About this table

Description	German Core Self-Evaluation Scale (G-CSES, 12 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

Size and shape

Responses	7,344
Respondents	612
Items	12
Response categories	5
Responses per respondent	12
Responses per item	612
Density	1
Longitudinal	FALSE

Classification

age range	Adult (18+)
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.

Codebook

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.

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant metadataHugging Face, Kaggle, OpenML

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_cse")

# Python
pip install irw

import irw
df = irw.fetch("algner2022_cse")

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_cse, clear

How to cite

@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 }

Version and provenance

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

algner2022_cse