README.md

In ItemRest: Automated Item Removal Strategies for Exploratory Factor Analysis

ItemRest

ItemRest is an R package developed to automate item removal strategies in Exploratory Factor Analysis (EFA). It helps researchers identify low-quality items using statistical criteria and simulate the impact of different removal combinations on the factor structure and internal consistency of the scale.

🔧 Features

• Automatically identifies cross-loading and low-loading items based on customizable thresholds.
• Tests all possible combinations of flagged items.
• Reports explained variance, Cronbach’s alpha, and factor loading range for each solution.
• Highlights optimal strategies with no cross-loading items.
• Built on top of psych, GPArotation, EFAtools, and qgraph packages.

📦 Installation

Install the package directly from GitHub using the devtools package:

# First install devtools if not already installed:
install.packages("devtools")

# Then install ItemRest from GitHub:
devtools::install_github("ahmetcaliskan1987/ItemRest")

Example

Load the library

library(ItemRest) # We will use the ‘bfi’ dataset from the ‘psych’ package for a realistic example. # This requires the ‘psych’ package to be installed. # The ‘bfi’ dataset contains responses to 25 personality items.

1. Prepare the data: Select the personality items (first 25 columns)

and remove rows with missing values for this example.

data(bfi, package = “psych”) example_data \<- bfi[, 1:25] example_data \<- na.omit(example_data)

2. Run the item removal analysis.

Based on theory, the Big Five model has 5 factors.

Let’s run the analysis with n_factors = 5.

results \<- itemrest( data = example_data, n_factors = 5, cor_method = “pearson” # Data is not ordinal, so pearson is appropriate )

3. Print the report for optimal strategies.

This will show the final table after the analysis is complete.

print(results, report = “optimal”)

📄 License

This package is distributed under the MIT License. See the LICENSE file for more details.

ItemRest documentation built on April 13, 2026, 5:07 p.m.