All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.

Research Article Open Access

Determining Dimensionality of Binary Variables: A Monte Carlo Simulation Study


Objective: The present study aimed to evaluate four criteria—Kaiser, empirical Kaiser, parallel analysis, and profile likelihood for determining the dimensionality of binary variables.

Methods: A large scale Monte Carlo simulation was conducted to evaluate these criteria across combinations of correlation matrices (Pearson r or tetra choric ρ) and analysis methods (principal component analysis or exploratory factor analysis), and combinations of study characteristics sample sizes (100, 250, 1000), variable splits (10%/90%, 25%/75%, 50%/50%), dimension (1, 3, 5, 10), and items per dimension (3, 5, 10).

Results: Parallel analysis performed best out of the four criteria, recovering dimensionality in 87.9% of replications when using principal component analysis with Pearson correlations.

Conclusion: Our findings suggested that dimensionality of a binary variable data matrix is best determined by parallel analysis using the combination of principal component analysis with a correlation matrix based on Pearson r. We provided recommendations for selecting criteria in different study conditions.

Ting Dai1*, Adam Davey2

To read the full article Download Full Article | Visit Full Article