4,560 responses from 304 respondents to 15 items.
| Description | 15-item Geriatric Depression Scale, yes/no; adults aged 60+ in Kilimanjaro, Tanzania. 304 respondents x 15 items = 4560 responses. |
|---|---|
| Reference | Mboya, I. (2020). Depression in elderly population [Data set]. Mendeley Data. https://doi.org/10.17632/btrrmwtfmh |
| Licence | CC BY 4.0 |
| Source data | https://data.mendeley.com/datasets/btrrmwtfmh |
| Responses | 4,560 |
|---|---|
| Respondents | 304 |
| Items | 15 |
| Response categories | 2 |
| Responses per respondent | 15 |
| Responses per item | 304 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Elderly (minimum age >50) |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | swa |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Geriatric Depression Scale, 15-item short form (GDS-15) |
|---|---|
| Mean words per item | 8.867 |
| Mean characters per item | 43.467 |
| Mean characters per response | 2.500 |
| Flesch-Kincaid grade level | 102.746 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, mboya_2020_gds15.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). | intid |
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_age_band | Covariate 1 = 60-70 · 2 = 71-80 · 3 = >80 | age_cat_1 |
cov_alcohol | Covariate 0 = No · 1 = Yes | consumedalcoholicdrinkwithin12mo |
cov_asthma | Covariate 0 = No · 1 = Yes | beendiagnosedwithasthma |
cov_close_contacts | Covariate 1 = · 2 = 1 or 2 · 3 = 3 to 5 · 4 = 6 or more | peopleclosetoyou |
cov_cognitive_impairment | Covariate 0 = No · 1 = Yes | cognitiveimpairment |
cov_depression_history | Covariate 0 = No · 1 = Yes | depressionhistory |
cov_diabetes | Covariate 0 = No · 1 = Yes | beendiagnosedwithdiabetes |
cov_education | Covariate 1 = No education · 2 = Primary · 3 = Secondary or above | edulevel_cat_1 |
cov_hypertension | Covariate 0 = No · 1 = Yes | beendiagnosedwithhypertension |
cov_living_alone | Covariate 0 = No · 1 = Yes | living |
cov_marital_status | Covariate 1 = Not in marriage · 2 = Married/ cohabiting · 3 = Widower | marstat |
cov_occupation | Covariate 0 = No occupation · 1 = Employed · 2 = Farmer | occupation |
cov_perceived_concern | Covariate 1 = no concern · 2 = little concern · 3 = uncertain · 4 = some concern · 5 = alot of concern | concernpeopleshow |
cov_practical_help | Covariate 1 = very difficult · 2 = difficult · 3 = possible · 4 = easy · 5 = very easy | practicalhelp |
cov_religious_activity | Covariate 1 = always · 2 = often · 3 = sometimes · 4 = never | takepartinreligiousactivities |
cov_sex | Covariate 1 = male · 2 = female | sex |
cov_social_activity | Covariate 1 = always · 2 = often · 3 = sometimes · 4 = never | takepartinsocialactivity |
cov_social_group | Covariate 1 = Poor · 2 = Intermediate · 3 = Strong | social_grp |
cov_stressful_event | Covariate 0 = No · 1 = Yes | stressfulevent |
cov_stroke | Covariate 0 = No · 1 = Yes | beendiagnosedwithstroke |
cov_tobacco | Covariate 0 = No · 1 = Yes | everusedtobacco |
Covariate value labels are the source’s own, as the IRW recorded them. What item codes and any unlabelled 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("mboya_2020_gds15")
# Python
pip install irw
import irw
df = irw.fetch("mboya_2020_gds15")
@misc{https://doi.org/10.17632/btrrmwtfmh,
doi = {10.17632/BTRRMWTFMH},
url = {https://data.mendeley.com/datasets/btrrmwtfmh},
author = {Mboya, Innocent},
keywords = {Mental Health},
title = {Depression in elderly population},
publisher = {Mendeley},
year = {2020}
}
| IRW version | v569 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v13.0 |
| Redivis dataset DOI | 10.57761/w8zx-td92 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v43.0 |