Determining the Number of Factors in Exploratory Factor Analysis
An Activation Function: Softmax
the Comparison Data (CD) Approach
the Comparison Data Forest (CDF) Approach
Check and Install Python Libraries (numpy and onnxruntime)
Hierarchical Clustering for EFA
Various Indeces in EFA
K-means for EFA
Scree Plot
Simulate Data that Conforms to the theory of Exploratory Factor Analys...
Voting Method for Number of Factors in EFA
Empirical Kaiser Criterion
Extracting features According to Goretzko & Buhner (2020)
Extracting features for the pre-trained Neural Networks for Determinin...
Factor Analysis by Principal Axis Factoring
Factor Forest (FF) Powered by An Tuned XGBoost Model for Determining t...
Simulating Data Following John Ruscio's RGenData
the Hull Approach
Kaiser-Guttman Criterion
Load the the pre-trained Neural Networks for Determining the Number of...
Load the Scaler for the pre-trained Neural Networks for Determining th...
Load the Tuned XGBoost Model
Minimum Average Partial (MAP) Test
the pre-trained Neural Networks for Determining the Number of Factors
Feature Normalization for the pre-trained Neural Networks for Determin...
Parallel Analysis
Plot Methods
Prediction Function for the Tuned XGBoost Model with Early Stopping
Print Methods
Scree Test Optimal Coordinate (STOC)
Provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) <doi:10.1207/s15327906mbr0102_10>, Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis (PA) by Horn (1965) <doi:10.1007/BF02289447> based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) <doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012) <doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011) <doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) <doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner (2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors.