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Figure 2B shows the relative bias for the parameter values investigated.
Using these potentially heterogeneous data may be a source of bias for the parameter estimates.
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Based on our simulation study, we observed that an increase in the sample size decreased the estimation bias for the parameters in both submodels.
Firth (1993) proposed a general penalised regression procedure which eliminates the first-order bias of the parameter estimates; more importantly for the present work, it also avoids infinite parameter estimates.
The QTL located in chromosome 6 is especially interesting because it explained a high proportion of the genetic variance (17 %) with the lowest estimation bias for this parameter and with a high detection frequency (95 %) through CV runs.
The median bias for these parameters (see the gray region in Fig. 1) were found to be 17 % and 14%%, respectively (see Fig. 10 a).
The bias for the frailty parameter is in general quite small.
This leads to usually overestimate the latent trait level (and jointly underestimate item difficulties) producing negative bias for the v parameter except for the WORST method that systematically underestimates the latent trait level by only imputing negative responses.
For visualization, we plot the contra-ipsi bias for the two parameters.
Distribution of the estimates for β0, β1 and σ showed no systemic bias for the first parameters.
To test for bias in the parameter estimates we investigated whether any mean value of θ differed from the mean values for other scenarios within a given simulated value of θ.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com