The Economic Impact of Supervised Agricultural Experiences in Agricultural Education from a National Perspective
Type of Degreedissertation
Curriculum and Teaching
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The purpose of this quantitative study was to examine, on a national level, the economic impact of the agricultural education students supervised agricultural experience programs (SAE), and determine whether certain demographic variables could be statistically significant predictors of economic impact. Experiential learning, commonly called SAE, is a well-documented, valuable, and integral part of Agricultural Education (Cheek, Arrington, Carter & Randall, 1994; Dyer & Osborne, 1996; Moore, 1988). Measuring the cost and economic benefits of SAEs provide valuable information in communicating additional benefits of SAE programs. The SAEs make a strong statement about the impact of student projects on the local economy (Cole & Connell, 1993). The structure and format of this study was replicated from an SAE economic impact study by Hanagriff, Murphy, Roberts, Briers and Linder (2010). The study assessed values for the various SAE components and applied a multiplier (IMPLAN), that is widely used in industry, to project an economic impact for Texas SAEs (Hanagriff, Murphy, Roberts, Briers and Linder (2010). All 5,970 members of the National Association of Agriculture Educators (NAAE) were asked to participate in the study. There were 374 total responses representing all six regions of the NAAE. A multiple regression analysis was utilized to examine the relationship between the dependent variable (DV) economic impact with seven independent variables (IV) including region, school size, years of experience, number of teachers, number of students, number of FFA members and number of students using record books Analysis of Variance (ANOVA) was used for testing the differences between means. The model that included all of the IVs was not statistically significant. An analysis of standardized beta weights indicated statistical significance for region, and size of school. Follow-up analyses indicated that schools were statistically significantly different based on size with very large schools having a p-value of .005. Therefore a predictive model for statistical significance of economic impact could be developed thus rejecting the null hypothesis. When the IMPLAN multiplier is applied to the economic values derived from the survey, there is a total per program economic impact of $116,000. This would suggest a national economic impact of $694 million based on the target population of 5,970 agriculture teachers.