Author: 0000-0002-6606-5507 - Octopus

Organisation	Role	Department	Start date	End date
University of Otago Wellington	Senior Research Fellow	Not specified	2022-05-09
University of Cambridge	Postdoctoral Research Associate	Not specified	2020-03-16	2022-09-23
Victoria University of Wellington	Teaching Fellow	Not specified	2019-01-03	2019-06-30
University of Otago	Assistant Research Fellow	Not specified	2011	2011
University of Otago	Research assistant	Not specified	2007	2007
University of Otago	Research Assitant	Not specified	2006	2006

Organisation	Degree/title	Department	Start date	End date
Victoria University of Wellington	PhD	Not specified	2016-06-01	2020-09-30
University of Otago	MSciComm	Not specified	2009	2010
University of Otago	BSc (Hons)	Not specified	2003	2006

Title	DOI	Published date
The Misinformation Susceptibility Test (MIST): A psychometrically validated measure of news veracity discernment	10.3758/s13428-023-02124-2	2023-06-29
Transparent communication of evidence does not undermine public trust in evidence	10.1093/pnasnexus/pgac280	2022-11-01
COVID-19 risk perception and hoax beliefs in the US immediately before and after the announcement of President Trump's diagnosis	10.1098/rsos.212013	2022-08
Why are beliefs in different conspiracy theories positively correlated across individuals? Testing monological-network vs. unidimensional factor model explanations	10.31234/osf.io/xyf46	2022-07-22
Democracy and belief in conspiracy theories in New Zealand	10.1080/10361146.2022.2122773	2022-07-03
Why are beliefs in different conspiracy theories positively correlated across individuals? Testing monological network versus unidimensional factor model explanations	10.1111/bjso.12518	2022-07
Democracy and belief in conspiracy theories in New Zealand	10.31234/osf.io/s8qf4	2022-06-02
To what extent do beliefs in conspiracy theories change over time?	10.31234/osf.io/5q2ky	2022-03-31
Australasian Public Awareness and Belief in Conspiracy Theories: Motivational Correlates	10.1111/pops.12746	2022-02
Communicating expert consensus increases personal support for COVID‐19 mitigation policies	10.1111/jasp.12827	2022-01
The effects of President Trump’s COVID-19 diagnosis on hoax beliefs and risk perceptions of the virus in the U.S.	10.31234/osf.io/frxt8	2021-12-01
The Effects of Consensus Messages and the Importance of a Pre-Test: A Comment on Chinn and Hart (2021)	10.31234/osf.io/6fua5	2021-11-24
Associations between conspiracism and the rejection of scientific innovations	10.1177/09636625211007013	2021-10-16
The effects of communicating uncertainty around statistics on public trust: an international study	10.1101/2021.09.27.21264202	2021-09-29
Do Climate Change Consensus Messages Cause Reactance? A Comment on Chinn and Hart (2021)	10.31234/osf.io/kqsfz	2021-08-30
Correlates of intended COVID-19 vaccine acceptance across time and countries: results from a series of cross-sectional surveys	10.1136/bmjopen-2020-048025	2021-08
The Misinformation Susceptibility Test (MIST): A psychometrically validated measure of news veracity discernment	10.31234/osf.io/gk68h	2021-07-06
Right-wing authoritarianism and social dominance orientation predict rejection of science and scientists	10.1177/1368430221992126	2021-06
Effect of Information about COVID-19 Vaccine Effectiveness and Side Effects on Behavioural Intentions: Two Online Experiments	10.3390/vaccines9040379	2021-04-13
COVID-19 risk perception: a longitudinal analysis of its predictors and associations with health protective behaviours in the United Kingdom	10.1080/13669877.2021.1890637	2021-04-03
Communicating personalized risks from COVID-19: guidelines from an empirical study	10.1098/rsos.201721	2021-04
Political polarization on COVID-19 pandemic response in the United States	10.1016/j.paid.2021.110892	2021
Predictors of COVID-19 vaccine acceptance across time and countries	10.1101/2020.12.09.20246439	2020-12-11
Susceptibility to misinformation about COVID-19 around the world	10.1098/rsos.201199	2020-10
Risk perceptions of COVID-19 around the world	10.1080/13669877.2020.1758193	2020-08-02
The influence of politics and labelling on New Zealanders’ attitudes towards animal agriculture emissions policy	10.1080/00323187.2020.1800414	2020-05-03
Perceptions of scientific consensus do not predict later beliefs about the reality of climate change: A test of the gateway belief model using cross-lagged panel analysis	10.1016/j.jenvp.2018.08.012	2018-10
Changes in perceived scientific consensus shift beliefs about climate change and GM food safety	10.1371/journal.pone.0200295	2018-07-06
Benzylpiperazine in New Zealand: brief history and current implications	10.1080/03036758.2011.557036	2011-03
Pharmacological preconditioning with GYKI 52466: a prophylactic approach to neuroprotection	10.3389/fnins.2010.00054	2010
Selective reduction in synaptic proteins involved in vesicle docking and signalling at synapses in the ataxic mutant mousestargazer	10.1002/cne.21890	2009-01-01

