Tuition fee EU nationals (2024/2025)
2000.00 €Programme Structure for 2024/2025
Curricular Courses | Credits | |
---|---|---|
Data for Social Sciences
6.0 ECTS
|
Mandatory Courses | 6.0 |
Introduction to Visual Data Communication
6.0 ECTS
|
Mandatory Courses | 6.0 |
Sample Design and Estimation
6.0 ECTS
|
Mandatory Courses | 6.0 |
Inferential Analytic Methods
6.0 ECTS
|
Mandatory Courses | 6.0 |
Applied Regression Models
6.0 ECTS
|
Mandatory Courses | 6.0 |
Multiple Association and Clustering Methods
6.0 ECTS
|
Mandatory Courses | 6.0 |
Factorial Methods
6.0 ECTS
|
Mandatory Courses | 6.0 |
Moderation and Mediation Models
6.0 ECTS
|
Mandatory Courses | 6.0 |
Research Project on Data Analysis in the Social Sciences
6.0 ECTS
|
Mandatory Courses | 6.0 |
Computer Assisted Qualitative Data Analysis
6.0 ECTS
|
Conditioned Optional Courses | 6.0 |
Multilevel Modeling
6.0 ECTS
|
Conditioned Optional Courses | 6.0 |
Data for Social Sciences
LO1. To develop knowledge about the new challenges and potential for data access in digital societies;
LO2. Identify the main sources of data in social sciences and develop skills to enable access to them;
LO3. Develop skills in database preparation, management and data screening and validation;
LO4. To develop knowledge and skills to apply basic methods of descriptive analysis of data (univariate and bivariate) and presentation of results;
LO5. To develop basic skills in the use of SPSS software.
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. Periodic evaluation: Individual report, approximately 10 pages.
2. Evaluation by examination: Individual exercise to be done at the end of the school year (PGADCS examination season).
Title: Vartanian, T. P. (2011). Secondary data analysis. New York, NY: Oxford.
Maroco, J. (2018), Análise Estatística com utilização do SPSS Statistics 25, Lisboa, Edições Sílabo.
Laureano, Raul, Maria do Carmo Botelho (2017), SPSS, O Meu Manual de Consulta Rápida, Lisboa, Sílabo (3ª edição).
Foster, Ian (ed), (2016), Big data and social science: a practical guide to methods and tools, Taylor & Francis Group.
Goodwin, John (2012) (ed.), Secondary Data Analysis, Thousand Oaks, Sage Publications.
Bryman, Alan (2016), Social Research Methods, Oxford, Oxford University Press (5th ed.).
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Title: 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.
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Introduction to Visual Data Communication
LG1. Collect and organize information that best suits the goals of visual communication.
LG2. Distinguish between different types of statistical indicators and understand when and how they should be used.
LG3. Apply visual variables according to its properties, depending on the nature of the quantitative or qualitative information.
LG4. Using the most appropriate chart models and maximize its power of communication.
LG5. To analyze critically informative graphic expressions of in a variety of disciplinary contexts.
LG6. Make a graphical storytelling (or alternatively, a scientific poster, an abstract or graphic lead, a comic informative band, a visual/graphic explanation or an infographic), from the stage of collection, processing and selection of relevant information to the stage of portrayal, communication and construction of a visual storytelling.
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
Periodical evaluation:
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%).
Title: 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.
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Title: 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.
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Sample Design and Estimation
LO1: Identify the main statistical concepts with relevance in sampling and estimation
LO2: Select samples and calculation of sample size
LO3: To evaluate the quality of the adjustment of the sample to the target population.
LO4: Use and calculate weights
LO5: Diagnose the non-response pattern and propose forms of imputation.
LO6: Estimation ant test of parameters
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 size
2.3 Sample selection
2.4 Errors
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. Periodic evaluation:
Resolution of a case study (group exercise): 40%.
Individual assignment: 40%.
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%).
Title: 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.
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Title: 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.
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Inferential Analytic Methods
LG1: Identify the main statistical concepts for hypotheses tests
LG2: Select the most adequate hypothesis test, given the problem under analysis, the objective and the type of variables
LG3: Interpret and report the results
LG4: Use the statistical package SPSS to conduct inferential data analysis
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
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.
