Michael Höfler (PhD)
© M. Höfler
Michael Höfler (PhD)
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I am interested in meta-scientific questions, especially in assumptions beyond the data, which are not negotiated if they are not made transparent. For example, when causality is avoided outside of experiments, but (seemingly) tested by association. And anything that remains unmentioned and compromises the falsifiability of a claim.
Academic experience
| Since 2017 | Research associate, Chair for Clinical Psychology and Behavioral Neuroscience, Technische Universität Dresden, Dresden, Germany |
| 2006-2017 | Research associate, Institute for Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany |
| 1999-2005 | Research associate, research group Clinical Psychology and Epidemiology, Max Planck Institute for Psychiatry, Munich, Germany |
Education
|
2007 |
Dissertation (Dr.phil.; Ph.D.), Universität Basel, Switzerland |
| 1997 | Diploma in Statistics, Ludwig Maximilians Universität, Munich, Germany |
Publications
Ongoing projects
A form to assess severe testing of a paper's claims
How large must an associational mean difference be to support a causal effect? (with Katja Pronizius und Erin Buchanan, Shiny App and R package ViSe). Preprint
A graphical method for assessing the impact of prior evidence on Bayesian inference. (with Robert Miller, Shiny App and R package BayesROE). Submitted to Research Synthesis
Methodical publications
Höfler M, Giesche A. Avoidance of causality outside experiments: hypotheses from cognitive dissonance reduction. Science Progress. First published online. doi.org/10.1177/0036850424123550c
Höfler M, Kanske P, McDonald B, Miller R. Means to valuable exploration: II. How to explore data to modify existing claims and create new ones Meta-Psychology 2023, 7. doi.org/10.15626/MP.2022.3270
Höfler M, Scherbaum S. Kanske P, McDonald B, Miller R. Means to valuable exploration I. The blending of confirmation and exploration and how to resolve it. Meta-Psychology 2022, 6. doi.org/10.15626/MP.2021.2837
Höfler M, Trautmann S, Kanske P. Qualitative approximations to causality: non-randomizable factors in clinical psychology. Clinical Psychology in Europe. 2021, 3(2), Article e3873
Höfler M, Trautmann S. Letter to the editor: When does selection generate bias in clinical samples? Journal of Psychiatric Research 2019; 116:189-190
Höfler M, Venz J, Trautmann S, Miller R. Writing a discussion section: how to integrate substantive and statistical expertise. BMC Medical Research Methodology 2018; 18:34
Höfler M, Hoyer J. Population size matters: bias in conventional meta-analysis. International Journal of Social Research Methodology 2014, 17: 585-597.
Höfler M, Gloster AT, Hoyer J. Causal effects in psychotherapy: Counterfactuals counteract overgeneralization. Psychotherapy Research 2010; 20:668-79
Höfler M, Lieb R, Wittchen HU. Estimating causal effect from observational data conditionally on a model for multiple bias. International Journal of Methods in Psychiatry Research 2007; 16:77-87.
Höfler M, Brückl T, Bittner A, Lieb R. (2007). Visualizing multivariate dependencies with association chain graphs. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 3(1), 24–34.
Höfler M, Seaman SR. Re-interpreting conventional interval estimates taking into account bias and extra-variation. BMC Medical Research Methodology 2006; 6:51.
Höfler M. Getting causal considerations back on the right track. Emerging Themes in Epidemiology 2006; 3:8.
Höfler M. The Bradford Hill considerations on causality: A counterfactual perspective. Emerging Themes in Epidemiology 2005; 2:11
Höfler M. Causal inference based on counterfactuals. BMC Medical Research Methodology 2005; 5: 28.
Höfler M. The effect of misclassification on the estimation of association: a review. International Journal of Methods in Psychiatric Research 2005; 14: 92-101.
Teachining
I teach the methods module of the Master's program "Psychology with a focus on clinical psychology and psychotherapy".
Lecture topics include
- Measurement
- Associations
- Causality
- Bias due to confounding/selection/measurement/non-compliance
- Study designs
- Statistical inference
- Measures of associations and measuring association in regression models
Seminar topics include:
- Good scientific practice
- Questionable practices/Open science
- Confirmation vs. exploration
- Robust statistics
- Model building
- Longitudinal analyses