9,899 responses from 300 respondents to 33 items.
| Description | 33-item STEM Teaching Practices questionnaire, EFA sample (STP/KN/CH/PSE constructs; 1-4, declared 5-point Likert), from Malaysian STEM teachers |
|---|---|
| Reference | Karpudewan M, Krishnan P, Ali MN, Fah LY (2022) Designing instrument to measure STEM teaching practices of Malaysian teachers. PLoS ONE. |
| DOI | 10.1371/journal.pone.0268509 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0268509.s005 |
| Responses | 9,899 |
|---|---|
| Respondents | 300 |
| Items | 33 |
| Response categories | 4 |
| Responses per respondent | 32.997 |
| Responses per item | 299.970 |
| Density | 1.000 |
| Longitudinal | FALSE |
| sample | Workplace |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | may |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | STEM Teaching Practices Questionnaire (33-item exploratory-factor-analysis version) |
|---|---|
| Mean words per item | 7.848 |
| Mean characters per item | 52.667 |
| Mean characters per response | 5.750 |
| Flesch-Kincaid grade level | 10.462 |
iditemresp
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("karpudewan_2022_stp_efa")
# Python
pip install irw
import irw
df = irw.fetch("karpudewan_2022_stp_efa")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v7.0 |
| Redivis dataset DOI | 10.57761/pqqn-pm43 |
| Manifest pin for this IRW version | v7.0 |
| Metadata source | irw_meta v23.0 |