arXiv subjects

Johan Lyrvall

Publications and source records attributed to Johan Lyrvall.

3 recordsLinked to original sources

arXiv2026

Normative boundaries of AI in scientific work: Evidence from PhD researchers

Artificial intelligence (AI) is increasingly embedded in scientific work, but researchers may not evaluate its use uniformly across research tasks. This study examines task-specific attitudes towards AI among an international, self-selected sample of 3,785 PhD students in STEM and medical and health sciences who participated in Nature's Graduate Survey 2025. We analyse respondents' comfort with using AI for writing a research article, collecting and analysing data, designing experiments, tracking scientific literature, and summarising it. Latent class analysis identifies four distinct attitudinal profiles. The dominant profile reflects a "division of labour," in which AI is widely accepted for literature-related tasks but resisted in activities closely associated with intellectual contribution, such as writing, data analysis, and experimental design. A "status quo" profile is broadly uncomfortable across tasks, an "all-purpose" profile is broadly comfortable, and an "undecided" profile expresses substantial uncertainty. These patterns suggest that attitudes towards AI in research are organised less around a simple acceptance-rejection divide than around task-specific boundaries, likely concerning delegation, authorship, and responsibility. Because the survey measures comfort rather than legitimacy, the profiles are best interpreted as attitudinal configurations with a normative dimension. The findings highlight the importance of task-specific approaches to AI governance, doctoral training, disclosure, and research evaluation.

econ.GN↗

arXiv2026

An integration of decision trees into latent class modeling with covariates

We propose a novel methodology for fitting decision trees to latent classes. The latent class analysis methodological literature has previously been focusing on logistic models of class membership given covariates, which has important drawbacks in the presence of complex interactions between covariates: logistic models are easily misspecified by omitting some interaction terms and complexity of interpretation increases rapidly with inclusion of higher-order interaction terms. Our proposed approach directly integrates decision tree modeling into the latent class analysis framework to model the covariate effects as an easily interpretable tree leading the eye through combinations of predictors to a final conditional classification. The novel methodology does not require any non-traditional assumptions for latent class models with covariates, is based on well-established routines of model estimation, and can be readily implemented in existing software. In the present paper, we focus on decision trees for binary covariates. We present the proposed approach, describe two tree pruning strategies, and provide a real-data illustration. Important extensions include generalizing the approach to multinomial and continuous covariates by developing more advanced within-predictor splitting procedures, and developing and evaluating alternative tree pruning strategies. The ultimate aim of this work is to initiate a novel research line in the latent class analysis methodological literature, and to facilitate the advancement of applied research via a novel data analytical tool.

stat.ME↗

arXiv2024

multilevLCA: An R Package for Single-Level and Multilevel Latent Class Analysis with Covariates

This contribution presents a guide to the R package multilevLCA, which offers a complete and innovative set of technical tools for the latent class analysis of single-level and multilevel categorical data. We describe the available model specifications, mainly falling within the fixed-effect or random-effect approaches. Maximum likelihood estimation of the model parameters, enhanced by a refined initialization strategy, is implemented either simultaneously, i.e., in one-step, or by means of the more advantageous two-step estimator. The package features i) semi-automatic model selection when a priori information on the number of classes is lacking, ii) predictors of class membership, and iii) output visualization tools for any of the available model specifications. All functionalities are illustrated by means of a real application on citizenship norms data, which are available in the package.

stat.CO↗