Measuring Congruence Between Voters and Parties in Online Surveys: Does Question Wording Matter?

Congruence on policies between political parties and voters is a frequently assumed re­quirement for democracy. To be able to study this, we should be able to calculate accurate and precise measures of policy congruence in political systems. This could then tell us more about the political system we study, and the "distances" that exist between parties and voters on either issues or broader ideological dimensions. Here, I draw on experimental data from a Voting Advice Application to show that the wording of the issues can influence the degree of congruence one measures. Yet, this comes with the complication that this influence depends on the type of issue, the characteristics of the voters themselves, and the party the congruence is calculated with. These findings should serve as a warning for those who aim to measure congruence that even minor changes in question-wording can (but do not have to) cause relatively large changes in congruence, especially when many parties are involved and the differences between the congruences are small

Patterns in the Press Releases of Trade Unions: How to Use Structural Topic Models in the Field of Industrial Relations

Quantitative text analysis and the use of large data sets have received only limited attention in the field of Industrial Relations. This is unfortunate, given the variety of opportunities and possibilities these methods can address. We demonstrate the use of one promising technique of quantitative text analysis - the Structural Topic Model (STM) - to test the Insider-Outsider theory. This technique allowed us to find underlying topics in a text corpus of nearly 2,000 German trade union press releases (from 2000 to 2014). We provide a step-by-step overview of how to use STM since we see this method as useful to the future of research in the field of Industrial Relations. Until now the methodological publications regarding STM mostly focus on the mathematics of the method and provide only aminimal discussion of their implementation. Instead, we provide a practical application of STM and apply this method to one of the most prominent theories in the field of Industrial Relations. Contrary to the original Insider-Outsider arguments, but in line with the current state of research, we show that unions do in fact use topics within their press releases which are relevant for both Insider and Outsider groups.Quantitative Textanalysen und die Verwendung von großen Datensätzen wurden im Bereich der Industriellen Beziehungen bisher nur wenig Aufmerksamkeit geschenkt. Das ist bedauerlich, wenn man bedenkt, welche Vielfalt an Möglichkeiten diese Methoden bieten. Wir zeigen daher wie eine neue und vielversprechende Technik der quantitativen Textanalyse - Structural Topic Model (STM) - eingesetzt werden kann, um große Datenmengen zu reduzieren und die Insider-Outsider Theorie zu testen. Diese Technik ermöglicht es in einem Textkorpus von fast 2000 Pressemitteilungen deutscher Gewerkschaften zwischen 2000 und 2014 die zugrundeliegenden Muster zu finden. Wir geben eine schrittweise Einführung zur Anwendung von STM, weil wir die Zukunft der Forschungsmethodik im Feld der Industriellen Beziehungen auch bei quantitativen Analysetechniken sehen. Bisherige methodologische Literatur zu STM konzentriert sich überwiegend auf die Mathematik der Methodik und weniger auf deren Umsetzung und Diskussion. Daher geben wir ein praktisches Anwendungsbeispiel der STM und testen eine der bekanntesten Theorien im Bereich der Industriellen Beziehungen. Entgegen den ursprünglichen Annahmen der Insider-Outsider Theorie, aber im Einklang mit dem aktuellen Forschungsstand, zeigen wir, dass Gewerkschaften in ihren Pressemitteilungen Themen ansprechen, die sowohl für die Gruppe der Insider als auch für die Gruppe der Outsider relevant sind

Introduction to Quantitative Text Analysis

Welcome to our introductory textbook on quantitative text analysis! This book originated as a collection of assignments and lecture slides that we prepared for the ECPR Winter and Summer Schools in Methods and Techniques. Later, as we taught the Introduction to Quantitative Analysis course at the ECPR Schools, the MethodsNET Summer School, and seminars at the Max Planck Institute for the Study of Societies, Goethe University Frankfurt, Chalmers University, and the Cyprus University of Technology, we added more and more material and text, resulting in this book. The version you see today has been updated with the help of a grant from the [Learning Development Network] (https://ldn.cut.ac.cy/) at the Cyprus University of Technology. For now, the book focuses on some of the best-known quantitative text analysis methods in the field, showing what they are and how to run them in R. So why bother with quantitative content analysis? For one thing, we can say that developments over the last twenty years have made research using quantitative text analysis a particularly exciting proposition. First, the huge increase in computing power has made it possible to work with large amounts of text. Second, there is the development of R - a free, open-source, cross-platform statistical software. This development has enabled many researchers and programmers to develop packages for statistical methods for working with text. In addition, the spread of the internet has made many interesting sources of textual data available in digital form. Add to this the emergence of social media as a massive source of text generated daily by millions of users around the world. However, quantitative text analysis can be a daunting experience for someone unfamiliar with quantitative methods or programming. Our aim with this book is to guide you through it, combining theoretical explanations with a step-by-step explanation of the code. There are also several exercises designed for those with little or no experience in text analysis, R, or quantitative methods. Ultimately, we hope that you will find this book not only informative but also engaging and educational

Replication data for: Validating Wordscores

Replication material for Validating Wordscores: The Promises and Pitfalls of Computational Text Scalin

The Prevalence of mRNA Related Discussions during the Post-COVID-19 Era

Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy and skepticism are raising serious concerns for a portion of the population in many countries, including Sweden. In this study, we use Swedish social media data and structural topic modeling to automatically identify mRNA-vaccine related discussion themes and gain deeper insights into how people's refusal or acceptance of the mRNA technology affects vaccine uptake. Our point of departure is a scientific study published in February 2022, which seems to once again sparked further suspicion and concern and highlight the necessity to focus on issues about the nature and trustworthiness in vaccine safety. Structural topic modelling is a statistical method that facilitates the study of topic prevalence, temporal topic evolution, and topic correlation automatically. Using such a method, our research goal is to identify the current understanding of the mechanisms on how the public perceives the mRNA vaccine in the light of new experimental findings

Prefrontal Cortical to Mediodorsal Thalamus Projection Neurons Regulate Posterror Adaptive Control of Behavior

Adaptive control is the online adjustment of behavior to guide and optimize responses after errors or conflict. The neural circuits involved in monitoring and adapting behavioral performance following error are poorly understood. The prefrontal cortex (PFC) plays a critical role in this form of control. However, these brain areas are densely connected with many other regions, and it is unknown which projections are critical for adaptive behavior. Here, we tested the involvement of four distinct dorsal and ventral prefrontal cortical projections to striatal and thalamic target areas in adaptive control. We re-analyzed data from published experiments, using trial-by-trial analyses of behavior in an operant task for attention and impulsivity. We find that male rats slow their responses and perform worse following errors. Moreover, by combining retrograde labeling and chemogenetic silencing, we find that dorsomedial prefrontal pyramidal neurons that project to the lateral nucleus of the mediodorsal thalamus (MDL) are involved in posterror performance and timing of responses, specifically with unpredictable delays until stimulus presentation. Together, these data show that dorsal medial PFC (mPFC) projection neurons targeting the lateral MDT regulate adaptive control to flexibly optimize behavioral responses in goal-directed behavior