12,223 responses from 719 respondents to 17 items.
One of 2 tables from the same source; this one measures Brief Symptom Inventory (BSI). The others are listed under Related tables.
| Description | Cognitive Diagnosis Modeling for characterizing mental health symptom profiles, Brief Symptom Inventory (BSI) |
|---|---|
| Reference | Tan, Z., De la Torre, J., Ma, W., Huh, D., Larimer, M. E., & Mun, E. Y. (2023). A tutorial on cognitive diagnosis modeling for characterizing mental health symptom profiles using existing item responses. Prevention Science, 24(3), 480-492. |
| DOI | 10.1007/s11121-022-01346-8 |
| Licence | CC BY 4.0 |
| Source data | https://data.mendeley.com/datasets/97bzg6z28h/1 |
| Responses | 12,223 |
|---|---|
| Respondents | 719 |
| Items | 17 |
| Response categories | 5 |
| Responses per respondent | 17 |
| Responses per item | 719 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Educational |
| construct type | Affective/mental health |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Brief Symptom Inventory (BSI) |
These are classifications the IRW records, not the study’s own words. Some were annotated by hand and some written by an automated tagger whose output is published only where it clears a measured accuracy bar; which values came from which is not recorded per table. Coverage and accuracy per column, and what is held back, are on Tag quality.
This table is in 1 collection. Each membership records how it was decided:
q_matrix — by ruleMembership is the IRW’s judgment and is worth checking before you use a collection as a sample. A hand-curated list is a snapshot: it is not re-checked when tables are added, so a table that would qualify may be missing. See Collections.
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, cdm_mentalhealth_tan_2023.R. 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 |
| Other columns | ||
qmatrix__an | Not documented Item classifications for cognitive diagnostic modeling (Q-matrix). | not traced |
qmatrix__ap | Not documented Item classifications for cognitive diagnostic modeling (Q-matrix). | not traced |
qmatrix__de | Not documented Item classifications for cognitive diagnostic modeling (Q-matrix). | not traced |
qmatrix__ho | Not documented Item classifications for cognitive diagnostic modeling (Q-matrix). | not traced |
What item codes and covariate values stand for is in the codebook released with the source data.
Or load it directly in R (no account needed with irw 1.4.0 or later) or Python (needs a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("irw")
library(irw)
df <- irw_fetch("cdm_mentalhealth_tan_2023_bsi")
# Python
pip install irw
import irw
df = irw.fetch("cdm_mentalhealth_tan_2023_bsi")
Or in Stata, which reads the same no-account CSV as the button above:
* Stata
net install irw, from("https://raw.githubusercontent.com/itemresponsewarehouse/Stata-pkg/main/")
irw_fetch cdm_mentalhealth_tan_2023_bsi, clear
@article{Tan_2022, title={A Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses}, volume={24}, ISSN={1573-6695}, url={http://dx.doi.org/10.1007/s11121-022-01346-8}, DOI={10.1007/s11121-022-01346-8}, number={3}, journal={Prevention Science}, publisher={Springer Science and Business Media LLC}, author={Tan, Zhengqi and de la Torre, Jimmy and Ma, Wenchao and Huh, David and Larimer, Mary E. and Mun, Eun-Young}, year={2022}, month=feb, pages={480–492} }
| IRW version | v707 |
|---|---|
| Redivis dataset | item_response_warehouse v81.0 |
| Redivis dataset DOI | 10.57761/5sed-tj46 |
| Manifest pin for this IRW version | v81.0 |
| Metadata source | irw_meta v66.0 |