1. Linear Regression Model
1.1.Definition and assumptions
1.2.Estimation of parameter; multiple correlation coefficient and coefficient of multiple determination; Inference about the model
1.3.Partial and semi-partial correlation coefficients
1.4.Discussion and presentation of the results
1.5. Hierarchical regression
1.6.Applications with SPSS
2. Categorical regression: Binary Logistic
2.1.Logistic Regression versus Linear regression: models comparison
2.2.Logit transformation
2.3.Effect size of the model
2.4. Testing the model: Qui-square test
.2.5. Logistic Regression Coefficients: odds and odds ratio
2.6. Testing the coefficients: Wald test
2.7.Outliers: analysis of the residuals
2.8.Analyses with SPSS

There are two options for evaluation:
1. During the semester: individual written test at the end of the curricular unit (100%).
The student has to have a minimum of 70% attendance in class.
2.Evaluation by final exam: individual assignment at the end of the semester (100%).

Mandatory

Field, A. (2024). Discovering statistics using IBM SPSS statistics (6th ed.). Sage Publications.
Filho, D.F., Rocha, E., Paranhos, R., e Alexandre, J. (2015). Regressão logística em Ciência Política. Universidade Federal de Pernambuco.
Maroco, J. (2021). Análise Estatística com o SPSS (8ª edição). Report Number.

Optional

Hair, J., Black, W.C., Babin, B.J., and Anderson, R.E. (2018). Multivariate Data Analysis (8th ed.). Cengage Learning.
Hosmer Jr. D.W, Lemeshow, S., and Sturdivant, R.X. (2013). Applied Logistic Regression (3rd ed.). John Wiley & Sons Inc.
Menard, S. (2002). Applied Logistic Regression Analysis (2nded.) SAGE publications.
Menard, S. (2010). Logistic Regression: From Introductory to Advanced Concepts and Applications. SAGE publications.
Pampel, F. C. (2021). Logistic Regression. A Primer. SAGE publications.
Tabachnick, B., e Fidell, L. (2021). Using Multivariate Statistics (7th ed.), Pearson.

1. Social sciences research in the era of "big data". Types of data, challenges, opportunities and ethical issues;
2. Data sources: primary and secondary data;
3. Secondary data: types of sources and modes of access; structured and unstructured data;
4. Primary data: information collection tools, information collection methods, construction of databases (SPSS);
5. Preparation of databases (SPSS): validation (basic operations for error detection and correction); managing aggregated data from micro data; recoding of variables and construction of new variables;
6. Descriptive statistical analysis with SPSS: univariate analysis, bivariate analysis, report of tables and graphs, export of results to other programs.

Two possibilities:
1. Assessment throughout the semester: Individual report, approximately 10 pages.
2. Evaluation by examination: Individual exercise or individual report at the end of the school year (PGADCS examination season).

Mandatory

Bryman, Alan (2016), Social Research Methods, Oxford, Oxford University Press (5th ed.).
Goodwin, John (2012) (ed.), Secondary Data Analysis, Thousand Oaks, Sage Publications.
Foster, Ian (ed), (2016), Big data and social science: a practical guide to methods and tools, Taylor & Francis Group.
Laureano, Raul, Maria do Carmo Botelho (2017), SPSS, O Meu Manual de Consulta Rápida, Lisboa, Sílabo (3ª edição).
Maroco, J. (2018), Análise Estatística com utilização do SPSS Statistics 25, Lisboa, Edições Sílabo.

Optional

Reis, E., P. Melo, R. Andrade e T. Calapez (1997), Estatística Aplicada, vols. 1 e 2, Lisboa, Sílabo, 3ª ed.
Reis, E. (2008), Estatística Descritiva, Lisboa, Sílabo, 7ª ed.
Maroco, J. e R. Bispo (2003), Estatística aplicada às ciências sociais e humanas, Lisboa, Climepsi Editores.

PC 1. Hypotheses tests: main concepts
PC 2. Parametric tests: presentation, interpretation and implementation
PC 2.1.T Test for two independent samples
PC 2.2.T Test for two paired samples
PC 2.3. Two-way ANOVA
PC 3. Non parametric Tests: presentation, interpretation and implementation
PC 3.1. Non-parametric Tests: Kolmogorov-Smirnov, Shapiro-Wilk and Chi-Square
PC 3.2. Chi-Square independence test
PC 3.3. Mann-Whitney test
PC 3.4. Wilcoxon test
PC 3.5. Kruskall-Wallis test

The student can choose one of the following assessment methods:

1) Throughout the semester: individual essay with a minimum score of 10 required. The essays must be original, authored by the students themselves, both from the point of view of writing, as well as the organisation of ideas and construction of the argument, in accordance with the Iscte Code of Academic Conduct and the Iscte Code of Ethical Conduct in Research.

