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Table 2 Results data example. Parameter estimates (posterior means) of the multilevel model for the example ‘Trait Anxiety’ data with a random intercept using the IRT-based plausible values technique and the CTT-based scores as outcome variables

From: Why item response theory should be used for longitudinal questionnaire data analysis in medical research

  IRTa CTTb
  Meanc SD Meanc SD
Fixed effect     
γ Intercept 0.018 0.043 0.015 0.045
Random Effects     
Between individual (level-2)     
τ2 Intercept 0.733 0.067 0.685 0.074
Within individual (level-1)     
σ2 Residual variance 0.294 0.030 0.357 0.042
Intra Class Correlation     
ρ 0.287   0.343  
  1. aItem response theory based estimates
  2. bClassical test theory based estimates using sum-scores
  3. cMean of the coefficients resulting from fitting the structural model (longitudinal multilevel model) to the five draws of plausible values based on the IRT or CTT measurement models