"This book looks to set out best practice for conducing online experiments for all social science disciplines. Some basic content on designing experiments and the advantages and challenges will be included as a foundation and then the main body of the book will provide practical advice on how to design an online experiment. The end of the book will be real world experiments broken down into easy to follow steps"--
Abstract Similarly to other domains of the social sciences, behavioural science has grappled with a crisis concerning the effect sizes of research findings. Different solutions have been provided to answer this challenge. This paper will discuss analytical strategies developed in the context of computational social science, namely causal tree and forest, that will benefit behavioural scientists in harnessing heterogeneity of treatment effects in RCTs. As a mixture of theoretical and data-driven approaches, these techniques are well suited to exploit the rich information provided by large studies conducted using RCTs. We discuss the characteristics of these methods and their methodological rationale and provide simulations to illustrate their use. We simulate two scenarios of RCTs-generated data and explore the heterogeneity of treatment effects using causal tree and causal forest methods. Furthermore, we outlined a potential theoretical use of these techniques to enrich behavioural science ecological validity by introducing the notion of behavioural niche.
Ziel meines Beitrages ist es, einige Grundgedanken über politische Philosophie vorauszuschicken und dann zwei Standpunkte zum politischen Denken vorzustellen, die wesentlich mit den Persönlichkeiten des sefardischen Staatsmannes und Philosophen Isaak Abravanel und des venezianischen Rabbiners und Philosophen Simone Luzzatto zu identifizieren sind.
Combining citations and network analysis, this study examines information flows between 10 European elite newspapers from 2000 to 2009 and identifies this network's most central actors, sub-groups and structural features. At the same time, the article contributes to the literature with an alternative and network approach to the study of the European public sphere. Results indicate that The Times and The Guardian are the most quoted by other foreign newspapers, while the top monitors of information are The Guardian and El País. A longitudinal analysis of structural network metrics indicates a less dense but more inclusive information exchange that can be interpreted as sign of a qualitative transformation of the European communication space in the direction of a horizontal integration.
AbstractFollowing the outbreak of COVID‐19, scientists rushed to develop vaccines to protect individuals and ferry the world out of the pandemic. Unfortunately, vaccine hesitancy is a major threat to the success of vaccination campaigns. Research on previous pandemics highlighted the centrality of perceived risk and confidence as core determinants of vaccine acceptance. Research on COVID‐19 is less conclusive, and frequently it relies on one‐country, cross‐sectional data, thus making it hard to generalize results across contexts and observe these relationships over time. To bridge these gaps, in this article, we analyzed the association between perceived risk, confidence, and vaccine acceptance cross‐sectionally at individual and country levels. Then, we longitudinally explored whether a within‐country variation in perceived risk and confidence was correlated with a variation in vaccine acceptance. We used data from a large‐scale survey of individuals in 23 countries and 19 time‐points between June 2020 and March 2021 and comparative longitudinal multilevel models to estimate the associations at different levels of analysis simultaneously. Results show the existence of cross‐sectional relationships at the individual and country levels but no significant associations within countries over time. This article contributes to our understanding of the roles of risk perception and confidence in COVID‐19 vaccines' acceptance by underlining that these relationships might differ at diverse levels of analysis. To foster vaccine uptake, it might be important to address individual concerns and persisting contextual characteristics, but increasing levels of perceived risk and confidence might not be a sufficient strategy to increase vaccine acceptance rates.
Introduction -- Part I: The identity-otherness dynamics -- Chapter 1. Identity, otherness, and sociocultural dynamics (Sergio Salvatore) -- Chapter 2. Theories and methods (Alessia Rochira) -- Part II: Maps of Otherness -- Chapter 3. Immigration (Giuseppe A. Veltri) -- Chapter 4. Islam (Gordon Sammuta) -- Chapter 5. LGBT (Alina Pop) -- Part III: Symbolic resources for the representation of otherness -- Chapter 6. The semiotic construction of Otherness (Sergio Salvatore) -- Chapter 7. An interpretative model: Semiotic fields and semiotic forces (Sergio Salvatore) -- Chapter 8. Implications for policy-making and further developments (Giuseppe Veltri).
Frontmatter -- Preface -- Remarks -- Contents -- Discorso Circa Il Stato Degli Hebrei Et In Particolar Dimoranti Nell'Inclita Città Di Venetia / Luzzatto, Simone -- Discourse On The State Of The Jews And In Particular Those Dwelling In The Illustrious City Of Venice / Luzzatto, Simone -- The Venetian Context Of The Discourse / Ravid, Benjamin -- Individual Responsibility and Collective Punishment in the Thought of Rabbi Simone Luzzatto / Veltri, Giuseppe -- Jews on Trial and Their Sceptical Attorney: Philosophic Scepticism and Political Thought in Simone Luzzatto's Italian Works / Lissa, Anna -- Bibliography -- Glossary and Index of Names
The automated parsing of 130,213 news articles about the 2012 US presidential elections produces a network formed by the key political actors and issues, which were linked by relations of support and opposition. The nodes are formed by noun phrases and links by verbs, directly expressing the action of one node upon the other. This network is studied by applying insights from several theories and techniques, and by combining existing tools in an innovative way, including: graph partitioning, centrality, assortativity, hierarchy and structural balance. The analysis yields various patterns. First, we observe that the fundamental split between the Republican and Democrat camps can be easily detected by network partitioning, which provides a strong validation check of the approach adopted, as well as a sound way to assign actors and topics to one of the two camps. Second, we identify the most central nodes of the political camps. We also learnt that Clinton played a more central role than Biden in the Democrat camp; the overall campaign was much focused on economy and rights; the Republican Party (Grand Old Party or GOP) is the most divisive subject in the campaign, and is portrayed more negatively than the Democrats; and, overall, the media reported positive statements more frequently for the Democrats than the Republicans. This is the first study in which political positions are automatically extracted and derived from a very large corpus of online news, generating a network that goes well beyond traditional word-association networks by means of richer linguistic analysis of texts. ; This work was supported by EU-funded research projects CompLACS (FP7-ICT 270327); and ThinkBig (FP7-IDEAS-ERC 339365). ; Peer-reviewed ; Publisher Version