2) By examination: individual exercise with a minimum score of 10 points.

Mandatory

Laureano, Raul M. S. (2022), Testes de Hipóteses com o IBM SPSS Statistics, (3ª edição), Lisboa, Sílabo.
Laureano, Raul M. S. e Maria do Carmo Botelho (2017), SPSS Statistics: O meu manual de consulta rápida (3ª edição), Lisboa, Sílabo.
Marôco, João (2021), Análise Estatística com o SPSS Statistics (8ª edição), Pero Pinheiro, Report Number.
Marôco, João e Regina Bispo (2005), Estatística Aplicada às Ciências Sociais e Humanas (2ª edição), Lisboa, Climepsi Editores.
Vicente, Paula (2012), Estudos de Mercado e Opinião. Princípios e Aplicações de Amostragem, Lisboa, Edições Sílabo.

Optional

Bryman, Alan e Duncan Cramer (2003), Análise de Dados em Ciências Sociais: Introdução às Técnicas Utilizando o SPSS para Windows (3ª edição), Oeiras, Celta.
Cochran, William G. (1977), Sampling Techniques (3rd edition), New York, John Wiley & Sons.
Murteira, Bento J. F. (1993), Análise Exploratória de Dados: Estatística Descritiva, Lisboa, McGraw-Hill.
Reis, Elizabeth (2008), Estatística Descritiva (7º edição), Lisboa, Edições Sílabo.
Reis, Elizabeth, Paulo Melo, Rosa Andrade e Teresa Calapez (2021; 2019), Estatística Aplicada, Volumes 1 (7ª edição) e 2 (6ª edição), Lisboa, Edições Sílabo.
Vicente, Paulo, Elizabeth Reis e Fátima Ferrão (2001), Sondagens: A Amostragem como Factor Decisivo de Qualidade (2ª edição), Lisboa, Edições Sílabo.
Vinacua, Bienvenido Visauta e Joan Carles Martori I Canas (2003), Análisis Estadístico com SPSS para Windows, vols. I e II, Madrid, McGraw Hill.

Programatic contents (PC) articulated with the learning objectives.
I.Graphical and statistical literacy: learning a new language
PC1. Statistical indicators
PC2. Semiotics of communication and perception
PC3. Pictography
II.Charts: turning the trivial into the unusual
PC4. Communicating Graphics
PC5. Mixed or doble-use graphics
PC6. Exploratory graphics
PC7. Make a graphic story

Evaluation during the semester:
Preparation of a report with visual representations appropriate to the data and work objectives - 100%
Minimum score of 10 required.
Final exam:
Individual assignment (100%).

Mandatory

Alexandrino da Silva, A. (2006). Gráficos e mapas - representação de informação estatística. Lisboa: Lidel edições técnicas.

Beniger, J., & Robyn, D.L. (1978). Quantitative graphics in statistics: A brief history, The American Statistician, 32 (1), 1-11.

Cairo, A. (2013). The Functional Art: An introduction to information graphics and visualization. New Riders.

Cairo, A. (2019). How Charts Lie. Getting smarter about visual information. New York: W.W. Norton & Company, Inc.

Cleveland, W.S., & McGill, R. (1984a). Graphical perception: Theory, Experimentation, and application to the development of graphical methods, Journal of the American Statistical Association, 82, 419-423.

Laureano, R. e Botelho, M. C. (2017) SPSS - O Meu Manual de Consulta Rápida, 3ª ed.,Lisboa, Edições Sílabo.
Tufte, E. (1983). The Visual Display of Quantitative Information. Edition, Cheshire, CT: Graphics Press. USA.

Ware, C. (2012), Information Visualization, Perception for design, Morgan Kaufmann.

Optional

Alexandrino da Silva, A. (2009). Gráficos: às vezes é difícil fazer pior. In Maria de Fátima Salgueiro, Diana A. Mendes, Luís F. Martins (Eds.) Temas em Métodos quantitativos - Vol. 6 (ISCTE, Edições Sílabo).