Interpretation of an experiment to test the effects of different formats of communication of indirect uncertainty around the COVID-19 R number on a public audience

This experiment set out to investigate the effects of the communication of indirect uncertainty (the level of consensus between different estimates) in combination with direct uncertainty (the statistical range around a point estimate) in a pair of experiments within a single online survey. The first experiment presented people with a text form of the information (...

Published 2 years ago, by A. Freeman, J. Kerr, S. Dryhurst

Analysis

Analysis of an experiment to test the effects of different formats of communication of indirect uncertainty around the COVID-19 R number on a public audience

The R code for this analysis can be found in the OSF repository: https://osf.io/ypf6c/For each outcome, responses were analysed with a mixed two-way ANOVA, treating format as between-subjects factor and R range as a within-subjects factor. The Greenhouse-Geisser correction was applied when within-subjects factor violated the sphericity assumption (i.e., Mauchly's t...

Published 2 years ago, by J. Kerr

Results

Results of an experiment to test the effects of different formats of communication of indirect uncertainty around the COVID-19 R number on a public audience

This experiment was given ethical approval by the University of Cambridge’s Psychology Research Ethics Committee (PRE.2023.087)UK-based Participants were recruited via the platform Prolific.com using their ‘representative sample’ option to give a participant pool representative of the UK’s population on the basis of age (5 brackets), sex (2 groups) and ethnicity (5...

Published 2 years ago, by A. Freeman, J. Kerr, S. Dryhurst

Method

Method to test the effects of different formats of communication of indirect uncertainty around the COVID-19 R number on a public audience

A method to test the effects of different verbal/numeric and graphical communications of uncertainty around the Covid-19 R number (pre-registration)

Published 2 years ago, by S. Dryhurst, J. Kerr, A. Freeman

Interpretation

Interpretation: what the public already know, want to know and need to know, about their personal risks from COVID-19

This Interpretation is based on work published in: https://doi.org/10.1098/rsos.201721 The interviews and the survey responses combined help give us an overall view of the public’s information needs regarding their personal COVID-19 risk, as of July 2020.The majority of participants clearly showed a desire for quantitative information about COVID-19 (much of which...

Published 2 years ago, by A. Freeman, J. Kerr, A. Lawrence, G. Recchia, C. Schneider, S. Dryhurst, L. Finikarides, G. Luoni

Results

Results of surveys: finding out what the public already know, want to know and need to know, about their personal risks from COVID-19

These Results are based on work published in: https://doi.org/10.1098/rsos.201721 This study was approved by the Psychological Research Ethics Committee at the University of Cambridge (PRE.2020.070).All stages of the study were carried out between the 3 June 2020 and 23 July 2020.The questions were split over four different surveys carried out with members of the p...

Published 3 years ago, by A. Freeman, J. Kerr, G. Recchia, C. Schneider, S. Dryhurst

Interpretation

Interpretation of an analysis of the effects of different numerical formats on people’s perception of their risk of dying from COVID-19

This Interpretation is based on work published in: https://doi.org/10.1098/rsos.201721 The analysis of the data - from 2,500 members of the UK general public who were asked to rate how risky they felt five different numerical risk levels were (0.1%, 1%, 5%, 12%, 20%) when presented to them in a random order - confirms the collected findings of previous studies. The...

Published 3 years ago, by C. Schneider, J. Kerr, S. Dryhurst, G. Recchia

Method

Method for online surveys to find out what the public already know, want to know and need to know, about their personal risks from COVID-19

This Method is based on work published in: https://doi.org/10.1098/rsos.201721 A combination of in depth qualitative interviews and large-scale quantitative surveys are ideal for gaining insights into people’s understanding of a topic such as their risk from COVID-19, and their information needs.Quantitative surveys can give broad insights into opinions across a po...

