The goal of edmdata is to provide a set of an example assessment data sets for psychometric modeling.


You can install edmdata from github with:

# install.packages("devtools")

Data Sets Included

  • Examination for the Certificate of Proficiency in English (ECPE) (Templin & Bradshaw, 2014; Templin & Hoffman, 2013).
    • items_ecpe: N = 2922 subject responses to J = 28 items.
    • qmatrix_ecpe: J = 28 items and K = 3 traits.
    • TMSA Papers: Culpepper & Chen (2019)
  • Fraction Addition and Subtraction (Tatsuoka, 2002, 1984).
    • items_fractions: N = 536 subject responses to J = 20 items.
    • qmatrix_fractions: J = 536 items and K = 20 traits.
    • TMSA Papers: Chen et al. (2020), Culpepper (2019b), Culpepper & Chen (2019), Chen et al. (2018)
  • Revised PSVT:R (Culpepper & Balamuta, 2017; Yoon, 2011).
    • items_revised_psvtr: N = 516 subject responses to J = 30 items.
    • TMSA Papers: Culpepper & Balamuta (2017), Culpepper (2015)
  • Subset of Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999’s Approaches to Learning (NCES, 2010).
    • items_ordered_eclsk_atl: N = 13354 subject responses to J = 12 items.
    • TMSA Papers: Culpepper (2019a)
  • Last Series of the Standard Progressive Matrices (SPM-LS) (Myszkowski & Storme, 2018; Raven, 1941; Robitzsch, 2020).
    • items_spm_ls: N = 499 subject responses to J = 12 items.
  • Experimental Matrix Reasoning Test (OpenPsychometrics, 2012a).
    • items_matrix_reasoning: N = 400 subject responses to J = 25 items.
    • TMSA Papers: Chen et al. (2020)
  • Taylor Manifest Anxiety Scale (OpenPsychometrics, 2012b; Taylor, 1953).
    • items_taylor_manifest_anxiety_scale: N = 4468 subject responses to J = 50 items.
  • Narcissistic Personality Inventory (OpenPsychometrics, 2013; Raskin & Terry, 1988).
    • items_narcissistic_personality_inventory: N = 11243 subject responses to J = 40 items.
  • Pre-generated identified Q matrices.
    • qmatrix_oracle_k2_j12: J = 12 items and K = 2 traits.
    • qmatrix_oracle_k3_j20: J = 20 items and K = 3 traits.
    • qmatrix_oracle_k4_j20: J = 20 items and K = 4 traits.
    • qmatrix_oracle_k5_j30: J = 30 items and K = 5 traits.

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 mechansim, 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

# See the first 10 observations of the `items_revised_psvtr` dataset

# View the help documentation for `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

# View the help documentation for `items_revised_psvtr`

Build Scripts

Want to see how each data set was imported? Check out the data-raw folder!


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:





Chen, Y., Culpepper, S. A., Chen, Y., & Douglas, J. (2018). Bayesian estimation of the dina q matrix. Psychometrika, 83(1), 89–108.

Chen, Y., Culpepper, S. A., & Liang, F. (2020). A sparse latent class model for cognitive diagnosis. Psychometrika, 1–33.

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., & Chen, Y. (2019). Development and application of an exploratory reduced reparameterized unified model. Journal of Educational and Behavioral Statistics, 44(1), 3–24.

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.