306 results. Second, descriptive frequencies were cal- culated to determine how many respondents to the final survey (distributed to both the treatment and control groups) had watered their trees. Chi-square tests were conducted to determine if the differences in self-reported watering (from the final survey) between the treatment and control groups were sig- nificant. Fisher’s Exact Test results are reported in instances in which there were cell counts <5. Only final survey respondents who lived at their address over summer 2012 were included in this second analysis. To measure the impact of the outreach inter- vention on soil moisture, an independent samples t-test was conducted to identify significant dif- ferences in average soil moisture levels for the treatment and control groups. Means for both groups for 15 out of 17 weeks in which soil mois- ture was collected are reported (the data collec- tion team was on vacation for two of the 17 weeks, hence soil moisture is only reported for 15 weeks). To control for confounding variables not mea- sured, a difference-in-difference regression analysis was conducted, which is an analytical tool used in social science research and program evaluation to measure the differences in two groups before and aſter a treatment. One strength of this analysis is that it accounts for underlying variation between treatment and control groups (Lechner 2011). Difference-in-difference analysis has been used in forestry to evaluate programs that pay produc- ers of ecosystem services in developing countries (Pattanayak et al. 2010; Honey-Rosés et al. 2011; Arriagada et al. 2012). Difference-in-difference analysis has also been conducted in studies relevant to urban forestry that measured the interaction between household location decisions and urban green space (Stone et al. 2015), and also exam- ined the impact of vacant lot greening programs on crime and human health (Branas et al. 2011). In this study, the goal of the difference-in-dif- ference regression analysis was to compare the dif- ference in soil moisture between the treatment and control group in the week before a postcard was mailed to the difference in soil moisture between the treatment and control group in the week a postcard was mailed. The difference between these two differences can be interpreted as the impact of the postcard “treatment” on soil moisture. The difference-in-difference analysis was implemented ©2016 International Society of Arboriculture The fiſth variable, soil compaction (continuous), represents the penetrometer measurements at each tree. A sixth independent dummy variable, city watering (0 = not watered, 1 = watered), was added in the model for postcards four through eight to capture the four weeks (7, 9, 10, 12) in which the city watered some of the trees. A city watering score of 1 indicates that the tree was located in a neighborhood where the city watered that week. The reader will be guided in how to interpret the beta coefficients for the regressions to determine the impact of the postcards in the Results section. It was impossible to control for additional biophysical factors (average weekly temperature, precipitation, and evaporative demand) that may also affect soil moisture, as the data collected for these variables were constant across all of the trees. However, the difference-in-difference analysis accounts for any pre-existing differences in these variables between treatment and control groups. The implications of this limitation for this study will be addressed in the Discussion section. Moskell et al.: Engaging Residents in Street Tree Ownership using seven different multiple linear regression models, one each for postcards two through eight. For the purposes of explaining the following regression model, week X represents the week prior to a postcard mailing, and week Y represents the week a postcard was received. Five independent variables were entered into the regression model to predict the dependent variable, soil moisture in week Y. First, a dummy variable week (0 = week X, 1 = week Y) was created to represent the difference in soil moisture between weeks Y and X for the control group. Second, a postcard received dummy variable (0 = no postcard received, 1 = postcard received) which represents the difference in soil moisture between the treatment and control groups in week X. Third, a dummy variable, postcard-week, which indicates all of the trees planted at a residence that received a postcard in week Y, the week the postcard was mailed (0 = no postcard received in week Y, 1 = postcard received in week Y). The postcard-week variable represents the impact of the postcard, which can be understood with the following formula: [1] soil moisture in week X – Control soil mois- ture Postcard impact (bpostcard-week in week X) – (Treatment in week Y – Control soil moisture in week Y) ) = (Treatment soil moisture
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