Can Happiness Apps Generate Nationally Representative Datasets? - a Case Study Collecting Data on People’s Happiness Using the German Socio-Economic Panel

In the last few years, apps have become an important tool to collect data. Especially in the case of data on people’s happiness, two projects have received substantial attention from both the media and the scientific world: “Track your happiness” from Killingsworth and Gilbert (Science, 330, 932-932, 2010), and “Mappiness,” from MacKerron (2012). Both happiness apps used the experience sampling method to ask people a few times per day how they feel, what they do, with whom, and where. The collected data are then displayed for the participants in simple graphs to help them understand what makes them happy and what does not. Both studies have collected considerable data without giving participants any financial rewards. But quantity is not everything that matters with respect to data collection, and thus, understanding whether nationally representative datasets can be collected using such happiness apps is crucial. To address this question, we built a new happiness app and ran a case-study with over 4000 participants of the innovation sample of the German Socio-Economic Panel (Richter and Schupp in Schmollers Jahrbuch, 135(3), 389–399, 2015). Participants were informed that the app collects data on everyday happiness after a household interview and asked whether they would like to use the app. In the first year (2015), participants did not receive any reward, and in the second year (2016), a different group of participants received a 50 Euro Amazon voucher for their participation. The results showed that our happiness app cannot generate nationally representative datasets if it is not controlled that all demographic sub-groups have access to a smartphone, are highly motivated with a sufficient reward and data is collected with quota sampling.

     

The missing link between faces and names: Evidence from Alzheimer's disease patients

Retrieval of proper names is a cause of concern and complaint among elderly adults and it is an early symptom of patients suffering from neurodegenerative diseases such as Alzheimer's disease (AD). While it is well established that AD patients have deficits of proper name retrieval, the nature of such impairment is not yet fully understood. Specifically, it is unknown whether this deficit is due to a degradation of the links between faces and proper names, or due to deficits in intentionally accessing and retrieving proper names from faces. Here, we aim to investigate the integrity of the links between famous faces and proper names in AD while minimizing the impact of the explicit retrieval. We compare the performances of AD patients and elderly controls in a face-name priming task. We assess the integrity of the link between faces and names at two different levels: identity level - the name and face belong to the same person; and semantic level - the name and face belong to the same category (e.g., politicians). Our results reveal that AD patients compared with controls show intact semantic priming but reduced priming for person identity. This suggests that the deficits in intentionally retrieving proper names in AD are the result of a partial disruption of the network at the identity level, i.e., the links between known faces and proper names.