Data Analysis and Information Management I

Recommended Prerequisites

Not applicable.

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 univariate and bivariate information management techniques. In particular, students should: understand the generic concept of hypothesis testing; distinguish between parametric and non-parametric hypothesis tests; apply hypothesis tests to real situations; perform categorical analysis of data to determine the existence of dependence between variables, through appropriate hypothesis tests; gain an introductory understanding of the application of correspondence analysis tools.

Work Placement(s)

No

Syllabus

1. Hypothesis testing

1.1. Parametric tests

1.1.1. Hypothesis testing for the population mean and proportion

1.2. Non-parametric tests

1.2.1. Normality Test (Kolmogorov-Smirnov with Lilliefors correction)

1.2.2. Shapiro-Wilk's Test

1.3. Continuation of the theme of parametric tests

1.3.1. Hypothesis Test for Equality of Two Means

1.3.1.1. In case samples are independent

1.3.1.2. In case samples are paired

1.3.2. Multiple data groups comparison tests

1.3.2.1. ANOVA test

1.3.2.2. Kruskal-Wallis's Test

1.3.2.3. Mann-Whitney's Test

2. Analysis of data grouped into categories

2.1. Chi-square independence test

2.2. Fisher's exact test

2.3. ANACOR Procedure

2.4. Introduction to multiple correspondence analysis.

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.