- Last 7 days
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Why Most Advertisers Can’t Afford to Boycott Facebook. (2020, July 8). Pro Market. https://promarket.org/2020/07/08/why-most-advertisers-cant-afford-to-boycott-facebook/
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science.sciencemag.org science.sciencemag.org
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Haushofer, J., & Metcalf, C. J. E. (2020). Which interventions work best in a pandemic? Science. https://doi.org/10.1126/science.abb6144
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twitter.com twitter.com
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psyarxiv.com psyarxiv.com
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Adam-Troian, J., & Bagci, S. (2020). The pathogen paradox: Evidence that perceived COVID-19 threat is associated with both pro- and anti-immigrant attitudes. https://doi.org/10.31234/osf.io/948ch
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Fontana, M., Iori, M., Montobbio, F., & Sinatra, R. (2020). New and atypical combinations: An assessment of novelty and interdisciplinarity. Research Policy, 49(7), 104063. https://doi.org/10.1016/j.respol.2020.104063
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Spiegelhalter, D. (2020, July 5). Risks, R numbers and raw data: How to interpret coronavirus statistics. The Observer. https://www.theguardian.com/world/2020/jul/05/risks-r-numbers-and-raw-data-how-to-interpret-coronavirus-statistics
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Fontanet, A., Tondeur, L., Madec, Y., Grant, R., Besombes, C., Jolly, N., Pellerin, S. F., Ungeheuer, M.-N., Cailleau, I., Kuhmel, L., Temmam, S., Huon, C., Chen, K.-Y., Crescenzo, B., Munier, S., Demeret, C., Grzelak, L., Staropoli, I., Bruel, T., … Hoen, B. (2020). Cluster of COVID-19 in northern France: A retrospective closed cohort study. MedRxiv, 2020.04.18.20071134. https://doi.org/10.1101/2020.04.18.20071134
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Sperrin, M., Martin, G. P., Sisk, R., & Peek, N. (2020). Missing data should be handled differently for prediction than for description or causal explanation. Journal of Clinical Epidemiology, 0(0). https://doi.org/10.1016/j.jclinepi.2020.03.028
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Natalie E. Dean, PhD on Twitter: “THINK LIKE AN EPIDEMIOLOGIST: There are more new confirmed cases each day in the US than at any time during the earlier April peak. But is it really meaningful to compare those numbers? How do epidemiologists decide when to sound the alarm? A thread. 1/11 https://t.co/rPelzIvcxs” / Twitter. (n.d.). Twitter. Retrieved July 3, 2020, from https://twitter.com/nataliexdean/status/1278868210385915904
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Gupta, H., & Porter, M. A. (2020). Mixed Logit Models and Network Formation. ArXiv:2006.16516 [Physics, Stat]. http://arxiv.org/abs/2006.16516
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Lovato, J., Allard, A., Harp, R., & Hébert-Dufresne, L. (2020). Distributed consent and its impact on privacy and observability in social networks. ArXiv:2006.16140 [Physics]. http://arxiv.org/abs/2006.16140
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Starnini, M., Aleta, A., Tizzoni, M., & Moreno, Y. (2020). Impact of the accuracy of case-based surveillance data on the estimation of time-varying reproduction numbers. MedRxiv, 2020.06.26.20140871. https://doi.org/10.1101/2020.06.26.20140871
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psyarxiv.com psyarxiv.com
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Rahman, M., Ali, G. G. M. N., Li, X. J., Paul, K. C., & Chong, P. H. J. (2020). Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/fz4ry
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www.nature.com www.nature.com
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Lavezzo, E., Franchin, E., Ciavarella, C., Cuomo-Dannenburg, G., Barzon, L., Del Vecchio, C., Rossi, L., Manganelli, R., Loregian, A., Navarin, N., Abate, D., Sciro, M., Merigliano, S., De Canale, E., Vanuzzo, M. C., Besutti, V., Saluzzo, F., Onelia, F., Pacenti, M., … Crisanti, A. (2020). Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’. Nature, 1–1. https://doi.org/10.1038/s41586-020-2488-1
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- Jun 2020
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psyarxiv.com psyarxiv.com
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Rauschenberg, C., Schick, A., Goetzl, C., Röhr, S., Riedel-Heller, S., Koppe, G., Durstewitz, D., Krumm, S., & Reininghaus, U. (2020). Social isolation, mental health and use of digital interventions in youth during the COVID-19 pandemic: A nationally representative survey [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/v64hf
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psyarxiv.com psyarxiv.com
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Levita, L., Gibson Miller, J., Hartman, T. K., Murphy, J., Shevlin, M., McBride, O., Mason, L., Martinez, A. P., bennett, kate m, Stocks, T. V. A., McKay, R., & Bentall, R. (2020). Report2: Impact of Covid-19 on young people aged 13-24 in the UK- preliminary findings [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/s32j8
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psyarxiv.com psyarxiv.com
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Yamada, Y. (2020). Micropublishing during and after the COVID-19 era [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/8fum4
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medium.com medium.com
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Caching is dangerous if not done correctly. For example, making decisions based on outdated data as if it was current.
