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Clemens Stachl
Title
Prof. Dr.
Last Name
Stachl
First name
Clemens
Email
clemens.stachl@unisg.ch
ORCID
Phone
+41 71 224 7713
Now showing
1 - 10 of 44
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PublicationUsing machine learning to predict student retention from socio-demographic charac-teristics and app-based engagement metrics( 2023)
;Matz, S. C. ;Bukow, C. S. ;Peters, H. ;Deacons, C.Type: journal articleJournal: Scientific Reports -
PublicationBest Practices in Supervised Machine Learning: A Tutorial for Psychologists( 2023)
;Pargent, F. ;Schoedel, R.Supervised machine learning (ML) is becoming an influential analytical method in psychology and other social sciences. However, theoretical ML concepts and predictive-modeling techniques are not yet widely taught in psychology programs. This tutorial is intended to provide an intuitive but thorough primer and introduction to supervised ML for psychologists in four consecutive modules. After introducing the basic terminology and mindset of supervised ML, in Module 1, we cover how to use resampling methods to evaluate the performance of ML models (bias-variance trade-off, performance measures, k-fold cross-validation). In Module 2, we introduce the nonlinear random forest, a type of ML model that is particularly user-friendly and well suited to predicting psychological outcomes. Module 3 is about performing empirical benchmark experiments (comparing the performance of several ML models on multiple data sets). Finally, in Module 4, we discuss the interpretation of ML models, including permutation variable importance measures, effect plots (partial-dependence plots, individual conditional-expectation profiles), and the concept of model fairness. Throughout the tutorial, intuitive descriptions of theoretical concepts are provided, with as few mathematical formulas as possible, and followed by code examples using the mlr3 and companion packages in R. Key practical-analysis steps are demonstrated on the publicly available PhoneStudy data set (N = 624), which includes more than 1,800 variables from smartphone sensing to predict Big Five personality trait scores. The article contains a checklist to be used as a reminder of important elements when performing, reporting, or reviewing ML analyses in psychology. Additional examples and more advanced concepts are demonstrated in online materials (https://osf.io/9273g/).Type: journal articleJournal: Advances in Methods and Practices in Psychological ScienceVolume: 6(3) -
PublicationA global experience-sampling method study of well-being during times of crisis: The Co-Co project.( 2023)
;Scharbert, J. ;Sakel, S. ;Geukes, K. ;Gosling, S. D. ;Harari, G. ;Kroencke, L. ;Matz, S. ;Schoedel, R. ;Shani, M. ;Talaifar, S. ;Aguilar, N. M. A. ;Amante, D. ;Aquino, S. D. ;Biesanz, J. C. ;Bornamanesh, A.Back, M. D.We present a global experience-sampling method (ESM) study aimed at describing, predicting, and understanding individual differences in well-being during times of crisis such as the COVID-19 pandemic. This international ESM study is a collaborative effort of over 60 interdisciplinary researchers from around the world in the “Coping with Corona” (CoCo) project. The study comprises trait-, state-, and daily-level data of 7490 participants from over 20 countries (total ESM measurements = 207,263; total daily measurements = 73,295) collected between October 2021 and August 2022. We provide a brief overview of the theoretical background and aims of the study, present the applied methods (including a description of the study design, data collection procedures, data cleaning, and final sample), and discuss exemplary research questions to which these data can be applied. We end by inviting collaborations on the CoCo dataset.Type: journal articleJournal: Social and Personality Psychology Compass -
PublicationHow to e-mental health: A guideline for researchers and practitioners using digital technology in the context of mental health( 2023)
;Seiferth, C. ;Aas, B. ;Brandhorst, I. ;Carlbring, P. ;Conzelmann, A. ;Esfandiari, N. ;Finkbeiner, M. ;Hollmann, K. ;Lautenbacher, H. ;Meinzinger, E. ;Newbold, A. ;Opitz, A. ;Renner, T. J. ;Sander, L. B. ;Santangelo, P. S. ;Schoedel, R. ;Schuller, B.Löchner, J.Despite an exponentially growing number of digital or e-mental health services, methodological guidelines for research and practical implementation are scarce. Here we aim to promote the methodological quality, evidence and long-term implementation of technical innovations in the healthcare system. This expert consensus is based on an iterative Delphi adapted process and provides an overview of the current state-of-the-art guidelines and practical recommendations on the most relevant topics in e-mental health assessment and intervention. Covering three objectives, that is, development, study specifics and intervention evaluation, 11 topics were addressed and co-reviewed by 25 international experts and a think tank in the field of e-mental health. This expert consensus provides a comprehensive essence of scientific knowledge and practical recommendations for e-mental health researchers and clinicians. This way, we aim to enhance the promise of e-mental health: low-threshold access to mental health treatment worldwide.Type: journal articleJournal: Nature Mental HealthVolume: 1(8) -
PublicationGrouped feature importance and combined features effect plot( 2022)
;Au, Q. ;Herbinger, J. ;Bischl, B.Casalicchio, G.Type: journal articleJournal: Data Mining and Knowledge Discovery -
PublicationMobile sensing in psychological and educational research: Examples from two application fields.( 2022)
;Birtwistle, E. ;Schoedel, R. ;Bemmann, F. ;Wirth, A. ;Sürig, C. ;Bühner, M.Niklas, F.Type: journal articleJournal: International Journal of TestingIssue: 3-4 -
PublicationMeasurement practices exacerbate the generalizability crisis: Novel digital measures can help(Cambridge University Press, 2022)
;Davidson, B. I. ;Ellis, D.A. ;Taylor, P. J.Joinson, A. N.Psychology's tendency to focus on confirmatory analyses before ensuring constructs are clearly defined and accurately measured is exacerbating the generalizability crisis. Our growing use of digital behaviors as predictors has revealed the fragility of subjective measures and the latent constructs they scaffold. However, new technologies can provide opportunities to improve conceptualizations, theories, and measurement practices.Type: journal articleJournal: Behavioral and Brain SciencesVolume: 45 -
PublicationType: journal articleJournal: Future Generation Computer Systems
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PublicationAnalyzing GPS Data for Psychological Research: A Tutorial( 2022)
;Müller, S. R. ;Bayer, J. B. ;Ross, M. Q. ;Mount, J. ;Harari, G. M. ;Chang, Y.-J.Le, H. T. K.Type: journal articleJournal: Advances in Methods and Practices in Psychological ScienceVolume: 5 -
PublicationMeasurement practices exacerbate the generalizability crisis: Novel digital measures can helpPsychology’s tendency to focus on confirmatory analyses before ensuring constructs are clearly defined and accurately measured is exacerbating the generalizability crisis. Our growing use of digital behaviors as predictors has revealed the fragility of subjective measures and the latent constructs they scaffold. However, new technologies can provide opportunities to improve conceptualizations, theories, and measurement practices.Type: journal article