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There is no accepted method for determining a statistical threshold for a quantitative trait interaction in a context of a genome-wide association study (GWAS).
The Item – Trait Interaction Statistic (denoted by the chi-square value), reflects the degree of invariance across the trait.
A statistically nonsignificant probability value (p>0.05; chi-square) of the item trait interaction score indicates model fit.
After these trait levels were combined, the F × TRAIT interaction still could not be removed (Table 1).
An initial exploration of the overall fit of the 98 questions to the Rasch model was poor with a significant question – trait interaction (χ2 = 291.4, p < 0.0001).
(See Table 4; Fig 1) Then item 2 was split for age group; this resulted in excellent item fit and non-significant item trait interaction.
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Only the SCI data had a significant item-trait interaction.
The summary item-trait interaction statistic also showed misfit.
A third summary fit statistic is an item-trait interaction statistic reported as a Chi-Square.
anon significant chi-square item-trait interaction statistic is evidence of overall model fit.
1D-trait mapping ignores the trait-trait interaction completely, which is a major shortcoming.
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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