The goal of edmdata R data package is to provide a set of assessment
data sets for psychometric modeling.
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")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_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_psvtr- 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 (C. Tatsuoka, 2002; K. K. Tatsuoka,
1984).
items_fractions: N = 536 subject responses to J = 20 items.qmatrix_fractions: J = 536 items and K = 20 traits.- TMSA Papers: Yinghan Chen et al. (2021), Yinyin Chen et al. (2020), Culpepper (2019b), Culpepper & Chen (2019), Yinghan Chen et al. (2018)
- Elementary Probability Theory (Heller & Wickelmaier, 2013).
items_probability_part_one_full: N = 504 subject responses to J = 12 items.items_probability_part_one_reduced: N = 431 subject responses to J = 12 items.qmatrix_probability_part_one: J = 12 items and K = 4 traits.- TMSA Papers: Yinghan Chen et al. (2021)
- 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)
- Trends in International Mathematics and Science Study 2015 (TIMSS)
Grade 8 Student Background Survey Item Responses (Mullis et al.,
2016).
items_ordered_timss15_background: N = 9672 subject responses to J = 16 items.
- Calculus-based probability and statistics course homework problems
Jimenez et al. (2023)
items_ordered_pswc_hw: N = 288 subject responses to J = 29 items.
- Programme for International Student Assessment (PISA) 2012 U.S.
Student Questionnaire Problem-Solving Vignettes (Culpepper & Balamuta,
2021).
items_ordered_pisa12_us_vignette: N = 3075 subject responses to J = 12 items.
- Programme for International Student Assessment (PISA) 2012 U.S. Math
Assessment.
items_pisa12_us_math: N = 4978 subject responses to J = 76 items.
- 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.
- Human Connectome Project’s Penn Progressive Matrices Fluid
Intelligence Assessment
items_hcp_penn_matrix: N = 1201 subject responses to J = 24 items.items_hcp_penn_matrix_missing: N = 1201 subject responses with missing data indicators to J = 24 items.
- Experimental Matrix Reasoning Test (OpenPsychometrics, 2012a).
items_matrix_reasoning: N = 400 subject responses to J = 25 items.- TMSA Papers: Yinyin 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: 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.
Want to see how each data set was imported? Check out the
data-raw
folder!
James Joseph Balamuta, Steven Andrew Culpepper, Jeffrey Douglas
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")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. (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
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
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. 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/