PhD position in Statistics / Social Sciences (1.0 FTE)

This is a PhD position in Statistics/Social Sciences – Rethinking Model Fit Guidelines for Longitudinal Structural Equation Modeling (LSEM) in Research on Youth.

Job description
Are you interested in working with a dynamic team of researchers? Do you dare to challenge existing statistical methods and adapt them to more valid methods for future research in youth development and bullying research? Do you have the ambition to contribute to scientific progress as well as perform socially relevant research? Apply for this PhD position!

You will have the opportunity to work on an interdisciplinary project applying Longitudinal Structural Equation Modeling (LSEM) in research on youth and bullying. Many researchers within the field of social sciences focus on youth development within the rapid changing cultural and social environment. They regularly make use of Longitudinal Structural Equation Models (LSEM) to measure, test and evaluate models consisting of multiple individual and social factors. Evaluating model fit is a key ingredient in Longitudinal Structural Equation Modeling (LSEM). The most commonly used and reported model fit indices in the literature on this topic include the CFI, TLI, and RMSEA. Current model fit guidelines do not consider how certain model characteristics (number of indicators and/or occasions) influence model fit indices. This research project aims to develop and evaluate a tool in order to create unique model fit intervals that take these longitudinal model characteristics into account.

To develop and test our tool, this project aims to apply it to the topic of bullying perpetration and victimization. Notable links exist between bullying victimization and perpetration. However, knowledge is lacking on how these dynamics develop, at which timepoints these transmissions can be observed and how that is related with individual and social changes in the environment. Moreover, most existing longitudinal research focuses on adolescents only. This is in sharp contrast with the raising awareness of bullying in the workplace. This project aims to fill in this gap by analyzing individual’s trajectories from primary school, starting at the age of 10years old, until the age of 30, an age at which most participants will be working. Within these trajectories we will determine participants’ bidirectional transmissions between bullying perpetration and victimization. Moreover, we will analyze the role of several individual and social factors steering these dynamics. These insights are valuable for the development of intervention and prevention initiatives on bullying.

Data are already available for this project. More specifically, we will use the data of the TRAILS (TRacking Adolescents’ Individual Lives Survey) project. TRAILS is a longitudinal, multidisciplinary research project into the psychological, social and physical development of adolescents and young adults (N= 2,500). Participants have been tracked for more than 15 years by means of surveys, interviews and tests. One of the advantages of the dataset is that it includes a lot of information on the environment in which participants live. More information on TRAILS can be found here.  

Your responsibilities as a PhD candidate are:

• conducting the research (literature research/review, conducting simulations in R, designing and evaluating a unique model fit calculator based on LSEM, analysing data and models, preparing and storing data packages for sharing);
• writing international scientific publications and a dissertation that combines the theoretical and statistical aspects of the project;
• giving presentations at (inter)national scientific conferences;
• active participation in the research team of the UU department of Methodology and Statistics (M&S) and the Department of Interdisciplinary Social Sciences (ISW);
• knowledge utilization: collaborating and sharing findings with practitioners (e.g., professional publications and presentations for scholars/teachers/practitioners);
• following courses/trainings (e.g., statistical courses).

Your work will also include 10% to 20% teaching tasks. Of course, you will be well guided and supported by your two daily supervisors, Dr Beth Grandfield from Methodology and Statistics (M&S) and Dr Gaëlle Ouvrein from Interdisciplinary Social Sciences (ISW), as well as two senior supervisors (Prof Rens van de Schoot from M&S and Prof Catrin Finkenauer from ISW).