kim2020_ams

20,084 responses from 1,335 respondents to 17 items.

About this table

DescriptionAging Male Symptom (AMS) questionnaire (17 items); Korean men N=1335; 1-5 ordinal (some half-step values)
ReferenceKim, J.W. (2020). Optimizing Aging Male Symptom Questionnaire Through Genetic Algorithms Based Machine Learning Techniques [Dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/9V2I0P
LicenceCC0 1.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/9V2I0P

Size and shape

Responses20,084
Respondents1,335
Items17
Response categories5
Responses per respondent15.044
Responses per item1181.412
Density0.885
LongitudinalFALSE

Classification

measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)kor

Item text

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

InstrumentAging Males' Symptoms (AMS) scale
Mean words per item11.059
Mean characters per item77.059
Mean characters per response2.600
Flesch-Kincaid grade level10.448

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, kim2020_ams.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.

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).made in the build script
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Respondent covariates
cov_testosteroneCovariate Testo

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 (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("kim2020_ams")
# Python
pip install irw

import irw
df = irw.fetch("kim2020_ams")

How to cite

@misc{https://doi.org/10.7910/dvn/9v2i0p,
  doi = {10.7910/DVN/9V2I0P},
  url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/9V2I0P},
  author = {Kim, Jin Wook},
  keywords = {Medicine, Health and Life Sciences, Hypogonadism; Machine learning; Questionnaire design; Testosterone},
  title = {Optimizing Aging Male Symptom Questionnaire Through Genetic Algorithms Based Machine Learning Techniques},
  publisher = {Harvard Dataverse},
  year = {2020}
}

Version and provenance

IRW versionv549
Redivis datasetitem_response_warehouse_2 v33.0
Redivis dataset DOI10.57761/6m04-m698
Manifest pin for this IRW versionv33.0
Metadata sourceirw_meta v39.0