A collection of tools to automatically pair forced-choice items and examine their measurement performance


Forced-choice (FC) tests are gaining researcher’s interest increasingly for its faking resistance when well-designed. Well-designed FC tests should often be characterized by items within a block measuring different latent traits, and items within a block having similar magnitude, or high inter-item agreement (IIA) in terms of their social desirability. Other scoring models may also require factor loading differences or item locations within a block to be maximized or minimized.

Either way, decision on which items should be assigned to the same block - item pairing - is a crucial issue in building a well-designed FC test, which is currently carried out manually. However, given that we often need to simultaneously meet multiple objectives, manual pairing will turn out to be impractical and even infeasible, especially when the number of latent traits and/or the number of items per trait become relatively large.

The R package autoFC is developed to address these difficulties and provides a tool for facilitating automatic FC test construction. It offers users the functionality to:

  1. Customize one or more item pairing criteria and calculate a composite pairing index, termed “energy” with user-specified weights for each criterion.

  2. Automatically optimize the energy for the whole test by sequentially or simultaneously optimizing each matching rule, through the exchange of items among blocks or replacement with unused items.

  3. Construct parallel forms of the same test following the same pairing rules.

Users are allowed to create an FC test of any block size (e.g. Pairs, Triplets, Quadruplets).


You can install autoFC from CRAN:


You can install the development version of autoFC from GitHub:



Below is a brief explanation of all functions provided by autoFC.

  1. cal_block_energy() and cal_block_energy_with_iia() both calculate the total energy for a single item block, or a full FC test with multiple blocks, given a data frame of item characteristics. The latter function incorporates IIA metrics into energy calculation.
  1. make_random_block() takes in number of items and block size as input arguments and produces a test with blocks of randomly paired item numbers. Information about item characteristics is not required.

  2. get_iia() takes in item responses and a single item block (Or a full FC test with multiple blocks), then returns IIA metrics for each item block.

  3. sa_pairing_generalized() is the automatic pairing function which takes in item characteristics (and also individual responses for all items) and an initial FC test, then optimizes the energy of the test based on Simulated Annealing (SA) algorithm.