I am an Assistant Professor at the Federal University of Minas Gerais (UFMG), Brazil, with a PhD in Music. My academic and professional journey integrates Music, Music Therapy, Mental Health, and Psychometrics with a focus on developing and validating assessment tools and other strategies to measure outcomes in music and health. Since 2020, my work has increasingly incorporated Data Analysis, Statistics, and Artificial Intelligence (AI), including NLP and machine learning applications.

I lead research initiatives aimed at measuring the impact of music therapy interventions and collaborate with the Music Oriented Systems and Artificial Intelligence and Creativity group (MOSAIC/UFMG). My participation in the BEYOND program further hones my interdisciplinary skills in applying emerging technologies to music therapy and psychometric research.

My core interest lies in using robust quantitative methods and computational tools to advance our understanding and application of therapeutic interventions.

Here are a few highlights from my GitHub:

• 📊 DASS-21 Analysis: Psychometric Properties and Anxiety Predictors

  • Comprehensive psychometric evaluation (CFA, Reliability, Network Analysis) and predictor identification for the DASS-21 in a Brazilian undergraduate sample. Includes detailed R Markdown analysis.

• 🎵 Multimodal Song Circumplex Analysis (song_sent_scores)

  • R and Python functions for multimodal analysis of songs on the circumplex model of affect, integrating acoustic features and lyrical sentiment. Applications in music therapy and affective computing.

• 🕸️ Semantic Network Analysis for Content Validity (SNA)

  • Leveraging Semantic Network Analysis (SNA) with R and Python for robust content validity assessment of psychometric instruments. Includes scripts for text processing, network construction, and centrality analysis.

• 📄 MTDQ Scale Development & Validation (dissertation_data_analyses)

  • Data analysis scripts (R Markdown) detailing the development and validation of the Assessment Scale for Group Music Therapy in Substance Use Disorders (MTDQ), including CFA, reliability, and item analysis.

• ✨ Shiny Apps for Psychometric Measurement:

  • Reliable Change Index (RCI) Estimator:
    • Calculates the RCI to assess the clinical significance of change in individual scores.
    • App (PT): https://fredpedrosa.shinyapps.io/rci_app/
    • App (EN): https://fredpedrosa.shinyapps.io/jt_pt/
  • Individual Trajectory Estimation (4PL & Harmonic Linear Model):
    • Models individual change trajectories using advanced psychometric models.
    • App (PT): https://fredpedrosa.shinyapps.io/app_pt/
    • App (EN): https://fredpedrosa.shinyapps.io/estit/
  • Crawford's t-test & Bayesian Test Estimator:
    • Estimates Crawford's modified t-test for comparing an individual's score to a small normative sample.
    • App: https://vmsfue-frederico-pedrosa.shinyapps.io/t_crawford/

• Programming & Software:
  • R Language: Advanced (Data Wrangling, Statistical Modeling, Visualization, Function Development, Shiny Apps)
  • Python: (Intermediate, for AI/ML projects)
  • RStudio, GitHub, LaTeX, JASP, SPSS
• Statistical & Psychometric Methods:
  • Development & Validation of Psychometric Scales
  • Confirmatory Factor Analysis (CFA) & Structural Equation Modeling (SEM)
  • Network Psychometric Analysis
  • Reliability Analysis (Classical & Modern)
  • Bivariate & Multivariate Analyses
  • Nonparametric Tests
  • Mediation & Moderation Analysis
  • Item Response Theory (IRT)
  • Longitudinal Data Analysis / Trajectory Modeling
• Artificial Intelligence & Data Science:
  • Text Mining
  • Machine Learning
  • Music Information Retrivel
  • Computational Intelligence (Ongoing Specialization at UFV)
• Research Methods:
  • Quantitative Data Analysis
  • Thematic Analysis
  • Academic Writing & Peer-Reviewed Publications
  • Ethnographical Research

• Music and Health
• Psychometrics & Psychological Assessment
• Artificial Intelligence
• Statistical Modeling in R
• Mental Health
• Brazilian Musical Culture

• Email: fredericopedrosa@ufmg.br | frederico.musicoterapia@gmail.com
• Lattes CV
• LinkedIn
• ResearchGate
• ORCID:
• Google Scholar

Dedicated to advancing mental health through rigorous research and innovative data-driven approaches.