Degree Type

Creative Component

Semester of Graduation

Fall 2020

Department

Agronomy

First Major Professor

Dr. Bradley A. Miller

Degree(s)

Master of Science (MS)

Major(s)

Agronomy

Abstract

Data collection from corn yield trials produces large amounts of data that has been collected by a combination of breeders, research associates, research assistants, and part time labor. Expectations and specifications on how the data is collected are outlined in work instruction documents specific to the trait/traits being collected. During data collection training periods, an experienced individual will demonstrate the procedures that are explained in the work instruction documents to the data collection team. The trainer will then ask the data collection team to replicate the procedure that was demonstrated to them. Traditionally, once the experienced trainer “feels” that the team is ready they will proceed into the field to collect data.

Traditional data collection training provides satisfactory results in most cases. However, the person conducting the training does not have a quantified measure for the precision of the data collectors. How does the trainer identify individuals that are having difficulty understanding what is needed or that are not sufficiently consistent with the rest of the team? With demand from Bayer’s trait pipeline to improve data quality, there needs to be a way to objectively assess the performance of data collection teams in terms of data precision, individual performance, and group performance.

Here, I propose a statistical tool that can be used during a training event to analyze raw data and provide instant feedback. In order to provide this feedback, the statistical tool determines individual performance in the context of the group’s measurements..........

Copyright Owner

Moorberg, Matthew

File Format

PDF

Embargo Period (admin only)

11-16-2020

Data Quality Training Tool 11-15-2020.xlsx (114 kB)
Data Quality Training Tool's Microsoft Excel Program

Data Quality Training Tool 11-15-2020.xlsx (114 kB)

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