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Dimensionality assessment in ordinal data: a comparison between parallel analysis and exploratory graph analysis.

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  • 1Department of Primary Education, Democritus University of Thrace, Alexandroupolis, Greece.

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Summary

This study compares Parallel Analysis (PA) and Exploratory Graph Analysis (EGA) for scale dimensionality. EGA excels in complex structures, while PA is better for simpler, single-factor scales.

Keywords:
factor analysisfactor retentionpolychoric correlationscale validationsimulation study

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Area of Science:

  • Social Sciences
  • Psychometrics
  • Quantitative Psychology

Background:

  • Accurate scale dimensionality is vital for understanding social science constructs.
  • Choosing the right method for dimensionality assessment impacts construct validity.

Purpose of the Study:

  • To rigorously compare Parallel Analysis (PA) and Exploratory Graph Analysis (EGA) for assessing scale dimensionality.
  • To evaluate method performance across diverse conditions, including ordinal data, sample size, and factor structure complexity.

Main Methods:

  • Extensive simulation study evaluating PA and EGA.
  • Varied conditions included sample size, factor number and association, loading magnitudes, and item distribution symmetry/skewness.
  • Assessed performance under assumed normality and non-normality.

Main Results:

  • Exploratory Graph Analysis (EGA) generally outperforms Parallel Analysis (PA) in identifying the correct number of factors, especially in complex scenarios.
  • PA is recommended for simpler, single-factor structures with strong loadings and low inter-factor correlations.
  • Skewed item distributions significantly impacted both methods, particularly in complex scenarios.

Conclusions:

  • The choice between PA and EGA depends on the specific characteristics of the data and the underlying factor structure.
  • Findings offer guidance for researchers in scale development and validation to ensure accurate construct measurement.