Multivariate Data Analysis

Base Knowledge

Univariate and bivariate descriptive statistics.

Teaching Methodologies

The development of each topic in the course will have a similar structure, in particular: an introduction and characterization of the technique, with presentation and discussion of the options and outputs of SPSS using an example, and practical case resolution.

Learning Results

It is intended that students understand and apply the multivariate data analysis techniques covered, that they use specific software properly  and that they interpret critically the results obtained.

Program

Introduction: Data type. Classification of multivariate techniques. Examples.

Factor Analysis: The model. Estimation of loadings. Rotation of factors. Estimation of the values of the factors. Resolution of application examples using SPSS and interpretation of the outputs.

Principal Components Analysis: The model: construction, properties, geometric meaning and interpretation. ACP on standardized data vs. non-standardized data. Resolution of application examples using SPSS and interpretation of the outputs.

Multiple Linear Regression Analysis: The model; estimation of coefficients; inference on the model. Assumptions. Methods of sequential selection of variables. Resolution of applied examples using SPSS and interpretation of results.

Cluster Analysis: Dissimilarities between individuals. Hierarchical methods and nonhierarchical methods. Resolution of application examples using SPSS and interpretation of the outputs.

Introduction to Structural Equation Models: general characterization.

Curricular Unit Teachers

Grading Methods

Periodic Evaluation
  • - Group Work 1 - 25.0%
  • - Group work 2 - 25.0%
  • - Test - 50.0%
Exam
  • - Individual work 2 - 25.0%
  • - Exam - 50.0%
  • - Individual work 1 - 25.0%

Internship(s)

NAO

Bibliography

Hair, J. F., Black, W., Babin, B., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). CengageLearning.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). SAGE Publications.

Johnson, R. A., & Wichern, D. W. (2019). Applied Multivariate Statistical Analysis (6th ed.). Pearson. 

Malhotra, N. (2019). Pesquisa de Marketing: Uma Orientação Aplicada (7.ª ed.). Bookman. 

Marôco, J. (2021). Análise de Equações Estruturais: Fundamentos teóricos, software & aplicações (3.ª ed.). ReportNumber.

Marôco, J. (2021). Análise Estatística com o SPSS Statistics (8.ª ed.). ReportNumber.

Pestana, M. H., & Gageiro, J. N. (2014). Análise de Dados para Ciências Sociais: A complementaridade do SPSS (6.ª ed.). Edições Sílabo.