Statistical Analysis of Pre-Employment Predictive Indexing within the Farm Credit System
Tim Ulrich, Greeley, Colo., defended his thesis, “Statistical Analysis of Pre-Employment Predictive Indexing within the Farm Credit System,” Thursday, March 25, 2010. Ulrich is a Commercial Loan Analyst with Mountain Plains-Farm Credit Services in Greeley. He graduated from Kansas State University in May with a Master’s in Agribusiness (MAB).
Farm Credit Services, is an agricultural based lending organization with a focus on providing operating, term, and real estate lending options. Currently, it is composed of 93 banks and associations and five Farm Credit Banks (FCBs). In order to hire and retain the best employees for their positions, Farm Credit Council Services (FCCS) provides pre-employment screenings of applicants. Ulrich’s research analyzed the hiring and selection processes of five Farm Credit Services (FCS) Associations within the U.S. AgBank region to determine the effectiveness of potential employee testing and profiling practices as a predictor of tenure and retention as loan officers within the organization.
“Since a great deal of time and money is spent in onboarding and training a new employee, finding the right person is important. Pre-employment testing may be a good method to determine the best candidates for a position. For the loan officer position, nine character traits are evaluated during the screening process: focus, persistence, ego drive, competition, relator, command, discipline, critical thinking and value orientation,” Ulrich said.
While certain characteristics showed a greater accuracy in predicting employee behavior than others, Ulrich concluded overall testing is a valuable tool that can be used to support decisions to hire or not hire a potential employee and how they will fit into the organization.
Allen Featherstone, Professor of Agricultural Economics and Ulrich’s thesis advisor, said, “Tim went back and examined how successful pre-employment testing was in terms of predicting actual behavior. Understanding the success of pre-employment testing it critical to ensuring the right fit and adjusting the pre-employment process if necessary.”
The full thesis publication can be found online on K-State’s Research Exchange at http://hdl.handle.net/2097/14046.
