Jonas Haslbeck

New Preprint: Climate Change Coverage in The Guardian, 2010–2025

Fri, 17 Apr 2026 08:00:00 +0000

Leading news outlets play a central role in shaping how political leaders and the public understand the causes, impacts, and solutions to climate change. Here, we provide the first comprehensive assessment of how The Guardian, a globally influential newspaper widely recognized for high-quality climate journalism, has reported on climate change between 2010 and 2025. We applied a validated methodology based on large language models to analyze $N = 18,785$ articles and evaluate to what extent reporting covers scientifically grounded causes, impacts, mitigation strategies, and adaptation measures. We find that climate coverage increased markedly after 2018 and has remained structurally elevated relative to the preceding decade. Coverage of causes and mitigation is dominated by fossil fuels and renewable energy, whereas agriculture, overconsumption, carbon inequality, and economic growth are mentioned far less frequently. Aspects related to adaptation receive considerably less attention than aspects related to causes, impacts, and mitigation. Our findings highlight opportunities for more comprehensive coverage that better reflects full range of societal transformations needed to address climate change. The preprint is available here.

New Preprint: Techno-Optimistic beliefs Cause Lower Willingness to Contribute to Addressing Climate Change

Thu, 05 Mar 2026 08:00:00 +0000

Technological innovation is essential for mitigating climate change, but so are wider socio-economic transformations as well as lifestyle changes. \emph{Techno-optimism}, the belief that technological innovation alone is sufficient to address climate change, may reduce public willingness to engage in climate action. Here we use a structural causal model to estimate the causal effect of techno-optimism on willingness to contribute 1\% of one’s monthly household income to address climate change using a large, population-weighted sample from the Netherlands ($N = 23,395$). We find that being a techno-optimist reduces the probability of being willing to contribute by 18.7\% (95\% $\text{CI}: 15.6, 20.7)$. In addition, we find that the causal effect is much smaller for respondents on the political right, where overall willingness is already very low. We close by discussing the importance of adequately framing the role of technology when discussing climate change mitigation. The preprint is available here.

New Preprint: Mapping Climate Change Coverage: Causes, Consequences, and Solutions in German News Media, 2010–2024

Mon, 08 Dec 2025 08:00:00 +0000

The media shapes how political leaders and the public understand the causes, impacts, and solutions to climate change. Here, we provide the most extensive analysis to date of how German news media report on climate change between 2010 and 2024. We develop and validate a methodology based on large language models to analyze the contents of over 50,000 articles from seven major newspapers across the political spectrum. We found that aspects relating to causes, impacts, and mitigation were all covered substantially more often than aspects relating to adaptation. While most articles identified climate change as human-caused, coverage about causes was dominated by fossil fuels, with agriculture, overconsumption, carbon inequality, and economic growth rarely mentioned. Left-leaning outlets more frequently reported that climate change is human-caused, highlighted fossil fuels as a cause, emphasized the need to reduce their use, and discussed systemic and social drivers more often. Coverage patterns have remained largely stable over time, except for growing attention to net-zero targets and carbon taxes. Our findings highlight opportunities for more comprehensive climate journalism to better support public understanding and policy debate by reflecting the scientific consensus and the full range of societal transformations needed to address climate change. The preprint is available here.

New Preprint: Techno-optimistic scientists take fewer climate actions

Sat, 06 Dec 2025 08:00:00 +0000

Technological innovation is key to mitigating climate change, yet excessive faith in technology may undermine the wider societal transformations needed to address it. In their roles as knowledge producers and trusted public figures, scientists play a vital part in shaping how societies understand and respond to climate change. We examine techno-optimism — here defined as the belief that technology will largely solve the problems caused by climate change — among scientists using survey data from $N = 9,199$ scientists across $115$ countries. Our findings show that techno-optimism is most prevalent among scientists in applied and natural sciences, and among those with right-leaning political views. Techno-optimistic scientists are substantially less likely to engage in civic climate action (28\% lower) or make high-impact lifestyle changes (20\% lower). These results suggest that techno-optimistic worldviews within science may inadvertently constrain the behavioral and cultural shifts required for effective climate action. The preprint is available here.

Introducing openESM: A database of openly available experience sampling datasets including R/Python interface

Fri, 05 Dec 2025 08:00:00 +0000

Experience sampling via mobile devices enables unprecedented insights into daily life. However, individual studies often cannot answer research questions conclusively, and open data are scattered across repositories in different formats. This impedes research into robustness, generalizability, and heterogeneity. We address this issue by introducing openESM, an open-source database of openly available experience sampling datasets in a harmonized format. The growing database currently comprises 60 datasets with more than 16,000 participants and more than 740,000 observations. Metadata can be searched via our website (https://openesmdata.org to select and download datasets via packages in R and Python. We demonstrate the potential of openESM through an analysis of within-person correlations of positive and negative affect in 39 datasets, providing evidence for a large negative momentary correlation ($-0.49$, 95\% CI: [$-0.54$, $-0.42$]). We end by discussing the design principles that will allow openESM to become a continuously evolving community resource for cumulative experience sampling research. The preprint is available [Here].

New Preprint: Model Checking for Vector Autoregressive Models

Thu, 04 Dec 2025 08:00:00 +0000

Time series have become pervasive in psychological research and Vector Autoregressive (VAR) models have become one of the most popular classes of models to study within-person dynamics in such data. However, systematic checking of how well a VAR model fits the data is hardly ever performed. This is a problem, because model misfit can lead both to incorrect interpretations of model parameters and to missing effects in the data that would be theoretically interesting. We provide a tutorial that explains the theory behind model checking, introduces the most common types of VAR model misspecification in the context of psychological time series, and introduces diagnostics for them, using plots and simulations. We then apply these tools to assess model fit for a multilevel VAR model estimated on a typical empirical dataset of emotion measurements over three weeks of 179 persons. We conclude by discussing three complementary areas of research that could improve the modeling of psychological time series in the future. The preprint is available here and here is a Github repository with the R-code to reproduce all analyses shown in the paper.

