Political affiliation moderates subjective interpretations of Covid-19 graphs
Bibliographic Data
| ID | 5260427 |
|---|---|
| Authors | Jonathan D Ericson (0000-0001-9076-0596, Bentley University, corresponding author), William S Albert, William Albert (0000-0001-5851-7043, Bentley University), Ja-Nae Duane (0000-0002-4091-3264, Bentley University) |
| Year | 2022 |
| Volume | 9 |
| Issue | 1 |
| Pages | 20539517221080678-20539517221080678 |
| Publication date | 2022-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Big Data & Society (JOURNAL) |
| Journal identifiers | ISSN: 2053-9517 • E-ISSN: 2053-9517 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/20539517221080678 |
| PMID | 35281347 |
| OpenAlex | W4214901152 |
| Language | EN |
| Citations received | 1 |
| References cited | 61 |
We examined the relationship between political affiliation, perceptual (percentage, slope) estimates, and subjective judgements of disease prevalence and mortality across three chart types. An online survey (N = 787) exposed separate groups of participants to charts displaying (a) COVID-19 data or (b) COVID-19 data labeled 'Influenza (Flu)'. Block 1 examined responses to cross-sectional mortality data (bar graphs, treemaps); results revealed that perceptual estimates comparing mortality in two countries were similar across political affiliations and chart types (all ps > .05), while subjective judgements revealed a disease x political party interaction ( p < .05). Although Democrats and Republicans provided similar proportion estimates, Democrats interpreted mortality to be higher than Republicans; Democrats also interpreted mortality to be higher for COVID-19 than Influenza. Block 2 examined responses to time series (line graphs); Democrats and Republicans estimated greater slopes for COVID-19 trend lines than Influenza lines ( p < .001); subjective judgements revealed a disease x political party interaction ( p < .05). Democrats and Republicans indicated similar subjective rates of change for COVID-19 trends, and Democrats indicated lower subjective rates of change for Influenza than in any other condition. Thus, while Democrats and Republicans saw the graphs similarly in terms of percentages and line slopes, their subjective interpretations diverged. While we may see graphs of infectious disease data similarly from a purely mathematical or geometric perspective, our political affiliations may moderate how we subjectively interpret the data
Economics · Political economy · Political science · Politics · Positive economics · Sociology · Law · Misinformation and Its Impacts · Psychology · Psychology of Moral and Emotional Judgment · Social and Intergroup Psychology · Social Psychology
Using implementation intentions prompts to enhance influenza vaccination rates
The role of stress mindset in shaping cognitive, emotional, and physiological responses to challenging and threatening stress
Seeking Better Health Care Outcomes
Salience, Attention, and Attribution
The Partisan Brain
Representativeness Revisited
Motivated Reasoning and Public Opinion
Exposure to ideologically diverse news and opinion on Facebook
On the psychology of prediction.
Can humans perform mental regression on a graph? Accuracy and bias in the perception of scatterplots
The Psychology of Conspiracy Theories
Lessons from the Faith-Driven Response to the West africa Ebola Epidemic
Reluctance to vaccinate
Embodying Psychological Thriving
The dark side of meaning-making
Beyond the Turk
At the Nexus of Observational and Experimental Research
Logarithmic versus Linear Visualizations of Covid-19 Cases Do Not Affect Citizens’ Support for Confinement
The Hostile Audience
What breeds conspiracy antisemitism? The role of political uncontrollability and uncertainty in the belief in Jewish conspiracy
The Origins and Consequences of Affective Polarization in the United States
Availability
Learning from lines
The Availability Heuristic and Perceived Risk
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |