Package 'ItemRest'

Title: Automated Item Removal Strategies for Exploratory Factor Analysis
Description: Automates the identification and comparative evaluation of item-removal strategies in exploratory factor analysis, producing transparent summaries (explained variance, loading ranges, reliability) to support comfortable, reproducible decisions. The criteria are based on best practices and established heuristics (e.g., Costello & Osborne (2005) <doi:10.7275/jyj1-4868>, Howard (2016) <doi:10.1080/10447318.2015.1087664>). Includes flexible thresholds for factor loadings (min_loading) and cross-loading differences (loading_diff).
Authors: Ahmet Çalışkan [aut, cre], Abdullah Faruk Kılıç [aut]
Maintainer: Ahmet Çalışkan <[email protected]>
License: MIT + file LICENSE
Version: 0.2.5
Built: 2026-05-14 09:21:24 UTC
Source: https://github.com/ahmetcaliskan1987/itemrest

Help Index

Evaluate Item Removal Strategies for Exploratory Factor Analysis (EFA)

Description

This function automates the process of identifying low-quality items (those with low factor loadings or significant cross-loadings) in an Exploratory Factor Analysis (EFA). It systematically tests various combinations of removing these problematic items and evaluates the impact on model fit, returning a comprehensive summary of all tested strategies.

Usage

itemrest(
  data,
  cor_method = "pearson",
  n_factors = NULL,
  extract = "uls",
  rotate = "oblimin",
  min_loading = 0.3,
  loading_diff = 0.1
)

Arguments

data

A numeric data.frame or matrix for the analysis.
cor_method

The correlation method to use, e.g., "pearson" or "polychoric".
n_factors

The number of factors. If NULL, it's determined automatically by parallel analysis.
extract

The factor extraction (estimation) method. See psych::fa. Default is "uls".
rotate

The rotation method. See psych::fa. Default is "oblimin".
min_loading

Minimum factor loading threshold. Default is 0.30.
loading_diff

Threshold for cross-loading identification. Default is 0.10. Can be "howard".

Value

An object of class itemrest_result. This is a list containing the following components:

descriptive_stats

Basic descriptive statistics of the input data.
initial_efa

The results of the initial EFA before any items are removed.
problem_items

A list of items identified as low-loading or cross-loading.
removal_summary

A data.frame summarizing the results of all tested removal strategies.
optimal_strategy

The best-performing strategy that resulted in a clean factor structure (no cross-loadings).
settings

A list of the settings used for the analysis.

Examples

# We will use the 'bfi' dataset from the 'psych' package.
# This requires the 'psych' package to be installed.
if (requireNamespace("psych", quietly = TRUE)) {
  data(bfi, package = "psych")

  # 1. Prepare the data: Select the personality items (first 25 columns)
  #    and remove rows with missing values for this example.
  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.
  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 (default).
  print(results)

  # 4. Print the report for all tested strategies.
  print(results, report = "all")
}

Print method for itemrest_result class

Description

Print method for itemrest_result class

Usage

## S3 method for class 'itemrest_result'
print(x, report = "optimal", ...)

Arguments

x

An object of class itemrest_result.
report

The type of report to generate: "optimal" (default) or "all".
...

Other arguments (not used).

Value

No return value, called for side effects (prints the report to the console).

Examples

if (requireNamespace("psych", quietly = TRUE)) {
  data(bfi, package = "psych")
  example_data <- na.omit(bfi[, 1:25])
  results <- itemrest(example_data, n_factors = 5)

  # Print the default optimal report
  print(results)

  # Print the report of all strategies
  print(results, report = "all")
}