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Data Analysis for Business Sciences

Code 8255
Year 1
Semester S1
ECTS Credits 7,5
Workload TP(30H)
Scientific area Management
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;
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
Language Portuguese. Tutorial support is available in English.


Last updated on: 2012-05-21

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