| Code |
13969
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| Year |
1
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| Semester |
S1
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| ECTS Credits |
7,5
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| Workload |
TP(30H)
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| Scientific area |
Management
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Entry requirements |
None
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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.
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Syllabus |
1. Philosophy of research and data analysis – the relationship between ontology, epistemology, methodology and methods;
2. Data analysis software
3. Open data
4. Data munging
5. Data description
a. Numeric description
b. Visualisation
6. Analyses of interdependence;
a. Correlation and Crosstabulation;
b. Factor analysis and Principal Components Analysis;
c. Cluster analysis;
7. Dependency analyses;
a. Chi-square;
b. ANOVA;
c. Regression;
8. Introduction to second-generation analyses;
a. Structural Equation Analysis;
b. PLS.
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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
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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.
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Language |
Portuguese. Tutorial support is available in English.
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