8,862,532 responses from 633,038 respondents to 11 items.
| Description | Items from Tanzanian Form Two National Assessment & Primary School Leaving Examination |
|---|---|
| Reference | Brandt, Kasper, 2022, "Replication Data for: When Private Beats Public: A Flexible Value-Added Model with Tanzanian School Switchers", https://doi.org/10.7910/DVN/UMNYYR, Harvard Dataverse, V1, UNF:6:/7r9mSRgjyqiFqRGtLx/og== [fileUNF] Brandt, K. (2023). When private beats public: A flexible value-added model with Tanzanian school switchers. Economic Development and Cultural Change, 72(1), 159-206. |
| DOI | 10.1086/718893 |
| Licence | CC0 1.0 |
| Source data | https://dataverse.harvard.edu/file.xhtml?fileId=5429719&version=1.0 |
| Responses | 8,862,532 |
|---|---|
| Respondents | 633,038 |
| Items | 11 |
| Response categories | 5 |
| Responses per respondent | 14 |
| Responses per item | 805684.727 |
| Density | 1.273 |
| Longitudinal | TRUE |
| age range | Child (<18y) |
|---|---|
| child age (for child-focused studies) | Child (6-12y), Adolescent (12-18y) |
| sample | Educational |
| construct type | Cognitive/educational |
| measurement tool | Test |
| item format | Likert Scale/selected response |
| primary language(s) | swa, eng |
| construct name | Items from Tanzanian Form Two National Assessment & Primary School Leaving Examination |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Tanzanian national school examinations: Primary School Leaving Examination (PSLE) and Form Two National Assessment (FTNA) |
|---|---|
| Mean words per item | 1.091 |
| Mean characters per item | 8.455 |
| Mean characters per response | 1 |
| Flesch-Kincaid grade level | 18.269 |
cluster_idcov_femalecov_privatecov_yeariditemrespwave
This table is larger than Redivis serves without a login, so download it with one of the packages below or while signed in to Redivis.
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("ftna_kasper_2022")
# Python
pip install irw
import irw
df = irw.fetch("ftna_kasper_2022")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse v53.0 |
| Redivis dataset DOI | 10.57761/4g08-xt41 |
| Manifest pin for this IRW version | v53.0 |
| Metadata source | irw_meta v23.0 |