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The interaction between item type and group was not significant.
The model predicts an interaction between item frequency and print-to-sound consistency analogous to what has been found for English, as well as a language-specific regularity effect particular to Chinese.
This model additionally revealed that the positive effect of item difficulty was smaller among strong test-takers as indicated by the significant negative interaction between item difficulty and cognitive skill.
Contrary to expectations, the effect of item difficulty was not significantly attenuated among strong test-takers, as indicated by the non-significant negative interaction between item difficulty and cognitive skill.
The effect of item history was non significant, nor was its interaction with the between-subject factor deception, while the interaction between item history and reward bias was significant both overall (F 1,30) = 5.041, p<0.04, η2p = 0.144) and in its second order interaction with deception (F 1,30) = 7.306, p<.02, η2p = 0.196).
As is often the case in reaction time experiments, the analyses showed that responses became somewhat faster over the course of the experiment, but in none of the experiments was there an interaction between item number and delay, showing that participants' responses to delays did not change over time.
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We found a significant crossover interaction between item-type and speech condition, F 1, 35) = 10.87, p = .002, η 2 = 0.23.
An interaction analysis between item difficulty and schools was conducted to examine the difference of students' oral English performance on the eight assessment items across the three different schools.
In order to evaluate whether scores on the eight items of the two assessment tasks were significantly different across schools, interaction analysis between item difficulty and schools was conducted.
In each case properties or facts at one level are realized by complex interactions between items at an underlying level.
Humans are able to deal with few items of information simultaneously in their working memory, and any interactions between items held in their working memory also require working memory capacity, reducing the number of items that can be dealt with simultaneously [45].
Write better and faster with AI suggestions while staying true to your unique style.
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