Following Political Science Students Through Their Methods Training: Statistics Anxiety, Student Satisfaction, and Final Grades in the COVID Year 2021/22

Vierus, Paul; Elis, Jonas; Goerres, Achim; Ziller, Conrad; Karem Höhne, Jan Article — Published Version Following Political Science Students Through Their Methods Training: Statistics Anxiety, Student Satisfaction, and Final Grades in the COVID Year 2021/22 Politische Vierteljahresschrift Provided in Cooperation with: Springer Nature Suggested Citation: Vierus, Paul; Elis, Jonas; Goerres, Achim; Ziller, Conrad; Karem Höhne, Jan (2025) : Following Political Science Students Through Their Methods Training: Statistics Anxiety, Student Satisfaction, and Final Grades in the COVID Year 2021/22, Politische Vierteljahresschrift, ISSN 1862-2860, Springer Fachmedien Wiesbaden GmbH, Wiesbaden, Vol. 66, Iss. 4, pp. 867-884, https://doi.org/10.1007/s11615-025-00613-x This Version is available at: https://hdl.handle.net/10419/330909 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/deed.de RESEARCH NOTE https://doi.org/10.1007/s11615-025-00613-x Politische Vierteljahresschrift (2025) 66:867–884 Following Political Science Students Through Their Methods Training: Statistics Anxiety, Student Satisfaction, and Final Grades in the COVID Year 2021/22 Paul Vierus · Jonas Elis · Achim Goerres · Conrad Ziller · Jan Karem Höhne Received: 23 April 2024 / Revised: 4 March 2025 / Accepted: 17 March 2025 / Published online: 24 April 2025 © The Author(s) 2025 Abstract Teaching empirical social research methods as a compulsory part of a curriculum involves several challenges. Students are often unaware of the relevance of methodological training for their political science education and its value as a transferable skill. In addition, some students are afraid of the mathematical components of their applied statistics training. These challenges can have a diminishing effect on student success. We examine three different perspectives of students’ satisfaction with methods courses at a large political science department in a German university. We describe temporal changes in student satisfaction over the course of a complete term (6 months) and use a set of independent variables to explain the outcomes. To do this, we fielded a longitudinal survey in five in-person methods and statistics courses during 2021/2022 after the height of the COVID-19 pandemic in Germany. We demonstrate that statistics anxiety—the self-reported worries about getting lower grades, becoming nervous, or feeling helpless when solving tasks that focus on statistics—has a substantial negative effect both on student satisfaction and on their final grades. This clear pattern raises the question of how to optimally support stuPaul Vierus · Jonas Elis · Achim Goerres · Conrad Ziller Department of Political Science, University of Duisburg-Essen, Duisburg, Germany E-Mail: [email protected] Paul Vierus E-Mail: paul.[email protected]e Jonas Elis E-Mail: [email protected] Conrad Ziller E-Mail: [email protected] Jan Karem Höhne German Centre for Higher Education Research and Science Studies (DZHW), Leibniz University Hannover, Hannover, Germany E-Mail: [email protected] K 868 P. Vierus et al. dents who exhibit high levels of negative emotions towards statistics. Our findings contribute to the understanding of course satisfaction in academic methodological training and can be used to improve the design of courses in order to significantly reduce failure and dropout rates. Keywords Teaching · Longitudinal · Methodology · University · Germany · Student panel Die Begleitung von Politikwissenschaftsstudierenden durch ihre Methodenausbildung: Statistikangst, Zufriedenheit der Studierenden und Abschlussnoten im COVID-Jahr 2021/22 Zusammenfassung Die Vermittlung von Methoden der empirischen Sozialforschung als obligatorischer Bestandteil der Curricula bringt mehrere Herausforderungen mit sich. Die Studierenden sind sich oft nicht über die Bedeutung der Methodenausbildung für ihre politikwissenschaftliche Ausbildung bewusst. Darüber hinaus haben einige Angst vor den mathematischen Komponenten in angewandter Statistik. Diese Herausforderungen können sich nachteilig auf den Erfolg der Studierenden auswirken. Unser Beitrag untersucht die Zufriedenheit der Studierenden mit den Methodenkursen an einem großen politikwissenschaftlichen Institut in Deutschland aus drei verschiedenen Perspektiven. Wir beschreiben die zeitlichen Veränderungen in der Zufriedenheit der Studierenden über den Verlauf eines kompletten Semesters und verwenden mehrere unabhängige Variablen, um die Messungen zu erklären. Dazu wurde eine Längsschnittdatenerhebung in fünf Methodenund Statistikkursen durchgeführt, welche nach dem Höhepunkt der COVID-19-Pandemie in Deutschland in Präsenz stattfanden. Wir zeigen, dass Statistikangst, also die selbstberichtete Sorge, schlechtere Noten zu bekommen, nervös zu werden oder sich hilflos zu fühlen, wenn Aufgaben mit Statistikbezug gelöst werden, einen wesentlichen negativen Einfluss sowohl auf die Kurszufriedenheit als auch auf die Abschlussnoten hat. Dieses Muster wirft die Frage auf, wie Studierende mit einem hohen Maß an negativen Emotionen gegenüber Statistik optimal unterstützt werden können. Unsere Ergebnisse tragen zum Verständnis der Kurszufriedenheit in der akademischen Methodenausbildung bei und können dazu genutzt werden, die Gestaltung von Kursen zu verbessern, um das Scheitern in Prüfungen und die Abbrecherquote der Studierenden zu minimieren. Schlüsselwörter Lehre · Längsschnitt · Methodik · Universität · Deutschland · Studierendenpanel 1 Introduction Empirical methods training enables political science students to critically assess empirical research findings and to conduct their own research projects. It also endows them with a set of marketable skills for future jobs. Quantitative methods form K Following Political Science Students Through Their Methods Training: Statistics Anxiety,... 