The assessment by examination is based on an individual exercise with a minimum score of 10 points.
Title: 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.
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Title: 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.
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Applied Regression Models
Students who successfully complete this unit will be able to:
LG1. Identify the main goal of each of the methods explored in the course.
LG2. Apply and interpret the results of a Linear Regression model.
LG3. Apply and interpret the results of Binary Logistic Regression model.
LG4. Conduct, with SPSS, a Linear Regression and a Binary Logistic Regression.
LG5. Summarize, present and interpret the results in order to write a data analysis report or an article.
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.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
Individual assignment with a minimum score of 10 required.
The evaluation can also be made through a final examination with a minimum score of 10 required.
Title: Maroco, J., 2018. Análise Estatística com o SPSS, 7ª edição, Pero Pinheiro, Report Number.
Field, A., 2018. Discovering Statistics Using IBM SPSS Statistics, London, Sage Publications, 5th Edition.
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Title: Tabachnick, B., e Fidell, L., 2013. Using Multivariate Statistics, Pearson International Edition, 6th Edition.
Pampel, F. C., 2000. Logistic Regression. A Primer (Quantitative Applications in the Social Sciences) A Sage University Paper, London, SAGE publications.
Menard, S. 2010. Logistic Regression: From Introductory to Advanced Concepts and Applications, London, SAGE publications.
Menard, S. 2002. Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences) 2nd Edition, A Sage University Paper, London, SAGE publications.
Hosmer Jr. D.W, Lemeshow, S., and Sturdivant, R.X., 2013. Applied Logistic Regression 3rd Edition, New Jersey, John Wiley & Sons Inc.
Hair, J., Black, W.C., Babin, B.J., and Anderson, R.E., 2014. Multivariate Data Analysis, Pearson Educational, 7th Edition.
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Multiple Association and Clustering Methods
LO1 | Acquire and develop knowledge about Multiple Correspondence Analysis (MCA).
LO2 | Acquire and develop knowledge about Cluster Analysis.
LO3 | Apply Principal Components Analysis with SPSS.
LO4 | Apply and articulate cluster analysis with other multivariate analysis methods with SPSS.
LO5 | Analyze and interpret the statistical results.
LO6 | Summarize and present the results in order to write a data analysis report or a paper.
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 and with Multiple Correspondence Analysis: presentation and discussion of examples. Interpretation and writing of results.
There are two options for evaluation:
1. Periodic evaluation: individual written test at the end of the curricular unit.
In the performance evaluation, 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
Title: Hair, J., Anderson R., Tatham, R. and Black, W. (2010). Multivariate Data Analysis: A Global Perspective, Upper Saddle River, Pearson International Edition (7ª ed).
Carvalho, H. (2017) Análise de Multivariada de Dados Qualitativos, Utilização da Análise de Correspondências Múltiplas com o SPSS. 2ª Edição. Lisboa. Ed. Sílabo.
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Title: Reis, Elizabeth (1997), Estatística Multivariada Aplicada, Lisboa, Edições Sílabo.
Maroco, J. (2010). Análise Estatística com o PASW Statistics (ex-SPSS), Pero Pinheiro, ReportNumber.
Hair, Joseph F. and William C. Black (2000) Cluster Analysis, in Grimm, Laurence G. e Paul R. Yarnold (Eds), Reading and Understanding More Multivariate Statistics, Washington, DC, American Psychology Association.
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Factorial Methods
LG1 | Acquire and develop knowledge about Principal Components Analysis (PCA)
LG2 | Acquire and develop knowledge about Confirmatory Factorial Analysis (CFA)
LG3 | Apply Principal Components Analysis
LG4 | Apply Confirmatory Factorial Analysis
LG5 | Analyze and interpret the results of factorial analyses
LG6 | Report the results in a paper, a thesis
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
1.9. Interpretation and reporting results in a paper, a thesis
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
2.9. Interpretation and reporting results in a paper, a thesis
There are two options for evaluation:
1. Periodic evaluation: individual written test at the end of the curricular unit.
In the performance evaluation, 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
Title: Reis, E. (2001) Estatística Multivariada Aplicada, Lisboa, Edições Sílabo, 2ªed.
Tabachnick, B. & Linda, F. (2006) Using Multivariate Statistics, USA, Person International Edition, 5ªed.
