Item Response Warehouse / Tables / kim2020_amskim2020_ams
20,084 responses from 1,335 respondents to 17 items.
About this table
Description Aging Male Symptom (AMS) questionnaire (17 items); Korean men N=1335; 1-5 ordinal (some half-step values) Reference Kim, 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 Licence CC0 1.0 Source data https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/9V2I0P
Size and shape
Responses 20,084 Respondents 1,335 Items 17 Response categories 5 Responses per respondent 15.044 Responses per item 1181.412 Density 0.885 Longitudinal FALSE
Classification
measurement tool Survey/questionnaire item format Likert 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.
Instrument Aging Males' Symptoms (AMS) scale Mean words per item 11.059 Mean characters per item 77.059 Mean characters per response 2.600 Flesch-Kincaid grade level 10.448
Columns
cov_testosterone 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("kim2020_ams")
# Python
pip install irw
import irw
df = irw.fetch("kim2020_ams")
Version and provenance
IRW version v393 Redivis dataset item_response_warehouse_2 v19.0 Redivis dataset DOI 10.57761/qtx5-4m80 Manifest pin for this IRW version v19.0 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_2 v19.0.