76,908 responses from 142 respondents to 2,202 items.
| Description | Word recognition task administered to Mturkers |
|---|---|
| Reference | Ratcliff, R., Hendrickson, A.T. Do data from mechanical Turk subjects replicate accuracy, response time, and diffusion modeling results?. Behav Res 53, 2302–2325 (2021). https://doi.org/10.3758/s13428-021-01573-x |
| DOI | 10.3758/s13428-021-01573-x |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/za9y8/ |
| Responses | 76,908 |
|---|---|
| Respondents | 142 |
| Items | 2,202 |
| Response categories | 2 |
| Responses per respondent | 541.606 |
| Responses per item | 34.926 |
| Density | 0.246 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | General/non-specific |
| construct type | Cognitive/educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Word Recognition Task (MTurk DDM Recognition) |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Item recognition (word recognition memory) task, Ratcliff & Hendrickson (2021) Experiment 1 |
|---|---|
| Mean words per item | 1 |
| Mean characters per item | 6.378 |
| Mean characters per response | 8 |
| Flesch-Kincaid grade level | 1726.556 |
blockiditemorderresprt
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("mturkddm_recognition")
# Python
pip install irw
import irw
df = irw.fetch("mturkddm_recognition")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse v53.0 |
| Redivis dataset DOI | 10.57761/4g08-xt41 |
| Manifest pin for this IRW version | v53.0 |
| Metadata source | irw_meta v23.0 |