| Code |
17222
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| Year |
1
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| Semester |
S2
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| ECTS Credits |
5
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| Workload |
T(15H)/TP(30H)
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| Scientific area |
Mathematics
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Entry requirements |
none
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Learning outcomes |
The curricular unit Statistics and Data Analysis aims to provide students with a solid foundation in statistics, equipping them with the skills necessary for the appropriate application of statistical methods.
Upon completion of the curricular unit, students are expected to be able to:
a. Demonstrate the ability to select and justify the most appropriate statistical methods in accordance with the specific objectives of a study;
b. Apply suitable statistical methods for the analysis of a given dataset;
c. Demonstrate autonomy and independence in the use of statistical software, particularly SPSS;
d. Interpret, discuss, and clearly communicate, both in written and oral form, the results obtained from statistical analyses;
e. Develop the ability to critically interpret statistical results and articulate them within the broader research context.
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Syllabus |
1. Introduction to Statistics and Sampling Concepts:
1.1. Introduction to SPSS software
1.2. Descriptive Analysis
1.3. Sampling Theory and Sample Size Determination
2. Hypothesis Testing: One and Two Samples:
2.1. Methodology of Hypothesis Testing
2.2. Parametric and Nonparametric Hypothesis Tests
3. Hypothesis Testing: More than Two Samples:
3.1. One-Way ANOVA and Repeated Measures ANOVA
3.2. Kruskal–Wallis Test and Friedman Test
4. Linear Regression Analysis:
4.1. Correlation and Simple Linear Regression
4.2. Multiple Linear Regression
4.3. Assessment of Model Goodness-of-Fit
5. Introduction to Logistic Regression Analysis:
5.1. The Logistic Regression Model and Odds Ratio
5.2. Variable Selection Methods
5.3. Model Significance and Goodness-of-Fit
6. Effect Size:
6.1. Introduction to the Concept of Effect Size
6.2. Magnitude of Differences Between Groups and Relationships Between Variables
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Main Bibliography |
- Bryman, A., & Duncan, C. (2002). Quantitative Data Analysis with SPSS Release 10 for Windows. A guide for social scientists. New York: Routledge.
- Guimarães, R.C. & Sarsfield Cabral, J.A. (2010). Estatística, 2ª Ed. Verlag Dashöfer
- Fritz, C.O., Morris, P.E. & Richler, J.J. (2012). Effect size estimates: current use, calculations, and interpretation. J Exp Psychol: General, 141(2).
- Marôco, J. (2014). Análise Estatística com o SPSS Statistics. 6ª edição. Edições Sílabo.
- Montgomery, D.C., Peck, E.A. & Vining, G.G. (2012). Introduction to linear regression analysis. 5th ed. Wiley series in probability and statistics.
- Pereira, A. (2006). SPSS - Guia prático de utilização – Análise de dados para Ciências Sociais e Psicologia. 7ª ed. Edições Sílabo.
- Pestana, M.H., & Gageiro, J.N. (2014). Análise de dados para Ciências Sociais: A complementaridade do SPSS. 6ª ed. Lisboa: Edições Sílabo.
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Teaching Methodologies and Assessment Criteria |
1) Teaching and Learning Assessment (AP):
M1 – Written Work (graded up to 6 points, 30% of the final grade)
A practical assignment in which students must answer the proposed questions by applying appropriate statistical methodologies to a dataset. This assignment will be carried out in groups of two students.
M2 – Experimental Research Work (graded up to 14 points, 70% of the final grade)
A group research project (maximum of 4 students): 8 points for the written report and 6 points for the individual oral presentation and discussion of the work.
Final AP Grade = 30% × M1 + 70% × M2
Grading:
Pass: AP >= 9.5 points
Admitted to Exam: 6 <= AP < 9.5 points
Not Admitted: AP < 6 points
2) Final Exam Assessment:
The exam consists of an improved version of the research work, following the same structure as M2:
- written report (11 points)
- individual oral presentation (9 points)
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Language |
Portuguese. Tutorial support is available in English.
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