2,880 responses from 160 respondents to 18 items.
One of 2 tables from the same source; this one is the bmq table. The others are listed under Related tables.
| Description | Beliefs about Medicines Questionnaire (BMQ), 18 items on a 1-5 scale, Malaysian hypertensive outpatients. 2,880 responses from 160 respondents on 18 items. |
|---|---|
| Reference | Aziz F, Malek S, Mhd Ali A, Wong MS, Mosleh M, Milow P (2020). Determining hypertensive patients' beliefs towards medication and associations with medication adherence using machine learning methods. PeerJ, 8, e8286. https://doi.org/10.7717/peerj.8286 |
| DOI | 10.7717/peerj.8286 |
| Licence | CC BY 4.0 |
| Source data | https://europepmc.org/article/PMC/PMC7075362 |
| Responses | 2,880 |
|---|---|
| Respondents | 160 |
| Items | 18 |
| Response categories | 5 |
| Responses per respondent | 18 |
| Responses per item | 160 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Clinical |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | may |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, aziz_2020_hypertension.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 |
| Respondent covariates | ||
cov_age | Covariate | Age |
cov_education | Covariate | Educational_level |
cov_ethnicity | Covariate | Ethnicity |
cov_gender | Covariate | Gender |
cov_marital_status | Covariate | Marital_status |
cov_monthly_income | Covariate | Monthly_income |
What item codes and covariate values stand for is in the source’s questionnaire, peerj-08-8286-s014.docx, which shows the item wording.
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("aziz_2020_bmq")
# Python
pip install irw
import irw
df = irw.fetch("aziz_2020_bmq")
@article{Aziz_2020, title={Determining hypertensive patients’ beliefs towards medication and associations with medication adherence using machine learning methods}, volume={8}, ISSN={2167-8359}, url={http://dx.doi.org/10.7717/peerj.8286}, DOI={10.7717/peerj.8286}, journal={PeerJ}, publisher={PeerJ}, author={Aziz, Firdaus and Malek, Sorayya and Mhd Ali, Adliah and Wong, Mee Sieng and Mosleh, Mogeeb and Milow, Pozi}, year={2020}, month=Mar, pages={e8286} }
| IRW version | v566 |
|---|---|
| Redivis dataset | item_response_warehouse_6 v5.11 |
| Redivis dataset DOI | 10.57761/qahq-j855 |
| Manifest pin for this IRW version | v5.11 |
| Metadata source | irw_meta v43.0 |