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Research

As an academic, I am interested in dyadic data analysis, mediation and moderation analysis, longitudinal data analysis, intervention research, and personal relationships. I have written about the distinction between partial and full mediation and the importance of effect sizes as well as the analysis of dyadic data using Multilevel Modeling (MLM) and Structural Equation Modeling (SEM). A complete list of my publications is available on Google Scholar. R code accompanying my methodological papers is available for download on my GitHub account. I offer workshops on quantitative data analysis techniques, particularly the analysis of dyadic data.

Models and methods introduced

– The Common Fate Growth Model for analyzing change at the dyadic level (Ledermann & Macho, 2014).                
– The Multilevel Common Fate Model for analyzing effects at the individual and dyadic level, the Two-Factor Common Fate Model with method factors, and dyadic hybrid models (Ledermann & Kenny, 2012).                 
– The mediation and moderation Actor-Partner Interdependence Model (Garcia, Kenny, & Ledermann, 2015; Ledermann & Bodenmann, 2006; Ledermann, Macho, & Kenny, 2011).     
– The Multimember Multigroup Actor-Partner Interdependence Model for analyzing group effects in dyads and triads (Ledermann, Rudaz, & Grob, 2017).                                                  
– Parameter k for testing patterns in the Actor-Partner Interdependence Model (Kenny & Ledermann, 2010).                 
– The Phantom Model Method for testing and comparing specific effects in complex models (Macho & Ledermann, 2011).

Concepts and scales introduced

– The Caring for Bliss Scale (CBS; Rudaz, Ledermann, Ross, & Fincham, 2020).
– The Genuine Happiness Scale (GHS; Rudaz, Ledermann, & Fincham, 2022).
– The Relational Cultural Intelligence (RCI) scale (Su & Ledermann, 2025).

Method papers

Ledermann, T., Rudaz, M., & Fritz, M. S. (2025). On partial versus full mediation and the importance of effect sizes. Advances in Methods and Practices in Psychological Science, 8(3). https://doi.org/10.1177/25152459251355585
Iida, M., Savord, A., & Ledermann, T. (2023). Dyadic longitudinal models: A critical review. Personal Relationships, 30(2), 356-378. https://doi.org/10.1111/pere.12468
Ledermann, T., Rudaz, M., Wu, Q., & Cui, M. (2022). Determine power and sample size for the simple and mediation Actor–Partner Interdependence Model. Family Relations, 71(4), 1452-1469. http://dx.doi.org/10.1111/fare.12644
Ledermann, T., & Kenny, D. A. (2017). Analyzing dyadic data with Multilevel Modeling versus Structural Equation Modeling: A tale of two methods. Journal of Family Psychology, 31(4), 442–452. https://doi.org/10.1037/fam0000290
Ledermann, T., Rudaz, M., & Grob, A. (2017). Analysis of group composition in multimember multigroup data. Personal Relationships, 24(2), 242–264. https://doi.org/10.1111/pere.12176
Ledermann, T., & Kenny, D. A. (2015). A toolbox with programs to restructure and describe dyadic data. Journal of Social and Personal Relationships, 32(8), 997-1011. https://doi.org/10.1177/026540751455527
Ledermann, T., & Macho, S. (2014). Analyzing change at the dyadic level: The Common Fate Growth Model. Journal of Family Psychology, 28(2), 204-213. https://doi.org/10.1037/a0036051
Ledermann, T., & Kenny, D. A. (2012). The common fate model for dyadic data: Variations of a theoretically important but underutilized model. Journal of Family Psychology, 26(1), 140–148. https://doi.org/10.1037/a0026624
Ledermann, T., Macho, S., & Kenny, D. A. (2011). Assessing mediation in dyadic data using the actor-partner interdependence model. Structural Equation Modeling: A Multidisciplinary Journal, 18(4), 595-612. https://doi.org/10.1080/10705511.2011.607099
Macho, S., & Ledermann, T. (2011). Estimating, testing, and comparing specific effects in structural equation models: The phantom model approach. Psychological Methods, 16(1), 34-43. https://doi.org/10.1037/a0021763
Ledermann, T., & Macho, S. (2009). Mediation in dyadic data at the level of the dyads: A Structural Equation Modeling approach. Journal of Family Psychology, 23(5), 661–670. https://doi.org/10.1037/a0016197
Ledermann, T., & Bodenmann, G. (2006). Moderator- und Mediatoreffekte bei dyadischen Daten: Zwei Erweiterungen des Akteur-Partner-Interdependenz-Modells [Moderator and mediator effects in dyadic research: Two extensions of the Actor-Partner Interdependence Model]. Zeitschrift für Sozialpsychologie, 37(1), 27-40. https://doi.org/10.1024/0044-3514.37.1.27

Psychometric papers

Rudaz, M., Ledermann, T., & Fincham, F. D. (2023). Initial development and validation of a brief scale to measure genuine happiness. Journal of Religion and Health, 62(3), 2163–2180. https://doi.org/10.1007/s10943-022-01659-6
Macho, S. & Ledermann, T. (2022). A psychometric analysis of signal detection measures from ratings versus repeated and non-repeated forced choices. Journal of Experimental Psychology: Learning, Memory, and Cognition, 48(12), 1923–1946. https://doi.org/10.1037/xlm0001043
Rudaz, M., Ledermann, T., May, R. W., & Fincham, F. D. (2020). A brief scale to measure caring for bliss: Conceptualization, initial development, and validation. Mindfulness, 11, 615-626. https://doi.org/10.1007/s12671-019-01267-8
Gygi, J. T., Ledermann, T., Grob, A., Rudaz, M., & Hagmann-von Arx, P. (2019). The Reynolds Intellectual Assessment Scales (RIAS): Measurement invariance across four language groups. Journal of Psychoeducational Assessment, 37(5), 590-602. https://doi.org/10.1177/0734282918780565
Ledermann, T., Bodenmann G., Gagliardi, S., Charvoz, L., Verardi, S., Rossier, J., Bertoni, A., & Iafrate, R. (2010). Psychometrics of the dyadic coping inventory in three language groups. Swiss Journal of Psychology, 69(4), 201–212. https://doi.org/10.1024/1421-0185/a000024

Theoretical papers

Macho, S., & Ledermann, T. (2024). Elementary probabilistic operations: A framework for probabilistic reasoning. Thinking & Reasoning, 30(2), 259-300. https://doi.org/10.1080/13546783.2023.2259541

By the numbers

100+ publications
2 career grants from the Swiss National Science Foundation
1 international postdoc supported by a career grant from the home country
9 doctoral students mentored, including 5 current students
14 master’s students mentored
2 undergraduate honors thesis students
2 students mentored as part of the undergraduate research opportunity program
18 method workshops conducted
6 trainings conducted with Myriam Rudaz