Project portfolio
About me
I am a junior academic working at the Unviersity of Lincoln. I specialise in teaching quantitative research skills and I supervise masters’ final projects. I am interested in applying advanced quantitative research techniques in social sciences and, implementing open and transparent research protocols in line with the Open Science movements, and I am enthusiastic about teaching quntatitative research methods to varied audiences and especially those who experience maths and statistics related anxiety. I have previously worked as an associate lecutrer, research assistant, and I completed a number of psychology-related internships.
Education
- MSc Psychological Research Methods with Data Science / Distinction (avg 78.4%) / University of Sheffield / 2021-2022
- Study Abroad Exchange Programme / University of Padua / GPA 3.88 (avg 97%) / 2019-2020
- BSc (Hons) Psychology with Clinical Psychology (International Study) / First Class Honours (avg 80.5%) / University of Lincoln / 2017-2021
Achievements & Grants
- The School of Psychology Prize / University of Lincoln / 2021 / Awarded to exceptionally well performing students
- The Michael Siegal Prize / University of Sheffield / 2022 / Awarded to the recipient of the highest dissertation mark
- CoASSH Interdisciplinary Research Fund (£1,000) / University of Lincoln / 2024 / Research grant co-awarded to researchcers pioneering interdisciplinary research in social sciences
Brief employment history
- Psychology Intern / Educational Care Centre - Brno, Veslarska, CZ / 02/2020
- Research Assistant / University of Reading & Masaryk University, CZ / 07-12/2020
- Associate Lecturer / School of Psychology, Unviersity of Lincoln / 02-05/2023
- Teaching Fellow / Lincoln International Business School, University of Lincoln / 07/2023 - present
Key skills & certification
- Programming, Data Manipulation, & Statistics
- R
- Statistics for Brain and Cognitive Sciences (University of Padua, 2019)
- The Data Scientist’s Toolbox (John Hopkins University & Coursera, 2020)
- Data Analysis and Visualisation (Unviersity of Sheffield, 2021)
- Data Analysis with R Programming (Google & Coursera, 2024)
- Python
- Python for Data Science series (University of Sheffield Research Computing Group, 2021-2022)
- Learn to Program: The Fundamentals (University of Toronto & Coursera, )
- R
- Data Management, Analysis, & Statistics
- Excel - Microsoft Excel Expert (Microsoft, 2023)
- MATLAB
- Introduction to Matlab (University of Sheffield Research Computing Group, 2021)
- MATLAB Onramp (MATLAB, 2021)
- SQL - SQL Programming (LinkedIn Learning, 2023)
- SPSS - Research Skills I-IV, Advanced Multivariate Statistics (Unviersity of Lincoln, 2017-2021)
- Stata - Accessing and using’real-world’ study data 2024 (UCL, CLOSER, & UK Data Service, 2024)
Projects
Postgraduate projects
Covid-19 Vaccination Uptake in the Czech Republic
Data Management and Visualisastion Skills employed: programming (R), data science (data extraction, manipulation, and transformation), Data visualisation and analytics (data exploration, visualisation selection and customisation) Areas of research: public health, health statistics*
The aim of this project was to visualise Covid-19 vaccine uptake in the Czech Republic. Completing this project required engaging with the Czech healthcare system, extracting data from public healthcare repositories, cleaning the data, and matching it with public statistical reports to provide as accurate region-based estimates as possible. The project site depicts the step-by-step process of creating the visualisation, which is accompanied by a critical appraisal and interpretation.
Examining a Covariate Multiverse informed by Crowdsourced and Multiverse Analyses of the ‘Many Analysts, One Dataset’ Study
Data Science & Advanced Statistics; Open Science - Research Transparency
Skills employed: R programming, Python programming, Data science - data extraction, Advanced statistics - multilevel modelling, logistic regression, critical research evaluation
Areas of research: Open Science, sports statistics, social psychology, cognitive psychology
Making decisions in research leads to researcher degrees of freedom that can be seen as exploring a subset of options and outcomes. They lead to over-reporting significance and under-reporting non-significance. Globally, this trend contributed to the replication crisis in psychology; a large proportion of reported effects cannot be replicated because they are unlikely to exist. Principles of Open science such as pre-registration, transparency, full procedure disclosure and data sharing aim to eliminate reporting biases but are faced with practical limitations and the issue of parallel ‘viable’ choices leading to different results. Crowdsourced studies further identified idiosyncratic variability unaccounted for by experience, knowledge, procedures, or peer-rated study quality. Multiverse analyses allow researchers to analyse all possible outcomes of any given specification, which is researcher-driven and therefore cannot eliminate human choices. Multiverse analyses of a sports data set are discussed whereby the impact of covariates and functional forms on whether skin tone ratings can predict the award of red cards is examined. This study provides new specifications to extend the multiverse and explores related issues of overall multiverse model performance and average model performance based on covariate grouping.
Cleaned data capturing player-referee interactions are used to construct a multiverse of multilevel logistic regression models with red card awards an outcome, averaged skin tone ratings a predictor, and a selection of covariates. Non-independence is treated using random intercepts.
Overfitting was detected and eliminated. Model performance was modest both overall and after grouping models by included covariates. Estimates of skin tone ratings showed a stronger effect and a high proportion of statistical significance after overfitting elimination. This revealed a change in estimate distribution compared to previous studies.
Multiverse analyses have excellent exploratory power but are limited by human choices. They should be used in conjunction with replication or crowdsourcing. Limitations and future directions are discussed.
Other projects (CPD)
Captsone Project (Google Data Analytics Certification)
Coming soon!