Published 3 years ago, by A. Freeman, C. Schneider, J. Kerr, G. Recchia, S. Dryhurst

Rationale / Hypothesis

Finding out what the public already know, and want to know, about their personal risks from COVID-19

This Hypothesis/Theoretical rationale is based on work published in: https://doi.org/10.1098/rsos.201721One method of approaching risk communication is the ‘mental models’ approach, whereby researchers use both qualitative and quantitative methods to build up an understanding of the audience’s intuitive model of causality in a situation, as well as the probabilitie...

Published 3 years ago, by A. Freeman, J. Kerr, G. Recchia, S. Dryhurst, C. Schneider, A. Lawrence

Analysis

Analysis of the effects of different numerical formats on people’s perception of their risk of dying from COVID-19

Analysis of data from a study in which participants rated COVID-19 infection-fatality risks presented in different formats.

Published 3 years ago, by J. Kerr

Results

Results of testing the effects of different numerical formats on people’s perception of their risk of dying from COVID-19

These Results are based on work published in: https://doi.org/10.1098/rsos.201721The data from this experiment is available in the Open Science Framework repository: https://doi.org/10.17605/OSF.IO/AUF8H Survey 4.This study was approved by the Psychology Research Ethics Committee at the University of Cambridge (PRE.2020.070).The survey was carried out online, with...

Published 3 years ago, by A. Freeman, J. Kerr, C. Schneider, G. Recchia, S. Dryhurst

Method

Testing the effects of different numerical formats on people’s perception of their risk of dying from COVID-19

This Method is based on work published in: https://doi.org/10.1098/rsos.201721This method was pre-registered before data was collected, at: https://doi.org/10.17605/OSF.IO/RDTC4 Participants will be asked to imagine they have received a personalised estimate of their risk of dying if infected with COVID-19. They will be asked ‘How would you classify that risk in yo...

Published 3 years ago, by A. Freeman, C. Schneider, G. Recchia, J. Kerr

Research Problem

What information do the public need and want to know about their personal risk from COVID-19?

This Problem is based on work published in: https://doi.org/10.1098/rsos.201721Different communicators might have different aims when approaching the communication of personalized risk from COVID-19. Some may feel it important to encourage people to take mitigating actions, and hence to ensure that risks are perceived as relatively high to increase motivation [1,2]...

Published 3 years ago, by A. Freeman, J. Kerr, G. Recchia, S. Dryhurst, A. Lawrence, C. Schneider

Research Problem

When presenting people with their personal risk from COVID-19, what format should they be shown the numbers in?

This Problem is based on work published in: https://doi.org/10.1098/rsos.201721Probabilistic information can be represented numerically in different ways, and research has shown that these different formats can affect the perception of likelihoods as well as affecting the ease with which people can make mental comparisons and manipulations of the information [1-5....

Published 3 years ago, by A. Freeman, G. Recchia, S. Dryhurst, J. Kerr, C. Schneider

John Kerr

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Octopus publications

Interpretation of an experiment to test the effects of different formats of communication of indirect uncertainty around the COVID-19 R number on a public audience

Analysis of an experiment to test the effects of different formats of communication of indirect uncertainty around the COVID-19 R number on a public audience

Results of an experiment to test the effects of different formats of communication of indirect uncertainty around the COVID-19 R number on a public audience

Method to test the effects of different formats of communication of indirect uncertainty around the COVID-19 R number on a public audience

Interpretation: what the public already know, want to know and need to know, about their personal risks from COVID-19

Results of surveys: finding out what the public already know, want to know and need to know, about their personal risks from COVID-19

Interpretation of an analysis of the effects of different numerical formats on people’s perception of their risk of dying from COVID-19

Method for online surveys to find out what the public already know, want to know and need to know, about their personal risks from COVID-19

Finding out what the public already know, and want to know, about their personal risks from COVID-19

Analysis of the effects of different numerical formats on people’s perception of their risk of dying from COVID-19

Results of testing the effects of different numerical formats on people’s perception of their risk of dying from COVID-19

Testing the effects of different numerical formats on people’s perception of their risk of dying from COVID-19

What information do the public need and want to know about their personal risk from COVID-19?

When presenting people with their personal risk from COVID-19, what format should they be shown the numbers in?