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Related Experiment Video

Updated: Sep 17, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Comparing raw score difference, multilevel modeling, and structural equation modeling methods for estimating

Amber McEnturff1, Qi Chen2, Robin K Henson2

  • 1Alexandria City Public Schools, Alexandria, VA, United States.

Frontiers in Psychology
|July 4, 2025
PubMed
Summary
This summary is machine-generated.

Raw score difference (RSD) and structural equation modeling (SEM) methods are recommended for estimating dyadic discrepancy scores. Multilevel modeling (MLM) showed poor reliability, making it unsuitable for practical application in psychological research.

Keywords:
Monte Carlo simulationdyadic analysisdyadic discrepancymultilevel modelingstructural equation modeling

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Area of Science:

  • Psychology
  • Statistics
  • Quantitative Research Methods

Background:

  • Dyadic data analysis is crucial in psychological research for examining relationships within pairs (e.g., parent-child, spouses).
  • Accurate estimation of dyadic discrepancy scores is essential for reliable outcome prediction.
  • Several methods exist for calculating these scores, but their comparative accuracy is not well-established.

Purpose of the Study:

  • To compare the accuracy of different dyadic discrepancy score estimation methods.
  • To identify which method provides the most reliable discrepancy estimates and best outcome prediction.
  • To evaluate the influence of various design factors on estimation accuracy.

Main Methods:

  • A Monte Carlo simulation was employed to compare three discrepancy score estimation methods: raw score difference (RSD), multilevel modeling (MLM), and structural equation modeling (SEM).
  • Key simulation factors included intraclass correlation (ICC), cluster number, reliability, effect size of discrepancy, and effect size variance.

Main Results:

  • Multilevel modeling (MLM) yielded discrepancy estimates with poor reliability, particularly under conditions of high ICC, high effect size variance, and low cluster numbers.
  • Raw score difference (RSD) and structural equation modeling (SEM) methods demonstrated comparable and stable performance across simulation conditions.
  • The design factors investigated did not significantly impact the accuracy of RSD or SEM estimates.

Conclusions:

  • Raw score difference (RSD) and structural equation modeling (SEM) are recommended for practical application in estimating dyadic discrepancy scores due to their reliability and stability.
  • Multilevel modeling (MLM) is not advised for discrepancy score estimation because of its comparatively poor reliability.
  • Future research should consider the performance of these methods under diverse dyadic data structures.