Data Analysis and Information Management II

Recommended Prerequisites

None.

Teaching Methods

Teaching methods will be used that involve students in the analysis and application of concepts in real public-private administration contexts, resulting in the acquisition of essential theoretical and methodological skills. In each of the techniques discussed, the Professor will always start by solving a case together with the students, which will be followed by exercises to be solved by the students, with the Professor acting as a moderator. Whenever possible, topics will be addressed with the aid of data analysis and information management software.

Learning Outcomes

Provide students with knowledge so that they can analyze and interpret a wide variety of data using bivariate information management techniques. In particular, students should: perform natural groupings of observations through cluster analysis; to be able to simplify an analytical reality through the reduction of information redundancy; make predictions for certain scenarios using regression analyses.

Work Placement(s)

No

Syllabus

1. Construction of observations' clusters and profile analysis

1.1. Theoretical and practical framework of observation grouping techniques and profile analysis

1.1.1. Two-Step method

1.1.2. K-means method

1.1.3. Hierarchical grouping method

2. Information redundancy reduction

2.1. Theoretical and practical framework of information redundancy reduction techniques

2.1. Exploratory factor analysis

2.2. CATPCA method

3. Regression analysis

3.1. Theoretical and practical framework of regression analysis techniques

3.1.1. Categorical regression

3.1.2. Logistic regression

3.1.3. Discriminant analysis

3.1.4. Multiple linear regression.

Head Lecturer(s)

Pedro Miguel Alves Ribeiro Correia

Assessment Methods

Assessment
Resolution Problems: 10.0%
Synthesis work: 40.0%
Frequency: 50.0%

Bibliography

Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2018). Multivariate Data Analysis. (8th Edition). Edinburg Gate: Pearson Prentice Hall.

Maroco, J. (2021) Análise Estatística com o SPSS Statistics. (8ª Edição). Lisboa: Edições Sílabo.

Pestana, M. H. & Gageiro, J. N. (2014). Análise de Dados para as Ciências Sociais - A complementaridade do SPSS. (6ª Edição). Lisboa: Edições Silabo.

Reis, E. Melo, P. Andrade, R. & Calapez, T. (2014). Exercícios de Estatística Aplicada. (2ª Edição, Vol. 2.). Lisboa: Edições Sílabo.

Reis, E. Melo, P. Andrade, R. & Calapez, T. (2016). Estatística Aplicada - Vol. 2. ( 5ª Edição). Lisboa: Edições Sílabo.

Tacq, J. (1997). Multivariate Analysis Techniques in Social Science Research. London: SAGE Publications.