Alexandrino da Silva, A. (2006). Pictogramas estatísticos antes de 1935 (data de criação do INE) com Olga Mendes, Poster, Jornadas de classificação e análise de dados (JOCLAD-06 Lisboa - Universidade Lusíada). Disponível em http://graficosemapas.wordpress.com/o-autor/visualizacao-de-dados/postersartigos/

Cleveland, W.S. (1987b). Research in statistical graphics, Journal of the American Statistical Association, 82, 419-423.

Dias, M.H. (1991). Leitura e comparação de mapas temáticos em geografia. Lisboa: Centro de Estudos Geográficos - Universidade de Lisboa.

Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press.

Programatic contents (PC) articulated with the learning objectives.
PC1. Introduction
1.1 Statistics and parameters
1.2 Probabilities
1.3 Normal Distribution
PC2. Sampling
2.1 Population list
2.2 Sample selection
2.3 Errors
2.4 Sample size
PC3. Weights
Construction and activation of weights
PC4. Non-response
5.1 Diagnosis and pattern analysis
5.2 Imputation
PC5. Estimation
5.1 Point estimation
5.2 Interval estimation
5.3 Robust estimation (the problem of outliers)
PC6. Statistical tests
6.1 Hypotheses, Errors, Decision.
6.2 One sample t-test
PC7. Quality of the adjustment
7.1 Kolmogorov-Smirnov and Shapiro-Wilk
7.2 Chi-square test of goodness of fit

There are two options for evaluation:
1. Assessment throughout the semester:
Resolution of a case study (group exercise): 40%.
Individual assignment: 60%.
In order to get a positive grade in the curricular unit, the mark of each of the assignments cannot be below 8.
In the performance evaluation, the student has to have a minimum of 70% attendance in class.
2. Evaluation by final exam: individual assignment (100%).

Mandatory

Field, A. (2009) Discovering Statistics Using SPSS, 3th ed., London, SAGE.
Graham, J. W. (2012) Missing Data_ Analysis and Design, New York, Springer-Verlag.
Laureano, R. (2020) Testes de hipóteses e regressão: o meu manual de consulta rápida, Lisboa, Edições Sílabo.
Laureano, R. e Botelho, M. C. (2017) SPSS - O Meu Manual de Consulta Rápida, 3ª ed.,Lisboa, Edições Sílabo.
Maroco, J. (2018) Análise Estatística com o SPSS Statistics, 7ª ed., Pero Pinheiro, ReportNumber.
Vicente, P. (2012) Estudos de Mercado e Opinião. Princípios e Aplicações de Amostragem, Lisboa, Edições Sílabo.

Optional

Cochran, W. (1997) Sampling Techniques, USA, John Wiley & Sons, 3ª ed.
Maroco, J. e Bispo, R. (2003) Estatística Aplicada às Ciências Sociais e Humanas, Lisboa, Climepsi Editores.
Murteira, B.J. (1993) Análise Exploratória de Dados? Estatística Descritiva, Lisboa, McGraw-Hill.
Reis, E. (1998) Estatística Descritiva, Lisboa, Lisboa, Edições Sílabo.
Reis, E., Melo, P., Andrade, R. e Calapez, T. (1997) Estatística Aplicada, Volumes 1 e 2, Lisboa, Edições Sílabo.
Little, R.J.A., & Rubin, D.B. (2002) Statistical analysis with missing data, Hoboken, Wiley.

1. Principal Component Analysis (PCA)
1.1. Introduction
1.2. Definition of principal components
1.3. Eigenvalues and communalities
1.4. Criteria's for extraction the principal components
1.5. Rotation of the components: orthogonal and non-orthogonal methods
1.6. Definition and interpretation of factor scores
1.7. Reliability analysis (Cronbach's Alpha) and computing summated scales
1.8. Applying with SPSS software or equivalent
1.9. Interpretation and reporting results in a paper, a thesis
1.10. Comparison with Exploratory Factor Analysis (EFA)
2. Confirmatory Factorial Analysis (CFA)
2.1. Comparison between PCA and CFA
2.2. Measurement model
2.3. Variables, constructs and errors
2.4. Specifying the model
2.5. CFA Goodness-of-Fit Statistics
2.6. Construct validity
2.7. CFA Assumptions
2.8. Applying with AMOS software or equivalent
2.9. Interpretation and reporting results in a paper, a thesis

There are two options for evaluation:
1. Assessment during the semester: individual written test at the end of the curricular unit.
Access to assessment requires attendance at least 70% of the classes in the course.
2. Assessment by final exam: Individual exam to be taken at the end of the academic year during the postgraduate exam season.

