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Paulina Pankowska (PhD) is a postdoctoral researcher at the Department of Communication Science and the Department of Sociology, Vrije Universiteit Amsterdam. Her research relates primarily to data quality. She has also published in the field of official statistics, survey research methods, and machine learning.

She is currently working on the development of an online participant recruitment platform for social science and humanities research in the Netherlands. She is also a senior quantitative methodologist in the BAM: Becoming a Minority Project, which studies the lives of people without a migration background, who are living in ethnically diverse neighborhoods.

In November 2020 she defended her PhD dissertation titled: ‘Measurement error: estimation, correction, and analysis of implications’, which investigated the feasibility of using hidden Markov models (a latent variable modelling technique) to account and correct for measurement error in survey and administrative data. The project was conducted in collaboration with Statistics Netherlands.


Dutch participant recruitment platform for SSH research

Much research within the Social Sciences and Humanities relies on input that can only be obtained from people. Such research includes experiments or surveys, development of tests and measurement instruments, and (digital) humanities studies requiring human annotations of textual data, artefacts, and images. Although international online platforms exist to recruit human participants (e.g., MTurk, Prolific), these are unusable for research bound to the Dutch linguistic and/or cultural context. Cases in point are, e.g., the development of Dutch verbal memory tests, annotation of Dutch texts for machine learning, classification of Dutch cultural artefacts or art, or testing Dutch health messages’ effectiveness. Moreover, GDPR regulations prohibit Dutch researchers from storing data outside EU borders. Finally, criticism regarding (especially) MTurk’s data quality and ethics signals a need for homegrown alternatives.

The current projectaddresses these challenges by developing an affordable, sustainable, and secure online participant/annotator recruitment platform for Netherlands-based academic researchers. Development of the platform will help Dutch SSH scholars to remain internationally competitive, while serving Dutch societal research needs. It will also strengthen ties between Dutch Social Sciences and Humanities research communities. The platform will be developed in cooperation with not-for-profit platform developer EYRA together with established Dutch academic partner SURF.

The BaM project

The BaM project looks at the lives of people without a migration background living in ethnically diverse neighborhoods where everybody belongs to a minority now. This situation is referred to as a majority-minority context and it is becoming more and more common in many neighborhoods of large Western European cities. The BaM project focses on the harbor cities Rotterdam, Antwerp and Malmö and on the service sector cities Amsterdam, Hamburg and Vienna. Part of the research is an international survey among people living in in these six cities in majority-minority neighborhoods.


Designed and taught a PhD level course on clustering analysis using SPSS and R.

Experience as a teaching assistant for Master's level courses in Sociology and Communication Science that focused on quantitative research methods and applied statistics using the SPSS statistical package.


Ancillary activities

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Academic qualification

Survey Methodology/Official Statistics, PhD, Vrije Universiteit Amsterdam

1 Oct 201517 Nov 2020

Award Date: 17 Nov 2020

Quantitative research methods, University of Michigan, Ann Arbor

Award Date: 31 Aug 2016

Public Policy, Master, UNU-MERIT

Award Date: 31 Aug 2013

Public Policy, Master, Universiteit Maastricht

Award Date: 31 Aug 2013

Economics and Business, Bachelor, Universiteit van Amsterdam

Award Date: 30 Jun 2012

Social Sciences, The University of Melbourne

Award Date: 30 Jun 2011


  • H Social Sciences (General)
  • Labour market research
  • Research methods
  • Survey methodology
  • Official statistics
  • Measurement error
  • Total Survey Error
  • Latent variable modelling
  • Hidden Markov Models
  • Big data
  • Machine Learning
  • crowdfunding platforms
  • Online recruitment platforms
  • Data quality
  • Migration Studies


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