arXiv subjects

Malte Schierholz

Publications and source records attributed to Malte Schierholz.

4 recordsLinked to original sources

arXiv2025

The Impact of Question Framing on the Performance of Automatic Occupation Coding

Occupational data play a vital role in research, official statistics, and policymaking, yet their collection and accurate classification remain a challenge. This study investigates the effects of occupational question wording on data variability and the performance of automatic coding tools. We conducted and replicated a split-ballot survey experiment in Germany using two common occupational question formats: one focusing on "job title" (Berufsbezeichnung) and another on "berufliche Tätigkeit" (loosely translated as occupation or occupational task). Our analysis reveals that automatic coding tools, such as CASCOT and OccuCoDe, exhibit sensitivity to the form and origin of the data. Specifically, these tools were more efficient when coding responses to the job title question format than the occupational task format, suggesting a potential way to improve the respective questions for many German surveys. In a subsequent "detailed tasks and duties" question, providing a guiding example prompted respondents to give longer answers without broadening the range of unique words they used. These findings highlight the importance of harmonising survey questions and and ensuring that automatic coding tools are robust to differences in question wording. Further research is needed to optimise question design and coding tools for greater accuracy and applicability in occupational data collection.

stat.AP↗

arXiv2025

Sources of Uncertainty in Supervised Machine Learning -- A Statisticians' View

Supervised machine learning and predictive models have achieved an impressive standard today, enabling us to answer questions that were inconceivable a few years ago. Besides these successes, it becomes clear, that beyond pure prediction, which is the primary strength of most supervised machine learning algorithms, the quantification of uncertainty is relevant and necessary as well. However, before quantification is possible, types and sources of uncertainty need to be defined precisely. While first concepts and ideas in this direction have emerged in recent years, this paper adopts a conceptual, basic science perspective and examines possible sources of uncertainty. By adopting the viewpoint of a statistician, we discuss the concepts of aleatoric and epistemic uncertainty, which are more commonly associated with machine learning. The paper aims to formalize the two types of uncertainty and demonstrates that sources of uncertainty are miscellaneous and can not always be decomposed into aleatoric and epistemic. Drawing parallels between statistical concepts and uncertainty in machine learning, we emphasise the role of data and their influence on uncertainty.

stat.ML↗

arXiv2024

Cracking the Code: Evaluating Zero-Shot Prompting Methods for Providing Programming Feedback

Despite the growing use of large language models (LLMs) for providing feedback, limited research has explored how to achieve high-quality feedback. This case study introduces an evaluation framework to assess different zero-shot prompt engineering methods. We varied the prompts systematically and analyzed the provided feedback on programming errors in R. The results suggest that prompts suggesting a stepwise procedure increase the precision, while omitting explicit specifications about which provided data to analyze improves error identification.

cs.SE↗

arXiv2022

When regression coefficients change over time: A proposal

A common approach in forecasting problems is to estimate a least-squares regression (or other statistical learning models) from past data, which is then applied to predict future outcomes. An underlying assumption is that the same correlations that were observed in the past still hold for the future. We propose a model for situations when this assumption is not met: adopting methods from the state space literature, we model how regression coefficients change over time. Our approach can shed light on the large uncertainties associated with forecasting the future, and how much of this is due to changing dynamics of the past. Our simulation study shows that accurate estimates are obtained when the outcome is continuous, but the procedure fails for binary outcomes.

stat.ME↗