liu_2022_mice_skills

1,520 responses from 95 respondents to 16 items.

About this table

Description	16-item employability-skills importance rating scale (1-5 Likert) for meetings/incentives/conferences/exhibitions (MICE) management students and professionals, N=95.
Reference	Liu X, Seevers R, Lin H (2022). Employability skills for MICE management in the context of ICTs. PLOS ONE 17(7):e0271430.
DOI	10.1371/journal.pone.0271430
Licence	CC BY 4.0
Source data	https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271430

Size and shape

Responses	1,520
Respondents	95
Items	16
Response categories	5
Responses per respondent	16
Responses per item	95
Density	1
Longitudinal	FALSE

Classification

sample	Educational, Workplace
measurement tool	Survey/questionnaire
item format	Likert Scale/selected response
primary language(s)	chi

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

Instrument	MICE employability skills questionnaire (Liu, Seevers & Lin, 2022)
Mean words per item	1
Mean characters per item	2
Mean characters per response	7.200
Flesch-Kincaid grade level	-3.400

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, liu_2022_mice_skills.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).	`No`
`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_gender`	Covariate	`Gender`
`cov_mice_degree`	Covariate	`Do you have a degree in MICE (4-year)`
`cov_work_experience`	Covariate	`Years of working experience in MICE`

What item codes and covariate values stand for is in the codebook released with the source data.

Get the data

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

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("liu_2022_mice_skills")

# Python (needs a free Redivis account)
pip install irw

import irw
df = irw.fetch("liu_2022_mice_skills")

How to cite

@article{Liu_2022, title={Employability skills for MICE management in the context of ICTs}, volume={17}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0271430}, DOI={10.1371/journal.pone.0271430}, number={7}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Liu, Xiao and Seevers, Randy and Lin, Hongyi}, editor={Laso, María Cristina Cristina}, year={2022}, month=July, pages={e0271430} }

Version and provenance

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