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Czarnek, G., Szwed, P., & Kossowska, M. (2020). Political ideology and attitudes toward vaccination: Study report. https://doi.org/10.31234/osf.io/uwehk
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psyarxiv.com psyarxiv.com
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Im, H., & Chen, C. (2020). Social Distancing Around the Globe: Cultural Correlates of Reduced Mobility [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/b2s37
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Syed, M., & Donnellan, B. (2020). Registered Reports with Developmental and Secondary Data: Some Brief Observations and Introduction to the Special Issue [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/gnhxk
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www.youtube.com www.youtube.com
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Supporting Open Science Data Curation, Preservation, and Access by Libraries. (2020, June 25). https://www.youtube.com/watch?v=SbmGWHpzAHs&feature=youtu.be
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zoom.us zoom.us
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Welcome! You are invited to join a webinar: Supporting Open Science Data Curation, Preservation, and Access by Libraries. After registering, you will receive a confirmation email about joining the webinar. (n.d.). Zoom Video. Retrieved June 28, 2020, from https://zoom.us/webinar/register/2615905946283/WN_W6dYUXQFTqGQjGAZPRB74w
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www.sciencedirect.com www.sciencedirect.com
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Rosenberg, E. S., Tesoriero, J. M., Rosenthal, E. M., Chung, R., Barranco, M. A., Styer, L. M., Parker, M. M., John Leung, S.-Y., Morne, J. E., Greene, D., Holtgrave, D. R., Hoefer, D., Kumar, J., Udo, T., Hutton, B., & Zucker, H. A. (2020). Cumulative incidence and diagnosis of SARS-CoV-2 infection in New York. Annals of Epidemiology. https://doi.org/10.1016/j.annepidem.2020.06.004
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Science Magazine on Twitter: “Democrats & Republicans have seemingly taken a divided stance on how to handle the #COVID19 pandemic, & their Twitter accounts might provide the best evidence to date. Watch the latest video from our #coronavirus series on research from @ScienceAdvances: https://t.co/BSCdWS011J https://t.co/sJyF357wje” / Twitter. (n.d.). Twitter. Retrieved June 27, 2020, from https://twitter.com/sciencemagazine/status/1276282194596675590
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Green, J., Edgerton, J., Naftel, D., Shoub, K., & Cranmer, S. J. (2020). Elusive consensus: Polarization in elite communication on the COVID-19 pandemic. Science Advances, eabc2717. https://doi.org/10.1126/sciadv.abc2717
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psyarxiv.com psyarxiv.com
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Parsons, Sam. ‘Reliability Multiverse’, 26 June 2020. https://doi.org/10.31234/osf.io/y6tcz.
