README.md

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.
Chen, Yinyin, Culpepper, S. A., & Liang, F. (2020). A sparse latent class model for cognitive diagnosis. *Psychometrika*, 1–33.
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.
Culpepper, S. A. (2015). Bayesian estimation of the DINA model with gibbs sampling. *Journal of Educational and Behavioral Statistics*, *40*(5), 454–476.
Culpepper, S. A. (2019a). An exploratory diagnostic model for ordinal responses with binary attributes: Identifiability and estimation. *Psychometrika*, *84*(4), 921–940.
Culpepper, S. A. (2019b). Estimating the cognitive diagnosis $Q$ matrix with expert knowledge: Application to the fraction-subtraction dataset. *Psychometrika*, *84*(2), 333–357.
Culpepper, S. A., & Balamuta, J. J. (2017). A Hierarchical Model for Accuracy and Choice on Standardized Tests. *Psychometrika*, *82*(3), 820–845.
Culpepper, S. A., & Balamuta, J. J. (2021). Inferring latent structure in polytomous data with a higher-order diagnostic model. *Multivariate Behavioral Research*, 1–19.
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.
Heller, J., & Wickelmaier, F. (2013). Minimum discrepancy estimation in probabilistic knowledge structures. *Electronic Notes in Discrete Mathematics*, *42*, 49–56.
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/
Mullis, I. V. S., Martin, M. O., Goh, S., & Cotter, K. (Eds. ). (2016). *TIMSS 2015 encyclopedia: Education policy and curriculum in mathematics and science*. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: http://timssandpirls.bc.edu/timss2015/encyclopedia/.
Myszkowski, N., & Storme, M. (2018). A snapshot of g? Binary and polytomous item-response theory investigations of the last series of the standard progressive matrices (SPM-LS). *Intelligence*, *68*, 109–116.
NCES. (2010). *Early childhood longitudinal study, kindergarten class of 1998-99 (ECLS-k) kindergarten through fifth grade approaches to learning and self-description questionnaire (SDQ) items and public-use data files*.
OpenPsychometrics. (2012a). *Experimental matrix reasoning IQ test*.
OpenPsychometrics. (2012b). *Taylor manifest anxiety scale*.
OpenPsychometrics. (2013). *Narcissistic personality inventory*.
Raskin, R., & Terry, H. (1988). A principal-components analysis of the narcissistic personality inventory and further evidence of its construct validity. *Journal of Personality and Social Psychology*, *54*(5), 890.
Raven, J. C. (1941). Standardization of progressive matrices, 1938. *British Journal of Medical Psychology*, *19*(1), 137–150.
Robitzsch, A. (2020). Regularized latent class analysis for polytomous item responses: An application to SPM-LS data. *Preprint*.
Tatsuoka, C. (2002). Data analytic methods for latent partially ordered classification models. *Journal of the Royal Statistical Society: Series C (Applied Statistics)*, *51*(3), 337–350.
Tatsuoka, K. K. (1984). *Analysis of errors in fraction addition and subtraction problems. Final report.*
Taylor, J. A. (1953). A personality scale of manifest anxiety. *The Journal of Abnormal and Social Psychology*, *48*(2), 285.
Templin, J., & Bradshaw, L. (2014). Hierarchical diagnostic classification models: A family of models for estimating and testing attribute hierarchies. *Psychometrika*, *79*(2), 317–339.
Templin, J., & Hoffman, L. (2013). Obtaining diagnostic classification model estimates using mplus. *Educational Measurement: Issues and Practice*, *32*(2), 37–50.
Yoon, S. Y. (2011). *Psychometric properties of the revised purdue spatial visualization tests: Visualization of rotations (the revised PSVT: r)*. Purdue University.


tmsalab/ecdmdata documentation built on Aug. 24, 2024, 1:53 p.m.