Mandatory

Brown, Timothy A. (2015), Confirmatory Factor Analysis for Aplied Research, The Guilford Press, 2nd Edition.
Hair, J., Anderson R., Tatham, R. e Black, W. (2010). Multivariate Data Analysis: A Global Perspective, Upper Saddle River, Pearson International Edition (7ª ed).
Maroco, J. (2014). Equações Estruturais: Fundamentos teóricos, software e aplicações, Pero Pinheiro, ReportNumber.
Maroco, J. (2018), Análise Estatística com utilização do SPSS Statistics 25, Lisboa, Edições Sílabo
Reis, E. (2001) Estatística Multivariada Aplicada, Lisboa, Edições Sílabo, 2ªed.
Tabachnick, B. & Linda, F. (2019), Using Multivariate Statistics, Person International Edition, 7ªed.

Optional

Albright, J.J. and Park, H.M. (2009), “Confirmatory factor analysis using Amos, LISREL, Mplus, SAS/ STAT CALIS”, working paper, The University Information Technology Services (UITS) Center for Statistical and Mathematical Computing, Indiana University, Indiana University Publishing
Arbuckle, James L. (2017), IBM® SPSS® Amos™ 25 User’s Guide, IBM and Amos Development Corporation
Collier, Joel E. (2020), Applied Structural Equation Modeling using AMOS: Basic to Advanced Techniques, Routledge
Tabachnick, B. & Linda F. (2000) Computer-assisted research design and analysis, Boston: Ally and Bacon.

1. Moderator and mediated models
1.1. Moderation: interaction effect
1.2. Mediation: chain of effects
1.3. Exploring papers with moderation and mediation
2. Modelling moderation using OLS regression
2.1. Main effect and interaction effect
2.2. Quantitative moderator
2.3. Dummy moderator
2.4. Applying software (SPSS and PROCESS)
2.5. Report results in a thesis/paper
3. Modelling mediation by OLS regression
3.1. Modelling with quantitative mediator
3.2. Estimate and test indirect effect using bootstrapping
3.3. Partial and complete mediation
3.4. Applying software statistics (SPSS and PROCESS)
3.5. Report results in a thesis/paper

There are two options for assessment:

1. Assessment during the semester - individual work (100%)

Delivery of this work requires attendance at at least 70 per cent of the course unit's lessons.

2. Assessment by exam: written test to be taken at the end of the semester (100%).

Mandatory

Eliyana A, Pradana II. (2020). The Effect of Work-Family Conflict on Job Satisfaction with Organizational Commitment as the Moderator Variable. Sys Rev Pharm , 11(10): 429-437. doi:10.31838/srp.2020.10.66

Hair, J., Black, W., Babin, B. and Anderson, R. (2019) Multivariate Data Analysis, Pearson New International Edition (8th ed).

Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. 3rd, Guilford Press.

Jiang, L., J. and Matthew J. (2018). Work and Affective Commitment: A Moderated Mediation Model of Positive Work Reflection and Work Centrality. J Bus Psychol 33, 545?558. https://doi.org/10.1007/s10869-017-9509-6.

Maroco, J. (2010). Análise Estatística com o PASW Statistics (ex-SPSS), Pero Pinheiro.

Tabachnick, B. and Fidell, L. (2013). Using Multivariate Statistics, USA, Person International Edition, 6ª ed.

Important links:

https://davidakenny.net/cm/moderation.htm

https://davidakenny.net/cm/mediate.htm

Optional

Cohen, J., P. Cohen, S. West, L. Aiken (2003) Applied Multiple Regression/Correlation. Analysis for the Behavioral Sciences, Mahawh: Laurence Erlbaum, 3ª ed.

Baron, R e Kenny D. (1986). The Moderator-Mediator Variable Distinction in Social Psychological research: Conceptual, Strategic and Statistical Considerations, Journal of Personality and Social Psychology, 51, 1173-1182.

1. Multilevel models
1.1. Hierarchical structure of the data
1.2. Data requirements to use multilevel analysis
1.3. Intraclass correlation
2. Linear mixed effects model
2.1. Fixed effects and random effects
2.2. Mixed-linear model equations
2.3. Selection of random effects
3. Model interpretation and inferential objectives
4. Extensions ? nonlinear mixed effects models
5. Applications
6. Report data in a paper /thesis

Assessment during the semester" takes the form of an individual assignment (100%).