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psyarxiv.com psyarxiv.com
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Brik, A. B. (2020). COVID 19 FAMILY LIFE STUDY [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/gcqhp
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Stokel-Walker, C. (n.d.). Concerns raised about pubs collecting data for coronavirus tracing. New Scientist. Retrieved June 25, 2020, from https://www.newscientist.com/article/2246965-concerns-raised-about-pubs-collecting-data-for-coronavirus-tracing/
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www.youtube.com www.youtube.com
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EU Datathon 2020—Webinar on COVID-19 and media and data monitoring. (2020, April 22). https://www.youtube.com/watch?v=wyNgmEfi_vk&feature=youtu.be
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www.unforgettable.me www.unforgettable.me
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Register here: (n.d.). Google Docs. Retrieved May 5, 2020, from https://docs.google.com/forms/d/e/1FAIpQLSdqXWlf0sbRR9wSH_42shm4vU4tHcCe0bQZuC-6ngHaI4I32w/viewform??embedded=true&usp=embed_facebook
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psyarxiv.com psyarxiv.com
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Cheung, M. W.-L. (2020). Meta-Analytic Structural Equation Modeling [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/epsqt
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psyarxiv.com psyarxiv.com
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Minocher, R., Atmaca, S., Bavero, C., McElreath, R., & Beheim, B. (2020, June 24). Reproducibility of social learning research declines exponentially over 63 years of publication. https://doi.org/10.31234/osf.io/4nzc7
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psyarxiv.com psyarxiv.com
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Romero, E., López-Romero, L., Domínguez-Álvarez, B., Villar, P., & Gómez-Fraguela, J. A. (2020). Testing the effects of COVID-19 confinement in Spanish children: The role of parents’ distress, emotional problems and specific parenting [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/spxtw
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declaring-independence.org declaring-independence.org
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SPARC (2020) We Declared Independence and so can you. Retrieved from https://declaring-independence.org/
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covid19mm.github.io covid19mm.github.io
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Sixth Report. (2020, June 19). COVID-19 Mobility Monitoring Project. https://covid19mm.github.io//in-progress/2020/06/19/sixth-report.html
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psyarxiv.com psyarxiv.com
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Bulbulia, J., Barlow, F., Davis, D. E., Greaves, L., Highland, B., Houkamau, C., Milfont, T. L., Osborne, D., Piven, S., Shaver, J., Troughton, G., Wilson, M., Yogeeswaran, K., & Sibley, C. G. (2020). National Longitudinal Investigation of COVID-19 Lockdown Distress Clarifies Mechanisms of Mental Health Burden and Relief [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/cswde
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blogs.lse.ac.uk blogs.lse.ac.uk
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Carrying out qualitative research under lockdown – Practical and ethical considerations. (2020, April 20). Impact of Social Sciences. https://blogs.lse.ac.uk/impactofsocialsciences/2020/04/20/carrying-out-qualitative-research-under-lockdown-practical-and-ethical-considerations/
Tags
- qualitative research
- technique
- data collection
- study design
- COVID-19
- pandemic
- ethics
- is:blog
- scientific practice
- lang:en
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blogs.lse.ac.uk blogs.lse.ac.uk
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Using LinkedIn for Social Research. (2019, July 9). Impact of Social Sciences. https://blogs.lse.ac.uk/impactofsocialsciences/2019/07/09/using-linkedin-for-social-research/
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agromonitoring.com agromonitoring.com
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theplosblog.plos.org theplosblog.plos.org
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Yeolekar, M. (2020, April 30). The Digital Migration: Lessons About Open Science Arising from the COVID19 Crisis. The Official PLOS Blog. https://theplosblog.plos.org/2020/04/the-digital-migration-lessons-about-open-science-arising-from-the-covid19-crisis/
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twitter.com twitter.com
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twitter.com twitter.com
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Katz, M. MD. (2020, June 21). "Amazing what a difference four weeks makes since Memorial Day Weekend in #COVID19 New cases/day by U.S. region, adjusted for population". Twitter. https://twitter.com/subatomicdoc/status/1274702408317374466
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twitter.com twitter.com
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psyarxiv.com psyarxiv.com
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Antonakis, J., Bastardoz, N., & Jacquart, P. (2020). In praise of the impact factor [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/h4p9e
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n this project we see a shift from a citizen-based model to a consumer model for urban planning, where all citizens’ ‘personal and environmental data is an economic resource.’
Called survillance capitlism
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reutersinstitute.politics.ox.ac.uk reutersinstitute.politics.ox.ac.uk
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Newman, N. (n.d.). Reuters Institute Digital News Report 2020. 112.
