Postdoctoral Researcher @ UZH

Pascal
Kueng.

Investigating dyadic data, intensive longitudinal designs, and interpretable empirical evidence.

Explore my work → Get in touch

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Research Focus

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Methods shaped by theoretical questions.

Models and visual explanations for interdependent observations, partner effects, and relational processes.

APIMinterdependencerelational structure

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Selected Work

Highlighting dyadic health behavior change.

Peer-Reviewed Publications

Health-related social control in romantic couples

My core research investigates daily associations of social control with physical activity and affect in couples. Published in British Journal of Health Psychology, Psychology & Health, and Annals of Behavioral Medicine.

View all on ORCID →

R Package

wbCorr: Within- and between-cluster correlations

An R package designed to cleanly compute bivariate within- and between-cluster correlations, a common requirement in intensive longitudinal and dyadic data analysis.

View on GitHub →

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Methods Demos

Visualizing multilevel models.

When teaching intensive longitudinal methods and EMA, visualizing what random intercepts and slopes actually mean helps bridge the gap between equations and intuition. Play with the parameters below.

Parameters

Fixed Intercept4.0

SD Random Intercepts1.0

Fixed Slope0.40

SD Random Slopes0.20

Corr. Intercept-Slope0.50

Fixed effects line

10 simulated individuals

Y = (\overset{\color{#2563EB}{4.0}}{\color{#2563EB}{B_0}} + \overset{\color{#60A5FA}{1.0}}{\color{#60A5FA}{u_{0i}}}) + (\overset{\color{#E11D48}{0.40}}{\color{#E11D48}{B_1}} + \overset{\color{#FB7185}{0.20}}{\color{#FB7185}{u_{1i}}})X \quad | \quad \text{Cor}(u_{0}, u_{1}) = \overset{\color{#9333EA}{0.50}}{\color{#9333EA}{\rho}}

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Contact

Let's
connect.

Interested in collaboration or have questions about dyadic data analysis? My inbox is open.

Email Me →

GitHub ORCID

Google Scholar GitHub