pang_2023_behavioral_intent
1,545 responses from 309 respondents to 5 items.
About this table
| Description | 5-item Behavioral Intention scale (1-5 Likert), the outcome construct of a media-based NEV adoption model |
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| Reference | Pang J, Ye J, Zhang X (2023) Factors influencing users' willingness to use new energy vehicles. PLoS ONE. |
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| DOI | 10.1371/journal.pone.0285815 |
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| Licence | CC BY 4.0 |
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| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0285815.s001 |
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Size and shape
| Responses | 1,545 |
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| Respondents | 309 |
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| Items | 5 |
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| Response categories | 5 |
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| Responses per respondent | 5 |
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| Responses per item | 309 |
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| Density | 1 |
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| Longitudinal | FALSE |
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Classification
| sample | Educational, Internet-based |
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| measurement tool | Survey/questionnaire |
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| item format | Likert Scale/selected response |
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| primary language(s) | chi |
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Item text
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Behavioral intention (BI) subscale, Factors Influencing Users' Willingness to Use New Energy Vehicles questionnaire |
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| Mean words per item | 1 |
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| Mean characters per item | 2 |
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| Mean characters per response | 11 |
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| Flesch-Kincaid grade level | -3.400 |
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Columns
cov_agecov_educationcov_gendercov_professioniditemresp
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("pang_2023_behavioral_intent")
# Python
pip install irw
import irw
df = irw.fetch("pang_2023_behavioral_intent")
Version and provenance
| IRW version | v393 |
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| Redivis dataset | item_response_warehouse_4 v7.0 |
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| Redivis dataset DOI | 10.57761/cpj5-vm97 |
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| Manifest pin for this IRW version | v7.0 |
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| Metadata source | irw_meta v23.0 |
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