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In fact, the BK-plot was originally introduced to elucidate Simpson's paradox for the simplest case that both the treatment variable and the confounding variable are binary.
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The treatment variable is area of residence, rural vs. urban.
Controlled variables should not be affected by the treatment variable of interest [42, 43].
With the gpscore programme, the study first took a logarithmic transformation of the treatment variable.
The design used between-subjects variation and involved information feedback as the treatment variable.
Only the treatment variable of perfusing solution was found to be significant (p<0.05).
The third row displays the estimates of the coefficient of the treatment variable (land security in equation).
Much of the work of propensity score analysis has focused on cases where the treatment variable is binary.
Afterwards, a quasi-experimental design was employed, with day-or-night video of the view as the treatment variable.
The treatment variable was a factor in the ANOVA.
Enter the treatment variable as a dependent or response factor.
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