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https://github.com/tmsalab/edmdata
Supplementary data package for the edm package
https://github.com/tmsalab/edmdata
cognitive-diagnostic-models data edm r
Last synced: 26 days ago
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Supplementary data package for the edm package
- Host: GitHub
- URL: https://github.com/tmsalab/edmdata
- Owner: tmsalab
- License: other
- Created: 2018-03-04T16:55:00.000Z (almost 7 years ago)
- Default Branch: main
- Last Pushed: 2024-08-21T03:24:35.000Z (5 months ago)
- Last Synced: 2024-08-21T22:32:36.342Z (5 months ago)
- Topics: cognitive-diagnostic-models, data, edm, r
- Language: R
- Homepage: https://tmsalab.github.io/edmdata
- Size: 1.41 MB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
bibliography: bibliography.bib
csl: apa-single-spaced.csl
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# edmdata
[](https://github.com/tmsalab/edmdata/actions)
[](https://opensource.org/licenses/MIT)
[](https://CRAN.R-project.org/package=edmdata)
The goal of `edmdata` R data package is to provide a set of assessment data sets
for psychometric modeling.
## Installation
The `edmdata` package is available on both
[CRAN](https://CRAN.R-project.org/package=edmdata) and
[GitHub](https://github.com/tmsalab/edmdata). The CRAN version is considered
stable while the GitHub version is in a state of development and may break.
You can install the stable version of the `edmdata` package with:
```{r}
#| label: cran-installation
#| eval: false
install.packages("edmdata")
```
For the development version, you can install the `edmdata` package from GitHub with:
```{r}
#| label: gh-installation
#| eval: false
# install.packages("remotes")
remotes::install_github("tmsalab/edmdata")
```
## Using data in the package
There are two ways to access the data contained within this package.
The first is to load the package itself and type the name of a data set.
This approach takes advantage of *R*’s lazy loading mechanism, which
avoids loading the data until it is used in *R* session. For details on
how lazy loading works, please see [Section 1.17: Lazy
Loading](https://cran.r-project.org/doc/manuals/r-release/R-ints.html#Lazy-loading)
of the [R
Internals](https://cran.r-project.org/doc/manuals/r-release/R-ints.html)
manual.
``` r
# Load the `edmdata` package
library("edmdata")
# See the first 10 observations of the `items_revised_psvtr` dataset
head(items_revised_psvtr)
# View the help documentation for `items_revised_psvtr`
?items_revised_psvtr
```
The second approach is to use the `data()` command to load data on the
fly without loading the package. After using `data()`, the data set
will be available to use under the given name.
``` r
# Loading `items_revised_psvtr` without a `library(edmdata)` call
data("items_revised_psvtr", package = "edmdata")
# See the first 10 observations of the `items_revised_psvtr` dataset
head(items_revised_psvtr)
# View the help documentation for `items_revised_psvtr`
?items_revised_psvtr
```
## Data Sets Included
```{r}
#| echo: false
library(edmdata)
```
- Examination for the Certificate of Proficiency in English (ECPE) [@Templin:2013:DCMECPE; @Templin:2014:HierarchicalDCM].
- `items_ecpe`: N = `r nrow(items_ecpe)` subject responses to J = `r ncol(items_ecpe)` items.
- `qmatrix_ecpe`: J = `r nrow(qmatrix_ecpe)` items and K = `r ncol(qmatrix_ecpe)` traits.
- **TMSA Papers:** @Culpepper:2019:ErRUM
- Fraction Addition and Subtraction [@Tatsuoka:1984:FractionSubtraction; @Tatsuoka:2002:FractionSubtractionRelease].
- `items_fractions`: N = `r nrow(items_fractions)` subject responses to J = `r ncol(items_fractions)` items.
- `qmatrix_fractions`: J = `r nrow(items_fractions)` items and K = `r ncol(items_fractions)` traits.
- **TMSA Papers:** @Chen:2021:InferK, @Chen:2020:SLCMDC, @Culpepper:2019:EGDM, @Culpepper:2019:ErRUM, @Chen:2018:EDINA
- Elementary Probability Theory [@Heller:2013:ProbabilityKS].
- `items_probability_part_one_full`: N = `r nrow(items_probability_part_one_full)`
subject responses to J = `r ncol(items_probability_part_one_full)` items.
- `items_probability_part_one_reduced`: N = `r nrow(items_probability_part_one_reduced)`
subject responses to J = `r ncol(items_probability_part_one_reduced)` items.
- `qmatrix_probability_part_one`: J = `r nrow(qmatrix_probability_part_one)`
items and K = `r ncol(qmatrix_probability_part_one)` traits.
- **TMSA Papers:** @Chen:2021:InferK
- Revised PSVT:R [@Yoon:2011:RevisedPSVTR; @Culpepper:2017:ChoiceIRT].
- `items_revised_psvtr`: N = `r nrow(items_revised_psvtr)` subject responses
to J = `r ncol(items_revised_psvtr)` items.
- **TMSA Papers:** @Culpepper:2017:ChoiceIRT, @Culpepper:2015:BayesianDINA
- Subset of Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999's
Approaches to Learning [@ECLSK:2010:ATLData].
