Portfolio

As part of the systematic scoping review, we collected and coded a wide range of theoretical and methodological manuscripts on psychological acculturation. From the literature, we were able to collect and code 233 acculturation scales as well as 93 acculturation theories. Given that this undertaking, to the best of our knowledge, brought together the largest collection of acculturation scales and theories to date, we decided to make the scales and theories as well as their attributes accessible to the readership. To this aim, we created an interactive acculturaction directory. This directory is part of the publication 'The Migration Experience: A Conceptual Framework and Systematic Scoping Review of Psychological Acculturation'. We designed the dashboard to address three main functions of (1) selection, (2) accessibility, and (3) exploration of the review results. Additional information is also available via our OSF repository. The original code of the directory interface is available in our GitHub repository.

In collaboration with the Center for Psychological Gun Research and the Center for Peace and Conflict Studies at Wayne state, we integrated 15 diverse datasets into a single, dynamic ecosystem. We wrangled with a range of diverse datasets, met software needs, and ensured that the pipeline could handle new datasets being added in the future.

In an APA Observer article, we describe how we collected data on the psychological and behavioral impacts of the COVID-19 pandemic across cultures using online surveys. We discuss how we make these types of data accessible and more broadly how large-scale studies in the social sciences might be able to approach data communication.

In an APA Observer article, we describe how we tracked psychological responses to the COVID-19 pandemic across over 60,000 participants in 30 languages, with the goal of informing strategies to contain the pandemic and prepare for future crises.

As part of the the PsyCorona initiative, we developed an interactive dashboard that allows users to assess country-level information of the PsyCorona dataset. For comparability, we focus on the initial waves of the survey and to protect the privacy of the particiapnts we focus on roughly 60,000 people (excluding countries with relatively few participants). We also offer users the possibility to assess variable and country (co-)developments across the first 30 survey waves.

As data managers at the PsyCorona initiative, we developed a data management pipeline bringing together the many data sources, preparing individual datasets for analysts, and developing a GDPR-compliant data sharing procedure. We automatized almost all steps to reduce human error, ensure reproducibility, and protect the data of our participants. We ultimately prepared over 100 individual datasets from over 23 diverse data sources.

As part of the PsyCorona project, we shared a dataset of over 75,000 participants, who privided psychological self-reports during the Covid-19 pandemic for up to over 30 waves. The data is from a multinational study on psychological factors related to responses to the COVID-19 pandemic and public health measures. As part of the data sharing procedures we were the main data managers and later implemented a step-wise publicly accessible dataset. As part of this process we developed a comprehensive strategy from the informed consent to the enventual publication.

In a recent article, we used a random forests analysis to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries. The analysis allowed us to compare a wide range of psychological, contexutal, and policy predictors in the wake of a public health crisis.

Psychological researchers and practitioners collect increasingly complex time series data aimed at identifying differences between the developments of participants or patients. Past research has proposed a number of `dynamic features’ that describe meaningful developmental patterns for psychological data (e.g., instability, inertia, linear trend). We propose that feature-based clustering offers an ideal fit for this type of data. We cluster participants based on the dynamic measures directly using common clustering algorithms, which means that the approach is flexible, transparent, and well-grounded. This illustration accompanies the publication “A Gentle Introduction and Application of Feature-Based Clustering with Psychological Time Series” and provides a hands-on tutorial-style code documentation. We provide annotated R code of all analysis steps as well as an associated R package that collects several of the functions we created along the way.

tsFeatureExtracR is a package for R that provides a set of functions for feature extraction from time series data. The focus is on meaningful time series features for psychological (ESM) data. Several of the calculations are based on the amazing work of the team working on the tsfresh python package. The package additionally offers simple tools for analyzing and dealing with missing data in extracted time series features. These functionalities are aimed at understanding meaningful features of psychological time series and accompany a research paper on feature-based time series clustering in ESM research.

We created an easily accessible codebook for a project in which we merged 15 different datasets. For this, we collaborated with the Center for Psychological Gun Research and the Center for Peace and Conflict Studies at Wayne State University. The main challenge was to give users easy access to the measured variables with extensive filtering possibilities. If you want to understand how we made the code work, have a look here. For the shiny hosted codebook have a look here.

Moving behavioral methodologies into Online Survey Engines like Qualtrics is crucial because it significantly enhances the accessibility, scalability, and efficiency of conducting complex behavioral research, allowing for broader participant reach and more diverse data collection. a comprehensive guide for researchers implementing the gun shooter task in online research. It offers a pre-built survey template (for Qualtrics), a detailed manual on the functional elements of the Online Shooter Simulation (OSS), and guidance on data processing and analysis. The manual covers topics like template access and implementation, functional elements of the OSS, setting up the task, and saving the data. It aims to provide an easy-to-use guide for implementing the OSS in online studies.