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ISLA Santarém 13030

Data Analysis and Processing

Human Resources Management
  • ApresentaçãoPresentation
    The course develops skills in data collection, organization, analysis, and processing, using statistical software to produce and interpret numerical and graphical results in order to support decision-making in contexts of variability and uncertainty based on real data.  
  • ProgramaProgramme
    1 Introduction to SPSS software 1.1 Work environment 1.2 Designing a data file 1.3 Defining variable properties 2 Univariate Descriptive Statistics with SPSS 2.1 General information 2.1.1 Population, Sample, and Statistical Unit 2.2 Creating frequency tables, graphs, and calculating descriptive measures with SPSS 2.3 Measures of location and dispersion 2.4 Measures of asymmetry and skewness 3 Bivariate Descriptive Statistics with SPSS 3.1 Scatter plot 3.2 Measures of association 3.3 Simple linear regression 3.4 Cross-tabulation of variables 4 Statistical Inference with SPSS 4.1 Estimation theory 4.2 Point and interval estimation 4.3 Decision theory 4.4 Confidence intervals versus hypothesis testing 4.5 Parametric and nonparametric tests 
  • ObjectivosObjectives
    O1. Understand and apply fundamental statistical techniques that support decision-making in contexts of variability and uncertainty, using real data. O2. Develop skills in data collection, organization, analysis, and processing, using statistical software to produce and interpret numerical and graphical outputs.
  • BibliografiaBibliography
    Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Harlow: Pearson. Marôco, J. (2018). Análise Estatística com o SPSS Statistics (7.ª ed.). Pêro Pinheiro: ReportNumber. Pereira, A. (2015). SPSS – Guia Prático de Utilização (edição atualizada). Lisboa: Edições Sílabo. Pestana, M. H., & Gageiro, J. N. (2017). Análise de Dados para Ciências Sociais – A Complementaridade do SPSS (6.ª ed.). Lisboa: Edições Sílabo
  • MetodologiaMethodology
    Face-to-face: 1. Expository method: the defined program content is explored. In addition, the demonstrative method is used to present concrete examples of how the content can be applied. 2. Laboratory practice: based on the Problem-Based Learning (PBL) methodology, aiming to find solutions to problems identified by students or proposed by the teacher. Independent: 3. Guided research proposed by the teacher, aiming to consolidate the topics under study and providing input for the practical work developed by students.
  • LínguaLanguage
    Português
  • TipoType
    Semestral
  • ECTS
    6
  • NaturezaNature
    Mandatory
  • EstágioInternship
    Não
  • AvaliaçãoEvaluation

    O método de avaliação curricular consiste desenvolvimento de um trabalho de grupo aplicado a um caso real com recurso a SPSS (50%) e uma prova escrita no final do semestre (50%). 
    A avaliação final será concretizada através de exame (100%).