65,705 responses from 701 respondents to 19 items.
One of 3 tables from the same source; this one measures Stanford WELL for Life survey (CFA19 items). The others are listed under Related tables.
| Description | Stanford WELL for Life survey items (19 items in the authors' CFA set), 1-5 (two items 1-6), waves 1-5; 701 adults in California (Stanford WELL for Life), repeated surveys after the March 2020 shelter-in-place order (wave = the file's occasions t1-t5); cov_age, cov_gender, cov_income, cov_marital, cov_education, cov_race_5cat (codes, no codebook). |
|---|---|
| Reference | Moore, J. (2023). Benefit finding and well-being over the course of the COVID-19 pandemic [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/GNIOTF |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/GNIOTF |
| Responses | 65,705 |
|---|---|
| Respondents | 701 |
| Items | 19 |
| Response categories | 6 |
| Responses per respondent | 93.730 |
| Responses per item | 3458.158 |
| Density | 4.933 |
| Longitudinal | TRUE |
| age range | Adult (18+) |
|---|---|
| sample | Program-based, Targeted/specific |
| construct type | Affective/mental health, Physical health/functioning |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Stanford WELL for Life survey (CFA19 items) |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, moore_2023_benefit_finding.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 |
wave | IRW standard Longitudinal wave indicator. | made in the build script |
| Respondent covariates | ||
cov_age | Covariate | dv_age2020_t0 |
cov_education | Covariate | dv_edu_5cat_well0 |
cov_gender | Covariate | core_gender_well0 |
cov_income | Covariate | core_income_well0 |
cov_marital | Covariate | cov_marital_t0 |
cov_race_5cat | Covariate | race_5cat |
What item codes and covariate values stand for is in the codebook released with the source data.
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("moore_2023_well")
# Python
pip install irw
import irw
df = irw.fetch("moore_2023_well")
@misc{https://doi.org/10.7910/dvn/gniotf,
doi = {10.7910/DVN/GNIOTF},
url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/GNIOTF},
author = {Moore, Jessie},
keywords = {Medicine, Health and Life Sciences, Social Sciences},
title = {Benefit finding and well-being over the course of the COVID-19 pandemic},
publisher = {Harvard Dataverse},
year = {2023}
}
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_5 v8.5 |
| Redivis dataset DOI | 10.57761/41xy-5k56 |
| Manifest pin for this IRW version | v8.5 |
| Metadata source | irw_meta v44.0 |