{"id":23069524,"url":"https://github.com/tmsalab/edmdata","last_synced_at":"2025-08-15T13:32:09.651Z","repository":{"id":46206137,"uuid":"123809139","full_name":"tmsalab/edmdata","owner":"tmsalab","description":"Supplementary data package for the edm package","archived":false,"fork":false,"pushed_at":"2024-10-01T22:21:34.000Z","size":1558,"stargazers_count":5,"open_issues_count":2,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-03T15:12:15.016Z","etag":null,"topics":["cognitive-diagnostic-models","data","edm","r"],"latest_commit_sha":null,"homepage":"https://tmsalab.github.io/edmdata","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tmsalab.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-03-04T16:55:00.000Z","updated_at":"2024-10-01T22:20:17.000Z","dependencies_parsed_at":"2022-07-23T10:34:33.654Z","dependency_job_id":null,"html_url":"https://github.com/tmsalab/edmdata","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/tmsalab/edmdata","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tmsalab%2Fedmdata","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tmsalab%2Fedmdata/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tmsalab%2Fedmdata/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tmsalab%2Fedmdata/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tmsalab","download_url":"https://codeload.github.com/tmsalab/edmdata/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tmsalab%2Fedmdata/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270578303,"owners_count":24610035,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-15T02:00:12.559Z","response_time":110,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cognitive-diagnostic-models","data","edm","r"],"created_at":"2024-12-16T06:15:49.389Z","updated_at":"2025-08-15T13:32:09.011Z","avatar_url":"https://github.com/tmsalab.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\nbibliography: bibliography.bib\ncsl: apa-single-spaced.csl\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"README-\"\n)\n```\n\n# edmdata\n\n\u003c!-- badges: start --\u003e\n[![R build status](https://github.com/tmsalab/edmdata/workflows/R-CMD-check/badge.svg)](https://github.com/tmsalab/edmdata/actions)\n[![Package-License](http://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat)](https://opensource.org/licenses/MIT)\n[![CRAN status](https://www.r-pkg.org/badges/version/edmdata)](https://CRAN.R-project.org/package=edmdata)\n\u003c!-- badges: end --\u003e\n\nThe goal of `edmdata` R data package is to provide a set of assessment data sets\nfor psychometric modeling.\n\n## Installation\n\nThe `edmdata` package is available on both \n[CRAN](https://CRAN.R-project.org/package=edmdata) and\n[GitHub](https://github.com/tmsalab/edmdata). The CRAN version is considered \nstable while the GitHub version is in a state of development and may break.\n\nYou can install the stable version of the `edmdata` package with:\n\n```{r}\n#| label: cran-installation\n#| eval: false\ninstall.packages(\"edmdata\")\n```\n\nFor the development version, you can install the `edmdata` package from GitHub with:\n\n```{r}\n#| label: gh-installation\n#| eval: false\n\n# install.packages(\"remotes\")\nremotes::install_github(\"tmsalab/edmdata\")\n```\n\n\n## Using data in the package\n\nThere are two ways to access the data contained within this package.\n\nThe first is to load the package itself and type the name of a data set.\nThis approach takes advantage of *R*’s lazy loading mechanism, which\navoids loading the data until it is used in *R* session. For details on\nhow lazy loading works, please see [Section 1.17: Lazy\nLoading](https://cran.r-project.org/doc/manuals/r-release/R-ints.html#Lazy-loading)\nof the [R\nInternals](https://cran.r-project.org/doc/manuals/r-release/R-ints.html)\nmanual.