3,672 responses from 612 respondents to 6 items.
One of 9 tables from the same source; this one is the oss table. The others are listed under Related tables.
| Description | Occupational Self-Efficacy Scale-Short Form (OSS-SF, 6 items), Study 2 sample N=612 |
|---|---|
| Reference | Algner & Lorenz (2022). You're Prettier When You Smile: Construction and Validation of a Questionnaire to Assess Microaggressions Against Women in the Workplace. Frontiers in Psychology, 13. |
| DOI | 10.3389/fpsyg.2022.809862 |
| Licence | CC BY 4.0 |
| Source data | https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.809862/full |
| Responses | 3,672 |
|---|---|
| Respondents | 612 |
| Items | 6 |
| Response categories | 6 |
| Responses per respondent | 6 |
| Responses per item | 612 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Internet-based |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | ger |
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 | Occupational Self-Efficacy Scale - Short Form (OSS-SF; Rigotti, Schyns, & Mohr, 2008) |
|---|---|
| Mean words per item | 9.667 |
| Mean characters per item | 45.667 |
| Mean characters per response | 6.333 |
| Flesch-Kincaid grade level | 2.259 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, algner2022_mimi.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). | lfdn |
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_dur_worklife | Covariate | durWorklife |
cov_duration | Covariate | duration |
cov_education | Covariate | education |
cov_equality | Covariate | equality |
cov_feminism | Covariate | feminism |
cov_gender | Covariate | gender |
cov_kldb | Covariate | kldb |
cov_occupational_status | Covariate | occupational status |
cov_size_residence | Covariate | size_residence |
cov_size_workplace | Covariate | size_workplace |
cov_weekly_hours | Covariate | weeklyHours |
cov_workenvironment | Covariate | workenvironment |
What item codes and covariate values stand for is in the codebook released with the source data.
Respondent uniqueness. 41% of this table’s respondents are alone in their combination of the 4 covariates above — no one else in the table shares it. That is a necessary condition for re-identification and not re-identification itself, and on a sample this size it is largely arithmetic. The IRW republishes the demographics its sources published and never at finer granularity: see the data standard.
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("algner2022_oss")
# Python
pip install irw
import irw
df = irw.fetch("algner2022_oss")
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 algner2022_oss, clear
@article{Algner_2022, title={You’re Prettier When You Smile: Construction and Validation of a Questionnaire to Assess Microaggressions Against Women in the Workplace}, volume={13}, ISSN={1664-1078}, url={http://dx.doi.org/10.3389/fpsyg.2022.809862}, DOI={10.3389/fpsyg.2022.809862}, journal={Frontiers in Psychology}, publisher={Frontiers Media SA}, author={Algner, Mona and Lorenz, Timo}, year={2022}, month=Mar }
| 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 |