Statistical analysis of pre-employment predictive indexing within the farm credit system

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Show simple item record Ulrich, Timothy Creed 2012-07-18T19:46:22Z 2012-07-18T19:46:22Z 2012-07-18
dc.description.abstract This thesis analyzes the hiring and selection processes of five Farm Credit Services (FCS) Associations within U.S. AgBank to determine the effectiveness of potential employee testing and profiling practices as a predictor of success (defined as tenure and retention) within the organization. The data provided by the five FCS Associations were used to analyze whether that the results are a successful tool in predicting the success of a potential employee. Firm managers are acutely aware of the high cost of onboarding a new employee regardless of the industry in which the firm operates. Since employee training and education often takes months, and in some cases, years, it is critical that organizations select qualified, driven, and success oriented employees so that they can minimize the cost of hiring of new employees. To select the best candidates, many firms use personality profiling examinations to determine the candidate’s fit, not only for the job, but also for the company culture. Analyzing past results can assist managers in evaluating the outcomes of the time and cost spent seeking the best employee possible. Analysis was conducted by estimating a binomial logistic regression model using the test scores for loan officer hires from five Farm Credit Associations for the time period of 1999-2009. Each of the examined character traits was an independent variable, along with variables for gender and whether the candidate was a recommended-hire. The dependent variable is whether the employee is still employed with the Farm Credit Association. Results show that while some of the independent variables are statistically significant in predicting the success of an employee, others are not. The implications therein justify the value of the predictive index as an asset to hiring managers, and also provides direction on which traits are most highly correlated with one another and with the overall composite score. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Pre-employment en_US
dc.subject Predictive en_US
dc.subject Index en_US
dc.subject Examination en_US
dc.subject Farm en_US
dc.subject Credit en_US
dc.title Statistical analysis of pre-employment predictive indexing within the farm credit system en_US
dc.type Thesis en_US Master of Agribusiness en_US
dc.description.level Masters en_US
dc.description.department Department of Agricultural Economics en_US
dc.description.advisor Allen M. Featherstone en_US
dc.subject.umi Business (0310) en_US
dc.subject.umi Economics, Agricultural (0503) en_US
dc.subject.umi Economics, Labor (0510) en_US 2010 en_US May en_US

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