706 responses from 353 respondents to 2 items.
One of 3 tables from the same source; this one is the belonging table. The others are listed under Related tables.
| Description | 2-item sense-of-belonging scale (feel accepted / feel insignificant in STEM settings; 1-6) from a survey of underrepresented minority and women STEM PhD students |
|---|---|
| Reference | Fisher AJ, Mendoza-Denton R, Patt C, Young I, Eppig A, Garrell RL, et al. (2019) Structure and belonging: Pathways to success for underrepresented minority and women PhD students in STEM fields. PLoS ONE. |
| DOI | 10.1371/journal.pone.0209279 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0209279.s001 |
| Responses | 706 |
|---|---|
| Respondents | 353 |
| Items | 2 |
| Response categories | 6 |
| Responses per respondent | 2 |
| Responses per item | 353 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Educational |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
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 has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | California Alliance graduate student survey (sense of belonging items) |
|---|---|
| Mean words per item | 14 |
| Mean characters per item | 88.500 |
| Mean characters per response | 2 |
| Flesch-Kincaid grade level | 13.470 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, fisher_2019_belonging.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 |
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("fisher_2019_belonging")
# Python
pip install irw
import irw
df = irw.fetch("fisher_2019_belonging")
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 fisher_2019_belonging, clear
@article{Fisher_2019, title={Structure and belonging: Pathways to success for underrepresented minority and women PhD students in STEM fields}, volume={14}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0209279}, DOI={10.1371/journal.pone.0209279}, number={1}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Fisher, Aaron J. and Mendoza-Denton, Rodolfo and Patt, Colette and Young, Ira and Eppig, Andrew and Garrell, Robin L. and Rees, Douglas C. and Nelson, Tenea W. and Richards, Mark A.}, editor={Koniaris, Leonidas G.}, year={2019}, month=Jan, pages={e0209279} }
| IRW version | v674 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v15.0 |
| Redivis dataset DOI | 10.57761/6rg4-6f73 |
| Manifest pin for this IRW version | v15.0 |
| Metadata source | irw_meta v59.0 |