869 a sizeable part of these empirical methods, which reflects the increased level of applied statistics in the social sciences (Włodzimierz 2012; Clogg 1992; Maravelakis 2019). Before having familiarised themselves with the details of the curriculum, many students-to-be are not aware that studying social sciences includes a substantial proportion of training in methods and statistics, a proportion that varies among universities. This lack of prior awareness might negatively affect students’ satisfaction with their political science methods courses and jeopardise their studying success. The didactics of political science methods are especially important for instructors, much more so than in other subfields of political science. Political science methods is a subject-specific amalgam of methods used in adjacent disciplines, such as anthropology, economics, history, psychology, and sociology. Its contents are, furthermore, built on the philosophy of science, logic, mathematics, and statistics. Whereas students are most motivated to learn methods hands-on when they apply them to political science topics, the fundamentals of the methods and the added value of knowing how to use them properly originate from and reach far beyond political science. Methods instructors thus need to provide courses that (1) provide an overview of the abstract fundamentals, often examined through written exams, and (2) give students the possibility of applying methods to political science research problems, often examined through written project reports. Little is known about the in-class reception of political science methods training, especially about how the training evolves over the semester. This empirical research note answers the following two overarching research questions: 1. What drives student satisfaction across a semester of methods training in political science? 2. How important is student satisfaction for study success as measured by grades? We will distinguish between structural factors that shape the student experience before they enter university—for instance, their parents0educational background and the student0s final grades in high school—along with course characteristics such as attendance, bachelor0sormaster 0s level, or whether a course is mandatory, as well as individual characteristics such as optimism, procrastination, and self-efficacy. To motivate the relevance of student satisfaction, previous research suggests that students’ perceived satisfaction with courses, as well as the individual learning process, is pivotal to their performance and appears to mediate how teaching quality translates into study success (Keri et al. 2021). Other studies, however, find hardly any correlation between student course satisfaction and student performance, as both have distinct roots: noncognitive factors versus cognitive factors (cf. Blanz 2014). Given the prominent role of student satisfaction ratings in course evaluations, and the attributed high value of these evaluations—for instance, in application packages for professorships—it is critically important to the political science profession to investigate the link between student satisfaction and performance, as well as the role of potential confounders. To explore the ambiguous relationship between student satisfaction and performance in the context of methods training in the study of political science, we K 870 P. Vierus et al. investigated the relationship between these two factors. The relationship can be attributed to (1) course-related characteristics, (2) personal attributes of the evaluator, and (3) other, rather arbitrary factors (e.g. gender or the migration background of students). To investigate this relationship, we fielded the longitudinal Pulse Survey in the Department of Political Science at the University of Duisburg-Essen with political science students in a single-major political science study programme. We repeatedly surveyed 219 students in five empirical methods and statistics courses across four points in time between October 2021 and February 2022 to “feel their pulse” during the difficult “COVID semester”. These five courses differed in the amount of applied statistics, but all of them had at least some elements of it. Moving beyond previous studies on student satisfaction with methods curricula—studies that have focused on satisfaction with methods training as a predictor of self-assessed competencies in a cross-sectional research design (Auspurg et al. 2015)—we drew a more nuanced picture of the determinants of student satisfaction by including a range of course-related and student-related factors, and we collected data across a range of courses and over time. Moreover, we asked students for permission to merge their final course grades with their survey answers. This allowed us to investigate student satisfaction, together with several aspects of the courses they took, to assess their actual studying success. The data collection was implemented during the height of the COVID-19 pandemic in the winter semester