Submission of this work requires attendance at at least 70% of the course unit's classes.

This course does not include assessment by examination.

Mandatory

Aguinis, H., Gottfredson, R. K., & Culpepper, S. A. (2013). Best-practice recommendations for estimating cross-level interaction effects using multilevel modeling. Journal of Management, 39 (6), 1490-1528.

Maas, C. J. M., & Hox, J. J. (2006). Sufficient sample sizes for multilevel modelling. Methodology, 1(3), 86?92.

Kreft, I. G. G., & de Leeuw, J. (1998). Introducing multilevel modeling. Newbury Park, CA: Sage.

Hox, J. (2010). Multilevel Analysis: Techniques and Applications, 2nd edition. New York: Routledge.

Snijders, T., & Bosker, R. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, 2nd edition. Los Angeles, CA: Sage.

Optional

-

1.Multiple Correspondence Analysis (MCA)
1.1.Introduction
1.2.MCA input matrices
1.3.Optimal and multiple quantification of qualitative data
1.4.Eigenvalues, inertia and discrimination measures
1.5.Selection and interpretation of the dimensions
1.6.Graphical display of the variables, categories and objects
1.7.Suplementar (or non-optimal) variables
2.Cluster analysis - hierarquical methods:
2.1.Objectives
2.2.Measures of similarity / distance
2.3.Cluster methods
2.4.Reading the dendrogram and selection of the number of the clusters
2.5.Validation and description of the clusters
3.Cluster analysis - non-hierarchical Cluster Analysis
3.1.Distinction between optimization and hierarchical methods
3.2.K-Means method
4.Clusters analysis combined with Principal Component Analysis
5 Clusters analysis combined with Multiple Correspondence Analysis.

There are two options for evaluation:
1. During the semester: individual written test at the end of the curricular unit (100%).
The student has to have a minimum of 70% attendance in class.
2.Evaluation by final exam: individual assignment at the end of the semester (100%).

Mandatory

Carvalho, H. (2017). Análise de Multivariada de Dados Qualitativos, Utilização da Análise de Correspondências Múltiplas com o SPSS. 2ª Edição. Lisboa: Sílabo.
Hair, J., Black, W.C., Babin, B.J., and Anderson, R.E. (2018). Multivariate Data Analysis (8th ed.). Cengage Learning.

Optional

Hair, Joseph F. and William C. Black (2000) Cluster Analysis, in Grimm, L. G. & Yarnold, P. R. (Eds), Reading and Understanding More Multivariate Statistics. American Psychology Association.
Maroco, J. (2021). Análise Estatística com o SPSS (8ª edição). Report Number.
Reis, Elizabeth (1997), Estatística Multivariada Aplicada, Lisboa, Edições Sílabo.

1 Introduction to Content Analysis
1.1 Qualitative traditions and Research
1.2 Content Analysis (Theory): history, myths and definitions
1.3 Content Analysis (practice): goals, organization and examples
2 Data set organization
2.1 Documents and material
2.2 Rules in constructions. categories
2.3 The variables
3 Coding
3.1 Types of coding (and implications)
3.2 Colors and types of categories
4 Outputs
4.1 Classic textual
4.2 Words
4.3 Graphs
Document portraits
Codeline
Document comparison chart
World cloud
4.4 Quantitative
Categories frequencies Frequências das categorias
Subcodes statitsics
Code Matrix Browser
Code Relation Browser
Crosstabs
Similarity Analysis
5 Presentation and discussion of results
5.1 Content Analysis Design
5.2 Presentation and discussion of results

There are two options for the assessment:
1. During the semester: students will be assessed based on carrying out a content analysis report of individual interviews, group interviews or document analysis of the student's choice (100% of the final grade).
Access to the assessment requires attendance at at least 70% of the classes in the curricular unit.
2. Assessment by exam: practical test in MaxQda (100% of the grade).

