Carina Cornesse

Bio: Carina Cornesse is an academic researcher from University of Mannheim. The author has contributed to research in topics: Population & Nonprobability sampling. The author has an hindex of 9, co-authored 28 publications receiving 307 citations. Previous affiliations of Carina Cornesse include Leibniz Association.

The COVID-19 pandemic and subjective well-being: longitudinal evidence on satisfaction with work and family

Abstract: This paper provides a timely evaluation of whether the main COVID-19 lockdown policies – remote work, short-time work and closure of schools and childcare – have an immediate effect on the German p...

A review of conceptual approaches and empirical evidence on probability and nonprobability sample survey research

Abstract: There is an ongoing debate in the survey research literature about whether and when probability and nonprobability sample surveys produce accurate estimates of a larger population. Statistical theory provides a justification for confidence in probability sampling as a function of the survey design, whereas inferences based on nonprobability sampling are entirely dependent on models for validity. This article reviews the current debate about probability and nonprobability sample surveys. We describe the conditions under which nonprobability sample surveys may provide accurate results in theory and discuss empirical evidence on which types of samples produce the highest accuracy in practice. From these theoretical and empirical considerations, we derive best-practice recommendations and outline paths for future research.

COVID-19 policies in Germany and their social, political, and psychological consequences

Abstract: Many policy analyses on COVID-19 have been focusing on what kind of policies are implemented to contain the spread of COVID-19 What seems equally important to explore are the social and political consequences of the confinement policies Does the public support strict confinement policies? What are the social, political, and psychological consequences of the confinement policies? The question of how legitimate a policy is among the public is at the core of democratic theory Its relevance also stems from the expected consequences of public support on behavior: The more someone supports a policy, the more someone is likely to follow the policy even if the policy is not strictly enforced In this paper, we will focus on Germany, briefly summarize the main policies during the first 6 weeks of confinement and then explore political attitudes, risk perceptions, and the social consequences of the lockdown

Does the Recruitment of Offline Households Increase the Sample Representativeness of Probability-Based Online Panels? Evidence From the German Internet Panel

Abstract: The past decade has seen a rise in the use of online panels for conducting survey research. However, the popularity of online panels, largely driven by relatively low implementation costs and high rates of Internet penetration, has been met with criticisms regarding their ability to accurately represent their intended target populations. This criticism largely stems from the fact that (1) non-Internet (or offline) households, despite their relatively small size, constitute a highly selective group unaccounted for in Internet panels, and (2) the preeminent use of nonprobability-based recruitment methods likely contributes a self-selection bias that further compromises the representativeness of online panels. In response to these criticisms, some online panel studies have taken steps to recruit probability-based samples of individuals and providing them with the means to participate online. Using data from one such study, the German Internet Panel, this article investigates the impact of including offline households in the sample on the representativeness of the panel. Consistent with studies in other countries, we find that the exclusion of offline households produces significant coverage biases in online panel surveys, and the inclusion of these households in the sample improves the representativeness of the survey despite their lower propensity to respond.

Internet, phone, mail and mixed-mode surveys: the tailored design method: reseña

Abstract: Resena de la obra de Don A. Dillman, Jolene D. Smyth y Leah Melani Christian: Internet, Phone, Mail and Mixed-Mode Surveys. The Tailored Design Method. New Jersey: John Wiley and Sons

Collider bias undermines our understanding of COVID-19 disease risk and severity

Abstract: Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage. Many published studies of the current SARS-CoV-2 pandemic have analysed data from non-representative samples from populations. Here, using UK BioBank samples, Gibran Hemani and colleagues discuss the potential for such studies to suffer from collider bias, and provide suggestions for optimising study design to account for this.

Collider bias undermines our understanding of COVID-19 disease risk and severity

Abstract: Observational data on COVID-19 including hypothesised risk factors for infection and progression are accruing rapidly, often from non-random sampling such as hospital admissions, targeted testing or voluntary participation. Here, we highlight the challenge of interpreting observational evidence from such samples of the population, which may be affected by collider bias. We illustrate these issues using data from the UK Biobank in which individuals tested for COVID-19 are highly selected for a wide range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the sampling mechanisms that leave aetiological studies of COVID-19 infection and progression particularly susceptible to collider bias. We also describe several tools and strategies that could help mitigate the effects of collider bias in extant studies of COVID-19 and make available a web app for performing sensitivity analyses. While bias due to non-random sampling should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.

Why Working from Home Will Stick

Abstract: COVID-19 drove a mass social experiment in working from home (WFH). We survey more than 30,000 Americans over multiple waves to investigate whether WFH will stick, and why. Our data say that 20 percent of full workdays will be supplied from home after the pandemic ends, compared with just 5 percent before. We develop evidence on five reasons for this large shift: better-than-expected WFH experiences, new investments in physical and human capital that enable WFH, greatly diminished stigma associated with WFH, lingering concerns about crowds and contagion risks, and a pandemic-driven surge in technological innovations that support WFH. We also use our survey data to project three consequences: First, employees will enjoy large benefits from greater remote work, especially those with higher earnings. Second, the shift to WFH will directly reduce spending in major city centers by at least 5-10 percent relative to the pre-pandemic situation. Third, our data on employer plans and the relative productivity of WFH imply a 5 percent productivity boost in the post-pandemic economy due to re-optimized working arrangements. Only one-fifth of this productivity gain will show up in conventional productivity measures, because they do not capture the time savings from less commuting.