reliability control;

4. Visual Methodologies: understand the importance of visual methodologies in social sciences;

5. Grounded Theory: generating theories rooted in intensive and systematic qualitative data analysis; coding procedures: open, axial and selective; integration and analysis procedures.

Head Lecturer(s)

Daniel Neves da Costa

Assessment Methods

Assessment
Frequency: 50.0%
Laboratory work or Field work: 50.0%

Bibliography

Bardin, Laurence (2009), Análise de Conteúdo. Coimbra: Edições 70.

Krippendorf, Klaus, 2012, Content Analyses. An Introduction to Its Methodology. Thousand Oaks, London, New Delhi, Sage Publications.

Lewins, Ann; Silver, Christina (2007), Using Software in Qualitative Research: A Step-by-Step Guide. London: Sage.

Miles, Mathew B. e Huberman, A. Mitchael (1994), Qualitative Data Analysis. Thousand Oaks, London, New Delhi, Sage Publications.

Rose, Gilian (2007), Visual Methodologies: An Introduction to the Interpretation of Visual Materials. London: Sage.

Saldanha, John (2009), The Coding Manual for Qualitative Researchers. Londres, Thousand Oaks, Nova Deli, Singapura: Sage Publications Ltd

Strauss, Anselm e Corbin, Juliet (2009), Pesquisa qualitativa: técnicas e procedimentos para o desenvolvimento de teoria fundamentada. Porto Alegre: Artmed Editora/Bookman Companhia Editora. Artmed.