At the start of the study, group differences on demographic, health status and past stressful life events were assessed with chi-square and independent samples Significant t-tests. differences (p < .05) between Nature Group Walkers and Non-Group Walkers existed on age, gender, ethnicity, education, disability, past physical activity, and past stressful life events. These group differences could confound analyses of the influence of nature-based group walks on well-being. To adjust for confounders and make the groups comparable, propensity score matching (PSM) (Rosenbaum & Rubin, 1983) was used. PSM has been used in public health research investigations of outdoor physical exercise (Boer, Zheng, Overton, Ridgeway, & Cohen, 2007; Cohen et al., 2013; Hendriksen, Simons, Garre, & Hildebrandt, 2010). A propensity score is the conditional probability that a study participant belongs to the treatment group (i.e. Nature Group Walkers) given all the observed covariates. Propensity scores were estimated using logistic regression with group walk participation as the outcome variable and selected covariates (i.e. age, gender, ethnicity, education, disability, past physical activity, past stressful life events) as predictors. Participants were matched using 1:1 nearest neighbour matching with replacement, the recommended approach when there are than participants (Dehejia & Wahba, 1999). Each fewer ‘control’ ‘treated’ ‘control’ participant (Non-Group Walker) was thus matched to one or more participant(s) ‘treated’ (Nature Group Walkers) with the most similar propensity participants score. ‘Control’ a frequency weight that reflects the number of times they were selected as a “receive match” (Stuart, 2010 p. 13). To a caliper of .25 standard deviations of the ensure ‘good’ matches, logit of the propensity score was applied (Ho, Imai, King, & Stuart, 2007); ‘control’ participants not identified as a match for a participant were from the ‘good’ ‘treated’ ‘pruned’ sample (Stuart, 2010). The end result is a reduced sample of participants that have similar propensity scores. The PSM procedure was performed using the SPSS PSM plug-in 2012). ‘psmatching’ (Thoemmes,
At the start of the study, group differences on demographic, health status and past stressful life events were assessed with chi-square and independent samples t-tests. Significant differences (p < .05) between Nature Group Walkers and Non-Group Walkers existed on age, gender, ethnicity, education, disability, past physical activity, and past stressful life events. These group differences could confound analyses of the influence of nature-based group walks on well-being. To adjust for confounders and make the groups comparable, propensity score matching (PSM) (Rosenbaum & Rubin, 1983) was used. PSM has been used in public health research investigations of outdoor physical exercise (Boer, Zheng, Overton, Ridgeway, & Cohen, 2007; Cohen et al., 2013; Hendriksen, Simons, Garre, & Hildebrandt, 2010). A propensity score is the conditional probability that a study participant belongs to the treatment group (i.e. Nature Group Walkers) given all the observed covariates. Propensity scores were estimated using logistic regression with group walk participation as the outcome variable and selected covariates (i.e. age, gender, ethnicity, education, disability, past physical activity, past stressful life events) as predictors. Participants were matched using 1:1 nearest neighbour matching with replacement, the recommended approach when there are fewer ‘control’ than ‘treated’ participants (Dehejia & Wahba, 1999). Each ‘control’ participant (Non-Group Walker) was thus matched to one or more ‘treated’ participant(s) (Nature Group Walkers) with the most similar propensity score. ‘Control’ participants “receive a frequency weight that reflects the number of times they were selected as a match” (Stuart, 2010 p. 13). To ensure ‘good’ matches, a caliper of .25 standard deviations of the logit of the propensity score was applied (Ho, Imai, King, & Stuart, 2007); ‘control’ participants not identified as a ‘good’ match for a ‘treated’ participant were ‘pruned’ from the sample (Stuart, 2010). The end result is a reduced sample of participants that have similar propensity scores. The PSM procedure was performed using the SPSS PSM plug-in ‘psmatching’ (Thoemmes, 2012).