# Biography

I am a second year Master’s student enrolled in the programmes Methodology and Statistics of the Behavioural, Biomedical and Social Sciences and Sociology and Social Research at Utrecht University. During these programmes, I have experienced the difficulties applied researchers encounter when answering their research questions. My goal is to enable these researchers with the best procedures and statistical methods, to make optimal use of expensively collected research data and foster scientific progress.

Together with Gerko Vink and Stef van Buuren, I am involved in a project on synthetic data generation based on methods for the multiple imputation for missing data, where we aim to foster the dissemination of research data without disclosing sensitive participant data. Additionally, with Irene Klugkist I work on Bayesian Evidence Synthesis, which is a novel method developed for the aggregation of results from studies with varying designs that cannot be combined using meta-analysis. On the one hand, we study the performance of this method under various circumstances to make improvements where possible. On the other hand, I am involved in applying Bayesian Evidence Synthesis to multiple studies that measure trust among actors in cooperative relations, together with Vincent Buskens and Werner Raub.

### Interests

• Evidence Synthesis
• Bayesian Statistics
• Synthetic Data
• Missing Data
• Data Science
• Programming (in R)
• Sociology

### Education

• MSc in Methods and Statistics for the Behavioural, Biomedical and Social Sciences, 2022

Utrecht University

• MSc in Sociology and Social Research, 2022

Utrecht University

• BA in Liberal Arts & Sciences, 2019

Utrecht University

### Teaching statistics

Over the years, I have teached in courses with topics ranging from structural equation modeling and missing data methods to social network analysis.

### Research

My research is concentrated around Bayesian methods concerning the aggregation of results from multiple studies and multiple imputation (for missing data and synthetic data).

### Consultation

Since I started my Master’s programme, I have engaged in projects with several applied researchers. Drop me a line if you are interested in statistical consultation as well.

# Experience

#### Utrecht University

Sep 2019 – Present Utrecht
Over the years, I have assisted Dr. Rebecca Kuiper and Dr. Peter Lugtig by visualizing results of their research, developing course materials and revising academic papers.

#### Utrecht University

Sep 2018 – Present Utrecht
As a teaching assistant, I have been involved in multiple Bachelor’s, Master’s and post-graduate courses on Programming in R, Missing data, Structural equation modeling, Social Network Analysis and various other standard statistical techniques.

#### Utrecht University

Apr 2018 – Aug 2019 Utrecht
I have contributed to the organization of 10+ Methodology and Statistics courses offered in Utrecht Summer School. Main tasks included planning, contacting students and assisting course coordinators with required tasks.

# Education

#### Methodology and Statistics of the Behavioural, Biomedical and Social Sciences

Research Master’s programme

• Thesis: Bayesian Evidence Synthesis - Aggregating studies with varying designs
• Weighted average: 9.0

#### Sociology and Social Research

Research Master’s programme

• Thesis: Applying Bayesian Evidence Synthesis on multiple studies measuring trust
• Weighted average: 8.5

#### Liberal Arts & Sciences

Bachelor’s programme with a major in Pedagogical Sciences and a minor in Sociology & Social Research.

# Projects

#### Bayesian Evidence Synthesis

Bayesian Evidence Synthesis is a method to integrate the results of multiple studies with varying, seemingly incompatible, designs using Bayes Factors, to enhance the aggregation of scientific evidence.

#### Multiple Imputation of Synthetic Data

Synthetic data allows for openly sharing of research data, without disclosing identifying information of the participants, that could be as informative as the actually observed data.

# Recent Publications

### Anonymiced Shareable Data: Using mice to Create and Analyze Multiply Imputed Synthetic Datasets

We show how the R-package mice can be used to create and analyze multiply imputed synthetic data sets.

# Contact

• t.b.volker@uu.nl
• Padualaan 14, Utrecht, 3584 CH
• Monday - 09:00 to 17:00
Tuesday - 09:00 to 17:00
Wednesday - 09:00 to 17:00
Thursday - 09:00 to 17:00
Friday - 09:00 to 17:00