\n\n``` r\n# Load the `edmdata` package\nlibrary(\"edmdata\")\n\n# See the first 10 observations of the `items_revised_psvtr` dataset\nhead(items_revised_psvtr)\n\n# View the help documentation for `items_revised_psvtr`\n?items_revised_psvtr\n```\n\nThe second approach is to use the `data()` command to load data on the\nfly without loading the package. After using `data()`, the data set\nwill be available to use under the given name. \n\n``` r\n# Loading `items_revised_psvtr` without a `library(edmdata)` call\ndata(\"items_revised_psvtr\", package = \"edmdata\")\n\n# See the first 10 observations of the `items_revised_psvtr` dataset\nhead(items_revised_psvtr)\n\n# View the help documentation for `items_revised_psvtr`\n?items_revised_psvtr\n```\n\n## Data Sets Included\n\n```{r}\n#| echo: false\nlibrary(edmdata)\n```\n\n\n- Examination for the Certificate of Proficiency in English (ECPE) [@Templin:2013:DCMECPE; @Templin:2014:HierarchicalDCM]. \n    - `items_ecpe`: N = `r nrow(items_ecpe)` subject responses to J = `r ncol(items_ecpe)` items.\n    - `qmatrix_ecpe`: J = `r nrow(qmatrix_ecpe)` items and K = `r ncol(qmatrix_ecpe)` traits. \n    - **TMSA Papers:** @Culpepper:2019:ErRUM\n- Fraction Addition and Subtraction [@Tatsuoka:1984:FractionSubtraction; @Tatsuoka:2002:FractionSubtractionRelease].\n    - `items_fractions`: N = `r nrow(items_fractions)` subject responses to J = `r ncol(items_fractions)` items.\n    - `qmatrix_fractions`: J = `r nrow(items_fractions)` items and K = `r ncol(items_fractions)` traits. \n    - **TMSA Papers:** @Chen:2021:InferK, @Chen:2020:SLCMDC, @Culpepper:2019:EGDM, @Culpepper:2019:ErRUM, @Chen:2018:EDINA\n- Elementary Probability Theory [@Heller:2013:ProbabilityKS].\n    - `items_probability_part_one_full`: N = `r nrow(items_probability_part_one_full)`\n      subject responses to J = `r ncol(items_probability_part_one_full)` items.\n    - `items_probability_part_one_reduced`: N = `r nrow(items_probability_part_one_reduced)`\n      subject responses to J = `r ncol(items_probability_part_one_reduced)` items.\n    - `qmatrix_probability_part_one`: J = `r nrow(qmatrix_probability_part_one)` \n     items and K = `r ncol(qmatrix_probability_part_one)` traits. \n    - **TMSA Papers:** @Chen:2021:InferK\n- Revised PSVT:R [@Yoon:2011:RevisedPSVTR; @Culpepper:2017:ChoiceIRT].\n    - `items_revised_psvtr`: N = `r nrow(items_revised_psvtr)` subject responses\n      to J = `r ncol(items_revised_psvtr)` items.\n    - **TMSA Papers:** @Culpepper:2017:ChoiceIRT, @Culpepper:2015:BayesianDINA\n- Subset of Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999's\n  Approaches to Learning [@ECLSK:2010:ATLData].\n    - `items_ordered_eclsk_atl`: N = `r nrow(items_ordered_eclsk_atl)` subject responses\n      to J = `r ncol(items_ordered_eclsk_atl)` items.\n    - **TMSA Papers:** @Culpepper:2019:EODM\n- Trends in International Mathematics and Science Study 2015 (TIMSS) Grade 8\n  Student Background Survey Item Responses [@TIMSS:2015:Background].\n    - `items_ordered_timss15_background`: N = `r nrow(items_ordered_timss15_background)` subject responses\n      to J = `r ncol(items_ordered_timss15_background)` items.\n- Calculus-based probability and statistics course homework problems [@Culpepper:2014:SequentialIRT, @Jimenez:2023:OPGEDM]\n    - `items_ordered_pswc_hw`: N = `r nrow(items_ordered_pswc_hw)`\n      subject responses to J = `r ncol(items_ordered_pswc_hw)` items.