han_2015_peer_assisted_learning
2,362 responses from 197 respondents to 12 items.
About this table
| Description | Medical students' 12-item 5-point Likert evaluation of a peer-assisted gross anatomy dissection course |
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| Reference | Han ER, Chung EK, Nam KI (2015). Peer-Assisted Learning in a Gross Anatomy Dissection Course. PLOS ONE. |
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| DOI | 10.1371/journal.pone.0142988 |
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| Licence | CC BY 4.0 |
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| Source data | https://doi.org/10.1371/journal.pone.0142988.s001 |
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Size and shape
| Responses | 2,362 |
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| Respondents | 197 |
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| Items | 12 |
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| Response categories | 5 |
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| Responses per respondent | 11.990 |
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| Responses per item | 196.833 |
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| Density | 0.999 |
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| Longitudinal | FALSE |
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Classification
| sample | Educational |
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| measurement tool | Survey/questionnaire |
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| item format | Likert Scale/selected response |
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| primary language(s) | kor |
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Item text
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Anatomy Dissection Course Questionnaire, Section B (self-assessment of upper-limb dissection learning objectives) |
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| Mean words per item | 12.167 |
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| Mean characters per item | 73.833 |
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| Mean characters per response | 10 |
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| Flesch-Kincaid grade level | 8.694 |
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Columns
cov_gendercov_groupiditemresp
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("han_2015_peer_assisted_learning")
# Python
pip install irw
import irw
df = irw.fetch("han_2015_peer_assisted_learning")
Version and provenance
| IRW version | v393 |
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| Redivis dataset | item_response_warehouse_4 v7.0 |
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| Redivis dataset DOI | 10.57761/cpj5-vm97 |
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| Manifest pin for this IRW version | v7.0 |
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| Metadata source | irw_meta v23.0 |
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