Mandatory

Bardin, Laurence (2008), Análise de Conteúdo, Edições 70. | Bryman, A. (2012). Social Research Methods. Oxford: Oxford University Press | Denzin, N.K., & Lincoln, Y.S. (2000). Handbook of Qualitative Research. London: Sage | Freitas, F. (2013). Coding qualitative data using MAXQDA 11. In Rosaline Barbour’s Introducing Qualitative Research: A Student's Guide. London: SAGE Publications. | Kuckartz, Udo; Stefan Rädiker (2019), Analyzing Qualitative Data with MAXQDA. Text, Audio, and Video; Springer, Berlin. | MAXQDA 12 Reference Manual, Verbi Software, Berlin (disponível online) | Rädiker, Stefan; Udo Kuckartz (2020), Focused Analysis of Qualitative Interviews with MAXQDA. Step by step, Maxqda Press (disponível online)

Optional

Atkinson, Paul (2005), Qualitative Research – Unity and diversity, Forum: Qualitative Social Research, Vol. 6 (3), art. 26. | Evers, Jeanino (2011), From the past into the future. How technological developments change our ways of data collection, transcription and analysis, Forum: Qualitative Social Research, Vol. 12 (1), art. 38. | Freitas, F. (2013). Coding qualitative data using MAXQDA 11. In Rosaline Barbour’s Introducing Qualitative Research: A Student's Guide. London: SAGE Publications. | Gobo, Giampietro (2005), The renaissance of qualitative methods, Forum: Qualitative Social Research, Vol. 6 (3), art. 42. | Mayring, Philipp (2000), Qualitative content analysis, Forum: Qualitative Social Research, Vol. 1 (2), art. 20. | Teixeira, Alex Niche e Fernando Becker (2001), Novas possibilidades da pesquisa qualitativa via sistemas CAQDAS, Sociologias, Porto Alegre, ano 3, nº 5, pp. 91–113.

1. Structure of a data analysis report: main sections of a report and dimension
2. Selection the suitable statistical methods to answer the research questions/hypotheses
3. Report of the main results (tables and graphs)
4. Joint the main report and appendices.

Students prepare a report of data analysis where should be applied some of the statistical methods developed during the different curricular units.
For that purpose, students can use own research data or, alternatively, data provided by the coordination of the curricular unit.
In the performance evaluation, students have to have a minimum of 60% attendance of all classes of the post-graduation curricular unit.
This curricular unit does not include the possibility of evaluation by final exam.

Mandatory

Tabachnick, B., L. Fidell, 2006, Using Multivariate Statistics, USA, Person International Edition, 5ª ed.
Reis, E., 2001, Estatística Multivariada Aplicada, Lisboa, Sílabo, 2ª ed.
Maroco, J. e R. Bispo, 2003, Estatística aplicada às ciências sociais e humanas, Lisboa, Climepsi Editores.
Maroco, J., 2010, Análise Estatística com o PASW Statistics (ex-SPSS), Pero Pinheiro, ReportNumber.
Hair, J., R. Anderson, R. Tatham e W. Black, 1995, Multivariate Data Analysis, Upper Saddle River: Pearson, 6ª ed.
Field, A., 2009, Discovering statistics using SPSS, London, Sage, 3ªed.
Carvalho, H., 2008, Análise Multivariada de Dados Qualitativos. Utilização da Análise de Correspondências Múltiplas com o SPSS, Lisboa, Edições Sílabo.

Optional

Vicente, P., E. Reis e F. Ferrão,1996, Sondagens. A amostragem como factor decisivo de qualidade, Lisboa, Sílabo.
Tacq, J.,1997, Multivariate Analyses Techniques in Social Science Research. From Problems to Analysis, London, Sage.
Tabachnick, B., L. Fidell, 2000, Computer-assisted research design and analysis, Boston: Ally and Bacon.
Reis, E., P. Melo, R. Andrade e T. Calapez, 1997, Estatística Aplicada, vols. 1 e 2, Lisboa, Sílabo, 3ª ed.
Reis, E., 2008, Estatística Descritiva, Lisboa, Sílabo, 7ª ed.
Ghiglione R. e B. Matalon, 1996, O Inquérito-teoria e prática, Oeiras, Celta Editora.
Foddy, W.,1996, Como perguntar-teoria e prática da construção de perguntas em entrevistas e questionários, Oeiras, Celta Editora.
Calapez, T., 2001, ?A medida nas ciências sociais: um conceito em evolução?, Temas em Métodos Quantitativos 2, Lisboa, Edições Sílabo.
Bryman, A. e D. Cramer, 2003, Análise de dados em Ciências Sociais. Introdução às Técnicas Utilizando o SPSS para Windows, Oeiras, Celta Editora, 3ª ed.
Botelho, Maria do Carmo e Laureano, Raul, 2010, SPSS - O Meu Manual de Consulta Rápida, Lisboa, Edições Sílabo.