224 Wall et al.: Factors Influencing Participation in Urban and Community Forestry Programs fective in increasing the R2 while keeping an economic vari- able in the model. Political affiliation of the state was also evaluated. A dummy variable with 1 Republican and 0 Democrat was added to the model. The variable represented the direc- tion the state voted in the 2004 presidential election. One could argue this is a “conservative” or “liberal” variable or that one party is more “progovernment” and community ac- tion-oriented. Another common way to reevaluate the data are to add higher order forms of variables such as squares, cubes, logs, square roots, and reciprocals. For the most part, the best and simplest form for this model was linear. However, a simple Participationi = 79698 + 0.23875 Communities2 se = 26462 t = 3.01 0.0436 5.47 graph of variable shape showed that the dependent variable, participation, was exponentially shaped and the independent variable Communities was slightly exponential in shape. Transforming this independent variable by squaring it better matched participation and alone raised the R2 from 0.4467 to 0.5218 and the adjusted R2 from 0.3519 to 0.4398. All other variables showed very little of a nonlinear trend when graphed. Other functional transformations or models such as a semilog or log-linear model also proved to be poor fits. The improved regression equation is shown subsequently and the variables are described in Table 2. The improved form of regression with standard errors and t-values below coeffi- cients is shown below. i − 652.7581 WorkingPopi − 56.9062 Forestlandi 343.585 −1.90 25.870 −2.20 419.4388 Income*i + 3536.4228 Politicali − 364.7847 %Education$i + ei 174.709 −2.40 1489.95 2.37 152.634 −2.39 R2 0.5218 Adj-R2 0.4398 In the improved regression, the R2 increased to 0.5218, andthe adjusted R2 increased to 0.4398. The new model sat- isfied several criteria. It maximized the R2 and adjusted R2 while keeping the model as simple as possible. All of the independent variables were significant at the alpha 0.10 level. All previously determined theoretical criteria were sat- isfied with an education variable, economic variable, age structure variable, participating communities variable, and forested land variable. The impact of political affiliation on public participation produced an interesting result. Holding all other variables constant, the states that voted Republican in the 2004 presi- dential election have on average, 3,536 more days of public volunteer assistance per million people than states that voted for the Democratic candidate. This could be explained by something as simple as “conservative” versus “liberal,” but it is likely much more complex than that. It could be the result of wealth (amount of free time), concern for the environment, attitude toward the government, or any of a multitude of factors that interact with that variable. It is also interesting to note that the coefficients for WorkingPop with that Forestland, Income,* and %Educa- tion$ were all negative. Holding the other variables constant, if the percent of people in the state between the ages of 18 and 65 increased by one percentage point, then on average, the ©2006 International Society of Arboriculture days of volunteer assistance will decrease by approximately 653 days per million people. This possibly indicates that throughout the United States, middle-aged people, those who must work and provide for themselves participate less in U&CF programs. It is logical to think that many projects, especially those that require outdoor activity such as planting trees in parks, engage children and adolescents. Also, older people may have more time and therefore may participate more in technical, educational, or administrative projects than do middle-aged people. Forestland (percent of the state that is forested) seems to serve as an “equalizing” or “weighting” variable in the model constant, if the percentage of forested land in the state in- creased by one percentage point, then on average, the number of days of volunteer assistance will decrease by approxi- mately 57 days per million people. This agrees with the pre- vious hypothesis that forest land and participation will be negatively correlated. States with fewer forests tend to par- ticipate more in U&CF programs than states with more for- ests. People in states that have less forest land may place more importance on their urban forests and trees than people who live in states that are more heavily forested. Income* was slightly more complicated to interpret be- cause it was defined as disposable income as a percentage of household income. As the proportion of disposable income to
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