Publication in the Diário da República: Despacho n.º 6419/2017, de 24-07-2017
6 ECTS; 1º Ano, 2º Semestre, 20,0 T + 20,0 TP , Cód. 92999.
Lecturer
- Ricardo Jorge Viegas Covas (1)(2)
(1) Docente Responsável
(2) Docente que lecciona
Prerequisites
Not applicable.
Objectives
Provide students with knowledge about bi and multivariate data analysis, specifically in terms of inferential models.
Program
1. Advanced Research Methods
1.1. Development of research topics and bases of the research process
1.2. Methodological advantages and difficulties associated with the choice of specific types of study and methodological approach
1.3. Preparation of a research project
1.4. Publishing process: preparation, submission and revision of papers and scientific research studies
1.5. Public presentation of a scientific research project
2. Advanced Methods of Data Analysis
2.1. Parametric and non-parametric inferential analyses
2.2 Factor analysis
2.3 Simple and multiple linear regression models
2.4 Path Analysis
2.5 Structural equation model
2.6 Assessment of measurement and structure submodels
2.7 Distinction between mediation and moderation effects
2.8 Applications with software and how to report results in report/paper
Evaluation Methodology
- Continuous assessment: two tests (classified from 0 to 20 points each) during the semester. The student is exempted from exam, that is, the student is approved by continuous assessment if the average obtained from the classification of written frequencies, rounded to the nearest units, is equal to or greater than 10 values.
- Assessment by exam: written test (exam) with all the subjects taught in the course (rated from 0 to 20), with a weight of 100%. The student is approved to the course unit if the final classification, rounded to the units, is equal to or higher than 10 values.
Bibliography
- Barañano, A. (2008). Métodos e Técnicas de Investigação em Gestão: Manual de Apoio à Realização de Trabalhos de Investigação. Lisboa: Edições Sílabo
- Bryman, A. e Buchanan, D. (2011). The SAGE: handbook or organizational research methods. London, England: SAGE
- Hair, J. e Hult, G. e Ringle, C. e Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). (Vol. 1). (pp. 1-390). Los Angels: SAGE
- Marôco, J. (2007). Análise Estatística com utilização do SPSS. Lisboa: Edições Sílabo
- Marôco, J. (2011). Análise de Equações Estruturais: Fundamentos teóricos, Software e Aplicações. Lisboa: ReportNumber
Teaching Method
Expository lessons promoting brainstorming and debate supported by literature review to illustrate key models, methods and techniques.
Software used in class
The IBM SPSS and SmartPLS packages. From time to time, the Excel spreadsheet can be used.