- `items_ordered_eclsk_atl`: N = `r nrow(items_ordered_eclsk_atl)` subject responses
to J = `r ncol(items_ordered_eclsk_atl)` items.
- **TMSA Papers:** @Culpepper:2019:EODM
- Trends in International Mathematics and Science Study 2015 (TIMSS) Grade 8
Student Background Survey Item Responses [@TIMSS:2015:Background].
- `items_ordered_timss15_background`: N = `r nrow(items_ordered_timss15_background)` subject responses
to J = `r ncol(items_ordered_timss15_background)` items.
- Calculus-based probability and statistics course homework problems [@Culpepper:2014:SequentialIRT, @Jimenez:2023:OPGEDM]
- `items_ordered_pswc_hw`: N = `r nrow(items_ordered_pswc_hw)`
subject responses to J = `r ncol(items_ordered_pswc_hw)` items.
- Programme for International Student Assessment (PISA) 2012
U.S. Student Questionnaire Problem-Solving Vignettes [@Culpepper:2021:OHOEGDM].
- `items_ordered_pisa12_us_vignette`:
N = `r nrow(items_ordered_pisa12_us_vignette)`
subject responses to J = `r ncol(items_ordered_pisa12_us_vignette)` items.
- Programme for International Student Assessment (PISA) 2012
U.S. Math Assessment.
- `items_pisa12_us_math`:
N = `r nrow(items_pisa12_us_math)`
subject responses to J = `r ncol(items_pisa12_us_math)` items.
- Last Series of the Standard Progressive Matrices (SPM-LS) [@Raven:1941:SPM; @Myszkowski:2018:IRTSPMLS; @Robitzsch:2020:IRTRCLMSPMLS].
- `items_spm_ls`: N = `r nrow(items_spm_ls)`
subject responses to J = `r ncol(items_spm_ls)` items.
- Human Connectome Project's Penn Progressive Matrices Fluid Intelligence Assessment
- `items_hcp_penn_matrix`: N = `r nrow(items_hcp_penn_matrix)`
subject responses to J = `r ncol(items_hcp_penn_matrix)` items.
- `items_hcp_penn_matrix_missing`: N = `r nrow(items_hcp_penn_matrix_missing)`
subject responses with missing data indicators to J = `r ncol(items_hcp_penn_matrix_missing)` items.
- Experimental Matrix Reasoning Test [@OpenPsychometrics:2012:IQ1].
- `items_matrix_reasoning`: N = `r nrow(items_matrix_reasoning)`
subject responses to J = `r ncol(items_matrix_reasoning)` items.
- **TMSA Papers:** @Chen:2020:SLCMDC
- Taylor Manifest Anxiety Scale [@Taylor:1953:TMI; @OpenPsychometrics:2012:TaylorAnxietyScale].
- `items_taylor_manifest_anxiety_scale`: N = `r nrow(items_taylor_manifest_anxiety_scale)`
subject responses to J = `r ncol(items_taylor_manifest_anxiety_scale)` items.
- Narcissistic Personality Inventory [@Raskin:1988:NPI; @OpenPsychometrics:2013:NPI].
- `items_narcissistic_personality_inventory`: N = `r nrow(items_narcissistic_personality_inventory)`
subject responses to J = `r ncol(items_narcissistic_personality_inventory)` items.
- Pre-generated identified Q matrices.
- `qmatrix_oracle_k2_j12`: 12 items and 2 traits.
- `qmatrix_oracle_k3_j20`: 20 items and 3 traits.
- `qmatrix_oracle_k4_j20`: 20 items and 4 traits.
- `qmatrix_oracle_k5_j30`: 30 items and 5 traits.
- Pre-generated strategy sets.
- `strategy_oracle_k3_j20_s2`: 20 items, 3 traits, and 2 strategies.
- `strategy_oracle_k3_j30_s2`: 30 items, 3 traits, and 2 strategies.
- `strategy_oracle_k3_j40_s2`: 40 items, 3 traits, and 2 strategies.
- `strategy_oracle_k3_j50_s2`: 50 items, 3 traits, and 2 strategies.
- `strategy_oracle_k4_j20_s2`: 20 items, 4 traits, and 2 strategies.
- `strategy_oracle_k4_j30_s2`: 30 items, 4 traits, and 2 strategies.
- `strategy_oracle_k4_j40_s2`: 40 items, 4 traits, and 2 strategies.
- `strategy_oracle_k4_j50_s2`: 50 items, 4 traits, and 2 strategies.
## Build Scripts
Want to see how each data set was imported? Check out the
[`data-raw`](https://github.com/tmsalab/edmdata/tree/master/data-raw)
folder!
## Authors
James Joseph Balamuta, Steven Andrew Culpepper, Jeffrey Douglas
## Citing the `edmdata` package
To ensure future development of the package, please cite `edmdata`
package if used during an analysis or simulation study. Citation information
for the package may be acquired by using in *R*:
```{r, eval = FALSE}
citation("edmdata")
```
## License
MIT
## References