Overview

ShortForm is an R package for constructing short-form assessments from larger item banks using reproducible, optimization-based workflows.

It provides implementations of metaheuristic search algorithms to automate item selection while preserving prespecified psychometric properties. The package is designed for researchers and practitioners who need scalable, transparent, and reproducible methods for reducing assessment length.

Key Features

Automated short-form construction from full item banks
Multiple optimization algorithms:
- Ant Colony Optimization (ACO)
- Tabu Search (TS)
- Simulated Annealing (SA)
Integration with lavaan and Mplus workflows
Support for custom objective functions and model specifications
Reproducible, scriptable workflows for assessment design

Use Cases

ShortForm is useful when you need to:

Reduce assessment length while maintaining measurement validity
Automate item selection workflows for large-scale assessments
Build reproducilbe pipelines for psychometric modeling and evaluation

Engineering Highlights

This package demonstrates:

Development and maintenance of an R package distributed via CRAN
Implementation of optimization algorithms for applied statistical workflows
Automated build and testing pipelines using CI/CD
Integration with other modeling tools
Reproducible examples using structured simulated data

This document was created on 2026-08-14.

Installation

install.packages("ShortForm") # the CRAN-approved version
require("devtools")
devtools::install_github("AnthonyRaborn/ShortForm", branch = "devel") # the developmental version

Quick Examples

set.seed(58310)
library(ShortForm)
# Example workflow:
# 1. Define item pool and model
# 2. Run optimization algorithm
# 3. Evaluate resulting short form
result_ACO <- antColony(
data = simulated_test_data,
initialModel = exampleAntModel,
itemsPerFactor = 15,
steps = 100,
fit.indices = c("cfi", "rmsea"),
fit.statistics.test = "(cfi > 0.98)&(rmsea < 0.03)",
maxIterations = 200
)
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summary(result_ACO)
## Algorithm: Ant Colony Optimization
## Total Run Time: 3.973 secs
##
## lavaan 0.7-2 ended normally after 59 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 48
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 123.385
## Degrees of freedom 104
## P-value (Chi-square) 0.094
##
##
## Final Model Syntax:
## Ability =~ Item15 + Item16 + Item19 + Item2 + Item20 + Item30 + Item31 + Item37
## + Item4 + Item42 + Item45 + Item50 + Item53 + Item54 + Item55
## Outcome ~ Ability
##
## Fit Indices: cfi, rmsea
## Fit Test: (cfi > 0.98)&(rmsea < 0.03)
## Final Model Values: cfi = 0.991, rmsea = 0.014
plot(result_ACO)

set.seed(58310)
result_SA <- simulatedAnnealing(
  initialModel = paste("Ability =~", paste0("Item", 1:56, collapse = " + ")),
  originalData = simulated_test_data,
  maxIterations = 20,
  itemsPerFactor = 40,
  items = paste0("Item", 1:56),
  setChains = 4
)
##  Initializing short form creation.
##  The initial short form is:
##  Ability =~ Item10 + Item43 + Item35 + Item31 + Item42 + Item49 + Item48 + Item20 + Item32 + Item18 + Item36 + Item34 + Item45 + Item50 + Item3 + Item54 + Item2 + Item44 + Item24 + Item7 + Item22 + Item46 + Item53 + Item13 + Item39 + Item38 + Item51 + Item21 + Item15 + Item56 + Item8 + Item29 + Item40 + Item26 + Item23 + Item47 + Item1 + Item52 + Item27 + Item9
##  
##  Using the short form randomNeighbor function.
##  Finished initializing short form options.
##   Current Progress: 
##  Chain number 1 complete. 
##  Chain number 2 complete. 
##  Chain number 3 complete. 
##  Chain number 4 complete.
summary(result_SA)
##  Algorithm: Simulated Annealing
##  Total Run Time: 2.059 secs
##  
##  lavaan 0.7-2 ended normally after 34 iterations
##  
##    Estimator                                         ML
##    Optimization method                           NLMINB
##    Number of model parameters                       120
##  
##    Number of observations                          1000
##  
##  Model Test User Model:
##                                                        
##    Test statistic                               789.761
##    Degrees of freedom                               740
##    P-value (Chi-square)                           0.100
##  
##  
##  Final Model Syntax:
##  Ability =~ Item10 + Item43 + Item35 + Item31 + Item42 + Item49 + Item48 + Item20
##    + Item32 + Item18 + Item28 + Item34 + Item45 + Item50 + Item3 + Item25 +
##    Item2 + Item44 + Item24 + Item7 + Item22 + Item46 + Item53 + Item13 + Item39 +
##    Item38 + Item51 + Item21 + Item15 + Item56 + Item8 + Item29 + Item40 + Item26
##    + Item23 + Item47 + Item19 + Item52 + Item27 + Item9
##  
##  Criterion: "cfi" (maximized)
##  Final Model Value: 0.989
plot(result_SA)

