such as serving on their local county Farm Bureau boards. Leadership is a function of

these variables and this regression analysis will determine which variables are the

greatest predictors of why individuals chose to participate or not participate in leadership

roles in their local county Farm Bureau boards.

 There are several procedures available for selecting the independent variables in

 the multiple regression equation. Stepwise selection is the most commonly used method

 (Ary et al., 1996). A type of stepwise selection, a backward selection, is a method of

 multiple regression in which each time a predictor is added to the equation, a removal test

 is made of the least useful predictor. The regression equation is constantly being

 reviewed to see whether any redundant predictors can be removed. The first step in this

 model is placing all the predictors in the model and calculating the contribution of each.

 If a predictor meets the criteria for removal, if it is not making a statistically significant

 contribution, then it is removed from the model and then the model is recalculated for the

 remaining predictors (Field, 2000).

 The first variable considered is the one with the largest positive or negative

 correlation with the criterion, this correlation is the Pearson correlation and is the second

 table (after the descriptive statistics table) given in a multiple regression analysis using

 SPSS 12.0 for Windows (Ary et al., 1996). Agresti and Finlay (1997) state that the

 larger the absolute value of r, the Pearson correlation, the stronger the degree of linear

 association between the independent and dependent variable.

 In a multiple regression analysis, there are several other variables that need to be

considered when deciding if a dependent variable may be used to predict an independent

variable. The value j or Beta value is the second variable that is examined. It is reported