9,180 responses from 306 respondents to 30 items.
| Description | English listening test, 30 items in four tasks (two multiple-choice tasks of 6 items, two note-form tasks of 9 items); each student heard two tasks once and two twice, 0/1; treat = 1 when the task was heard twice (answer after the second listening); N = 306 secondary-school students taking an English listening test; treat. |
|---|---|
| Reference | Holzknecht, F. (2022). Repeating the listening text: effects on student performance, metacognitive processing, and anxiety [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/WWN1TS |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/WWN1TS |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Responses | 9,180 |
|---|---|
| Respondents | 306 |
| Items | 30 |
| Response categories | 2 |
| Responses per respondent | 30 |
| Responses per item | 306 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Educational, General/non-specific |
|---|---|
| construct type | Cognitive/educational |
| measurement tool | Test |
| item format | Mixed |
| primary language(s) | eng |
| construct name | English listening test (multiple-choice and note-form tasks) |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, holzknecht_2022_listening.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 |
treat | IRW standard Treatment group assignment in experimental studies. | made in the build script |
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 (both need a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("holzknecht_2022_listening")
# Python
pip install irw
import irw
df = irw.fetch("holzknecht_2022_listening")
@misc{https://doi.org/10.7910/dvn/wwn1ts,
doi = {10.7910/DVN/WWN1TS},
url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/WWN1TS},
author = {Holzknecht, Franz},
keywords = {Social Sciences},
title = {Repeating the listening text: effects on student performance, metacognitive processing, and anxiety},
publisher = {Harvard Dataverse},
year = {2022}
}
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_5 v8.5 |
| Redivis dataset DOI | 10.57761/41xy-5k56 |
| Manifest pin for this IRW version | v8.5 |
| Metadata source | irw_meta v44.0 |