You need to activate javascript for this site.
Menu Conteúdo Rodapé
  1. Home
  2. Courses
  3. Management
  4. Data Analysis for Business Sciences

Data Analysis for Business Sciences

Code 13969
Year 1
Semester S1
ECTS Credits 7,5
Workload TP(30H)
Scientific area Management
Entry requirements None
Learning outcomes Using quantitative techniques of data analysis in scientific research.
The students should understand the connection between the research problem, methodology, data collection and quantitative data analysis. To choose the appropriate quantitative data analyses according to the research methodology, nature and purpose. To perform analyses in statistical software, namely SPSS, AMOS and SmartPLS. To interpret, discuss and report analyses’ results.
Syllabus 1. Research philosophy and Data Analysis – relationship among ontology, epistemology, methodology and method;
2. Interdependence Analysis;
a. Factor Analysis
b. Cluster Analysis
3. Dependency Analysis;
a. ANOVA
b. Regression
4. Second Generation Analysis.
a. Structural Equation Modelling
b. PLS
Main Bibliography Easterby-Smith, M.; Thorpe, R. & Jackson, Paul R. (2008) Management Research 3rd ed., Londres, UK: Sage
Hair, Joseph F., Bill Black, Barry Babin, Rolph E. Anderson (2010) Multivariate Data Analysis, 7/e, Upper Saddle River, US: Prentice Hall, ISBN13: 9780138132637
Bollen, K.A. (1989); Structural Equations with Latent Variables; New York, NY, US: John Wiley & Sons
Dunbar, R. (1996). What is This Thing Called Science? In The trouble with science (pp. 12-33). Harvard University Press.
Hunt, S. D. (2002). The Morphology of Theory. In Foundations of marketing theory: Toward a General Theory of Marketing (pp. 191-221). Armonk, New York: US: ME Sharpe.
Nunnaly, Jum C.; Bernstein, Ira H. (1994); Psychometric Theory, 3rd. Ed.; New York, NY, US: McGraw Hill
Putnam, H. (1987). The Many Faces of Realism. Open Court. ISBN: 0812690435
Teaching Methodologies and Assessment Criteria The classes will have an expository component, in which the topics and analyses are presented. The learning of the software and the techniques of manipulation and analysis of data will be done using the computer. The evaluation will consist of three elements. A scientific paper with open data, up to 6000 words, formatted according to the model provided and written preferably in English. Two reviews of articles by colleagues. An individual written examination.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2023-11-22

The cookies used in this website do not collect personal information that helps to identify you. By continuing you agree to the cookie policy.