Two New Preprints on Multilevel Hidden Markov Models

Thu, 16 Oct 2025 08:00:00 +0000

Hidden Markov models (HMMs) are powerful models to capture the complex behaviours of psychological processes that switch between different latent states. Examples include manic and depressive states in bipolar disorder, recovery and relapse states as seen in addiction, and “normal” and “depressive” mood states in major depressive disorder. In addition to detecting latent mood/behavior states empirically, each of which is associated with different subjective experiences, HMMs model the tendency to switch between different latent states over time. For instance, inferring the probability of remaining in a depressive state or switching to a manic state from one moment to the next. This is something that typically used models (e.g., autoregressive models) cannot do. Emmeke Aarts and myself have two new preprints on multilevel HMMs: In the first (https://osf.io/preprints/psyarxiv/prm3t_v1), we provide gentle introduction to multilevel HMMs and a fully reproducible tutorial on model specification, estimation, selection, and interpretation on EMA emotion time series dataset. In the second (https://osf.io/preprints/psyarxiv/b5mxk_v2) we conduct an extensive simulation study to evaluate whether existing software works as intended and how well multilevel HMMs can be estimated in typical time series designs in psychology.

Tutorial Paper on Movement Tracking of Psychological Processes Using mousestrap

Thu, 16 Oct 2025 08:00:00 +0000

Movement tracking is a novel process-tracing method that promises unique access to the temporal dynamics of psychological processes. The method involves high-resolution tracking of a hand or handheld device (e.g., a computer mouse) while it is used to make a choice. In contrast to other process-tracing methods, which mostly focus on information acquisition, movement tracking focuses on the processes of information integration and preference formation. In this article, we present a tutorial on movement tracking of psychological processes with the mousetrap R package. We address all steps of the research process,from design to interpretation, with a particular focus on data processing and analysis and featuring both established and novel approaches. Using a representative working example, we demonstrate how the various steps of movement-tracking analysis can be implemented with mousetrap and provide thorough explanations of their theoretical background and interpretation. Finally, we present a list of recommendations to assist researchers in addressing their own research questions using movement tracking of psychological processes. [Here] is the paper published in Behavioral Research Methods.

New Preprint: A Stochastic Block Prior for Clustering in Graphical Models

Thu, 15 May 2025 08:00:00 +0000

Graphical models facilitate the representation of psychological variables as complex systems of interacting variables structured as a network. However, their existing statistical analyses overlook the assumption of clustering—the grouping of subsets of variables that are more densely connected within the network—despite its central role in many psychological theories. We address this gap by proposing the use of the Stochastic Block Model (SBM) as a prior distribution on the network structure of a graphical model for binary and ordinal data. The SBM assumes that variables belong to latent clusters, where the probability of an edge depends on the cluster membership of the nodes. Embedding this prior in a Bayesian graphical modeling framework allows researchers to formally incorporate theoretical expectations about clustering, test hypotheses about the number of clusters, and estimate cluster assignments from cross-sectional data. We demonstrate the benefits of this approach in a simulation study and reanalyze 30 openly available empirical datasets to test for clustering. This work highlights how the Bayesian framework can embed theoretical assumptions into network models via priors and introduces a new tool for latent cluster inference in psychological network analysis. [Preprint]

New Preprint: Enhancing Scale Development: Pseudo Factor Analysis of Language Embedding Similarity Matrices

Sat, 12 Apr 2025 08:00:00 +0000

We build on recent work using Large Language Models (LLMs) in psychometrics to generate pseudo-discrimination parameters. While earlier work focused on pseudo-discrimination at the item-by-construct level, we introduce Pseudo-Factor Analysis to support scale design. It is a data-free, model-based approach to evaluating key aspects of a latent construct’s measurement model, such as dimensionality and relations between factors and indicators. In two studies using Five- and Six-factor personality frameworks, various sentence transformer models, and three encoding methods (atomic, atomic reversed, and macro), pseudo-factor analyses recovered theoretically expected structures. These structures aligned closely with empirical factor structures based on human rating data from prior research. We propose Pseudo-Factor Analysis as a useful method for evaluating and refining items after generation and before trialing. A Shiny app is provided to compute pseudo-factor parameters and related psychometric estimates. [Preprint]

Jonas Haslbeck

New Preprint: Climate Change Coverage in The Guardian, 2010–2025

New Preprint: Techno-Optimistic beliefs Cause Lower Willingness to Contribute to Addressing Climate Change

New Preprint: Mapping Climate Change Coverage: Causes, Consequences, and Solutions in German News Media, 2010–2024

New Preprint: Techno-optimistic scientists take fewer climate actions

Introducing openESM: A database of openly available experience sampling datasets including R/Python interface

New Preprint: Model Checking for Vector Autoregressive Models

Two New Preprints on Multilevel Hidden Markov Models

Tutorial Paper on Movement Tracking of Psychological Processes Using *mousestrap*

New Preprint: A Stochastic Block Prior for Clustering in Graphical Models

New Preprint: Enhancing Scale Development: Pseudo Factor Analysis of Language Embedding Similarity Matrices

Tutorial Paper on Movement Tracking of Psychological Processes Using mousestrap