ma2021_sme_covid

6,552 responses from 234 respondents to 28 items.

About this table

DescriptionCOVID-19 impact questionnaire for Chinese SMEs (28 items); N=234; 1-7 Likert; covers financial performance, operations, costs, and government relief
ReferenceMa, Z., Liu, Y., & Gao, Y. (2022). Replication Data for: Valid questionnaire for small and medium-sized enterprises [Dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/0ADT0D
DOI10.1371/journal.pone.0257036
LicenceCC0 1.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/0ADT0D

Size and shape

Responses6,552
Respondents234
Items28
Response categories7
Responses per respondent28
Responses per item234
Density1
LongitudinalFALSE

Classification

sampleWorkplace
measurement toolSurvey/questionnaire
item formatLikert 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.

InstrumentQuestionnaire of impact of COVID-19 on small and medium-sized enterprises
Mean words per item15.429
Mean characters per item109.607
Mean characters per response2.714
Flesch-Kincaid grade level13.529

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, ma2021_sme_covid.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.

ColumnWhat it isSource column
IRW standard columns
idIRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).number
itemIRW standard Item identifier.made in the build script
respIRW 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.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("ma2021_sme_covid")
# Python
pip install irw

import irw
df = irw.fetch("ma2021_sme_covid")

How to cite

@article{Ma_2021, title={Research on the impact of COVID-19 on Chinese small and medium-sized enterprises: Evidence from Beijing}, volume={16}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0257036}, DOI={10.1371/journal.pone.0257036}, number={12}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Ma, Zhengwei and Liu, Yiran and Gao, Yida}, editor={Vasa, László}, year={2021}, month=Dec, pages={e0257036} }

Version and provenance

IRW versionv566
Redivis datasetitem_response_warehouse_2 v33.0
Redivis dataset DOI10.57761/6m04-m698
Manifest pin for this IRW versionv33.0
Metadata sourceirw_meta v43.0