Arboriculture & Urban Forestry 42(1): January 2016 Data recorded included the following: 1) sam- pling characteristics, including interviewer, loca- tion, date, and weather (based on two temperature descriptions, “cold” or “hot”; these helped dis- tinguish between a hot day in the summer and a cold day in autumn, thus reducing subjective impressions); 2) answers to the question on what degree of importance does the respondent place the trees in the city, on a 1–5 scale, with 5 being highest [a similar ordinal rating scale to that of Schroeder et al. (2006), but focused on all the trees in the city and not just on the tree in front of a respondent’s home]; 3) answers to the question on what makes the trees in the city important, with up to two verbatim responses; and 4) demo- graphic characteristics. For demographic char- acteristics, and based on the literature review, the study authors recorded the following: decade in which respondents were born (from 1920s to 1990s); sex (female/male); occupation (in their own words); and whether they were members of any environmental group (yes/no). Based on pilot surveys, the authors recognized that many people do not have any recollection of or desire to dis- close personal information related to their place of residence or economic situation, even if these are factors influencing people’s perception of the urban forest, as suggested by the literature review. Verbatim nominal data were processed further to facilitate analysis. Codes for occupation and value responses were assigned using interpreta- tive analysis techniques focused on condensing textual information into themes (Strauss and Corbin 2008). Since there were almost 25 occupa- tion codes, termed here original occupation codes, and given the high number of student respondents, occupation data were also re-coded as student/ non-student (Table 1). Moreover, the two verbatim responses to the question, “What makes the trees in the city important?” (termed first and second mentions) were converted into codes of value themes. Although the codes were grounded in the data, the final terms reflect terminology used in the literature (see Peckham et al. 2013). Some value codes were combined when the same idea was conveyed (e.g., oxygen became air quality; see RESULTS and DISCUSSION). The code of environmental quality referred to ideas imply- ing improvement or cleaning of the environment, 49 Table 1. Coding examples for the verbatim responses related to occupation and what respondents consid- ered important about the urban forest, based on the survey results from Fredericton, Halifax, and Winnipeg. Item Occupation Verbatim response “Administrative assistant” “Full-time mom” “Clerk at local store” “Dancer” “Pensioner” “Priest” Values Code Management Home parent Customer service Artist Retired Faith professional “Gives a better look to the city” Aesthetics “Improves the environment” “Filters the air” “Creates a buffer zone for animals” “Create oxygen” “Makes me feel good to see greenspace” “Makes me feel at home” “Because of David Suzuki” z Oxygen code becomes air quality (see DISCUSSION). whereas more specific ideas also related to environ- mental quality, such as regulation of air pollutants, soil quality, water quality, and noise, were coded separately (Table 1). No responses were coded as “no response.” Themes that were mentioned fewer than ten times and themes that were diffi- cult to categorize were coded as “other” (Table 1). Data were imported into statistical software (SPSS) and analyzed. Since it is mostly unneces- sary to use complex analysis for simple numeri- cal ratings given that simple procedures can yield reliable results (Schroeder 1984), research- ers analyzed the data on ratings of importance for variance (one-way ANOVA), using the cal- culated mean (the median was not used given that it was always the same = 5) according to all independent variables including city, weather (hot/cold), date, time and place of delivery, interviewer, sex, age (decade born), occupation (original and student/non-student), and envi- ronmental group membership (yes/no). The magnitude of the ANOVA statistic was used to interpret the relative differences of ratings within a significant variable. Two simple tests for means, the parametric two-sided t-test and the non-parametric Mann-Whitney test, were also carried out in variables with two possible out- comes to corroborate the differences between means. All tests were done for 95% confidence. ©2016 International Society of Arboriculture Environmental quality Air quality Biodiversity Oxygenz “Takes away from the concrete Naturalness feeling” Personal well-being Sense of place Other
January 2016
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