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Local file Local file
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Informal mentorship was captured using the following retrospective question from Wave 3 of the AddHealth data: "Other than your parents or step-parents, has an adult made an important positive difference in your life at any time since you were 14 years old?" Based on this question, I created a binary indicator for mentorship coded 1 if the young person had an informal mentor and 0 if they did not. Respondents were then asked "How is this person related to you?", and given response options like "family,""teacher/counselor,""friend's parent,""neighbor,"and "religious leader.
Defining informal mentorship in the survey data
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Middle-income subsample 3,158
Middle-income subsample for analysis was 3,158
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1. "Middle-income" is defined as anyone living in a household making two-thirds to double the median income (Pew Research Center, 2016). In 1994, the median income for a family of four was $46,757(US Bureau of Statistics, 1996). Thus, "middle-income" families would be those making between $30,860 and $93,514. Because I only have data available in $25,000 increments, I am defining middle-income families as those making between $25,000 and $100,000 a year in Wave 1.
Middle-income = families making $25k-$100k a year in Wave 1
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Defining low-,middle-, and high-income groupsDue to the limitation in the data described above, all incomes had to be converted in to categorical responses, with the smallest possible category size of $25,000 dollars. This created five categories for all incomes:
Defining income groups: under $25k, $25k-$49999, $50k-$74999, $75k-$99999, and $100k+.
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Wave 1 income was collected as a continuous variable, with an average of $45,728, (N=15,351, SD=$51,616). Low-income respondents (with incomes below $25,000) had an average of $9,837 (N=3,049, SD=4,633). Wave 4 income was recorded as a categorical variable, however, where respondents indicated if they made under $5,000, between $5,000 and $10,000, between $10,000 and $15,000, etc. These categories were of different sizes, getting larger as the income grew larger. Therefore, in order to create comparable measures between Wave 1 and Wave 4, both incomes were converted to 5 groups, (1) household income of less than $25,000, (2) household income of $25,000 to $49,999, (3) household income of $50,000 to $74,000, (4) household income of $75,000 to $99,000, and (5) household income of over $100,000
Upward mobility (dependent variable); data surrounding household incomes of Wave 1 and Wave 4
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stratum. This sampling method yielded a sample of 20,745 students in 7thto 12thgrade, with oversampling of some minority racialethnic groups, students with disabilities, and twins(Harris, 2018). Data were also collected from the parents of the in-home survey respondents, with an 85% success rate (Chen & Chantala, 2014).Wave 1 participants also reported their home address, which was then linked to a number of state-, county-, and Census tract-level variables from other sources. The present study used the school survey data, the in-home interview data, the parent survey data, and the data that was linked to state, county, and census-tracts, as described above. This study also used data from two subsequent waves of in-home interviews, specifically waves 3 and 4 (no new information relevant to the present study was collected in Wave 2). For each subsequent wave, AddHealth survey administrators recruited from the pool of Wave 1 respondents, no matter if they had responded to any wave since Wave 1. The present study used Wave 1 data for information about the youth’s socioeconomic status, social capital and other related variables. This wave collected from 1994 to 1995, when most respondents were between11 and 19 years old (n=20,745 youth) (Harris, 2013).This study also used information from the third wave of in-home interview data, namely all questions on informal mentoring. This wave wascollected in 2001 and 2002 when the youth (N=15,197) were 18 to 26 years old. The fourth wave of data was collected in 2008 and 2009, when the respondents were 25 to 33 years old (n=15,701). Data from the fourth wave wereused to calculate economic mobility, the key dependent variable for this study.
Data source
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DataTo address these questions, this study used three wavesofthe restricted-use version of the National Longitudinal Study of Adolescent Health (AddHealth). AddHealth is a multi-wave longitudinal, nationally representative study of youth who have been followed since adolescence through to adulthood. The AddHealth data were collected by sampling 80 high schools stratified across region, school type, urbanicity, ethnic mix, and school size during the 1994-1995 academic year. Fifty-two feeder schools(commonly middle schools whose students were assumed to go to these study high schools)were also sampled, resulting in a total of 132 sample schools. (Chen & Chantala, 2014, Harris, 2013). When sample high schools had grades 7 to 12, feeder schools were not recruited, as the lower grades served the role of feeding in younger students (Chen, 2014). Seventy nine percent of schools approached agreed to be in the study (Chen & Chantala, 2014). An in-school survey was then administered to over 90,000 students from these 132 schools. This survey was given during a single day within a 45-to 60-minute class period (Chen & Chantala, 2014). Subsequent recruitment for in-home interviews was done by stratifying students in each school by grade and sex and then randomly choosing 17 students from each
Data source
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www.wsj.com www.wsj.com
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Governments’ use of purchased location data has exploded in recent months, as officials around the world have sought insights on how people are moving around during the Covid-19 pandemic. In general, governments have assured their citizens that any location data collected by the marketing industry and used by public health entities is anonymous. But the movements of a phone give strong clues to its ownership—for example, where the phone is located during the evenings and overnight is likely where the phone owner lives. The identity of the phone’s owner can further be corroborated if their workplace, place of worship, therapist’s office or other information about their real-world activities are known to investigators.
private data is not anonymous as is purported
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covid19-projections.com covid19-projections.com
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COVID-19 Infections Tracker. (n.d.). COVID-19 Projections Using Machine Learning. Retrieved June 20, 2020, from https://covid19-projections.com/infections-tracker/
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Starr, T. N., Greaney, A. J., Hilton, S. K., Crawford, K. H., Navarro, M. J., Bowen, J. E., Tortorici, M. A., Walls, A. C., Veesler, D., & Bloom, J. D. (2020). Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding [Preprint]. Microbiology. https://doi.org/10.1101/2020.06.17.157982
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www.jclinepi.com www.jclinepi.com
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van Smeden, M., Groenwold, R.H.H., & Moons, K. GM. (2020, June 18). A cautionary note on the use of the missing indicator method for handling missing data in prediction research. Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2020.06.007
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www.the100.ci www.the100.ci
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Crisis research, fast and slow. (2020, March 26). The 100% CI. http://www.the100.ci/2020/03/26/crisis-research-fast-and-slow/
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Giles, C., & Burn-Murdoch, J. (2020, May 28). UK suffers second-highest death rate from coronavirus | Free to read. https://www.ft.com/content/6b4c784e-c259-4ca4-9a82-648ffde71bf0
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psyarxiv.com psyarxiv.com
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Yamagata, M., Teraguchi, T., & Miura, A. (2020, April 10). The Relationship between Infection-Avoidance Tendency and Exclusionary Attitudes towards Foreigners: A Case Study of the COVID-19 Outbreak in Japan. https://doi.org/10.31234/osf.io/vhrqn
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van Rooij, B., de Bruijn, A. L., Reinders Folmer, C., Kooistra, E., Kuiper, M. E., Brownlee, M., … Fine, A. (2020, April 22). Compliance with COVID-19 Mitigation Measures in the United States. https://doi.org/10.31234/osf.io/qymu3
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royalsociety.org royalsociety.org
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DELVE group publishes evidence paper on the use of face masks in tackling Coronavirus (COVID-19) pandemic | Royal Society. (2020 May 04). https://royalsociety.org/news/2020/05/delve-group-publishes-evidence-paper-on-use-of-face-masks/
Tags
- DELVE
- droplet
- COVID-19
- infection
- management
- physical distancing
- publication
- Data Evaluation and Learning for Viral Epidemics
- policy
- SAGE
- lang:en
- asymptomatic
- evidence
- learning
- public health
- transmission reduction
- Royal Society
- behavioral change
- face mask
- social distancing
- is:webpage
Annotators
URL
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covid19.gleamproject.org covid19.gleamproject.org
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Gleam Project | COVID-19 Mobility USA
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psyarxiv.com psyarxiv.com
