16,759 responses from 683 respondents to 26 items.
One of 3 tables from the same source; this one is the spai26 table. The others are listed under Related tables.
| Description | Smartphone Addiction Inventory (SPAI, 26 items 4-pt Likert Strongly disagree-Strongly agree) with demographic covariates, Lebanese university students N=683 |
|---|---|
| Reference | Matar Boumosleh J, Jaalouk D (2017) Depression, anxiety, and smartphone addiction in university students- A cross sectional study. PLOS ONE. |
| DOI | 10.1371/journal.pone.0182239 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0182239.s001 |
| Responses | 16,759 |
|---|---|
| Respondents | 683 |
| Items | 26 |
| Response categories | 4 |
| Responses per respondent | 24.537 |
| Responses per item | 644.577 |
| Density | 0.944 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Smartphone Addiction Inventory (SPAI) |
|---|---|
| Mean words per item | 14.192 |
| Mean characters per item | 81.385 |
| Mean characters per response | 15.500 |
| Flesch-Kincaid grade level | 8.252 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, matarboumosleh_2017_smartphone_depr_anx.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). | ID |
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_class | Covariate | Class |
cov_faculty | Covariate | Faculty |
cov_gender | Covariate | Gender |
cov_personality_type | Covariate | Prsnlty_type |
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:
# R (no account needed)
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("matarboumosleh_2017_spai26")
# Python (needs a free Redivis account)
pip install irw
import irw
df = irw.fetch("matarboumosleh_2017_spai26")
@article{Matar_Boumosleh_2017, title={Depression, anxiety, and smartphone addiction in university students- A cross sectional study}, volume={12}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0182239}, DOI={10.1371/journal.pone.0182239}, number={8}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Matar Boumosleh, Jocelyne and Jaalouk, Doris}, editor={Hayashi, Naoyuki}, year={2017}, month=Aug, pages={e0182239} }
| IRW version | v540 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v13.0 |
| Redivis dataset DOI | 10.57761/gpg7-sg87 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v37.0 |