Arboriculture & Urban Forestry 44(2): March 2018 and fully transcribed. This paper makes use of open-ended questions addressing recruitment and motivations, but the interviews also addressed top- ics of learning outcomes and civic engagement. For each respondent, researchers also identified the number of city blocks inventoried by each individual, which was recorded in the TC2015 app. Data Analysis Assessment Assessment responses were downloaded from Sur- veyMonkey into Microsoſt® Excel™ and examined for missing data. Responses to multiple-choice questions were presented in a summary format, and where applicable, included in statistical analy- ses. Organization names identified by respondents in open-ended questions were standardized. All statistical analyses were conducted in R 3.2.3 (R Core Team 2017). Researchers compared early and late responders to the assessment to check for nonresponse bias, using t-tests for continuous data and χ2 63 codes together into broader themes, but specific subcategories were also retained. The thematic clus- ters emerged out of key phrases, repeated language, and common ideas (Ryan and Bernard 2003). Researchers then assigned themes as present or absent for each respondent. Also included was each motivation theme as the dependent vari- able in logistic regressions with a set of demo- graphic variables as the independent variables (Table 1). Logistic regressions were conducted using the glm package for R (R Core Team 2017). Nagelkerke pseudo-R2 regression, using the pscl package for R, and also calculated goodness-of-fit χ2 were calculated for each tests compar- ing the full regression model to an intercept- only model, using the lmtest package for R. tests for categorical data (Groves 2006). In Excel, researchers qualitatively analyzed open-ended responses to the question, “Why did you decide to participate in TreesCount! 2015?” (Q10, SM1). Responses to questions were coded separately by two different researchers via an open coding scheme that identified key phrases and concepts (Lofland et al. 2005). These initial codes were compared and discussed, and discrepancies were examined using an iterative approach until consensus was reached among the coders, thereby enhancing reliability (Neuman 2003). Thematic clusters were then created to aggregate common Format Interviews Interview transcripts were analyzed using NVivo Pro 11 (QSR International Pty Ltd. 2015). Quali- tative coding of the semi-structured interviews focused on volunteer motivations to partici- pate in tree-counting activities (Q2, SM2). Re- searchers began with the initial set of thematic codes that were identified through coding the assessment data (see Table 2), and also identi- fied new themes that emerged from the more in-depth interview data (Strauss and Corbin 1990; Charmaz 2001). Finally, researchers com- bined assessment and interview results for individuals participating in both and associ- ated those results with the NYC Parks TC2015 database, which includes information on map- ping amount and location by participant. Table 1. Variables derived from the assessment and included in statistical analysis. Variable Motivations Themes coded from open-ended questions, recoded as factor variable (1 = motivation mentioned, 0 = motivation not mentioned). Gender (female) Multiple choice (female, male, other), other responses dropped from analysis, recoded as a factor variable (female = 1, male = 0). Political views Race (white) Age Likert scale 1 = very liberal, 7 = very conservative, continuous. Multiple choice, recoded as a factor variable (white/Caucasian = 1, other categories = 0). Continuous number. Education Income Multiple choice, recoded as a factor variable (bachelor’s degree or higher = 1, other levels of education = 0). Multiple choice. Proportion of life Derived from years of life in NYC divided by age, continuous. in NYC ©2018 International Society of Arboriculture
March 2018
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