833 responses from 119 respondents to 7 items.
One of 2 tables from the same source; this one measures Use of visual mental representations across everyday situations. The others are listed under Related tables.
| Description | Use of mental imagery vs. words across 7 cognitive tasks (recollection, comprehension, anticipation, planning, problem-solving, decision, memorization), autistic and control adults |
|---|---|
| Reference | Bled C, Guillon Q, Soulières I, Bouvet L (2021). Thinking in pictures in everyday life situations among autistic adults. PLOS ONE, 16(7), e0255039. https://doi.org/10.1371/journal.pone.0255039 |
| DOI | 10.1371/journal.pone.0255039 |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.1371/journal.pone.0255039.s001 |
| Responses | 833 |
|---|---|
| Respondents | 119 |
| Items | 7 |
| Response categories | 3 |
| Responses per respondent | 7 |
| Responses per item | 119 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Targeted/specific |
| construct type | Cognitive/educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | fre |
| construct name | Use of visual mental representations across everyday situations |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Use of visual mental representations in everyday life situations (Bled et al., 2021 online questionnaire, items 5-11) |
|---|---|
| Mean words per item | 21.429 |
| Mean characters per item | 112.714 |
| Mean characters per response | 6 |
| Flesch-Kincaid grade level | 8.815 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, bled_2021_pheno_imagery.py. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
id | IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task). | made in the build script |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_age | Covariate | age |
cov_education | Covariate | education [1=BEP/CAP; 2=BAC; 3=BAC+2; 4=BAC+3; 5=BAC+5; 6=BAC+8] |
cov_group | Covariate | group [0=control; 1=autism] |
cov_sex | Covariate | sex [1=M; 2=F] |
What item codes and covariate values stand for is in the codebook released with the source data.
Or load it directly in R or Python:
# R (no account needed)
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("bled_2021_imagery_use")
# Python (needs a free Redivis account)
pip install irw
import irw
df = irw.fetch("bled_2021_imagery_use")
@article{Bled_2021, title={Thinking in pictures in everyday life situations among autistic adults}, volume={16}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0255039}, DOI={10.1371/journal.pone.0255039}, number={7}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Bled, Clara and Guillon, Quentin and Soulières, Isabelle and Bouvet, Lucie}, editor={Dritschel, Barbara}, year={2021}, month=July, pages={e0255039} }
| IRW version | v540 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v13.0 |
| Redivis dataset DOI | 10.57761/gpg7-sg87 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v37.0 |