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Raihani, N., & de-Wit, L. (2020, April 21). Factors Associated With Concern, Behaviour & Policy Support in Response to SARS-CoV-2. https://doi.org/10.31234/osf.io/8jpzc
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doi.org doi.org
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Willem, L., Hoang, T. V., Funk, S., Coletti, P., Beutels, P., & Hens, N. (2020). SOCRATES: An online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19 [Preprint]. Epidemiology. https://doi.org/10.1101/2020.03.03.20030627
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DaveLagnado on Reddit
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firebase.google.com firebase.google.com
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An index of groups can help a great deal here:
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proandroiddev.com proandroiddev.com
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normalizing our dabatase will help us. What means normalize? Well, it simply means to separate our information as much as we can
directly contradicts firebase's official advice: denormalize the structure by duplicating some of the data: https://youtu.be/lW7DWV2jST0?t=378
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twitter.com twitter.com
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Twitter. (n.d.). Twitter. Retrieved June 18, 2020, from https://twitter.com/devisridhar/status/1273528564793643008
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twitter.com twitter.com
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Twitter. (n.d.). Twitter. Retrieved June 18, 2020, from https://twitter.com/scibeh/status/1273519745762037765
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www.dkv.global www.dkv.global
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COVID-19 Regional Safety Assessment | DKG. (n.d.). DKV. Retrieved June 17, 2020, from https://www.dkv.global/covid-safety-assessment-200-regions
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www.reddit.com www.reddit.com
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r/BehSciMeta—Comment by u/UHahn on ”What is the impact of retraction of scientific studies reported in news media?”. (n.d.). Reddit. Retrieved June 17, 2020, from https://www.reddit.com/r/BehSciMeta/comments/gyw43b/what_is_the_impact_of_retraction_of_scientific/ftp5w1p
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www.thelancet.com www.thelancet.com
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Kucharski, A. J., Klepac, P., Conlan, A. J. K., Kissler, S. M., Tang, M. L., Fry, H., Gog, J. R., Edmunds, W. J., Emery, J. C., Medley, G., Munday, J. D., Russell, T. W., Leclerc, Q. J., Diamond, C., Procter, S. R., Gimma, A., Sun, F. Y., Gibbs, H. P., Rosello, A., … Simons, D. (2020). Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: A mathematical modelling study. The Lancet Infectious Diseases, 0(0). https://doi.org/10.1016/S1473-3099(20)30457-6
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Zelner, J., Riou, J., Etzioni, R., & Gelman, A. (2020). Accounting for Uncertainty During a Pandemic. ArXiv:2006.08745 [Physics, q-Bio, Stat]. http://arxiv.org/abs/2006.08745
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github.com github.com
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aaronpeikert. (2020). Aaronpeikert/reproducible-research [TeX]. https://github.com/aaronpeikert/reproducible-research (Original work published 2019)
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zoom.us zoom.us
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Oxford Internet Institute. Webinar Registration - "Data, data (science), get us out of here! Recommendations for resilient and fair policy-making in a crisis"
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twitter.com twitter.com
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Kazemi, D. (2020, May 23). "NPR is promoting this article again. Without access to the study we have no way of knowing how "bot" was estimated or measured, we simply have to go on the reputation and past research of this lab, which I dug into last night here: https://twitter.com/tinysubversion..." Twitter. https://twitter.com/tinysubversions/status/1263965246416318465
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wip.mitpress.mit.edu wip.mitpress.mit.edu
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Building the New Economy · Works in Progress. (n.d.). Works in Progress. Retrieved June 16, 2020, from https://wip.mitpress.mit.edu/new-economy
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twitter.com twitter.com
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Nivi Mani on Twitter. "I cannot stop smiling! Here is a first peek at the data from our online browser-based intermodal preferential looking set-up! We replicate the prediction effect (boy eats big cake, Mani & Huettig, 2012) using our online webcam testing software @julien__mayor @Kindskoepfe_Lab" / Twitter. (n.d.). Twitter. Retrieved June 15, 2020, from https://twitter.com/nivedita_mani/status/1265556217486815232
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Raleigh, V. S. (2020). Tackling UK’s mortality problem: Covid-19 and other causes. BMJ, 369. https://doi.org/10.1136/bmj.m2295
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assets.publishing.service.gov.uk assets.publishing.service.gov.uk