Data Analysis

Recommended Prerequisites

NA

Teaching Methods

The methodology includes lectures, using audiovisual means and practices in computer room. It seeks to promote an active and participatory learning, focusing on the interaction, in order to better understand the logic that precedes the use of statistical tests. Knowledge evaluation is performed by conducting a written work (80%) and correspondent presentation and discussion in the class (20%). The classification is assigned from zero to twenty values, being guaranteed approval with the minimum rating of ten values. The final mark is the result of weighted average of the written document and the presentation/discussion.

Learning Outcomes

This course aims at sensitizing the students to the importance of data analysis methodologies in what concerns health science research. Moreover, it presents the various phases of the empirical research prior to data analysis, from the statement of the problem to the data collection techniques and the definition of research strategy, variables choice and operationalization, as well as sampling methods. Finally, a better knowledge of the statistical proceedings is created, namely the simple descriptive statistics, and the hypothesis testing to study comparisons and association among variables. At the end of this course, students will be able to, independently, produce the statistical analysis they need in preparing their dissertation, and later on in their professional lives.

Work Placement(s)

No

Syllabus

INTRODUCTION TO STATISTICS – Basic concepts. The nature of the data. Uses and abuses of statistics. Steps of a statistical study. Experimental design. ANALYZING DATA IN SPSS – Creation of databases. UNIVARIATE DESCRIPTIVE STATISTICS – Thinking in statistical terms. Organization and presentation of data. Indicators of central location and dispersion. Coefficient of variation. Indicators of asymmetry and kurtosis. CORRELATION, REGRESSION AND INDEPENDENCE – Linear correlation. Simple linear regression models. INFERENTIAL STATISTICS – Estimation and hypothesis testing. Inference about mean. Difference between means. FITNESS AND INDEPENDENCE – Independence test and Chi-square test. MULTIVARIATE STATISTICS – Principal component analysis. Factor analysis.

Head Lecturer(s)

Pedro Augusto Melo Lopes Ferreira

Assessment Methods

Assessement
Knowledge evaluation is performed by conducting a written work (80%) and correspondent presentation and discussion in the class (20%). The classification is assigned from zero to twenty values, being guaranteed approval with the minimum rating of ten values. The final mark is the result of weighted average of the written document and the presentation/discussion.: 100.0%

Bibliography

Lisboa JV, Augusto MG, Ferreira PL. Estatística aplicada à gestão. Porto: Vida Económica, 2012.

Maroco J. Análise estatística: com utilização do SPSS. Lisboa: Edições Sílabo, 2010.

Pestana MH, Gageiro JN. Análise de dados para ciências sociais, a complementaridade do SPSS. Lisboa: Edições Sílabo, 2008.

Ferreira PL. Análise e tratamento de dados – Apontamentos e guiões para as aulas. Coimbra: FEUC, 2011.