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:
items_ecpe: N = 2922 subject responses to J = 28 items.qmatrix_ecpe: J = 28 items and K = 3 traits.items_fractions: N = 536 subject responses to J = 20 items.qmatrix_fractions: J = 536 items and K = 20 traits.items_probability_part_one: N = 504 subject responses to J = 12 items.qmatrix_probability_part_one: J = 12 items and K = 4 traits.items_revised_psvtr: N = 516 subject responses to J = 30 items.items_ordered_eclsk_atl: N = 13354 subject responses to J = 12 items.items_spm_ls: N = 499 subject responses to J = 12 items.items_matrix_reasoning: N = 400 subject responses to J = 25 items.items_taylor_manifest_anxiety_scale: N = 4468 subject responses to J = 50 items.items_narcissistic_personality_inventory: N = 11243 subject responses to J = 40 items.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.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.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
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_psvtrThe 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_psvtrWant to see how each data set was imported? Check out the data-raw folder!
James Joseph Balamuta, Steven Andrew Culpepper, Jeffrey Douglas
edmdata packageTo 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:
MIT
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. (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., & 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.
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. https://doi.org/10.1016/j.intell.2018.03.010
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. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2010070
OpenPsychometrics. (2012a). Experimental matrix reasoning IQ test. https://openpsychometrics.org/_rawdata/IQ1.zip
OpenPsychometrics. (2012b). Taylor manifest anxiety scale. https://openpsychometrics.org/_rawdata/TMA.zip
OpenPsychometrics. (2013). Narcissistic personality inventory. https://openpsychometrics.org/_rawdata/NPI.zip
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. https://doi.org/10.1037/0022-3514.54.5.890
Raven, J. C. (1941). Standardization of progressive matrices, 1938. British Journal of Medical Psychology, 19(1), 137–150. https://doi.org/10.1111/j.2044-8341.1941.tb00316.x
Robitzsch, A. (2020). Regularized latent class analysis for polytomous item responses: An application to SPM-LS data. Preprint. https://doi.org/10.20944/preprints202007.0269.v1
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. https://doi.org/10.1111/1467-9876.00272
Tatsuoka, K. K. (1984). Analysis of errors in fraction addition and subtraction problems. Final report. https://eric.ed.gov/?id=ED257665
Taylor, J. A. (1953). A personality scale of manifest anxiety. The Journal of Abnormal and Social Psychology, 48(2), 285. https://doi.org/10.1037/h0056264
Templin, J., & Bradshaw, L. (2014). Hierarchical diagnostic classification models: A family of models for estimating and testing attribute hierarchies. Psychometrika, 79(2), 317–339. https://doi.org/10.1007/s11336-013-9362-0
Templin, J., & Hoffman, L. (2013). Obtaining diagnostic classification model estimates using mplus. Educational Measurement: Issues and Practice, 32(2), 37–50. https://doi.org/10.1111/emip.12010
Yoon, S. Y. (2011). Psychometric properties of the revised purdue spatial visualization tests: Visualization of rotations (the revised PSVT: r). Purdue University. https://docs.lib.purdue.edu/dissertations/AAI3480934/