Suchergebnisse

This is a case study of my doctoral work, in which I used a novel distinction to group participants according to how often they code-switch, that is, switch between languages within a conversation. Here, I address many of the practicalities regarding defining and categorizing code-switching habits, characterizing code-switching frequency according to a rigorously tested assessment created for this purpose, and subsequently recruiting the small percentage of the population who exhibit extreme code-switching behavior along the assessment scale. Once categorized, it was then important to match monolinguals and bilingual "switchers" who tend to code switch and "non-switchers" who abstain from this behavior on measures known to affect cognition, including language proficiency, socioeconomic status, age, and working memory. I discuss the practical aspects of defining and successfully recruiting matched groups for an event-related potential study regarding neural differences between switchers, non-switchers, and monolinguals related to the ability to suppress interference from irrelevant information while performing cognitive tasks.

During the onset of the COVID-19 pandemic, the COVIDiSTRESS Consortium launched an open-access global survey to understand and improve individuals' experiences related to the crisis. A year later, we extended this line of research by launching a new survey to address the dynamic landscape of the pandemic. This survey was released with the goal of addressing diversity, equity, and inclusion by working with over 150 researchers across the globe who collected data in 48 languages and dialects across 137 countries. The resulting cleaned dataset described here includes 15,740 of over 20,000 responses. The dataset allows cross-cultural study of psychological wellbeing and behaviours a year into the pandemic. It includes measures of stress, resilience, vaccine attitudes, trust in government and scientists, compliance, and information acquisition and misperceptions regarding COVID-19. Open-access raw and cleaned datasets with computed scores are available. Just as our initial COVIDiSTRESS dataset has facilitated government policy decisions regarding health crises, this dataset can be used by researchers and policy makers to inform research, decisions, and policy.

This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available. ; publishedVersion ; Fil: Yamada, Yuki. Kyushu University; Japón. ; Fil: Ćepulić, Dominik Borna. Catholic University of Croatia; Croacia. ; Fil: Coll Martín, Tao. Universidad de Granada; España. ; Fil: Debove, Stéphane. Independent Researcher; Francia. ; Fil: Gautreau, Guillaume. Universite Paris Saclay; Francia. ; Fil: Han, Hyemin. University of Alabama at Birmingahm; Estados Unidos. ; Fil: Rasmussen, Jesper. University Aarhus; Dinamarca. ; Fil: Tran, Thao P. State University of Colorado - Fort Collins; Estados Unidos. ; Fil: Travaglino, Giovanni A. University Of Kent; Reino Unido. ; Fil: Blackburn, Angélique M. Texas A&M University; Estados Unidos. ; Fil: Boullu, Loïs. Independent Researcher; Francia. ; Fil: Bujić, Mila. Universidad de Tampere; Finlandia. ; Fil: Byrne, Grace. Vrije Universiteit Amsterdam; Países Bajos. ; Fil: Caniëls, Marjolein C. J. Open University of The Netherlands; Países Bajos. ; Fil: Flis, Ivan. Catholic University of Croatia; Croacia. ; Fil: Kowal, Marta. University of Wroclaw; Polonia. ; Fil: Rachev, Nikolay R. Sofia University St. Kliment Ohridski; Bulgaria. ; Fil: Reynoso Alcántara, Vicenta. Universidad Nacional Autónoma de México; México. ; Fil: Zerhouni, Oulmann. Université Paris Nanterre; Francia. ; Fil: Ahmed, Oli. University of Chittagong; Bangladesh. ; Fil: Amin, Rizwana. Bahria University; Pakistán. ; Fil: Aquino, Sibele. Pontifícia Universidade Católica do Rio de Janeiro; Brasil. ; Fil: Areias, João Carlos. Universidad de Porto; Portugal. ; Fil: Aruta, John Jamir Benzon R. de la Salle University; Filipinas. ; Fil: Bamwesigye, Dastan. Mendel University in Brno; República Checa. ; Fil: Bavolar, Jozef. Pavol Jozef Safarik University; Eslovaquia. ; Fil: Bender, Andrew R. Michigan State University; Estados Unidos. ; Fil: Bhandari, Pratik. Universitat Saarland; Alemania. ; Fil: Bircan, Tuba. Vrije Unviversiteit Brussel; Bélgica. ; Fil: Reyna, Cecilia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina. ; Fil: Reyna Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Psicológicas; Argentina.

During the onset of the COVID-19 pandemic, the COVIDiSTRESS Consortium launched an open-access global survey to understand and improve individuals' experiences related to the crisis. A year later, we extended this line of research by launching a new survey to address the dynamic landscape of the pandemic. This survey was released with the goal of addressing diversity, equity, and inclusion by working with over 150 researchers across the globe who collected data in 48 languages and dialects across 137 countries. The resulting cleaned dataset described here includes 15,740 of over 20,000 responses. The dataset allows cross-cultural study of psychological wellbeing and behaviours a year into the pandemic. It includes measures of stress, resilience, vaccine attitudes, trust in government and scientists, compliance, and information acquisition and misperceptions regarding COVID-19. Open-access raw and cleaned datasets with computed scores are available. Just as our initial COVIDiSTRESS dataset has facilitated government policy decisions regarding health crises, this dataset can be used by researchers and policy makers to inform research, decisions, and policy.

Funder: Consejo Nacional de Ciencia y Tecnología (CONCYT); doi: https://doi.org/10.13039/501100007350 ; Funder: Research Foundation Flanders (FWO) postdoctoral fellowship ; Funder: The HSE University Basic Research Program ; Funder: JSPS KAKENHI Grant JP20K14222 ; Abstract: This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.