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To mitigate concerns that our findings are a consequence of the smaller sample size in our propensity score matched sample, we adopt two additional matching approaches.
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Due to the size of our sample, we adopted a p-value=0.15 to draw conclusions on the executed tests.
Considering also that the ESEM presented a more reliable factor structure for our sample, we adopted the ESEM factor structure for the subsequent analyses.
In the reduced version (second sample), we adopted the suggestion by Leão (2012) to use three response options: "nothing" (0), "medium" (1), and "completely" (02).
To achieve a representative sample, we adopted a sampling method that has been used in large household surveys among immigrants in the Netherlands[ 21].
To compute the EMT score of a sample, we adopted a similar approach to that used in ssGSEA (Verhaak et al, 2013).
To calculate the probability sample, we adopted a 95% confidence interval level, 20% accuracy and design effect (deff) of 2. The sample size calculation was based on the DMFT (2.3) and standard deviation (2.72) of epidemiological survey previously conducted [ 36].
For down sampling, we adopt Lanczos2 low-pass filtering to eliminate high-frequency components to prevent from the aliasing effect.
To deal with the imbalanced samples, we adopt cost-sensitive ensemble technique to build the prediction models.
Within samples, we adopted grain ages that overlapped with the other grain ages within ±3σ to calculate the weighted mean age of each sample (Table 3; Fig. 3).
Among the several configurations sampled, we adopted the solution with the highest surface complementarity and appropriate orientation relative to the membrane plane.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com