Item Response Warehouse / Tables / shi_2021_gentrificationshi_2021_gentrification
4,807 responses from 209 respondents to 23 items.
About this table
Description 23-item driving-factors-of-gentrification scale (1-5 Likert), Chinese urban planning professionals Reference Shi J, Duan K, Xu Q, Li J (2021) Effect analysis of the driving factors of super-gentrification using structural equation modeling. PLOS ONE 16(3): e0248265. DOI 10.1371/journal.pone.0248265 Licence CC BY 4.0 Source data https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0248265.s001
Size and shape
Responses 4,807 Respondents 209 Items 23 Response categories 5 Responses per respondent 23 Responses per item 209 Density 1 Longitudinal FALSE
Classification
sample 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 Scale for the importance of super-gentrification driving factors (Shi, Duan, Xu & Li, 2021) Mean words per item 4.870 Mean characters per item 37.565 Mean characters per response 14.400 Flesch-Kincaid grade level 14.911
Columns
cov_age cov_collection_mode cov_education cov_experience cov_nationality cov_occupation id item resp
Get the data
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("shi_2021_gentrification")
# Python
pip install irw
import irw
df = irw.fetch("shi_2021_gentrification")
Version and provenance
IRW version v393 Redivis dataset item_response_warehouse_4 v7.0 Redivis dataset DOI 10.57761/cpj5-vm97 Manifest pin for this IRW version v7.0 Metadata source irw_meta v23.0
Part of the Item Response Warehouse , IRW v393. This page describes the table as released in item_response_warehouse_4 v7.0.