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Comparing Methods for Modeling Acquiescence in Multidimensional Partially Balanced Scales.

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Survey Methodology

Background:

  • Direct and reversed items are used to mitigate acquiescence bias in scales.
  • This strategy is insufficient to prevent response style distortions in scale covariance and means.
  • Acquiescence bias can significantly impact the psychometric properties of multidimensional scales.

Purpose of the Study:

  • To evaluate two methods for modeling acquiescence bias in partially balanced multidimensional scales.
  • To compare the performance of Exploratory Factor Analysis (EFA) with target rotation and Random Intercept Factor Analysis (RIFA).
  • To provide evidence on the effectiveness of these methods under varying simulation conditions.

Main Methods:

  • A simulation study was conducted to analyze the performance of EFA and RIFA.
  • Independent variables included sample size, items per factor, balance of loadings, acquiescence loadings, and inter-factor correlation.
  • The study focused on partially balanced multidimensional scales.

Main Results:

  • RIFA demonstrated superior performance across most simulated conditions, particularly in balanced scenarios.
  • The variance of acquiescence factor loadings influenced RIFA's performance.
  • EFA was significantly impacted by a low degree of balance between direct and reversed items.

Conclusions:

  • RIFA is identified as the most robust method for modeling acquiescence bias in these scales.
  • EFA is a viable alternative for scales with medium and full balance.
  • Understanding and mitigating acquiescence bias is crucial for accurate scale interpretation.