Introduction to your data set and questions of interest (this will help you draft your “Introduction” section of your

Summary

Connectedness to Nature as a Mediator in the Relationship Between Trait Mindfulness and Pro-Environmental Behavior

In the midst of an escalating ecological crisis, shifting towards pro-environmental and sustainable behaviors is becoming increasingly urgent. Behaviors involving transportation, food, and household operations have been found to be responsible for the majority of environmental problems (Brower & Leon, 1999). Stern (2000) asserts that environmentally significant behavior should be defined by its impact and that grounding research in those behaviors that have a large environmental impact will increase the utility of results for practical application. Brower and Leon (1999) suggest that these more impactful behaviors include reducing travel, using alternative methods of transportation (e.g. walking, biking, or taking public transportation), eating less meat, and conserving energy. If these behaviors are to be encouraged it is important to consider factors that may potentially increase the willingness to engage in pro-environmental behaviors.

A factor that has been found to influence pro-environmental behavior is mindfulness (Amel, Manning, & Scott, 2009). Mindfulness is defined as moment-to-moment, non-judgmental awareness, cultivated by paying attention in a specific way, that is, in the present moment, and as non-reactively, non-judgmentally, and open-heartedly as possible (Kabat-Zinn, 2015). Research has shown that the construct of mindfulness can be broken down into five facets: nonreactivity to inner experience (e.g. “I perceive my feelings and emotions without having to react to them”), observing sensations (e.g. “I sense my body, whether eating, cooking, cleaning, or talking”), acting with awareness (e.g. “I break or spill things because of carelessness, not paying attention, or thinking of something else”), labeling with words (e.g. “I can easily put my beliefs, opinions, and expectations into words”), and non-judging of experience (e.g. I tend to evaluate whether my perceptions are right or wrong”) (Baer, Smith, Hopkins, Krietemery, & Toney, 2006). Research indicates that mindfulness positively influences decision-making processes (Black, Sussman, Johnson, & Milan, 2012) and behavioral motivation (Levesque & Brown, 2007). Amel et al., (2009) found that mindfulness predicts sustainable behavioral choices and argue that mindfulness creates a greater self-world connection that enhances experiences with nature and motivates pro-environmental behavior.

To further understand the relationship between mindfulness and pro-environmental behavior, factors that may indirectly affect the relationship need to be considered. Barbaro & Pickette (2016) found that connectedness to nature significantly mediates the relationship between mindfulness and pro-environmental behavior. Past research has shown that mindfulness is significantly associated with greater connectedness to nature (Howell, Dopko, Passmore, & Buro, 2011). Mayer & Frantz (2004) define connectedness to nature as the extent to which one feels part of the natural world. It is suggested that the extent to which one incorporates others as part of the self increases a sense of closeness in a relationship (Aron, Aron, Tudor, & Nelson, 1991) and as relationship closeness increases, so does willingness to help (Cialdini, Brown, Lewis, Luce, & Neuberg, 1997). It is argued that individuals who have a strong sense of closeness in their relationship to nature are less likely to harm the environment and are more likely to demonstrate altruistic behavior towards the environment because they see the self as embedded in nature (Mayer & Frantz, 2004).

A study by Hood & Friedman (2011) indicates that connectedness to nature predicts engagement in pro-environmental behavior. Barbaro & Pickett (2016) suggest that connectedness to nature may motivate individuals to engage in behaviors that have minimal negative impacts on the natural environment. The authors conducted a study to assess the extent to which connectedness to nature mediates the relationship between mindfulness and pro-environmental behavior with 360 undergraduate students and 296 participants recruited from the general public through Amazon’s Mechanical Turk using the Five Facets of Mindfulness Questionnaire (Baer, et al., 2006), the Connectedness to Nature Scale (Mayer & Frantz, 2004), and the Pro-Environmental Behavior scale (Whitmarsh & O’Neill, 2010). It was found that mindfulness significantly predicted pro-environmental behavior and that connectedness significantly mediated the relationship between mindfulness and pro-environmental behavior in both samples. Additionally, the observation facet of mindfulness was significantly predictive of pro-environmental behavior and connectedness to nature significantly mediated that relationship as well.

The proposed study aims to replicate Barbaro & Pickett’s 2016 study with an undergraduate sample from Willamette University and a general public sample recruited through Prolific. The proposed study will also expand on Barbaro & Pickett’s 2016 study by using the Pro-Environmental Behavior Scale, a multi-faceted measure of pro-environmental behavior (Markle, 2013), rather than the Pro-Environmental Behavior measure (Whitmarsh & O’Neill, 2010) used in the original study. Markle (2013) argues that the Pro-Environmental Behavior Scale enables examination of those behaviors that have the greatest impact on the environment with the ultimate goal of developing policy interventions. Prior to her 2013 study, Markle argues that few previous pro-environmental behavior measures considered the environmental impact of the behavior they measured and were measuring marginally impactful behaviors (e.g. “If possible, I do not insist on my right of way and make the traffic stop before entering a crosswalk”: GEB; Kaiser & Wilson, 2000), that are of little use in developing strategic interventions to bring about meaningful change. Markle’s (2013) study showed that previous pro-environmental behavior measures demonstrated very little consistency (including the Pro-Environmental Behavior measure (PEB; Whitmarsh & O’Neill, 2010) used by Barbaro and Pickett (2016)). The Pro-Environmental Behavior Scale has also shown to be more accommodating toward a wider range of socioeconomic statuses (i.e. not measuring behaviors such as “Bought or built an energy-efficient home” or “Installed a more efficient heating system”: PEB; Whitmarsh & O’Neill, 2010) (Markle, 2013). Markle (2013) suggests that the Pro-Environmental Behavior Scale can serve as a tool for determining which factors influence pro-environmental behavior to aid researchers in developing interventions aiming to mitigate anthropogenic environmental impact.

