Michael Höfler (PhD)
Michael Höfler (PhD)
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I am interested in how science can become more falsifiable and more transparent in its assumptions, for example in causal assessments.
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 meta-tool for navigating causality
Methodical publications
Evaluate what is claimed to be confirmed: A falsification assessment form (FAF). Preprint The FAF, version 1.0
Höfler M, Pronizius K, Buchanan E. How large must an associational mean difference be to support a causal effect? ( Shiny App und R package ViSe). Methodology 2024, 20(4), 318-335. https://doi.org/10.5964/meth.14579
Höfler M, Giesche A. Avoidance of causality outside experiments: hypotheses from cognitive dissonance reduction. Science Progress 2024; 107(2). doi.org/10.1177/0036850424123550c
Robert Miller, Michael Höfler. Robert Miller, Michael Höfler. Regions of Evidence: a Bayesian method for assessing the impact of prior evidence on the credibility of hypotheses Shiny App und R package BayesROE. Preprint psyarxiv/mg23h_v2. Shiny App und R package BayesROE. Preprint psyarxiv/mg23h_v2
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.
Teaching
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
Consultation hours are in the winter semester 2024/25 on the following dates, Tue 9.20 - 10.50 in my office (room 368, Chemnitzer Str. 46), no pre-registration necessary:
15.4.2025
13.5.2025
17.6.2025
15.7.2025