Bayesian Estimation with Informative Priors is Indistinguishable from Data Falsification

Secularism, family ties and loneliness: A multilevel longitudinal study of ten European societies

OBJECTIVES

Mass media suggest rising political and religious concern about secularism-induced decline of the family. Implications for loneliness remain unexamined. The current study filled this gap.

METHODS

Data were from 10 national probability samples in the Survey of Health, Ageing and Retirement in Europe. Multilevel longitudinal models tested linkages of societal secularism with loneliness, their mediation by specific family relationships, and the role of this cultural dimension in weakening associations of family ties with loneliness. Both weighted Maximum Likelihood and unweighted Bayesian analyses were conducted, separately for each gender.

RESULTS

Societal secularism was not positively linked to either gender's loneliness. Associations with family ties were inconsistent, with only men's average partnered status lower in more secular settings. Nor did any positive indirect effects emerge. Moderation results were also inconsistent, with secularism only weakening linkages of some family dimensions with loneliness. Bayesian estimates were generally nonsignificant.

DISCUSSION

Societal secularism may not be a risk factor for loneliness or for weak family ties. Results stand at odds with religious and political rhetoric on secularism-induced decline of the family, and its individual and societal consequences.

Population genetic considerations for using biobanks as international resources in the pandemic era and beyond

The past years have seen the rise of genomic biobanks and mega-scale meta-analysis of genomic data, which promises to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limits the global understanding of disease risk but also inhibits viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable and computable and to operate without access to raw data due to privacy concerns. Comparability is key both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of the most common genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks (locally and internationally), increase the accuracy of association analyses and inform developmental efforts.