set.seed(58310)
shortAntModel <- "
Ability =~ Item1 + Item2 + Item3 + Item4 + Item5 + Item6 + Item7 + Item8
Ability ~ Outcome
"
result_TS <- tabuSearch(
  initialModel = shortAntModel,
  originalData = simulated_test_data, itemsPerFactor = 7,
  maxIterations = 10, tabu.size = 3, parallel = FALSE
)
##  Running iteration 1 of 10.   Running iteration 2 of 10.   Running iteration 3 of 10.   Running iteration 4 of 10.   Running iteration 5 of 10.   Running iteration 6 of 10.   Running iteration 7 of 10.   Running iteration 8 of 10.   Running iteration 9 of 10.   Running iteration 10 of 10.
summary(result_TS)
##  Algorithm: Tabu Search
##  Total Run Time: 1.497 secs
##  
##  lavaan 0.7-2 ended normally after 36 iterations
##  
##    Estimator                                         ML
##    Optimization method                           NLMINB
##    Number of model parameters                        22
##  
##    Number of observations                          1000
##  
##  Model Test User Model:
##                                                        
##    Test statistic                                18.858
##    Degrees of freedom                                20
##    P-value (Chi-square)                           0.531
##  
##  
##  Final Model Syntax:
##  Ability =~ Item1 + Item6 + Item3 + Item4 + Item5 + Item7 + Item8
##  Ability ~ Outcome
##  
##  Criterion: "cfi" (maximized)
##  Final Model Value: 1
plot(result_TS)

Workflow

flowchart LR
    A@{ shape: lean-r, label: "Full Item Bank" } --> B[Specify Model]
    B --> C[Run Optimization Algorithm]
    C --> D[Evaluate Candidate Forms]
    D --> |One or More Forms Fit| E@{ shape: diamond, label: "Select Short Form" }
    D --> |No Adequate Forms Found| C
    E --> F@{ shape: lean-l, label: "Reporting & Visualization" }

Outputs

ShortForm produces diagnostic outputs such as:

Objective function convergence plots
Algorithm-specific diagnostics
Summary statistics for selected item sets

Algorithms

Ant Colony Optimization (ACO)

Implementation adapted from Leite, Huang, & Marcoulides (2008).

Tabu Search (TS)

Based on Marcoulides & Falk (2018), extended for model search and short-form construction.

Simulated Annealing (SA)

Provides one of the first implementations of simulated annealing for psychometric model selection workflows. Implemented following Kirkpatrick et al. (1983).

Performance Notes

Computational time depends on:

Number and types of items
Dimensionality/model complexity
Algorithm convergence settings

Parallelization is supported for certain workflows.

Reproducibility

ShortForm is designed for reproducible analytical workflows:

Fully scriptable
Compatible with R Markdown and Quarto
Supports integration into larger modeling pipelines

Reproducible Docker Environment

This repository includes a Dockerfile for building and checking the package in a clean R environment.

docker build -t shortform-r .

The Docker build installs package dependencies, builds the package source tarball, and runs R CMD check on the built package. This provides an additional reproducibility check alongside the GitHub Actions CI workflow.

License

LGPL (>= 2.0, < 3) | Mozilla Public License

Citation

If you use this package, please cite:

Raborn A, Leite W (2024). ShortForm: Automatic Short Form Creation. doi:10.32614/CRAN.package. ShortForm https://doi.org/10.32614/CRAN.package.ShortForm, R package version 0.5.6, https://CRAN.R-project.org/package=ShortForm.