Analysis of Productivity for A.L. Burruss Institute projects
Disciplines
Business Analytics
Abstract (300 words maximum)
Since 1999, the A.L. Burrus Institute has been providing applied research skills and data collection services to nonprofits and local governments. One of their main services is survey data collection through their phone lab. When the lab is being actively used, productivity reports detailing the number of interviewers, dials, completed surveys, and hours worked are collected daily. The goal of this project is to learn how to improve efficiency for the lab by using this secondary data as well information about each project, including the cost and estimated competition date. We will be using regression analysis in R to build an equation to determine how many interviewers, supervisors, and hours spent dialing are needed to complete a project or a certain number of completed surveys. We will be using productivity data on all projects dating back to July 2016. This gives us access to 13 projects. We expect to be able to predict the estimated completed time for a project as well as examining the efficiency of each additional interviewer and each project.
Primary Investigator (PI) Name
Ryan Falvai
Analysis of Productivity for A.L. Burruss Institute projects
Since 1999, the A.L. Burrus Institute has been providing applied research skills and data collection services to nonprofits and local governments. One of their main services is survey data collection through their phone lab. When the lab is being actively used, productivity reports detailing the number of interviewers, dials, completed surveys, and hours worked are collected daily. The goal of this project is to learn how to improve efficiency for the lab by using this secondary data as well information about each project, including the cost and estimated competition date. We will be using regression analysis in R to build an equation to determine how many interviewers, supervisors, and hours spent dialing are needed to complete a project or a certain number of completed surveys. We will be using productivity data on all projects dating back to July 2016. This gives us access to 13 projects. We expect to be able to predict the estimated completed time for a project as well as examining the efficiency of each additional interviewer and each project.