brand_raffaelli_2024_recognition_20

59,200 responses from 592 respondents to 101 items.

One of 4 tables from the same source; this one measures recognition of brands. The others are listed under Related tables.

About this table

DescriptionBRAND2020 Recognition
ReferenceRaffaelli, C., Bocchi, E., Estes, Z. et al. BRAND: Brand recognition and attitude norms database. Behav Res 57, 17 (2025). https://doi.org/10.3758/s13428-024-02525-x
DOI10.3758/s13428-024-02525-x
LicenceCC BY 4.0
Source datahttps://researchbox.org/1892

Size and shape

Responses59,200
Respondents592
Items101
Response categories2
Responses per respondent100
Responses per item586.139
Density0.990
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleInternet-based
construct typeOpinion/attitude
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct namerecognition of brands

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

InstrumentBRAND (Brand Recognition and Attitude Norms Database) Study 1 old/new recognition memory task (Raffaelli, Bocchi, Estes & Adelman, Behav Res Methods 2025;57:17, doi:10.3758/s13428-024-02525-x); materials at researchbox.org/1892. The IRW table pools two subsamples: the brand-name task (item_family=name, Brand_Memory_Task_Prolific.qsf, loop rows 2-101) and the logo task (item_family=logo, Logo_Memory_Task_Prolific.qsf, loop rows 1-100), so an item code is a Qualtrics loop position, not a stimulus: the same code indexes a different serial position in the two subsamples, and within a subsample the stimulus at that position is set by the respondent's list assignment (cov_condition, List_1..List_10). Instruction text and the Old/New choice labels are taken verbatim from the two .qsf files; the item-code descriptions are the deposit's own codebook wording.
Mean words per item22.752
Mean characters per item158.693
Mean characters per response3
Flesch-Kincaid grade level2108.441

Codebook

This codebook is reconstructed by the IRW from the IRW data standard. 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.

ColumnWhat it isSource column
IRW standard columns
idIRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).not traced
itemIRW standard Item identifier.not traced
item_familyIRW standard Groups items that may violate local independence — testlets, clones, or clusters of similar items.not traced
respIRW standard Response value.not traced
Respondent covariates
cov_ageCovariate not traced
cov_genderCovariate not traced

What item codes and covariate values stand for is in the codebook released with the source data.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

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("brand_raffaelli_2024_recognition_20")
# Python (needs a free Redivis account)
pip install irw

import irw
df = irw.fetch("brand_raffaelli_2024_recognition_20")

How to cite

@article{Raffaelli_2024, title={BRAND: Brand recognition and attitude norms database}, volume={57}, ISSN={1554-3528}, url={http://dx.doi.org/10.3758/s13428-024-02525-x}, DOI={10.3758/s13428-024-02525-x}, number={1}, journal={Behavior Research Methods}, publisher={Springer Science and Business Media LLC}, author={Raffaelli, Carolina and Bocchi, Elena and Estes, Zachary and Adelman, James S.}, year={2024}, month=dec }

Version and provenance

IRW versionv540
Redivis datasetitem_response_warehouse v70.0
Redivis dataset DOI10.57761/4qxs-pq87
Manifest pin for this IRW versionv70.0
Metadata sourceirw_meta v37.0