Data Analysis Advanced Methods 2017/2018
- 6 ECTS
- Taught in Portuguese
- Continuous Assessment
- relevant skillset
After completing the course, the student will be able to:
• Understand the rationale behind data analysis procedures, using the IBM SPSS software;
• Know different advanced data analysis and their usability, linking them to the test of research hypotheses;
• Develop decision-making skills in the selection of analyses and tests, to solve research problems in psychology;
• Critically interpret and report research results in psychology, according to APA standards;
• Compare different groups in the studied variables;
• Examine the factor structure of a psychological measure;
• Evaluate the relationship between variables and test complex prediction models;
• Develop autonomy in the use of statistical procedures for the support of research projects, as well as in reading and interpreting research results presented in scientific publications.
Prior knowledge of basic descriptive statistics and probabilities is recommended, following contents learned in Statistics and Data Analysis 1, as well as of inferential statistics following contents learned in Statistics and Data Analysis 2.
The theoretical and practical classes will combine expository and active and participatory methodologies. Expository methods will be based on the use of multimedia supports, using for this purpose the IBM SPSS software, PowerPoint slides, reading and discussing documents, and managing databases. The active methods will be based on practical activities, including the exploration of databases and statistical analysis exercises using the SPSS. These practical activities will seek the active resolution of psychology research problems as well as reporting research results in the form of reports, individually and with mentoring from the teacher. The students will be stimulated to present and critically discuss research results, presenting small group assignments.
Body of Work
1. Revision of statistical concepts and basic descriptive inferential statistics techniques
2. Research designs, hypothesis testing and statistical significance
3. Advanced group comparisons
3.1 Analysis of variance (ANOVA)
3.2 Analysis of covariance (ANCOVA)
3.3 Multivariate analysis of variance (MANOVA)
4. Factor Analysis and Principal Component Analysis
5. Reliability analysis
6. Regression analysis:
6.1 The hierarchical multiple regression
6.2 Logistic regression
7. Interpreting and writing research results according to APA standards: description, tables and figures (transversal content)
American Psychological Association (2009). Publication manual of the American Psychological Association (6th ed.). Washington, DC: American Psychological Association.
Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: Sage Publications Ltd.
Howittt, D., & Cramer, D. (2011). Introduction to research methods in psychology (3rd. ed.). Harlow, England: Pearson Education Limited.
Maroco, J. (2014). Análise estatística com o SPSS Statistics (6ª ed.). Pêro Pinheiro: Report Number.
Martins, C. (2011). Manual de análise de dados quantitativos com recurso ao IBM SPSS: Saber decidir, fazer, interpretar e redigir. Braga: Psiquilíbrios.
Pereira, A. (2008). SPSS – Guia prático de utilização. Análise de dados para ciências sociais e psicologia (7ª edição). Edições Sílabo.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Allyn and Bacon.
Araújo, A. M., Teixeira, F., Amorim, D., Zenha, G., Azevedo, B., & Santos, L. (2016). Validação da Escala Multidimensional de Suporte Social Percebido em estudantes universitários do ensino superior privado. Psicologia, Educação e Cultura, XX(1), 172-190.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.
Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76, 408-420.
Páramo-Fernández, M., Araújo, A. M., Tinajero-Vacas, C., Almeida, L. S., & Rodríguez-González, M. S. (2017). Predictors of students’ adjustment during the transition to university in Spain. Psicothema, 29(1), 67-72.
Week 1: Presentation of course - goals, teaching and assessment methods, and expected results. Revision of statistical concepts and basic descriptive and inferential statistics techniques. Research designs, hypothesis testing and statistical significance.
Week 2: Advanced group comparisons: Analysis of variance (ANOVA).
Week 3: Advanced group comparisons: Analysis of variance (ANOVA) and covariance analysis (ANCOVA)
Week 4: Multivariate analysis of variance (MANOVA).
Week 5: Applications and revisions. Mini-test 1.
Week 6: Collaborative work for the development of the research project, with tutorial support. Checkpoint 1. Factor Analysis and Principal Component Analysis
Week 7: Factor Analysis and Principal Component Analysis
Week 8: Reliability analysis.
Week 9: Applications and revisions. Collaborative work for the development of the research project, with tutorial support from the teacher. Checkpoint 2.
Week 10: Regression Analysis: The hierarchical multiple regression
Week 11: Regression Analysis: The hierarchical multiple regression
Week 12: Logistic regression
Week 13: Applications and revisions. Minis-test 2.
Week 14: Collaborative work for the development of the research project, with tutorial support from the teacher. Checkpoint 3.
Week 15: Presentations of the group assignments.
Demonstration of the syllabus coherence with the curricular unit's objectives
In order to provide students with the acquisition of critical thinking skills about the usability and selection of statistical analysis procedures for the support of research in psychology, the syllabus include a variety of inferential data analysis, from group comparisons to the testing of prediction models, as well as factor analysis. In order that students associate these statistical procedures with research in psychology, the program contents include the analysis and reflection of data analysis results and testing of empirical hypotheses. Finally, in order to develop skills of interpretation and reporting research results, the syllabus include the analysis of statistical analysis outputs and writing scientific reports according to APA standards.
Demonstration of the teaching methodologies coherence with the curricular unit's objectives
The use of expository methods enables the transmission and analysis of new contents relating to advanced data analysis techniques using the SPSS software. These methods support the acquisition of basic knowledge in the field, by the students. The use of methods involving students’ active participation is accomplished through the analysis and interpretation of research results published in scientific papers, as well as problem solving applied to psychology with the selection and implementation of data analysis techniques. Group assignments will contribute to the acquisition of critical thinking skills about data analysis procedures, and the interpretation and reporting of research results. The combination of these different methods will aid to achieve the proposed goals for the course.
|relevant generic skill||improved?||assessed?|
|Achieving practical application of theoretical knowledge||Yes||Yes|
|Adapting to new situations||Yes||Yes|
|Analytical and synthetic skills||Yes||Yes|
|Balanced decision making||Yes||Yes|
|Commitment to effectiveness||Yes||Yes|
|Commitment to quality||Yes||Yes|
|Ethical and responsible behaviour||Yes||Yes|
|Event organization, planning and management||Yes||Yes|
|Foreign language proficiency||Yes|
|Information and learning management||Yes||Yes|
|Initiative and entrepreneurship capability||Yes||Yes|
|IT and technology proficiency||Yes||Yes|
|Problem Analysis and Assessment||Yes||Yes|
|Relating to others||Yes||Yes|
|Understanding multiculturalism and valuing diversity|
|Working in international context|
|Written and verbal communications skills||Yes||Yes|