Describing the Dutch Social Networks and Fertility Study and how to process it

BACKGROUND The social networks of people play a prominent role in theories on fertility. Investigating how networks shape behaviour is hard, because of the difficulty in measuring (large) networks among representative samples. Therefore, comprehensive studies of the variation in the structure and composition of networks and their impact on fertility outcomes are lacking. OBJECTIVE I aim to, first, describe the Dutch Social Networks and Fertility Study, and, second, describe the R-package FertNet that processes data from this study and transforms it into an easy-to-use format for researchers. METHODS The data used are from the Longitudinal Internet Social Survey (LISS) panel, a representative panel of Dutch households. The focus is on the Social Networks and Fertility Study that includes a subsample of women between the ages of 18‒40. Specific survey software was designed to capture each respondent’s personal network comprising 25 individuals with whom they had a relationship. In total, 758 women reported on over 18,750 relationships. For each person with whom the respondent had a relationship, several questions were asked about fertility-related topics. Uniquely, the connections between these people were also assessed. The R-package FertNet corrects data issues and transforms unstructured network data into alter-attribute and alter-tie datasets that can be handled by a diversity of network analytical approaches. CONTRIBUTION The Social Networks and Fertility Study is a unique resource that allows for a comprehensive investigation of how networks shape fertility behaviour. It provides better estimates of network characteristics than earlier literature based on smaller networks. The R-package FertNet assists researchers in their analyses.</p

Collecting large personal networks in a representative sample of Dutch women

In this study we report on our experiences with collecting large personal network data (25 alters) from a representative sample of Dutch women. We made use of GENSI, a recently developed tool for network data collection using interactive visual elements that has been shown to reduce respondent burden. A sample of 758 women between the ages of 18 and 40 were recruited through the LISS-panel; a longitudinal online survey of Dutch people. Respondents were asked to name exactly 25 alters, answer sixteen questions about these alters (name interpreter questions), and assess all 300 alter-alter relations. Nearly all (97%) respondents reported on 25 alters. Non-response was minimal: 92% of respondents had no missing values, and an additional 5% had fewer than 10% missing values. Listing 25 alters took 3.5 ± 2.2 (mean ± SD) minutes, and reporting on the ties between these alters took 3.6 ± 1.3 min. Answering all alter questions took longest with a time of 15.2 ± 5.3 min. The majority of respondents thought the questions were clear and easy to answer, and most enjoyed filling in the survey. Collecting large personal networks can mean a significant burden to respondents, but through the use of visual elements in the survey, it is clear that it can be done within reasonable time, with enjoyment and without much non-response

How might life history theory contribute to life course theory?

In this commentary, we consider how evolutionary biology’s life history theory (LHT) can be integrated with life course theorizing, to the benefit of both endeavors. We highlight areas where it can add value to existing work in life course theory (LCT), focusing on: how it can add an extra level of explanation, which may be helpful in understanding why individuals focus on their own health and happiness (or why they don’t); how insights from comparative work, both across species and across all kinds of human populations, can inform LCT; and how social and biological researchers can come together fruitfully to make progress on the tricky issue of understanding human agenc

Balancing bias and burden in personal network studies

Personal network data is increasingly used to answer research questions about the interplay between individuals (i.e., egos) and their social environment (i.e., alters). Researchers designing such data collections face a trade-off: When eliciting a high number of alters, study participation can be particularly burdensome as all data is obtained by surveying the ego. Eliciting a low number of alters, however, may incur bias in network characteristics. In the present study we use a sample of 701 Dutch women and their personal networks of 25 alters to investigate two strategies reducing respondent burden in personal network data collections: (1) eliciting fewer alters and (2) selecting a random subsample from the original set of elicited alters for full assessment. We present the amount of bias in structural and compositional network characteristics connected to applying these strategies for every possible network size (2–24 alters) as well as the potential study time savings as a proxy for respondent burden reduction. Our results can aid researchers designing a personal network study to balance respondent burden and bias in estimates for a range of compositional and structural network characteristics

Do Data from Large Personal Networks Support Cultural Evolutionary Ideas about Kin and Fertility?

