Clemens Stachl

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Prof. Dr.
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Stachl
First name
Clemens
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clemens.stachl@unisg.ch
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+41 71 224 7713

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    Predicting and Explaining Assessment Center Judgments: A Cross‐Validated Behavioral Approach to Performance Judgments in Interpersonal Assessment Center Exercises
    (Wiley, 2024-11-27)
    Eric Grunenberg
    ;
    Clemens Stachl  
    ;
    Simon M. Breil
    ;
    Philipp Schäpers
    ;
    Mitja D. Back
    <jats:title>ABSTRACT</jats:title><jats:p>Although Assessment Center (AC) role‐play assessments have received ample attention in past research, their reliance on actual behavioral information is still unclear. Uncovering the behavioral basis of AC role‐play assessments is, however, a prerequisite for the optimization of existing and the development of novel automated AC procedures. This work provides a first data‐driven benchmark for the behavioral prediction and explanation of AC performance judgments. We used machine learning models trained on behavioral cues (<jats:italic>C</jats:italic> = 36) to predict performance judgments in three interpersonal AC exercises from a real‐life high‐stakes AC (selection of medical students, <jats:italic>N</jats:italic> = 199). Three main findings emerged: First, behavioral prediction models showed substantial predictive performance and outperformed prediction models representing potential judgment biases. Comparisons with in‐sample results revealed overfitting of traditional approaches, highlighting the importance of out‐of‐sample evaluations. Second, we demonstrate that linear combinations of behavioral cues can be strong predictors of assessors' judgments. Third, we identified consistent exercise‐specific patterns of individual cues and cross‐exercise consistent behavioral patterns of behavioral dimensions and interpersonal strategies that were especially predictive of the assessors' judgments. We discuss implications for future research and practice.</jats:p>
    Type:journal article
    Journal:Human Resource Management
    Volume:64
    Issue:2
    DOI:10.1002/hrm.22252
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/121444
    Scopus© Citations 2
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    A workflow for human-centered machine-assisted hypothesis generation: Commentary on Banker et al. (2024).
    (2024-09)
    Hermida Carrillo, Alejandro
    ;
    Clemens Stachl  
    ;
    Talaifar, Sanaz
    Large language models (LLMs) have the potential to revolutionize a key aspect of the scientific process-hypothesis generation. Banker et al. (2024) investigate how GPT-3 and GPT-4 can be used to generate novel hypotheses useful for social psychologists. Although timely, we argue that their approach overlooks the limitations of both humans and LLMs and does not incorporate crucial information on the inquiring researcher's inner world (e.g., values, goals) and outer world (e.g., existing literature) into the hypothesis generation process. Instead, we propose a human-centered workflow (Hope et al., 2023) that recognizes the limitations and capabilities of both the researchers and LLMs. Our workflow features a process of iterative engagement between researchers and GPT-4 that augments-rather than displaces-each researcher's unique role in the hypothesis generation process. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
    Type:journal article
    Journal:The American psychologist
    Volume:79
    Issue:6
    DOI:10.1037/amp0001256
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/121441
    Scopus© Citations 7
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    Psychological well-being in Europe after the outbreak of war in Ukraine
    (2024)
    Julian Scharbert
    ;
    Sarah Humberg
    ;
    Lara Kroencke
    ;
    Thomas Reiter
    ;
    Sophia Sakel
    The Russian invasion of Ukraine on February 24, 2022, has had devastating effects on the Ukrainian population and the global economy, environment, and political order. However, little is known about the psychological states surrounding the outbreak of war, particularly the mental well-being of individuals outside Ukraine. Here, we present a longitudinal experience-sampling study of a convenience sample from 17 European countries (total participants = 1,341, total assessments = 44,894, countries with >100 participants = 5) that allows us to track well-being levels across countries during the weeks surrounding the outbreak of war. Our data show a significant decline in well-being on the day of the Russian invasion. Recovery over the following weeks was associated with an individual’s personality but was not statistically significantly associated with their age, gender, subjective social status, and political orientation. In general, well-being was lower on days when the war was more salient on social media. Our results demonstrate the need to consider the psychological implications of the Russo-Ukrainian war next to its humanitarian, economic, and ecological consequences.
    Type:journal article
    Journal:Nature Communications
    Volume:15
    Issue:1
    DOI:10.1038/s41467-024-44693-6
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/120224
    Scopus© Citations 58
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    Scopus© Citations 90
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    Best Practices in Supervised Machine Learning: A Tutorial for Psychologists
    (2023)
    Pargent, F.
    ;
    Schoedel, R.
    ;
    Clemens Stachl  
    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 article
    Journal:Advances in Methods and Practices in Psychological Science
    Volume:6(3)
    DOI:10.1177/25152459231162559
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/117799
    Scopus© Citations 103
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    A 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.
    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 article
    Journal:Social and Personality Psychology Compass
    DOI:10.1111/spc3.12813
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/117618
    Scopus© Citations 12
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    How 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.
    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 article
    Journal:Nature Mental Health
    Volume:1(8)
    DOI:10.1038/s44220-023-00085-1
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/117887
    Scopus© Citations 80
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    Grouped feature importance and combined features effect plot
    (2022)
    Au, Q.
    ;
    Herbinger, J.
    ;
    Stachl, Clemens  
    ;
    Bischl, B.
    ;
    Casalicchio, G.
    Type:journal article
    Journal:Data Mining and Knowledge Discovery
    DOI:10.1007/s10618-022-00840-5
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/109157
    Scopus© Citations 63
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    Mobile sensing in psychological and educational research: Examples from two application fields.
    (2022)
    Birtwistle, E.
    ;
    Schoedel, R.
    ;
    Bemmann, F.
    ;
    Wirth, A.
    ;
    Sürig, C.
    Type:journal article
    Journal:International Journal of Testing
    Issue:3-4
    DOI:10.1080/15305058.2022.2036160
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/109187
    Scopus© Citations 10
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    Measurement practices exacerbate the generalizability crisis: Novel digital measures can help
    (Cambridge University Press, 2022)
    Davidson, B. I.
    ;
    Ellis, D.A.
    ;
    Stachl, Clemens  
    ;
    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 article
    Journal:Behavioral and Brain Sciences
    Volume:45
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/109226