Objectives
The main objective is to develop specialized knowledge that provides content analysis (CA) skills in diverse contexts of research and intervention in SC.
Students should be able to: a) present and critically discuss the theoretical-epistemological assumptions behind the CA in HS, with an emphasis on its potential and challenges in broad and multidisciplinary contexts; b) distinguish and operationalize methodological approaches adjusted to different contexts, showing mastery in the use of different languages and techniques of CA; c) design effective plans for implementing CA, with sampling criteria, constitution of the corpus, manual and computer-assisted analytical strategies; d) construct interpretive categories and discuss the limits of inference in the analysis of small and big data; e) choose reasonably between different visualization techniques and write output products aiming the presentation and multi-format dissemination of scientific results.
General characterization
Code
531010
Credits
6
Responsible teacher
Available soon
Hours
Weekly -
Available soon
Total -
Available soon
Teaching language
Portuguese
Prerequisites
Not applicable to
Bibliography
Denzin, N.K., & Lincoln, Y. (ed.) (2018). Handbook of Qualitative Research (5th ed.). Thousand Oaks: Sage Publications.
Foster, I., Ghani, R., Jarmin, R.S., Kreuter, F., & Lane, J. (ed.). (2016). Big Data and Social Science: A Practical Guide to Methods and Tools.
Boca Raton: Chapman and Hall/CRC Press.
Krippendorff, K. (2019). Content Analysis: an introduction to its methodology (4th ed.). Thousand Oaks: Sage Publications.
Miles M. B., Huberman, M. A., & Saldaña, J. (2014). Qualitative data analysis: A methods sourcebook. 3rd ed. Los Angeles: Sage.
Neuendorf, K.A. (2017). The Content Analysis Guidebook (2nd ed.). London: Sage.
Teaching method
Lectures and practical lessons, tutorial follow-up and e-learning solutions.
Evaluation method
According to internal regulation, students may choose between a continuous assessment process or a final examination.
Continuous assessment: written essay with oral presentation and discussion in class (100%). Final examination: written examination (100%).
Subject matter
I. Theoretical-epistemological assumptions of content analysis in health sciences: outline, potentialities and challenges.
II. Methodological approaches: contexts, languages and techniques of content analysis.
III. Sampling, corpus, manual and computer-assisted analytical strategies.
IV. Categorization and inference: reliability and validity in the analysis of small and big data.
V. Visualization techniques, scientific writing and findings dissemination.
Programs
Programs where the course is taught:
