Left-hand Programing vs. Right-hand Programming?

by Haibin Shu

Left-hand Programing vs. Right-hand Programming?

by Haibin Shu

by Haibin Shu

Double programming is a gold standard in a team of statistical programming and analysis. The necessity of doing so, is to ensure that the data is being processed correctly and the analysis is being conducted correctly following the pre-specified requirements such as the SAP document. In the end, the beauty of the practice is that same results are achieved by two or more different approaches. Often times the approaches are independent from each other and might even begin with divergent understanding of particular analysis methods. Ultimately, accuracy will be achieved when differences are reconciled and critical understandings converge, just like the team-work between left-hand and right-hand!

Generally speaking, the initial programmer has certain advantages such as choosing/using naming conventions, setting up output layout formats, and applying statistical procedures etc. The QC programmer would generally focus on checking the accuracy of the content of the output and analysis results. Plus, they would follow the variety of conventions that has been set up by the initial programmer.

Some factors to consider when facilitating a strong collaboration between left-hand programming and right-hand programming are:

  • A win-win culture: approaches are independent but the goal is the same. Commonly, the final goal is to prevent any mistakes in any programming and analysis. The initial programmer should always self-check first for initial quality assurance before handing over to the QC programmer for independent reviews.
  • Avoid cosmetic over-do: both parties should stick to simple, common, and effective conventions. Making too many extremely detailed formatting efforts could result in extra time and more difficulty in reconciliation of non-essential content, e.g., concatenating variables by special characters and calculated spaces.
  • Having constant contact: both parties should talk to each other constantly in order to generate high productivity. Changes are usually inevitable. For example, analysis methods may change multiple times in the course of a study before the SAP finalization. Both parties may start revising the respective programs in parallel (not sequentially) had they all be informed of the change requests in the same time.

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