9,910 responses from 213 respondents to 4 items.
One of 2 tables from the same source; this one is the alcohol_consequences table. The others are listed under Related tables.
| Description | Alcohol consequences of the previous night, 4 yes/no items (hangover, nausea/vomiting, failing to remember events, intoxication-related injury), reported once a day. 213 people; wave is the study day. College students who drink regularly, two EMA studies pooled (study 1: 127 people, up to 14 days x 5 signals; study 2: 86 people, up to 22 days x 3 signals; data from Witkiewitz et al. 2012 and Smith et al. 2022). Covariates: cov_study, cov_gender, cov_age, cov_ddq_typical_drinks (from the OSF file). No timestamps, so no date. |
|---|---|
| Reference | Dora, J., McCabe, C. J., van Lissa, C. J., Witkiewitz, K., & King, K. M. (2024). A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized) Mixed-Effects Models. Advances in Methods and Practices in Psychological Science, 7(1). |
| DOI | 10.1177/25152459241235875 |
| Licence | CC BY 4.0 |
| Source data | https://zenodo.org/records/22809550 |
These data were found via openESM, where additional metadata for this dataset are available.
| Responses | 9,910 |
|---|---|
| Respondents | 213 |
| Items | 4 |
| Response categories | 2 |
| Responses per respondent | 46.526 |
| Responses per item | 2477.500 |
| Density | 11.631 |
| Longitudinal | TRUE |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, dora_2024_esm_alcohol.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 | age |
cov_ddq_typical_drinks | Covariate | DDQ.typ.drinks |
cov_gender | Covariate | gender |
cov_study | Covariate | study |
What item codes and covariate values stand for is in the source’s codebook, zy639.
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("dora_2024_alcohol_consequences")
# Python
pip install irw
import irw
df = irw.fetch("dora_2024_alcohol_consequences")
@article{Dora_2024, title={A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized) Mixed-Effects Models}, volume={7}, ISSN={2515-2467}, url={http://dx.doi.org/10.1177/25152459241235875}, DOI={10.1177/25152459241235875}, number={1}, journal={Advances in Methods and Practices in Psychological Science}, publisher={SAGE Publications}, author={Dora, Jonas and McCabe, Connor J. and van Lissa, Caspar J. and Witkiewitz, Katie and King, Kevin M.}, year={2024}, month=Jan }
This table was found via openESM; please also cite:
@article{siepe2026openesm, title={Introducing openESM: A database of openly available experience sampling datasets}, author={Siepe, Bj{\"o}rn S. and Haslbeck, Jonas M. B. and Kloft, Matthias and B{\"u}chner, Anabel and Zhang, Yong and Fried, Eiko I. and Heck, Daniel W.}, journal={Behavior Research Methods}, volume={58}, number={8}, pages={240}, year={2026}, doi={10.3758/s13428-026-03112-y}}
| IRW version | v584 |
|---|---|
| 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 v44.0 |