goldberg_2018_hpq_h

27,534 responses from 706 respondents to 39 items.

About this table

DescriptionHealth Practices Questionnaire (ESCS), the h block. resp 1-5. 706 respondents x 39 items = 27534 responses.
ReferenceGoldberg, L. R. (2018). Eugene-Springfield Community Sample [Data set]. Harvard Dataverse. https://dataverse.harvard.edu/dataverse/ESCS-Data
LicenceCC0 1.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SVGXVF

Size and shape

Responses27,534
Respondents706
Items39
Response categories5
Responses per respondent39
Responses per item706
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
measurement toolSurvey/questionnaire
primary language(s)eng

Item text

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

InstrumentHealth Practices Questionnaire (HPQ), Eugene-Springfield Community Sample: Health Attitudes and Behaviors
Mean words per item6.821
Mean characters per item37.103
Mean characters per response11.800
Flesch-Kincaid grade level4.111

Codebook

This codebook is reconstructed by the IRW from the IRW data standard. 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).not traced
itemIRW standard Item identifier.not traced
respIRW standard Response value.not traced
Respondent covariates
cov_ageCovariate not traced
cov_educationCovariate not traced
cov_employmentCovariate not traced
cov_ethnicityCovariate not traced
cov_marital_statusCovariate not traced
cov_sexCovariate not traced

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("goldberg_2018_hpq_h")
# Python
pip install irw

import irw
df = irw.fetch("goldberg_2018_hpq_h")

How to cite

@misc{https://doi.org/10.7910/dvn/svgxvf,
  doi = {10.7910/DVN/SVGXVF},
  url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/SVGXVF},
  author = {Goldberg, Lewis R.},
  keywords = {Social Sciences},
  title = {( 7) Health Practices Questionnaire (HPQ)},
  publisher = {Harvard Dataverse},
  year = {2018}
}

Version and provenance

IRW versionv569
Redivis datasetitem_response_warehouse_4 v13.0
Redivis dataset DOI10.57761/w8zx-td92
Manifest pin for this IRW versionv13.0
Metadata sourceirw_meta v43.0