Class Advanced Methods of Data Analysis in Clinical Psychology

It is intended to provide students with a structured and in-depth contact with advanced data analysis, under a quantitative and qualitative perspective, allowing them to acquire and develop solid knowledge and skills in scientific research in Clinical Psychology and apply it in their studies during the doctoral program. 

Not applicable

S1 Quantitative research and statistical modeling S2 Quantitative Data Analysis Software: SPSS, JASP and R S3 Inference for Regression S4 Moderation vs. Mediation Using Process MACRO S5 Logistic Regression S6 Longitudinal data analysis: repeated and mixed measures ANOVA S7 A posteriori or post-hoc tests vs. a priori or planned contrasts. Simple, principal and interaction effects analysis S8 Confirmatory factor analyses S9 Modern approach to longitudinal data analysis: regression to correlated data (GEE- marginal models) S10 Dyadic Data Analysis S11 Qualitative research: from concepts to data analysis using NVivo S12 Thematic analysis: Assumptions and analysis methodologies S13 Grounded Theory: Assumptions and Analysis Methodologies S14 Narrative Analysis: Assumptions and Analysis Methodologies S15 Interpretive phenomenological analysis: assumptions and analysis methodologies

The course presents a typology of seminar and tutorial guidance. Lectures rely on expository and demonstrative methods, focused on problem-solving using a collaborative and blended learning approachSeminar classes aim to deepen theoretical knowledge and advanced data analysis techniques to consolidate and/or increase students' proficiency in data analysis methods commonly used in clinical psychology research. To promote their autonomy in the learning process, students are able to choose and attend seminars that address data analysis methods they envisage using in their doctoral projects.

APA (2010). Publication manual of the American Psychological Association (6th ed.). Washington: APA Frost, N. (2011). Qualitative research methods in psychology. Combining core approaches. UK: McGraw Hill. Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. (2nd ed.). New York: Guilford. Hair, J., Black, W. Babin, B. & Anderson, R. (2014). Multivariate data analysis (7th ed.). Boston: Pearson Education Jackson, K. & Bazaley, P. (2019). Qualitative data analysis with Nvivo (3rd Edition). London: Sage. Kenny, D., Kashy, D., & Cook, W. (2006). Dyadic data analysis. New York: Guilford. Maroco, J. (2018). Análise estatística com o SPSS Statistics (7ª edição). Pêro Pinheiro: Report Number. Singer, J., & Willett, J. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. NY: Oxford University Press. Tabachnick, B. & Fidell, L (2013). Using Multivariate Statistics (6th ed.). Boston: Pearson.