Maroco, J. (2010). Análise Estatística com o PASW Statistics (ex-SPSS), Pero Pinheiro, ReportNumber.
Hair, J., Anderson R., Tatham, R. e Black, W. (2010). Multivariate Data Analysis: A Global Perspective, Upper Saddle River, Pearson International Edition (7ª ed).
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Title: Tabachnick, B. & Linda F. (2000) Computer-assisted research design and analysis, Boston: Ally and Bacon.
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Moderation and Mediation Models
LO1 | Define concepts of moderation and mediation; identify and distinguish the different effects
LO2 | To design moderated and mediated models
L03 | To evaluate the adequacy of Linear Regression for testing moderated and mediated models
L04 | To evaluate the assumptions of Multiple Linear Regression
L05 | Upgrade and deepen knowledge of linear regression models to test moderation and mediation models
L06 | Apply multiple linear regression to test moderation and mediation models
L07 | Analyze and interpret the results of different models
L08 | To present the results in a report or in a scientific paper
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 evaluation:
1.Periodic evaluation: individual written test at the end of the curricular unit.
In the performance evaluation, 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
Title: Passos, A. e Caetano, A. (2005). Exploring the effects of intragroup conflict and past performance feedback on team effectiveness, Journal of Managerial Psychology 20, 3/4, 231-244.
Maroco, J. (2010). Análise Estatística com o PASW Statistics (ex-SPSS), Pero Pinheiro.
MacKinnon, D. P., Fairchild, A. J. e Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593-614.
Hayes, A. F. (2017). Introduction to Mediation, Moderation, and Conditional Process Analysis. A Regression-Based Approach. 2nd ed. Guilford Press.
Frazier, P. A., Tix, A. P. e Barron, K. E. (2004). Testing moderator and mediator effects in counselling psychology research. Journal of Counselling Psychology, 51(1), 115-134.
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Title: Tabachnick, B., L. Fidell (2000) Computer-assisted research design and analysis, Boston: Ally and Bacon.
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.
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Research Project on Data Analysis in the Social Sciences
At the end of this unit students will be able to:
LO1 - Design a Report
LO2 - Select adequate statistical methods according the objectives of the research
LO2 - Present, analyse and interpret data.
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.
Title: 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.
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Title: 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.
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Computer Assisted Qualitative Data Analysis
Students who successfully complete this curricular unit will be able to:
LO1 ? Identify different strategies to develop content analysis with MaxQda and know how to adapt and use their potential to diverse documents such as interview, focus groups, press articles, among other;
LO2 -Plan and execute a content analysis methodological design using MAXQDA, adapted to the material in questions and to the analytical goals;
LO3 - Present MAXQDA outputs in reports and other scientific products.
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 evaluation:
a) Periodic Evaluation: the final grade is based on the execution of one report based on content analysis of interviews, focus groups or document analysis chosen by the student (100%).
In the performance evaluation, the student has to have a minimum of 70% attendance in class.
b) Evaluation by final exam: the final grade is based only in a practical individual test (100%).
It is expected that the students produce original work, based on criteria of responsible authorship, and they are the ones producing the text that puts together and in order the ideas they intend to transmit. Their essays will be evaluated with the authorship and bibliographical rigour, and the code of ethical conduct (https://www.iscte-iul.pt/assets/files/2022/01/24/1643046250963_Code_of_Ethical_Conduct_in_Research_ISCTE.pdf) in mind.
Title: 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)
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Title: 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 Qays 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.
Maryring, 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, nº5, pp. 91-113.
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Multilevel Modeling
LO1 - Understand the methodological implications related to the complex structure of data.
LO2 - Understand the theoretical framework and assumptions associated with multilevel modelling
LO3 - Specify appropriate multilevel models, considering the research questions.
LO4 - Conduct two-level model analyses using the statistical software (R and SPSS).
LO5 - Interpret and present results of MLM analyses.
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
Individual assignment.
In the performance evaluation, the student has to have a minimum of 70% attendance in class.
This curricular unit does not include the possibility of evaluation by final exam.
Title: Snijders, T., & Bosker, R. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, 2nd edition. Los Angeles, CA: Sage.
Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interaction effects in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31, 437-448.
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.
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.
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Title: -
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