\n- Programme for International Student Assessment (PISA) 2012\n  U.S. Student Questionnaire Problem-Solving Vignettes [@Culpepper:2021:OHOEGDM].\n    - `items_ordered_pisa12_us_vignette`: \n      N = `r nrow(items_ordered_pisa12_us_vignette)`\n      subject responses to J = `r ncol(items_ordered_pisa12_us_vignette)` items.\n- Programme for International Student Assessment (PISA) 2012\n  U.S. Math Assessment.\n    - `items_pisa12_us_math`: \n      N = `r nrow(items_pisa12_us_math)`\n      subject responses to J = `r ncol(items_pisa12_us_math)` items.\n- Last Series of the Standard Progressive Matrices (SPM-LS) [@Raven:1941:SPM; @Myszkowski:2018:IRTSPMLS; @Robitzsch:2020:IRTRCLMSPMLS].\n    - `items_spm_ls`: N = `r nrow(items_spm_ls)`\n      subject responses to J = `r ncol(items_spm_ls)` items.\n- Human Connectome Project's Penn Progressive Matrices Fluid Intelligence Assessment\n    - `items_hcp_penn_matrix`: N = `r nrow(items_hcp_penn_matrix)`\n      subject responses to J = `r ncol(items_hcp_penn_matrix)` items.\n    - `items_hcp_penn_matrix_missing`: N = `r nrow(items_hcp_penn_matrix_missing)`\n      subject responses with missing data indicators to J = `r ncol(items_hcp_penn_matrix_missing)` items.\n- Experimental Matrix Reasoning Test [@OpenPsychometrics:2012:IQ1].\n    - `items_matrix_reasoning`: N = `r nrow(items_matrix_reasoning)`\n      subject responses to J = `r ncol(items_matrix_reasoning)` items.\n    - **TMSA Papers:** @Chen:2020:SLCMDC\n- Taylor Manifest Anxiety Scale [@Taylor:1953:TMI; @OpenPsychometrics:2012:TaylorAnxietyScale].\n    - `items_taylor_manifest_anxiety_scale`: N = `r nrow(items_taylor_manifest_anxiety_scale)`\n      subject responses to J = `r ncol(items_taylor_manifest_anxiety_scale)` items.\n- Narcissistic Personality Inventory [@Raskin:1988:NPI; @OpenPsychometrics:2013:NPI].\n    - `items_narcissistic_personality_inventory`: N = `r nrow(items_narcissistic_personality_inventory)`\n      subject responses to J = `r ncol(items_narcissistic_personality_inventory)` items.\n- Pre-generated identified Q matrices.\n    - `qmatrix_oracle_k2_j12`: 12 items and 2 traits.\n    - `qmatrix_oracle_k3_j20`: 20 items and 3 traits.\n    - `qmatrix_oracle_k4_j20`: 20 items and 4 traits.\n    - `qmatrix_oracle_k5_j30`: 30 items and 5 traits.\n- Pre-generated strategy sets. \n    - `strategy_oracle_k3_j20_s2`: 20 items, 3 traits, and 2 strategies.\n    - `strategy_oracle_k3_j30_s2`: 30 items, 3 traits, and 2 strategies.\n    - `strategy_oracle_k3_j40_s2`: 40 items, 3 traits, and 2 strategies.\n    - `strategy_oracle_k3_j50_s2`: 50 items, 3 traits, and 2 strategies.\n    - `strategy_oracle_k4_j20_s2`: 20 items, 4 traits, and 2 strategies.\n    - `strategy_oracle_k4_j30_s2`: 30 items, 4 traits, and 2 strategies.\n    - `strategy_oracle_k4_j40_s2`: 40 items, 4 traits, and 2 strategies.\n    - `strategy_oracle_k4_j50_s2`: 50 items, 4 traits, and 2 strategies.\n\n\n\n## Build Scripts\n\nWant to see how each data set was imported? Check out the\n[`data-raw`](https://github.com/tmsalab/edmdata/tree/master/data-raw)\nfolder!\n\n## Authors\n\nJames Joseph Balamuta, Steven Andrew Culpepper, Jeffrey Douglas\n\n## Citing the `edmdata` package\n\nTo ensure future development of the package, please cite `edmdata`\npackage if used during an analysis or simulation study. Citation information\nfor the package may be acquired by using in *R*:\n\n```{r, eval = FALSE}\ncitation(\"edmdata\")\n```\n\n## License\n\nMIT\n\n## References\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftmsalab%2Fedmdata","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftmsalab%2Fedmdata","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftmsalab%2Fedmdata/lists"}