of 2021/2022. This period effect makes the findings particularly useful, as students were experiencing extraordinary levels of organisational and psychological strain. Over time, the dynamic of the pandemic created a volatile environment in which the methods and statistics training took place. In sum, we demonstrate that the “statistics anxiety index”—the self-reported worries at the first survey about getting lower grades in statistics courses, becoming nervous, or feeling helpless when solving tasks with a focus on statistics—has a clear and substantive effect both on the unified satisfaction index and on the students’ final grades. This clear pattern raises the question of how to optimally support students with high levels of negative emotions towards statistics. Section 2 presents the theoretical framework, including a review of previous research and the expectations for our own analyses. Section 3 lays out the Pulse Survey data and our analytical strategy. Section 4 displays the empirical results, and Section 5 summarises our contributions and suggests some wider implications of intense student surveying. 2 Measuring Student Performance in Political Science 2.1 Previous Research High-quality teaching in higher education is a desirable outcome, whether from the perspective of students, instructors, or employers. However, conceptualising, defining, and measuring high-quality teaching is a challenging task (Goerres et al. 2015; Lambach et al. 2017). Thinking of teaching situations as an increase in the knowledge base of students through knowledge transfer (Gow and Kember 1993), K Following Political Science Students Through Their Methods Training: Statistics Anxiety,... 871 this task requires some evaluation criteria. While student performance can to some extent be mapped by grades, using average grades as indicators of teaching quality is problematic because it reflects unobserved characteristics of students, for instance, their motivation, intelligence, skills, and experience. Since students are typically not randomly assigned to courses—they select themselves for a course depending on specific criteria—average course assessment grades are likely to be confounded by the composition of the group of students. To mute such hard-to-observe confounding factors, a few approaches have measured the knowledge or competencies of students before and after the course and compared the students’ progress over time (Wilson 2013). While standardised pre/post-testing would theoretically provide objective measurements, implementing such a system across numerous specialised courses, especially methods-related courses in which students typically possess little or no knowledge before the course starts, poses substantial practical challenges in terms of test development, validation, and administrative resources. Given the limited feasibility of implementing knowledge-based tests, a typical approach to measuring teaching quality has been to rely on the students’ subjective course evaluations (see Pineda and Steinhardt [2020] for an overview). This approach has various advantages, as it is time efficient and delivers comparable results across courses and over time. For example, studies show that student satisfaction, as a predictor variable, to some extent explains the perceived student performance, course grades, retention rates, and graduation rates (Keri et al. 2021; Kostagiolas et al. 2019). As a dependent variable, student satisfaction is explained by several academic and course-related factors, such as the quality of course instruction, advice, and class size (Tessema et al. 2012; Jamelske 2009), as well as by individual and psychological factors, such as expectations, self-esteem, and conscientiousness (Schaeper 2020). Consequently, some studies conceptualise student satisfaction as an intermediary variable that largely mediates aspects of teaching quality on student performance (Keri et al. 2021).1 Apart from studies that emphasise the role of satisfaction, other research finds that student satisfaction and performance are hardly correlated (Blanz 2014). At the same time, it is important to critically reflect on the validity of student perceptions in general and student satisfaction ratings more specifically. Several assumptions underlie the use of such perceptual measures: (1) it is assumed that students remember the course content correctly when they evaluate that content retrospectively, (2) it is assumed that students assess the course independently of individual characteristics (e.g. two students with the same experience of a course should come to a similar evaluation, regardless of their personal background), and (3) it is assumed that students take only course-related content into account rather than also considering arbitrary criteria (e.g. the perceived skin colour, attractiveness, age, or gender of the instructor). These three assumptions are not easy to defend. 