Package: autoFC 0.2.0

Mengtong Li

autoFC: Automatic Construction of Forced-Choice Tests

Forced-choice (FC) response has gained increasing popularity and interest for its resistance to faking when well-designed (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>). To established well-designed FC scales, typically each item within a block should measure different trait and have similar level of social desirability (Zhang et al., 2020 <doi:10.1177/1094428119836486>). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 <doi:10.31234/osf.io/hmnrc>). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 <doi:10.1177/0013164410375112>) or minimize item location differences (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>) depending on scoring models. Decision of which items should be assigned to the same block, termed item pairing, is thus critical to the quality of an FC test. This pairing process is essentially an optimization process which is currently carried out manually. However, given that we often need to simultaneously meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per trait are relatively large. To address these problems, autoFC is developed as a practical tool for facilitating the automatic construction of FC tests (Li et al., 2022 <doi:10.1177/01466216211051726>), essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods. Given characteristics of each item (and item responses), FC tests can be automatically constructed based on user-defined pairing criteria and weights as well as customized optimization behavior. Users can also construct parallel forms of the same test following the same pairing rules.

Authors:Mengtong Li [cre, aut], Tianjun Sun [aut], Bo Zhang [aut]

autoFC_0.2.0.tar.gz
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autoFC.pdf |autoFC.html
autoFC/json (API)

# Install 'autoFC' in R:
install.packages('autoFC', repos = c('https://tspsyched.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/tspsyched/autofc/issues

On CRAN:

17 exports 4 stars 1.19 score 98 dependencies 3 scripts 358 downloads

Last updated 2 months agofrom:be8373a707. Checks:OK: 1 ERROR: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 12 2024
R-4.5-winERRORSep 12 2024
R-4.5-linuxERRORSep 12 2024
R-4.4-winERRORSep 12 2024
R-4.4-macERRORSep 12 2024
R-4.3-winERRORSep 12 2024
R-4.3-macERRORSep 12 2024

Exports:build_scale_with_blueprintbuild_TIRT_var_namescal_block_energycal_block_energy_with_iiaconstruct_blueprintconvert_to_TIRT_responseempirical_reliabilityfacfunfit_TIRT_modelget_CFA_estimatesget_iiaget_simulation_matricesget_TIRT_long_datamake_random_blockplot_scoresRMSE_rangesa_pairing_generalized

Dependencies:abindaudiobackportsbeeprBHbriocallrcheckmateclicodetoolscolorspacecpp11crayoncurldescdiffobjdigestdistributionaldplyrevaluatefansifarverfsfuturefuture.applygenericsggplot2globalsgluegridExtragtableinlineirrCACisobandjsonlitelabelinglatticelavaanlifecyclelistenvloomagrittrMASSMatrixmatrixStatsmgcvmnormtmunsellmvtnormnlmenumDerivparallellypbapplypbivnormpillarpkgbuildpkgconfigpkgloadposteriorpraiseprocessxprogressrpspurrrquadprogQuickJSRR.methodsS3R.ooR.utilsR6RColorBrewerRcppRcppEigenRcppParallelrematch2rlangrprojrootRPushbulletrstanrstantoolsscalessessioninfoSimDesignsnowStanHeadersstringistringrtensorAtestthatthurstonianIRTtibbletidyrtidyselectutf8vctrsviridisLitewaldowithr

autoFC: An R Package for Automatic Item Pairing in Forced-Choice Test Construction

Rendered fromautoFC.Rmdusingknitr::rmarkdownon Sep 12 2024.

Last update: 2021-06-05
Started: 2021-06-02

Readme and manuals

Help Manual

Help pageTopics
Construct Forced-Choice Blocks Aligned with the Specifications in a Blueprintbuild_scale_with_blueprint
Build Variable Names for the Pairwise/Rank Responses in the TIRT Modelbuild_TIRT_var_names
Calculation of Item Block "Energy"cal_block_energy
Calculation of Item Block "Energy" with IIAs Includedcal_block_energy_with_iia
Build a Blueprint Data Frame for the Focal FC Scaleconstruct_blueprint
Convert the Latent Utility Values into Thurstonian IRT Pairwise/Rank Responses with Pre-Specified Block Designconvert_to_TIRT_response
Calculate the Empirical Reliability of the Latent Trait Scores, Following the Formula in Brown & Maydeu-Olivares (2018).empirical_reliability
Function for Checking If All Items in a Vector Are Uniquefacfun
Fit the Thurstonian IRT Model with Long Format Response Datafit_TIRT_model
Conduct Confirmatory Factor Analysis (CFA) and Obtain Parameter Estimatesget_CFA_estimates
Helper Function for Outputting IIA Characteristics of Each Blockget_iia
Generate Simulated Person and Item Parameter Matrices for the Thurstonian IRT Model Based on Confirmatory Factor Analysis Resultsget_simulation_matrices
Convert the TIRT Pairwise/Rank Response Data into Long Format Compatible with the thurstonianIRT Packageget_TIRT_long_data
Example HEXACO Response DataHEXACO_example_data
Construction of Random Item Blocksmake_random_block
Scatter Plot for True vs Estimated Scores, True Score vs Absolute Error, etc.plot_scores
Calculate the Overall RMSE of the Trait Scores, or the RMSE in a Certain Trait Score RangeRMSE_range
Automatic Item Pairing Method in Forced-Choice Test Constructionsa_pairing_generalized
Block Information for the Example Triplet Response Datatriplet_block_info
Example Triplet Response Datatriplet_example_data