230 Fleming et al.: Predicting Participation in Urban and Community Forestry Programs Sommer 1996). The support of nontraditional audiences is considered crucial to enhancing these programs (Iles 1998) and increased volunteers provide different skills, new ideas, and more effective outreach (Westphal and Childs 1994). For programs like Tree City USA expanded participation is seen as necessary to counter lagging fiscal support (Andresen 1989). To ensure public participation, one must first establish who the individuals are and when promoting these programs who needs to be targeted. The purpose of this study was to provide insight on continued public participation within the U&CF programs. The econometric model created in this study will show the likelihood of participation in these pro- grams for individuals based on personal characteristics. The data used in the model were described and analyzed by Straka et al. (2005). We used the same data to develop a predictive model that will help identify factors that impact participation and aid in projecting individual forest owner participation. Wall et al. (2006) described a similar econometric study. They also attempted to identify factors that led to U&CF program participation. That study used data from 42 of the states to quantify participation; we used data from a survey of South Carolina residents to attempt to do the same thing. Wall’s study attempted to identify variables that impacted participation, whereas this study produced a probability of participation. STUDY METHODS In the fall of 2003, a survey was mailed to 324 South Carolina residents to identify characteristics of participants and non- participants in U&CF programs and their attitudes toward the programs (Straka et al. 2005). Past participants were ran- domly selected from South Carolina Forestry Commission records, whereas nonparticipants were randomly selected from occupational groups that would be expected to exhibit equal interest in U&CF programs. The information on the 192 surveys returned was used to generate the econometric model. This is a 59% response rate; participants were 56% of the respondents. Econometrics involves the specification of a regression analysis model that forecasts or explains behavior. We de- veloped an econometric or regression model to predict par- ticipation in U&CF programs. Specific questions answered by both participants and nonparticipants were used to create the independent and dependent variables. “Regression analy- sis is concerned with describing and evaluating the relation- ship between a given variable (often called the explained or dependent variable, in our case participation) and one or more other variables (often called the explanatory variables or in- dependent variable)” (Maddala 2001). The responses to each question were placed in a Microsoft Excel document then ©2006 International Society of Arboriculture imported into SAS 9.0 (Statistical Analysis System for Win- dows) to create the regression model (SAS Institute 2002). Model formulation needed to describe the dependent variable, participation, was the primary task. The standard regression model using ordinary least squares could not be used because the dependent variable was nonnumeric, that is, questions were answered by responses like “yes” or “no” or “male” or female.” A linear probability model was first considered for the analysis with a dichotomous dependent variable, that is, the participation variable would take on a value of 1 or 0, yes or no, respectively (Maddala 2001). Participation would be an indicator variable that shows the incidence of an event or whether the person participated in the program, and we would have some independent variables that determine the likelihood of participation (Maddala 2001). The qualitative nature of the dependent variable proved inappropriate for the linear probability model. The logit model creates dummy variables for each of the dependent variables, that is, it accounts for the nonnumeric values by transforming the qualitative values into numeric values (0 or 1). This is achieved by creating dummy variables for each of the independent variables. Dummy variables were created to define each independent variable (Table 1). For the independent variable “age,” three dummy variables were created. The question was “What is your age?” The possible responses were: a) under 30 years old, b) 30 to 49 years old, c) 50 to 65 years old, or d) 66 years old or older. Of these four answers, three were chosen to become dummy variables. One answer was omitted because its effect can be seen in the models intercept. This approach was used throughout the model. The three answers retained for age were a, b, and d. They were defined as age1, age2, and age4. The logit regression analysis was computed using the SAS 9.0 system. This type of regression returns a numeric value between 0 and 1(which can be interpreted as a probability or percent) that describes how likely a certain individual (based on characteristics such as gender, age, and education level) will be to participate in U&CF programs. Once the value for participation of an individual is computed, if it is less than 0.50, we predicted that individual is not likely to participate in U&CF programs. Likewise, a value greater than or equal to 0.50 indicated that the individual is likely to participate in U&CF programs. Another way to interpret the participation value is to consider it a probability. If the value is 0.85, we predicted the individual will likely participate, but you can also say the individual is 85% likely to participate in U&CF programs. RESULTS AND DISCUSSION The logistic regression completed in SAS 9.0 yielded the following model (model 1) for participation:
September 2006
| Title Name |
Pages |
Delete |
Url |
| Empty |
Search Text Block
Page #page_num
#doc_title
Hi $receivername|$receiveremail,
$sendername|$senderemail wrote these comments for you:
$message
$sendername|$senderemail would like for you to view the following digital edition.
Please click on the page below to be directed to the digital edition:
$thumbnail$pagenum
$link$pagenum
Your form submission was a success. You will be contacted by Washington Gas with follow-up information regarding your request.
This process might take longer please wait
