Quality & Quantity最新文献

Digital trace data form a rich, growing source of data for social sciences and humanities. Data donation offers an innovative and ethical approach to collect these digital trace data. In data donation studies, participants request a copy of the digital trace data a data controller (e.g., large digital social media or video platforms) collected about them. The European Union's General Data Protection Regulation obliges platforms to provide such a copy. Next, the participant can choose to share (part of) this data copy with the researcher. This way, the researcher can obtain the digital trace data of interest with active consent of the participant. Setting up a data donation study involves several steps and considerations. If executed poorly, these steps might threaten a study's quality. In this paper, we introduce a workflow for setting up a robust data donation study. This workflow is based on error sources identified in the Total Error Framework for data donation by Boeschoten et al. (2022a) as well as on experiences in earlier data donation studies by the authors. The workflow is discussed in detail and linked to challenges and considerations for each step. We aim to provide a starting point with guidelines for researchers seeking to set up and conduct a data donation study.

The rise of populist radical right (PRR) ideas stresses the importance of understanding how individuals engage with PRR content online. However, this task is complicated by the variety of channels through which such engagement can take place. In this article, we systematically compare computational approaches for detecting PRR content in textual data. Using 66 dictionary, classic supervised machine learning, and deep learning (DL) models, we compare how these distinct approaches perform on the PRR detection task for three Germanophone test datasets and how their performance is affected by different modes of text preprocessing. In addition to individual models, we examine the performance of 330 ensemble models combining the above-mentioned approaches for the dataset with a particularly high volume of noise. Our findings demonstrate that the DL models, in combination with more computationally intense forms of preprocessing, show the best performance among the individual models, but it remains suboptimal in the case of more noisy datasets. While the use of ensemble models shows some improvement for specific modes of preprocessing, overall, it mostly remains on par with individual DL models, thus stressing the challenging nature of computational detection of PRR content.

Data sharing is increasingly becoming a highly encouraged or required practice for any federally funded research projects. However, the uptake of these practices in education science has been minimal. Research suggests that many researchers believe data sharing should be practiced always or often, but also suggests that many researchers rarely practice data sharing. This disconnect indicates a general lack of understanding around data sharing and suggests there are salient barriers that prevent education researchers from engaging in the practice. This work examines (a) the prevalence of positive attitudes and perceived barriers to data sharing in a sample of education researchers, and (b) if there is a difference between the perceived barriers for researchers who have different levels of data sharing experience. Results suggest education researchers generally hold positive attitudes towards data sharing, with 70% of the sample agreeing that it benefits their career, increases citations, and is good for science. However, barriers such as concerns about IRB issues and the potential for misinterpretation of shared data were prevalent among respondents. Additionally, researchers with more experience sharing data were less likely to agree with these barriers compared to those with less or no sharing experience.

Supplementary information: The online version contains supplementary material available at 10.1007/s11135-025-02188-6.

The life course paradigm emphasizes the need to study not only the situation at a given point in time, but also its evolution over the life course in the medium and long term. These trajectories are often represented by categorical data. This article aims to provide a comprehensive review of the multiple imputation methods proposed so far in the context of univariate categorical data and to assess their practical relevance through a simulation study based on real data. The primary goal is to provide clear methodological guidelines and improve the handling of missing data in life course research. In parallel, we develop the MICT-timing algorithm, which is an extension of the MICT algorithm. This innovative multiple imputation method improves the quality of imputation in trajectories subject to time-varying transition rates, a situation often encountered in life course data.

Supplementary information: The online version contains supplementary material available at 10.1007/s11135-024-02028-z.

The adage "the rich are getting richer" refers to increasingly skewed and heavily-tailed income distributions. For such distributions, the mean is not the best measure of the center, but the classical indices of income inequality, including the celebrated Gini index, are mean based. In view of this, it has been proposed in the literature to incorporate the median into the definition of the Gini index. In the present paper we make a further step in this direction and, to acknowledge the possibility of differing viewpoints, investigate three median-based indices of inequality. These indices overcome past limitations, such as: (1) they do not rely on the mean as the center of, or a reference point for, income distributions, which are skewed, and are getting even more heavily skewed; (2) they are suitable for populations of any degree of tail heaviness, and income distributions are becoming increasingly such; and (3) they are unchanged by, and even discourage, transfers among the rich persons, but they encourage transfers from the rich to the poor, as well as among the poor to alleviate their hardship. We study these indices analytically and numerically using various income distribution models. Real-world applications are showcased using capital incomes from 2001 and 2018 surveys from fifteen European countries.