edmdata

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

• 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.

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