DARIAH (Digital Research Infrastructure for the Arts and Humanities) is part of the European Strategy on Research Infrastructures. Among 38 projects originally on this roadmap, DARIAH is one of two projects addressing social sciences and humanities. According to its self-conception and its political mandate DARIAH has the mission to enhance and support digitally-enabled research across the humanities and arts. DARIAH aims to develop and maintain an infrastructure in support of ICT-based research practices. One main distinguishing aspect of DARIAH is that it is not focusing on one application domain but especially addresses the support of interdisciplinary research in the humanities and arts. The present paper first gives an overview on DARIAH as a whole and then focuses on the important aspect of technical, syntactic and semantic interoperability. Important aspects in this respect are metadata registries and crosswalk definitions allowing for meaningful cross-collection and inter-collection services and analysis.
There is evidence that survey interviewers may be tempted to manipulate answers to filter questions in a way that minimizes the number of follow-up questions. This becomes relevant when ego-centered network data are collected. The reported network size has a huge impact on interview duration if multiple questions on each alter are triggered. We analyze interviewer effects on a network-size question in the mixed-mode survey 'Panel Study 'Labour Market and Social Security'' (PASS), where interviewers could skip up to 15 follow-up questions by generating small networks. Applying multilevel models, we find almost no interviewer effects in CATI mode, where interviewers are paid by the hour and frequently supervised. In CAPI, however, where interviewers are paid by case and no close supervision is possible, we find strong interviewer effects on network size. As the area-specific network size is known from telephone mode, where allocation to interviewers is random, interviewer and area effects can be separated. Furthermore, a difference-in-difference analysis reveals the negative effect of introducing the follow-up questions in Wave 3 on CAPI network size. Attempting to explain interviewer effects we neither find significant main effects of experience within a wave, nor significantly different slopes between interviewers.
Panel surveys suffer from attrition. Most panel studies use propensity models or weighting class approaches to correct for non-random dropout. These models draw on variables measured in a previous wave or from paradata of the study. While it is plausible that they affect contactability and cooperativeness, panel studies usually cannot assess the impact of events between waves on attrition. The amount of change in the population could be seriously underestimated if such events had an effect on participation in subsequent waves. The panel study PASS is a novel dataset for labour market and poverty research. In PASS, survey data on (un)employment histories, income and education of participants are linked to corresponding data from respondents' administrative records. Thus, change can be observed for attritors as well as for continued participants. These data are used to show that change in household composition, employment status or receipt of benefits has an influence on contact and cooperation rates in the following wave. A large part of the effect is due to lower contactability of households who moved. Nevertheless, this effect can lead to biased estimates for the amount of change. After applying the survey's longitudinal weights this bias is reduced, but not entirely eliminated.
Survey methodologists worry about trade-offs between nonresponse and measurement error. Past findings indicate that respondents brought into the survey late provide low-quality data. The diminished data quality is often attributed to lack of motivation. Quality is often measured through internal indicators and rarely through true scores. Using administrative data for validation purposes, this article documents increased measurement error as a function of recruitment effort for a large-scale employment survey in Germany. In this case study, the reduction in measurement quality of an important target variable is largely caused by differential measurement error in subpopulations and respective shifts in sample composition, as well as increased cognitive burden through the increased length of recall periods among later respondents. Only small portions of the relationship could be attributed to a lack of motivation among late or reluctant respondents.
Few topics in psychology have generated as much controversy as sex differences in intelligence. For fluid intelligence, researchers emphasize the high overlap between the ability distributions of males and females, whereas research on sex differences in declarative knowledge often uncovers a male advantage. However, on the level of knowledge domains, a more nuanced picture emerged: while females perform better in health-related topics (e.g., aging, medicine), males outperform females in domains of natural sciences (e.g., engineering, physics). In this paper we show that sex differences vary substantially depending on item sampling. Analyses were based on a sample of n = 3306 German high-school students (Grades 9 and 10) who worked on the 64 declarative knowledge items of the Berlin Test of Fluid and Crystallized Intelligence (BEFKI) assessing knowledge within three broad content domains (science, humanities, social studies). Using two strategies of item sampling—stepwise confirmatory factor analysis and ant colony optimization algorithm—we deliberately manipulate sex differences in multi-group structural equation models. Results show that sex differences considerably vary depending on the indicators drawn from the item pool. Furthermore, ant colony optimization outperforms the simple stepwise selection strategy since it can optimize several criteria simultaneously (model fit, reliability, and preset sex differences). Taken together, studies reporting sex differences in declarative knowledge fail to acknowledge item sampling issues. On a more general stance, handling item sampling hinges on profound considerations
of the content of measures.
Background: Studies demonstrate an association between personality traits and
obesity as well as their prognostic influence on weight course. In contrast, only
few studies have investigated the association between personality disorders (PDs)
Objective: The present review summarizes through a comprehensive and critical
evaluation the results of 68 studies identified by database research (PubMed and
PsycINFO) covering the last 35 years that investigated the association between
PDs, overweight and obesity as well as the predictive value of PDs for the development
of obesity and the effectiveness of weight reduction treatments.
Results: Adults with any PD have a higher risk of obesity. In the female general
population, there is an association between avoidant or antisocial PD and severe
obesity. Further, women with paranoid or schizotypal PD have a higher risk of
obesity. Clinical studies including foremost female participants showed a higher
comorbidity of PDs, especially borderline PD and avoidant PD, in binge-eating
disorder. Regarding both genders, patients with PD show less treatment success
in conservative weight-loss treatment programmes for obesity than patients
Conclusions: In prevention and conservative weight-loss treatment strategies,
more care should be taken to address the special needs of patients with comorbid