PhD position on measuring the dynamic structure of affect

PhD position on measuring the dynamic structure of affect

Published Deadline Location
8 May 30 May Leuven

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Job description

The successful candidate will work in the Research Group of Quantitative Psychology and Individual Differences at KU Leuven and will develop novel methods for measuring the dynamic structure of affect, which plays an important role in well-being. Kim De Roover will be the promotor of the PhD-project and Eva Ceulemans the co-promotor. The research group offers an international, productive, collaborative, and interactive environment with about 6 professors and around 30 PhD-students and postdocs.

The KU Leuven is a research-oriented institution and is consistently ranked among the top research universities in Europe. Leuven is one of the oldest university towns in Europe, about 30 km from Brussels. It has a rich history and a unique friendly atmosphere.

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Many emotion researchers study the dynamics within and between momentary positive affect (PA) and negative affect (NA) across time, which play a crucial role in well-being and mental health. Momentary affect in daily life is measured by means of the Experience Sampling Methodology (ESM): Using a smartphone app, participants self-report on their momentary affect by responding to a number of questions (items) asking about the presence and intensity of positive and negative emotions at random moments throughout the day for the duration of one or more weeks. Based on the reported scores on multiple specific emotions (e.g., joy, pride, anger, fear), researchers then compute a PA and NA score. This is often done by summing or averaging the scores on positive and negative emotions, respectively, which helps in handling measurement error. Next, they model the dynamics of the resulting momentary PA and NA scores by means of the vector autoregressive model (VAR). In a VAR model, momentary PA and NA are predicted based on the preceding PA and NA scores. The VAR model incorporates auto-regressive effects, that reflect how long emotions linger over time, and cross-regressive effects, that reflect emotional blunting and augmentation.

The computation of momentary PA and NA implicitly relies on a so-called measurement model (MM) that specifies which observed items measure which construct of interest (e.g., negative emotions measure NA) and to what extent (e.g., each emotion contributes equally). Because the items used in ESM studies are often compiled in a rather ad hoc way, it is likely that the implicitly assumed MM does not hold for ESM, and such misspecification may lead to incorrect VAR parameter estimates. Moreover, with a MM, the quality of separate items can be evaluated and accounted for. It is thus essential not to implicitly assume but to explicitly study the MM when fitting VAR. The existing state-of-the-art methods for doing so have the important limitation that they require the user to prespecify which items measure which construct. This is often difficult in ESM as, for instance, even the typically assumed PA-NA distinction is not systematically found. Thus, an exploratory method is needed to infer the MM from the data, that is, to figure out which items are measuring which construct. Another challenge pertains to potential differences in the MM when comparing VAR parameters across persons and over time. To ensure comparability, the MM should be (at least partially) the same across participants and time points. This is referred to as measurement invariance. ESM is prone to violations of this condition due to person- or context-specific item interpretations, and these violations should be accounted for in the analysis. Therefore, in this PhD-project, we will develop new methods to (1) determine the appropriate MM for a particular ESM data set and account for it in VAR; and (2) to account for differences in the MM across time and/or across persons. These novel methods will give psychologists the tools to answer their important research questions, even when comparability is difficult. The project includes substantive applications of the novel methods and the development of user-friendly, open-source software.


KU Leuven



  • You have a master degree that implies a profound knowledge of statistics, data analysis and programming
  • You have affinity with psychology
  • You are precise, creative, highly motivated and enthusiastic.
  • You have excellent English communication and writing skills.
  • You can work on your own, but also enjoy working in an interdisciplinary and interuniversity team.
  • Knowledge of R and/or Matlab is an asset.
  • Familiarity with structural equation modeling and/or vector-autoregressive modeling is an asset.

Conditions of employment

Fixed-term contract: 4 years.


We offer:

  • a fully funded PhD position for four years, starting on October 1st, 2024.
  • an enthusiastic and supportive supervision team.
  • a research environment including top-level researchers in emotion, relationships, work, and culture, working with cutting edge data collection and statistical methods.
  • excellent research facilities, conference/travel budget, and a competitive salary with various additional benefits (in terms of holidays, health insurance, transport costs).


  • PhD; Research, development, innovation
  • Behaviour and society; Language and culture; Law; Economics; Health; Natural sciences
  • max. 40 hours per week
  • University graduate
  • AT KULEUVEN 20240508


Oude Markt 13, 3000, Leuven

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