The fertility decline associated with economic development has been attributed to a host of interrelated causes including the rising costs of children with industrialization, and shifts in family structure. One hypothesis is that kin may impart more pro-natal information within their networks than non-kin, and that this effect may be exacerbated in networks with high kin-density where greater social conformity would be expected. In this study, we tested these ideas using large personal networks (25 associates of the respondent) collected from a sample of Dutch women (N = 706). Kin (parents) were perceived to exert slightly more social pressure to have children than non-kin, although dense networks were not associated with greater pressure. In contrast, women reported talking to friends about having children to a greater extent than kin, although greater kin-density in the network increased the likelihood of women reporting that they could talk to kin about having children. Both consanguineal and affinal kin could be asked to help with child-care to a greater extent than friends and other non-kin. Overall, there was mixed evidence that kin were more likely to offer pro-natal information than non-kin, and better evidence to suggest that kin were considered to be a better source of child-care support

Friends, family, and family friends:Predicting friendships of Dutch women

Friends are an important source of well-being but people differ in who they consider to be friends. With a unique quantitative test of such differences based on 17,650 social relations of 706 Dutch women (aged 18–41), of whom 40% were considered friends, we examined (a) which kind of personal relations were typically identified as friends (e.g., family, colleague), (b) how this linked to relationship closeness, face-to-face and non-face-to-face contact, and (c) whether these relationship characteristics of friendships differed with age. Most friends were met at school (>70%) and 20% of family were considered friends. Friendships were often close relationships with more non-face-to-face contact, while meeting in person was less predictive. Relatively older women reported fewer friends. Even in this homogenous sample with multiple measures of tie strength, friendships were difficult to predict and often overlapped with other social roles, meaning that researchers should be careful in using friendship as distinct category

Simulating the evolution of height in the Netherlands in recent history

The Dutch have a remarkable history when it comes to height. From being one of the shortest European populations in the 19th Century, the Dutch grew some 20 cm and are currently the tallest population in the world. Wealth, hygiene, and diet are well-established contributors to this major increase in height. Some have suggested that natural selection may also contribute to the trend, but evidence is weak. Here, we investigate the potential role of natural selection in the increase in height through simulations. We first ask what if natural selection was solely responsible for the observed increase in height? If the increase in average height was fully due to natural selection on male height, then across six consecutive generations, men who were two standard deviation above average height would need to have eight times more children on average. If selection acted only through those who have the opportunity to reproduce, then reproduction would need to be restricted to the tallest third (37%) of the population in order to give rise to the stark increase in height over time. No linear relationship between height and child mortality is able to account for the increase over time. We then present simulations based on previously observed estimates of partnership, mortality, selection and heritability and show that natural selection had a negligible effect (estimates from 0.07 to 0.36 cm) on the increase in height in the period 1850 to 2000. Our simulations highlight the plasticity of height and how remarkable the trend in height is in evolutionary terms. Only by using a combination of methods and insights from different disciplines, including biology, demography, and history are we potentially able to address how much of the increase in height is due to natural selection versus other causes.</p

The Reproductive Ecology of Industrial Societies, Part I Why Measuring Fertility Matters

Is fertility relevant to evolutionary analyses conducted in modern industrial societies? This question has been the subject of a highly contentious debate, beginning in the late 1980s and continuing to this day. Researchers in both evolutionary and social sciences have argued that the measurement of fitness-related traits (e.g., fertility) offers little insight into evolutionary processes, on the grounds that modern industrial environments differ so greatly from those of our ancestral past that our behavior can no longer be expected to be adaptive. In contrast, we argue that fertility measurements in industrial society are essential for a complete evolutionary analysis: in particular, such data can provide evidence for any putative adaptive mismatch between ancestral environments and those of the present day, and they can provide insight into the selection pressures currently operating on contemporary populations. Having made this positive case, we then go on to discuss some challenges of fertility-related analyses among industrialized populations, particularly those that involve large-scale databases. These include “researcher degrees of freedom” (i.e., the choices made about which variables to analyze and how) and the different biases that may exist in such data. Despite these concerns, large datasets from multiple populations represent an excellent opportunity to test evolutionary hypotheses in great detail, enriching the evolutionary understanding of human behavior