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To address potential response bias issues, we will implement a propensity-based imputation methodology and pattern-mixture methods in a sensitivity analysis of the primary analyses described above.
By implementing a propensity score matching estimator method, we show that engaging in consulting activities has an overall negative relationship with the average number of ISI-publications.
Consequently, we implemented a propensity score matching method to reduce this bias 16 by first creating a propensity score to represent the relationship between multiple characteristics and the matching outcome: being in the prison group (treatments) or not (controls).
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Computational methods such as PREDITOP [ 2], PEOPLE [ 3], BEPITOPE [ 4] and BcePred [ 5] implemented a variety of physicochemical propensity scales, such as hydrophilicity, flexibility or solvent accessibility, in their prediction models.
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As the intensity of training measure is a continuous variable, we implement generalized propensity scores (GPS) with continuous treatments (Hirano and Imbens 2004) to estimate dose response functions on wage growth.
Since finding an exact match becomes increasingly difficult as more covariates are used, we implement the propensity score matching (PSM) technique first introduced by Rosenbaum and Rubin [14] and frequently used in the program evaluation literature.
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We therefore implemented a cluster analysis.
There are four approaches that are commonly used in implementing the propensity score (a) stratification on (hat{e}(Z)), (b) propensity score weighting, (c) optimal full matching, and (d) propensity score regression.
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CEO of Professional Science Editing for Scientists @ prosciediting.com