Item Response Warehouse / Tables / roettl_2018_brand_recognitionroettl_2018_brand_recognition
1,872 responses from 234 respondents to 8 items.
About this table
Description 8-item forced-choice brand recognition test (0/1 wrong/right) across 8 in-game brand categories, from the same study, with cov_condition Reference Roettl J, Terlutter R (2018) The same video game in 2D, 3D or virtual reality - How does technology impact game evaluation and brand placements? PLoS ONE. DOI 10.1371/journal.pone.0200724 Licence CC BY 4.0 Source data https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0200724.s004&type=supplementary
Size and shape
Responses 1,872 Respondents 234 Items 8 Response categories 2 Responses per respondent 8 Responses per item 234 Density 1 Longitudinal FALSE
Classification
sample Educational measurement tool Test item format Likert Scale/selected response primary language(s) ger
Item text
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
Instrument Recognition (adapted from Nelson et al., 2006) Mean words per item 1.250 Mean characters per item 8.500 Mean characters per response 5 Flesch-Kincaid grade level 8.498
Columns
cov_condition id item resp
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("roettl_2018_brand_recognition")
# Python
pip install irw
import irw
df = irw.fetch("roettl_2018_brand_recognition")
Version and provenance
IRW version v393 Redivis dataset item_response_warehouse_4 v7.0 Redivis dataset DOI 10.57761/cpj5-vm97 Manifest pin for this IRW version v7.0 Metadata source irw_meta v23.0
Part of the Item Response Warehouse , IRW v393. This page describes the table as released in item_response_warehouse_4 v7.0.