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Further F test confirmed that the joint effect of having both sons and daughters and its interaction item was still significant (F=7.86, p<0.001).
Thus, interaction item analysis is further conducted to study whether attributes of state ownership and non-state ownership have any impact on functions of occupational interaction.
After interaction item analysis, it was found that the interaction item coefficient for outward interaction and the state-owned sector is significantly negative, which signifies that in cities in China, the impact of heterogeneous social resources on income in non-state sectors is stronger than state-owned sectors.
Two interaction items are respectively added into the income models of the first job, the present job, and the first-order difference: the interaction item of internal interaction and the state-owned sector and the interaction item of outward interaction and the state-owned sector.
Since the main effect was not significant and the interaction effect was significant, we further run a joint F test of the number of children and its interaction item.
It is found that after the addition of such interaction items to first job, present job, or difference model, the coefficient of the interaction item of internal interaction and the state-owned sector is insignificant.
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Differences in coding were discussed with the first author, which resulted in adjustments to the learning interaction items.
This profile consists of 5 questions, 2 measurements (handgrip strength and body weight) and 2 interaction items.
PRRadj adjusted by the year and maternal age was calculated using the Poisson regression model, in which four factors (year, region, residential area, and maternal age) and one interaction items (Region*Residential area) were entered.
However, there are repeated concerns in the literature regarding objectivity, fairness, and extraneous factors from interpersonal interactions, item bias, reliability, and validity.
44.4% (4/9) of the participants preferred the usage of the electronic interventions over paper-based checklist (11.1%, 1/9) and posters (0/9), while looking for information about contraindications and drug-drug interactions (item U1).
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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