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edmdata

R build status Package-License CRAN status

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 and GitHub. 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:

install.packages("edmdata")

For the development version, you can install the edmdata package from GitHub with:

# 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 of the R Internals manual.

# 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.

# 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

Build Scripts

Want to see how each data set was imported? Check out the 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:

citation("edmdata")

License

MIT

References

Chen, Yinghan, Culpepper, S. A., Chen, Y., & Douglas, J. (2018). Bayesian estimation of the DINA q matrix. Psychometrika, 83(1), 89–108. https://doi.org/10.1007/s11336-017-9579-4
Chen, Yinyin, Culpepper, S. A., & Liang, F. (2020). A sparse latent class model for cognitive diagnosis. Psychometrika, 1–33. https://doi.org/10.1007/s11336-019-09693-2
Chen, Yinghan, Liu, Y., Culpepper, S. A., & Chen, Y. (2021). Inferring the number of attributes for the exploratory DINA model. Psychometrika, 86(1), 30–64. https://doi.org/10.1007/s11336-021-09750-9
Culpepper, S. A. (2014). If at first you don’t succeed, try, try again: Applications of sequential IRT models to cognitive assessments. Applied Psychological Measurement, 38(8), 632–644. https://doi.org/10.1177/0146621614536464
Culpepper, S. A. (2015). Bayesian estimation of the DINA model with gibbs sampling. Journal of Educational and Behavioral Statistics, 40(5), 454–476. https://doi.org/10.3102/1076998615595403
Culpepper, S. A. (2019a). An exploratory diagnostic model for ordinal responses with binary attributes: Identifiability and estimation. Psychometrika, 84(4), 921–940. https://doi.org/10.1007/s11336-019-09683-4
Culpepper, S. A. (2019b). Estimating the cognitive diagnosis \(`Q`\) matrix with expert knowledge: Application to the fraction-subtraction dataset. Psychometrika, 84(2), 333–357. https://doi.org/10.1007/s11336-018-9643-8
Culpepper, S. A., & Balamuta, J. J. (2017). A Hierarchical Model for Accuracy and Choice on Standardized Tests. Psychometrika, 82(3), 820–845. https://doi.org/10.1007/s11336-015-9484-7
Culpepper, S. A., & Balamuta, J. J. (2021). Inferring latent structure in polytomous data with a higher-order diagnostic model. Multivariate Behavioral Research, 1–19. https://doi.org/10.1080/00273171.2021.1985949
Culpepper, S. A., & Chen, Y. (2019). Development and application of an exploratory reduced reparameterized unified model. Journal of Educational and Behavioral Statistics, 44(1), 3–24. https://doi.org/10.3102/1076998618791306
Heller, J., & Wickelmaier, F. (2013). Minimum discrepancy estimation in probabilistic knowledge structures. Electronic Notes in Discrete Mathematics, 42, 49–56. https://doi.org/10.1016/j.endm.2013.05.145
Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023). A sequential exploratory diagnostic model using a pólya-gamma data augmentation strategy. British Journal of Mathematical and Statistical Psychology, 76(3), 513–538. https://doi.org/10.1111/bmsp.12307
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.