Statistics

Recommended Prerequisites

Intermediate knowledge (theoretical and methodological) of quantitative and qualitative research.

Teaching Methods

The methodology adopted allows the student to choose the path that most favors his or her doctoral research. The expository moments with discussions and simulations will motivate the active participation of the students, through the critical analysis of documents and the realization of practical studies of application of knowledge through the use of software (eg IBM SPSS, AMOS). This curricular unit will also provide the reading and critical analysis by the doctoral students of scientific articles derived from empirical studies in areas of psychology.

Learning Outcomes

- Deepen the learning process in the analysis of quantitative data relevant to planning and implementing scientific studies in the field of family psychology

- To know the advanced statistical methodologies more commonly used to answer to research questions in psychology, namely in the family domain

- To Know the validation assumptions of a statistical model and devise a robust analytical strategy to deal with detected problems

- Interpret the results/outputs produced by the appropriate multivariate statistical modeling in the presence of several types of dependent variables (continuous or categorical) or clustered data

- Know how to critically read publications in this area and be able to write research reports and contribute to the field. 

Work Placement(s)

No

Syllabus

- Statistics: Why? Type I, II Error and Power. Multiple statistical tests and the probability of spurious results. Statistical significance vs. practical meaning.

- Review of univariate and bivariate statistics.

- Examining / Cleaning of data.

- Comparisons of Means: MANOVA and ANCOVA.

- Advanced multiple regression models (including lienar and nonlinear models with category variables).

- Multilevel models useful for modeling of phenomena typical of studies with families, for example in the treatment of grouped data.

- Analysis of interpendences and adjustment of models to the data: Exploratory and confirmatory factorial analysis methods

- Structural Equations Modeling: Path analysis and full SEM with manifest and latent variables.

Head Lecturer(s)

José Manuel Tomás Silva

Assessment Methods

Assessment
Synthesis work: 100.0%

Bibliography

Brown, T. (2006). Confirmatory factor analysis for applied research. NY: The Guilford.

Field, A. (2013). Discovering Statistics Using IBM SPSS (4th ed.). London, UK: SAGE.

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2014). Multivariate data analysis (7ª ed). Harlow, USA: Pearson.

Long, J. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks: Sage Publications.

Marôco, J. (2016). Análise de Equações Estruturais (2ª ed.). Pêro Pinheiro: ReportNumber

Marôco, J. (2018). Análise Estatística com o SPSS Statistics (7ª ed.). ReportNumber: Pêro Pinheiro.

Pallant, J. (2013). SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS. London, UK: Open University

Snijders, T. A. B. & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). London: SAGE.

Tabachnick, B. G. & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston, USA: Pearson Education, Inc.