Class Métodos Avançados de Análise de Dados em Psicologia Clínica

  • Presentation


    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. 

  • Code


  • Syllabus


    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

  • Objectives


    LO1. Compare critically principal methods and techniques of data collection and analysis, assessing their advantages and disadvantages, depending on the design and research goals

    LO2. Select appropriate data analysis methods according to research objectives / hypotheses and the nature of the collected data

    LO3. Plan and conduct data collection and analysis protocols using the principal quantitative and qualitative research methods and techniques in the clinical psychological domain

    LO4. Interpret and understand statistical results in the light of the applied methods used, research goals and the scientific literature.

    LO5. Apply the principal methods and techniques of data collection and analysis that are intended to be used in the doctoral thesis project.

    LO6. Discuss how data collection and analysis methods contribute to the construction of empirical scientific knowledge in the field of clinical psychology.

  • Teaching methodologies and assessment

    Teaching methodologies and assessment

    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.

  • References


    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.


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