Ongoing Projects:

GitHub Project 1

Project 1 on ResearchGate

Exploring Expert Algorithmic Preference as Partial - yet Functional Predictor of Analytic Result Variation

A Meta-Research Follow-Up on “Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results” (Silberzahn et al., 2018)

Goals:

1. Statistical re-analysis and modeling of the original MAODS crowdsourcing soccer data. Then implementing the final modeling approach using different statistical software packages (viz. R, PYTHON, MATLAB, and SPSS + possibly others like JULIA, SAS, STATA). Finally, performing a comparison of their respective result outputs.

2. A more fine-grained re-analysis of the collected survey data looking more carefully at mentioned software characteristics, and the relationship between MAODS analysts’ three Software Expertise (SwE) related knowledge domains (viz. task-specific, product-specific, and general product-class), and their respective preferential Software Choice (SwC).

3. Elaborate a proposal for future research.

GitHub Project 2

Project 2 on ResearchGate

Measuring the Efficacy and Efficiency of Psychological Interventions

Goals:

1. To compare and contrast psychological intervention techniques in the area of personal development, by measuring their efficacy and efficiency in terms of the successful achievement of predefined objectives, over the number of interventions required to accomplish the desired outcome, in addition to measuring the durability of those interventions over time (i.e., maintaining personal success).

2. Pre- and post-intervention survey analysis (gauging confidence, fear and self-esteem).

3. Longitudinal time-series analysis of regular client follow-ups.

4. To alleviate the "Generalizability Crisis in Clinical Psychological Science" (Grubbs, J. B., 2021; Yarkoni, T., 2020).

5. To improve public mental health and promote citizen's well-beeing.

GitHub Project 3 (Currently Archived)

Project 3 on ResearchGate

Analysis of Switzerland’s Public Health Communication during the COVID-19 Pandemic

Goals:

1. To investigate the effects of the Swiss government’s public health risk and crisis communication on the social media public’s expressed fear and trust sentiment during the COVID-19 pandemic using natural language processing (NLP).

2. Possibly refining the FOPH's risk and crisis communication model.

3. Possibly constructing a social media sentiment dashboard to allow for real-time feedback on posted publications.