We propose a new method for analyzing and visualizing information on a large collection of personal networks to uncover the socio-centric structure of relationships among aggregated actors that we clustered into categories. The network of categories identifies the links between groups of sampled ego actors sharing a given attribute (e.g.,…

Mixed-mode surveys are popular as they can save costs and maintain (or improve) response rates relative to single-mode surveys. Nevertheless, it is not yet clear how design decisions like survey mode or questionnaire length impact measurement quality. In this study, we compare measurement quality in an experiment of three distinct survey designs…

Not knowing the population size is a common problem in data-limited contexts. Drawing on work in Sierra Leone, this short take outlines a four-step solution to this problem: (1) estimate the population size using expert interviews; (2) verify estimates using interviews with participants sampled; (3) triangulate using secondary data; and (4)…

This paper reports on students' experiences of describing and representing variation in hypothetical data. Fifty-six students (8-9 years-old) experienced collecting and working with quantitative data for two years as part of a STEM education project. The task described here was an end-of-year survey question, with three parts about a hypothetical…

How household-level data from censuses and surveys are analyzed to study household structure is an issue that has received little attention. The present study proposes a new methodological approach to address this gap. Specifically, we introduce the idea of the household configuration as a mathematical representation of observations from the…

In many developed countries, disagreement on important policy issues between groups with different social identities ('ideological polarisation') is increasing. In professional settings, these disagreements undermine cooperation and trust between employees, which negatively affects work relationships and managerial decision-making. We investigate…

Respondent-driven sampling (RDS) is a methodology for sampling and analysing survey data from rare and elusive populations that has gained increasing attention in migration research in recent years. Since its introduction in 1997, it has been applied in numerous surveys in the field of epidemiology (mainly for the estimation of HIV/AIDS prevalence…

A question asking for respondents' sex is one of the standard sociodemographic characteristics collected in a survey. Until now, it typically consisted of a simple question (e.g., "Are you…?") with two answer categories ("male" and "female"). In 2019, Germany implemented the additional sex designation divers for…

The purpose of this paper is to extend and evaluate methods of "Bayesian historical borrowing" applied to longitudinal data with a focus on parameter recovery and predictive performance. Bayesian historical borrowing allows researchers to utilize information from previous data sources and to adjust the extent of borrowing based on the…

The purpose of this paper is to extend and evaluate methods of "Bayesian historical borrowing" applied to longitudinal data with a focus on parameter recovery and predictive performance. Bayesian historical borrowing allows researchers to utilize information from previous data sources and to adjust the extent of borrowing based on the…

Researchers have become increasingly interested in better understanding the survey data collection process in interviewer-administered surveys. However, tools for analysing paradata capturing information about field processes, also called call record data, are still not yet fully explored. This paper introduces sequence analysis as a simple tool…

Descriptive analyses of socially important or theoretically interesting phenomena and trends are a vital component of research in the behavioral, social, economic, and health sciences. Such analyses yield reliable results when using representative individual participant data (IPD) from studies with complex survey designs, including educational…

Educational data mining is a developing research trend for exploring hidden patterns and natural associations among a set of student, teacher or school related variables. Discovering profiles of preservice science teachers using data mining methods would give important information about quality of teacher education programs and future science…

While violence against children (VAC) has been a long-established global concern, more recently attention has been drawn to how violence occurs in and around schools (VACS) and the role of education in preventing and responding to violence. Whilst awareness about VACS is growing, more robust data are needed to inform policy and practice. Concerns…

The world of work is being impacted by a fourth industrial revolution, transformed by artificial intelligence and other emerging technologies. With forecasts suggesting large shares of workers, displaced by automation, in need of upskilling/reskilling, the design of active skills policies is necessary. Conventional methods used to anticipate…