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Im Vergleich zu Umfragen an Wahrscheinlichkeitsstichproben bieten Umfragen an Access-Panels, die auf Nicht-Wahrscheinlichkeitsstichproben basieren, unbestreitbare wirtschaftliche Vorteile. Diese Vorteile gehen jedoch mit unvermeidbaren Qualitätseinbußen einher, die auch dann bestehen bleiben, wenn Erstere sehr niedrige Responseraten haben. Daher müssen die wirtschaftlichen Vorteile und die methodischen Einschränkungen gegeneinander abgewogen werden. Es wird argumentiert, dass diese Abwägung anhand normativer Festlegungen erfolgen muss. Unter Anwendung der hier vorgeschlagenen Maßstäbe kommt der Beitrag zu dem Schluss, dass die Qualitätsansprüche an über Massenmedien verbreitete Meinungsumfragen höher sein sollten als für rein (sozial)wissenschaftliche Zwecke. Compared with surveying respondents of a probability sample, surveying members of a self-selective custom online panel offers indisputable economic advantages. However, these advantages come with an unavoidable drop in quality. This disadvantage holds true even in comparison to probability sampling surveys with very low response rates. Therefore, it is necessary to balance the economic advantages against the methodological limitations. We argue that this consideration needs to be done according to normative determinations. Using the criteria proposed in this article, we conclude that public opinion research distributed through mass media should have higher quality standards than research with solely scientific purposes.

A review of all research papers published in the European Sociological Review in 2016 and 2017 (N = 118) shows that only a minority of papers clearly define the parameter of interest and provide sufficient reasoning for the selected control variables of the statistical analysis. Thus, the vast majority of papers does not reach minimal standards for the selection of control variables. Consequently, a majority of papers interpret biased coefficients, or statistics without proper sociological meaning. We postulate that authors and reviewers should be more careful about control variable selection. We propose graphical causal models in the form of directed acyclic graphs as an example for a parsimonious and powerful means to that end.

Pioneering scholarship links retrospective childhood conditions to mature adult health. We distinctively provide critical evidence with prospective state-of-the-art measures of parent income observed multiple times during childhood in the 1970s-1990s. Using the Panel Study of Income Dynamics, we analyze six health outcomes (self-rated health, heart attack, stroke, life-threatening chronic conditions, non-life-threatening chronic conditions, and psychological distress) among 40-65 year olds (N=3,813-3,944). Parent relative income rank has statistically and substantively significant relationships with five of six outcomes. The relationships with heart attack, stroke and life threatening chronic conditions are particularly strong. Parent income rank performs slightly better than alternative prospective and retrospective measures. At the same time, we provide novel validation on which retrospective measures (i.e. father’s education) perform almost as well as prospective measures. Further, we inform several perennial debates about how relative versus absolute income and other measures of socio-economic status and social class influence health.

Vast racial inequalities continue to prevail across the United States and are closely linked to economic resources. One particularly prominent argument contends that childhood wealth accounts for black–white (BW) disadvantages in life chances. This article analyzes how much childhood wealth and childhood income mediate BW disadvantages in adult life chances with Panel Study of Income Dynamics and Cross-National Equivalent File data on children from the 1980s and 1990s who were 30+ years old in 2015. Compared with previous research, we exploit longer panel data, more comprehensively assess adult life chances with 18 outcomes, and measure income and wealth more rigorously. We find large BW disadvantages in most outcomes. Childhood wealth and income mediate a substantial share of most BW disadvantages, although there are several significant BW disadvantages even after adjusting for childhood wealth and income. The evidence mostly contradicts the prominent claim that childhood wealth is more important than childhood income. Indeed, the analyses mostly show that childhood income explains more of BW disadvantages and has larger standardized coefficients than childhood wealth. We also show how limitations in prior wealth research explain why our conclusions differ. Replication with the National Longitudinal Survey of Youth and a variety of robustness checks support these conclusions.

Since COVID-19 became a pandemic, many studies are being conducted to get a better understanding of the disease itself and its spread. One crucial indicator is the prevalence of SARS-CoV-2 infections. Since this measure is an important foundation for political decisions, its estimate must be reliable and unbiased. This paper presents reasons for biases in prevalence estimates due to unit nonresponse in typical studies. Since it is difficult to avoid bias in situations with mostly unknown nonresponse mechanisms, we propose the maximum amount of bias as one measure to assess the uncertainty due to nonresponse. An interactive web application is presented that calculates the limits of such a conservative unit nonresponse confidence interval (CUNCI)., Survey Research Methods, Vol 14 No 2 (2020): Survey Research Methods During the COVID-19 Crisis

This article draws on the experience from an ongoing research project employing respondent-driven sampling (RDS) to survey (illicit) 24-hour home care workers. We highlight issues around the preparatory work and the fielding of the survey to provide researchers with useful insights on how to implement RDS when surveying populations for which the method has not yet been used. We conclude the article with ethical considerations that occur when employing RDS.

This paper compares the usability of data stemming from probability sampling with data stemming from nonprobability sampling. It develops six research scenarios that differ in their research goals and assumptions about the data generating process. It is shown that inferences from data stemming from nonprobability sampling implies demanding assumptions on the homogeneity of the units being studied. Researchers who are not willing to pose these assumptions are generally better off using data from probability sampling, regardless of the amount of nonresponse. However, even in cases when data from probability sampling is clearly advertised, data stemming from nonprobability sampling may contribute to the cumulative scientific endeavour of pinpointing a plausible interval for the parameter of interest.

Dieses Buch bietet eine Einführung in das Datenanalysepaket Stata und ist zugleich das einzige Buch über Stata, das auch Anfängern eine ausreichende Erklärung statistischer Verfahren liefert. „Datenanalyse mit Stata' ist kein Befehls-Handbuch sondern erläutert alle Schritte einer Datenanalyse an praktischen Beispielen. Die Beispiele beziehen sich auf Themen der öffentlichen Diskussion oder der direkten Umgebung der meisten Leser. Damit eignet sich diese Buch als Einstieg in Data Analytics in allen Disziplinen. Die neue Auflage bietet einen systematischeren Zugang zum Datenmanagement in Gegenwart von „Missing Values' und behandelt die in der Stata-Programmversion 14 implementierte Unicode-Codierung.

Was sollten Mitarbeiter in einem empirisch ausgerichteten Forschungsprojekt können, und welche Kernkompetenzen sollte die Ausbildung an den Universitäten daher vermitteln? Die Antworten auf diese Fragen hängen – wie sollte es anders sein – von der inhaltlichen Fragestellung und methodischen Ausrichtung des jeweiligen Forschungsprojektes ab. Natürlich sollten Projektmitarbeiter über Vorkenntnisse zum Forschungsthema verfügen. Natürlich sollten Kenntnisse des projektspezifischen (statistischen) Methodenarsenals vorliegen.