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The SOECBIAS data set is an output of the interdisciplinary research project SOECBIAS, funded by the Federal Ministry of Labour and Social Affairs in Germany and run at the Universität Hamburg. SOECBIAS studies income perceptions and redistributive preferences combining inequality research with social policy and welfare state research, in economics and sociology. SOECBIAS addresses three main questions: How do Europeans perceive national and European social policy? What explains the perception of one's own income position within the EU income distribution? What are the consequences of these perceptions for the assessment of redistribution measures? The dataset includes a survey experiment in four European countries that investigates the similarity of income perceptions at the supra-national level of the EU and that tests the effect of informing participants about their income position on preferences towards social policy measures in Europe.

ABSTRACTRecent advances in statistical estimation theory have resulted in the development of new procedures, called robust methods, that can be used to estimate the coefficients of a regression model. Because such methods take into account the impact of discrepant data points during the initial estimation process, they offer a number of advantages over ordinary least squares and other analytical procedures (such as the analysis of outliers or regression diagnostics).This paper describes the robust method of analysis and illustrates its potential usefulness by applying the technique to two data sets. The first application uses artificial data; the second uses a data set analyzed previously by Tufte [15] and, more recently, by Chatterjee and Wiseman [6].

This article introduces the new Family of Ethnic Power Relations (EPR) data sets, version 2014, which is the latest in a series of data sets on ethnicity that have stimulated civil war research in the past decade. The EPR Family provides data on ethnic groups' access to state power, their settlement patterns, links to rebel organizations, transborder ethnic kin relations, and intraethnic cleavages. The new 2014 version does not only extend the data set's temporal coverage from 2009 to 2013, but it also offers several new features, such as a new measure of regional autonomy that is independent of national-level executive power and a new data set component coding intraethnic identities and cleavages. Moreover, for the first time, detailed documentation of the EPR data is provided through the EPR Atlas. This article presents these novelties in detail and compares the EPR Family 2014 to the most relevant alternative data sets on ethnicity.

"Review of Available Data Sets" published on by Oxford University Press.

The SOECBIAS-COVREF data set is an output of two research projects: The interdisciplinary research project SOECBIAS, funded by the Federal Ministry of Labour and Social Affairs in Germany and the research project COVREF, funded by the German Research Foundation, both conducted at the Universität Hamburg. SOECBIAS started in May 2019 and ended in December 2021 and studied income perceptions and redistributive preferences combining inequality research with social policy and welfare state research in economics and sociology. SOECBIAS addressed three main questions: How do Europeans perceive national and European social policy? What explains the perception of one's own income position within the EU income distribution? What are the consequences of these perceptions for the assessment of redistribution measures?
Since the SOECBIAS survey period overlapped with the onset of the COVID-19 pandemic it became an additional focus of the project and evolved into a further research project (COVREF) which started in June 2022 and ended in June 2024. This research project focused on the individual affectedness by the COVID-19 pandemic from an objective and subjective point of view. The two leading questions for COVREF were: What influence do objectively measurable factors like illness, job loss or loss of income during the COVID-19 pandemic have on the subjective assessment of economic affectedness and the perception of one's own income position? What impact do comparisons with reference groups or reference points have on the perception and assessment of economic affectedness?

AbstractQualitative researchers sometimes talk about objectivity in relation to qualitative data sets. In this paper, I defend a reconstructed notion of objective qualitative data sets that may serve as a useful and reachable guiding ideal in qualitative data generation. In the first part of the paper, I develop the ideal. According to it, a qualitative data set is objective to the extent that it, in conjunction with true assumptions, possesses a combination of good-making features (epistemic values, epistemic virtues) in virtue of which the data set is suited to serve as evidence base for a satisfying answer to the research question under study. In the second part of the paper, I examine and reject two possible lines of objection to this ideal: One is that it picks out the wrong good-making features. The other is that the very focus on good-making features is misguided: the objectivity of a qualitative data set should instead be seen as a matter of how it was generated or evaluated.

International audience An important neef of corporations for internal audits is the ability to detect fraudulently reported financial data. Benford's Law is a probability distribution which is useful to analyse patterns of digits in numbers sets. A history of the origins of Benford's Law is given and the types of data sets expected to follow Benford's Law is discussed. This paper examines how BA students falsify financial numbers. The paper shows that they fail to imitate Benford's law and that there are cheating behaviour patterns coherent with previous empirical studies.

The Citadel annually publishes the Common Data Set, a set of standard data about the university including statistics about enrollment and persistence, freshman admission, transfer admission, academic offerings and policies, student life, annual expenses, financial aid, and instructional faculty and class size.

The Citadel annually publishes the Common Data Set, a set of standard data about the university including statistics about enrollment and persistence, freshman admission, transfer admission, academic offerings and policies, student life, annual expenses, financial aid, and instructional faculty and class size.

The Citadel annually publishes the Common Data Set, a set of standard data about the university including statistics about enrollment and persistence, freshman admission, transfer admission, academic offerings and policies, student life, annual expenses, financial aid, and instructional faculty and class size.