1It might also be possible that performance causally affects student satisfaction, while most empirical studies model performance as a consequence of satisfaction. This nonetheless makes it even more important to theoretically conceptualise, and empirically measure, specific aspects of satisfaction that go beyond a global measure of student satisfaction. K 872 P. Vierus et al. Regarding the question of accurate recall (assumption 1), research shows that recall bias is common in survey research (Blome and Augustin 2015). The longer that events date back, and the more complex the chain of events has been, the greater the likelihood of measurement error due to memory bias (Manzoni et al. 2010). At the same time, evaluations of a one-term course typically only require reasonable amounts of memory and cognitive capacity. Moreover, course evaluations typically ask students how often they attended the course, which might provide some insight into the reliability of their responses. The second assumption calls for a careful control strategy in which the impact of individual characteristics is held constant so that systematic variations across courses do not hamper comparisons. The third assumption—that only educational content should be relevant for evaluations made by students—is untenable. Decades of psychological research show that people evaluate others based on categories formed by socialisation and by experiential and societal processes (Rhodes and Baron 2019). While social categorisation represents a core psychological capacity that makes the social world accessible in an efficient and predictable way, it can also entail forms of stereotyping, prejudice, and discrimination. An example would be that teachers who are perceived as “outgroup” members receive extensively negative evaluations from students, not because of their teaching but because of their “otherness”. Quasi-experimental studies on bias in student evaluations show that, for example, instructors who are female and persons of colour receive lower scores on student evaluations than do white men (Chávez and Mitchell 2020). Similarly, instructors who, on average, assign better grades to students or assign a low course workload receive better evaluations than those who do otherwise (Clayson et al. 2006; Marsh and Roche 2000). Students also give higher scores to more attractive instructors (Rosar and Klein 2009). To carry arbitrary reasoning to extremes, the availability of cookies during class has been shown to improve students’ evaluations of teaching (Hessler et al. 2018). Taken together, the validity and usefulness of student satisfaction ratings as a meaningful correlate of teaching quality is far from unambiguous. However, instead of abandoning the whole concept of student satisfaction, we argue that it is all the more important to further investigate its determinants and consequences with appropriate research designs. After all, student satisfaction is positively linked to learning outcomes through motivation. 2.2 Our Expectations To disentangle the multidimensional input that informs student satisfaction ratings from the context of higher education, we included survey questions that refer to (1) satisfaction with the organisation of the course, (2) satisfaction with the preparation of the instructor, (3) satisfaction with the learning progress, and (4) overall satisfaction with the course. Such a multidimensional approach is congruent with current conceptualisations of student satisfaction (Keri et al. 2021) and should facilitate the separation of foundations that underlie an assessment of these various factors (Blanz 2014). Our explanatory framework employs a multidimensional resource model of satisfaction that guides the selection of relevant predictor variables (Marsh 1980; Green et al. 2015). Specifically, this model states that student satK Following Political Science Students Through Their Methods Training: Statistics Anxiety,... 873 isfaction only partially reflects teaching quality, as it also conveys the impact of other relevant factors that determine students’ expectations, as well as perceived and actual learning success. We can summarise this in hypothesis 1: Factors other than course characteristics have measurable effects on satisfaction. This assertion relates to research that shows that factors largely unrelated to courses can also have a strong relationship with perceived satisfaction. These factors include personality traits, student motivation, and stress management (Keri et al. 2021; Cotton et al. 2002). To assess the effects of alternative explanations, we included factors such as the students’ educational background before studying, motivational aspects, statistics anxiety, procrastination, and self-efficacy, while also including measures of course characteristics. Incorporating these personal characteristics is also expected to mitigate concerns about biased assessments due to unobserved confounders. Regarding the consequences of student satisfaction, we hypothesise—in line with previous research (Keri et al. 2021)—that student satisfaction with teaching is positively associated with studying success, measured as grades obtained in the final exams of the course (hypothesis 2). At the same time, we expect the empirical relationship between student satisfaction and studying success to be substantially absorbed once competing factors are taken into account (hypothesis 3). This contention is based on research that shows that student satisfaction and performance are hardly correlated because they are rooted in different processes (Blanz 2014). While student satisfaction is more systematically related to “noncognitive” factors, such as course-related factors, social competence, and personality traits, performance is largely based on “cognitive” factors, such as learning behaviour and previous grades (Blanz 2014: p. 282). Regarding the specific attributes of the scope of this study, teaching methodology in political science involves a number of challenges that can have a particular impact on students’ success in their studies and on how teachers cope with those challenges. Methods training is generally considered to be demanding, and psychological characteristics, especially statistics anxiety (Maloney and Beilock 2012), play a role here. We thus incorporated in our study indices of statistics anxiety, procrastination, and self-efficacy. This approach should map pitfalls specific to methods courses and help us to understand the role of time-constant characteristics and the resources of students, as well as the characteristics of the courses, in shaping student satisfaction and their academic success. 