©2023 International Society of Arboriculture 276 analyses revealed significant relationships at p = 0.05 (LOF1, X2 [3, 44] = 2.464, p = 0.482; LOF2, X2 [3, 44] = 3.365, p = 0.339; LOF3, X2 [3, 44] = 2.607, p = 0.456; POF1, X2 [4, 21] = 2.100, p = 0.717; POF2, X2 [4, 21] = 9.259, p = 0.055; POF3, X2 [4, 21] = 6.300, p = 0.178), and thus we concluded that there is no indication nonrespondents are different from respondents. In comparing self-reported financial and health risk tolerances from our DOSPERT questions, we failed to detect any differences between those in the USA and in the UK or Australia. Participants from the USA assessed the risk with our health scenarios slightly higher than the UK and Australian group (mean rating 7.2 versus 6.9), though this difference was not enough to pass our threshold for significance (P-value = 0.071). Financial risk ratings were even more closely aligned for the 2 demographics (7.3 for the USA and 7.2 for the UK and Australia) and not significant (P-value = 0.444). When assessing final risk ratings, we were not able to make direct comparisons between the assessments derived using the QTRA method and those derived from the TRAQ/ISA BMP method given differences in terminology. However, we have summarized these responses here for completeness. In assessing the 3 tree videos, the majority of assessments (n = 62) con- ducted by QTRA-trained arborists classified risk associated with the trees as “broadly acceptable” or having a risk of harm of 1:1,000,000 or less. The remaining assessments were nearly evenly split between “tolerable” (i.e., RoH between 1:1,000,000 and 10,000; 24%) or “unacceptable” (i.e., RoH greater than 1:10:000; 23%) ratings. Of the 82 risk assessments conducted by the TRAQ group, 14% were assessed as having “low” risk, 38% were assessed as having “moderate” risk, 43% were assessed as having “high” risk, and 5% were assessed as having “extreme” risk. In contrast, ISA BMP participants (n = 32) were more likely to rate risk as “extreme” (13%), less likely to rate risk as “high” (25%), less likely to rate risk as moderate (31%), and more likely to rate risk as low (31%). Fig- ure 1 shows a comparison of the risk ratings between both TRAQ and ISA BMP participants. In addition to looking at patterns of risk ratings, we compared the variability of the decisions made across the 3 methods tested. In running simple tests of homo- geneity of variances, we found that none of the methods Klein et al: Evaluating the Reproducibility of Tree Risk Assessment Ratings approximately 75% of certified arborists and 76% of TRAQ arborists were male (ISA, unpublished data). Most respondents (85%) conducted risk assessments as part of their work routines (Table 1). Similarly, Koeser and Smiley (2017) evaluated the impact that assessors have on tree risk assessment ratings and rec- ommended mitigation and found that 79.1% (n = 296) of participants conduct risk assessments as part of their job. When response rate is less than 100%, and espe- cially less than 70%, nonresponse error can be a lim- itation and should be examined (Bose 2001). One way to potentially limit this type of error is to assess whether the sample is representative of the target population (Bose 2001). Demographic data for the specific target audience (e.g., specific to the geo- graphical focus of the study) was not available. How- ever, our TRAQ respondents seem to align with the population they represent. According to a recent sur- vey conducted by the ISA, 54% of TRAQ certified arborists worldwide are 45 years of age or older (ISA, unpublished data). While our ability to conduct demo- graphic comparisons is limited, there is no indication the age of TRAQ participants does not align with the population. Another approach to controlling for nonresponse error is to group respondents into “early” and “late” respondent groups and compare them on key study variables (Bose 2001; Lindner et al. 2001; Lindner and Wingenbach 2002). This approach considers nonrespondents a “linear extension of the latest respondents” (Lindner et al. 2001), recognizing late respondents as more like nonrespondents than early respondents (Bose 2001). Thus, if trends are identi- fied, they could signify potential differences between respondents and nonrespondents and suggest a need to be especially concerned about nonresponse error. For the TRAQ and QTRA respondents for whom we had a survey postmark date, we split each group by the earliest half and latest half and compared them on key study variables (Lindner et al. 2001). For the TRAQ respondents, we compared one-year LOF for trees #1, #2, and #3 between early and late respon- dents. For QTRA respondents, we compared POF for trees #1, #2, and #3 between early and late respon- dents. Given the ordinal nature of LOF and POF, we employed 6 chi-square tests of independence to test the relationship between early/late respondents and LOF or POF, respectively. None of the chi-square
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