ordak_2026_vaccine_misreasoning
5,970 responses from 597 respondents to 10 items.
About this table
| Description | 597 anti-vaccination Facebook posts hand-coded for presence/absence of 10 statistical misreasoning types (binary 0/1 items) N=597 posts |
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| Reference | Ordak M. Statistical misreasoning in online content about vaccines: Implications and recommendations for addressing disinformation. PLOS ONE. |
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| DOI | 10.1371/journal.pone.0355341 |
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| Licence | CC BY 4.0 |
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| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0355341.s001 |
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Size and shape
| Responses | 5,970 |
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| Respondents | 597 |
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| Items | 10 |
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| Response categories | 2 |
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| Responses per respondent | 10 |
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| Responses per item | 597 |
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| Density | 1 |
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| Longitudinal | FALSE |
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Classification
| measurement tool | Observational rating |
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| item format | Likert Scale/selected response |
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| primary language(s) | pol |
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Item text
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Statistical misreasoning coding framework (ten categories) |
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| Mean words per item | 13.300 |
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| Mean characters per item | 100.100 |
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| Mean characters per response | 24 |
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| Flesch-Kincaid grade level | 16.036 |
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Get the data
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("ordak_2026_vaccine_misreasoning")
# Python
pip install irw
import irw
df = irw.fetch("ordak_2026_vaccine_misreasoning")
Version and provenance
| IRW version | v393 |
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| Redivis dataset | item_response_warehouse_3 v7.0 |
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| Redivis dataset DOI | 10.57761/pqqn-pm43 |
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| Manifest pin for this IRW version | v7.0 |
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| Metadata source | irw_meta v23.0 |
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