3 The Longitudinal Pulse Survey 2021/2022 3.1 Target Population and Field Period The data originates from a four-wave panel survey called the Pulse Survey at the University of Duisburg-Essen in Germany. The University of Duisburg-Essen has major programmes in social science, both at the bachelor of arts and the master of arts levels, with students from diverse socioeconomic backgrounds, both with roots in Germany and from abroad. The target population of students consisted of K 874 P. Vierus et al. all students from five courses with a political science methods focus at the Department of Political Science at the bachelor of arts and master of arts levels, both mandatory and elective and either lecture based or seminar based, that started in the winter semester 2021/22. All courses had at least some statistical components, either applied statistics or research designs that need statistical analysis during the implementation. That target population was about 450 students, with 198 who participated in the first survey wave. Due to panel dropouts, 98 students who took part in all four waves remained in the sample we used in the analyses. Considering the sample composition, no bias has been introduced by the panel dropouts (Online Appendix Table A.1). Online Appendix Table A.2 and Fig. A.1 show details of survey participation. Students were surveyed four times between November 2021 and February 2022 with a field period of 7 days per wave. During this period, some restrictions due to the COVID-19 pandemic were still in place. For example, courses were offered in person on campus and were then moved to remote teaching in December 2021. On average, survey completion time was between 5 minutes (wave 1) and 3 minutes (waves 2–4). Student characteristics with no, or expectedly low, variation over time (e.g. psychological measures) were covered in wave 1 only. The remaining surveys included questions on course satisfaction and course attendance. All students received two reminders per wave by their course instructor. For details on incentives, data handling, and ethics, see Note A.1 in the Online Appendix. 3.2 Variables 3.2.1 Dependent Variables We used questions about student satisfaction with the methods courses taken as well as the final grade achieved by students in each course as dependent variables. The questions on course-specific satisfaction pertained to overall course satisfaction, satisfaction with the organisation of the course, satisfaction with the course instructor’s performance, and satisfaction with the students’ own learning progress. Each dimension of satisfaction was measured using a seven-point rating scale with labelled endpoints ranging from “very dissatisfied” to “very satisfied”. We first calculated mean scores for each of the four satisfaction questions when a student was surveyed twice in different courses (n= 20; 10.1% of all students in wave 1). In a second step, we calculated the students’ mean for each question over the four survey waves. This way, we transformed our longitudinal data structure into a cross-sectional one. In order to derive a unified satisfaction index from the four questions, we used principal component analysis (PCA) to reduce these dimensions of satisfaction to a single component (eigenvalue: 2.88, 72% explained variance).2We used the esti2We find that using an exploratory factor analysis (EFA; with promax rotation), as well as a confirmatory factor analysis (all standardised factor loadings> 0.6), produces factor scores that are highly correlated with the scores obtained from a PCA (rPCA_EFA = 0.989, rPCA_EFA= 0.991). Using factor scores from an EFA leads to virtually congruent regression results, as reported in this research note. K Following Political Science Students Through Their Methods Training: Statistics Anxiety,... 881 Models 9 and 10 include all predictor variables, and in model 10 the course dummy variables are included. In both of these models, we find that frequency of attendance and the statistics anxiety index are significantly related to final grades. Moreover, the inclusion of these variables results in a drop in the coefficient estimate of the satisfaction measure. This pattern corroborates hypothesis 3. Specifically, the unified satisfaction index seems to have mediated student characteristics such as the statistics anxiety index. Once taken into account, the statistics anxiety index appears to be more indicative of the final grade than the unified satisfaction index. 5 Conclusions We present three major results in explaining student satisfaction and course performance in political science courses. First of all, student satisfaction with the courses attended during the semester follows a slight U shape over time. Satisfaction initially decreases and then rises again almost to the starting level towards the end of the lecture period (wave 4). This trend of satisfaction is caused by only a few students moving between very high and very low levels of satisfaction between time points. This shows that there is some variation in satisfaction between students and over time. Second, we see relatively few significant effects in two perspectives of the independent variables: characteristics of course participation and psychological as well as demographic characteristics of students. None of the course attributes has any precise effects on the unified satisfaction index. On the other hand, from the perspective of students’ attributes, it is interesting that a higher level of their parents’ education has a negative but statistically nonsignificant association with the unified satisfaction index. Most importantly, the statistics anxiety index shows a negative association with the unified satisfaction index. Also, it has a strong negative estimated effect on the unified satisfaction index and the student’s satisfaction with their own learning process. Satisfaction therefore depends less on the course content and more on time-constant and individual characteristics. Third, we analysed the effects of course and student characteristics on study success. The situation turns out to be similar to the final grade that students achieve for their course. Their final grade is primarily influenced by their own educational success in school (reported Abitur grade) and the statistics anxiety scale measured as an index (Mang et al. 2018; Förster and Maur 2015). The effects of maths anxiety—which can already have a negative impact on performance at school (Maloney and Beilock 2012)—also negatively influence satisfaction and grades at university. Another factor affecting study success is the students’ frequency of attendance of the course: A higher frequency of attendance has a positive and substantial effect on their final grade. In this specific setting, where attendance is generally voluntary, there is clear potential for improving study performance by mobilising students into course participation throughout the lecture period. Finally, satisfaction with the student’s own learning progress is associated with a better grade, while other dimensions of satisfaction show no precise effects. K 882 P. Vierus et al. The limitations of our study resemble those of other research projects that sample university students (Chávez and Mitchell 2020): The sample size is relatively small and is limited to one institution. However, it is worth noting that response rates throughout the longitudinal design are relatively high even before accounting for student dropout from the courses. It remains hard to assess the possible effects of the COVID-19 pandemic on study and response behaviour. Although we exercised a comprehensive control strategy—for instance, the inclusion of dummy variables for each course that absorbed differences in exams or differences in difficulty of course content—we need to emphasise that with the one-time measure of the final grade, it is not possible to map causal relationships between variables with our research design. Although randomised controlled trials are not easily feasible in the present context, panel studies with repeated measurement of satisfaction and performance outcomes are an important avenue for future research. Future research should therefore focus more closely on the specific effects of the subdimensions of student satisfaction. This would then go beyond the limitations of this research note. For political science lecturers, our results are sobering. We show that student satisfaction—comprising a family of indicators—only partially predicts grades. A teaching culture based on measuring student satisfaction will thus fail to bring to light important patterns. The finding that students’ statistics anxiety—as measured at the beginning of a course—maintains such a strong impact on both satisfaction and grades throughout the study period calls for a holistic approach to methods training. In this approach, teachers would collaborate with psychological specialists and would act as gatekeepers for strategies to deal with these negative emotions. While we are aware that a comprehensive implementation of these recommendations might be unrealistic, we nevertheless want, at least, to raise the awareness of instructors and students to these influential factors. Supplementary Information The online version of this article (https://doi.org/10.1007/s11615-02500613-x) contains supplementary material, which is available to authorized users. Acknowledgements We thank Joshua Claaßen for implementing the online survey in Unipark survey software. The study was financed by seed money of the University of Duisburg-Essen to Prof. Achim Goerres (incentives and research assistance). Jan Karem Höhne was a postdoctoral researcher in the working group of Empirical Political Science, where most of the work was carried out. Author Contribution Conceptualised research: AG; research design: all; literature research: PV; questionnaire design: CZ, JKH; pretesting: JKH; field management, data protection, and data linkage: JE; data management: PV; statistical analysis: CZ, PV; first draft: JE, PV, CZ, AG; final draft: all; revisions: AG (lead), all. PV and JE share equal first authorship in random sequence. Funding Open Access funding enabled and organized by Projekt DEAL. Conflict of Interest P. Vierus, J. Elis, A. Goerres, C. Ziller, and J. Karem Höhne declare that they have no competing interests. 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