“Big Questions”

This study aims to answer the following questions:

Does Mindfulness significantly predict Pro-Environmental Behavior?

Does Connectedness to Nature significantly mediate the relationship between Mindfulness and Pro-Environment?

My Process:

I did not do any data wrangling in R. My data cleaning process involved downloading excel data matrices of individual responses from Survey Monkey for the three different samples (Introduction to Psychology sample pool, Prolific online participant recruitment sample pool, and a Volunteer sample pool). The raw excel matrices from Survey Monkey were structured with each row being an individual respondent and all of their responses for each item, with the columns being each item/question from the survey. My first step when cleaning in the first round of data cleaning was deleting the entire row for any participant that failed the attention check placed midway through the survey or any participant that indicated they did not fill out the survey honestly and that we should not use their responses. I then reverse coded the responses for each item that was indicated by the scale instructions to be reverse coded (i.e. for responses on a 1-5 scale, I reverse coded them by inserting a new adjacent column for reverse coded scores and entered a function for “6 - the unreversed response option”). Following this process, I inserted columns for sub-scale and scale totals and averages. At this point, I inserted a rule to fill in each blank cell with black so that they would be easier to locate. This is when I used your recommendation to impute sub-scale averages for missing values. I did this for each blank cell. After this, I inserted a function to compute the average for each column to get an item, sub-scale, and scale average. Once this was done, I entered the columns for each sub-scale and scale total and average into a new excel spreadsheet.

For the second round of data cleaning, I intended to be able to replicate the results (averages and totals) from the first cleaning. The only difference in this process was that instead of cleaning each data set for the samples separately and then combining the sub-scale and scale totals and averages, I combined the raw data matrices for all three samples and then cleaned them following the same steps described above. Using this process I was able to replicate the same averages and totals I found after the first cleaning. This process left me with the sub-scale and scale totals and averages for all 336 individual responses ready for analysis.

Analyses

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.2.1     ✔ purrr   0.3.2
## ✔ tibble  2.1.3     ✔ dplyr   0.8.3
## ✔ tidyr   0.8.3     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0

## ── Conflicts ────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(readxl)
HonorsSurveyTotals_AveragesOct12 <- read_excel("HonorsSurveyTotals&AveragesOct12.xlsx")
survey=na.omit(HonorsSurveyTotals_AveragesOct12)
attach(survey)

Distributions of Main Variables of Interest

ggplot(survey, aes(x = `FFMQ TOTAL`))+
  geom_histogram(aes(fill = ..count..), binwidth = 5)

ggplot(survey, aes(x = `PEBS TOTAL`))+
  geom_histogram(aes(fill = ..count..), binwidth = 5)

ggplot(survey, aes(x = `CNS TOTAL`))+
  geom_histogram(aes(fill = ..count..), binwidth = 5)

PEBS as function of FFMQ

mod1<-lm(`PEBS TOTAL`~`FFMQ TOTAL`)
summary(mod1)

## 
## Call:
## lm(formula = `PEBS TOTAL` ~ `FFMQ TOTAL`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -25.3984  -8.3904  -0.2868   7.8932  29.5629 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  40.92224    4.02337   10.17  < 2e-16 ***
## `FFMQ TOTAL`  0.13212    0.03191    4.14 4.41e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.24 on 333 degrees of freedom
## Multiple R-squared:  0.04895,    Adjusted R-squared:  0.04609 
## F-statistic: 17.14 on 1 and 333 DF,  p-value: 4.407e-05

anova(mod1)

## Analysis of Variance Table
## 
## Response: PEBS TOTAL
##               Df Sum Sq Mean Sq F value    Pr(>F)    
## `FFMQ TOTAL`   1   2167 2166.82  17.139 4.407e-05 ***
## Residuals    333  42101  126.43                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ggplot(survey, aes(x=`FFMQ TOTAL`, y=`PEBS TOTAL`))+
  geom_point()+
  geom_abline(slope=mod1$coefficients[2], intercept=mod1$coefficients[1],
              color="blue", lty=2, lwd=1)+
  theme_bw()

CNS as a function of FFMQ

mod2<-lm(`CNS TOTAL`~`FFMQ TOTAL`)
summary(mod2)

## 
## Call:
## lm(formula = `CNS TOTAL` ~ `FFMQ TOTAL`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -25.7284  -6.3286  -0.2156   6.7567  24.4339 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  28.17512    3.41029   8.262 3.44e-15 ***
## `FFMQ TOTAL`  0.17050    0.02705   6.303 9.26e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.531 on 333 degrees of freedom
## Multiple R-squared:  0.1066, Adjusted R-squared:  0.1039 
## F-statistic: 39.73 on 1 and 333 DF,  p-value: 9.258e-10

anova(mod2)

## Analysis of Variance Table
## 
## Response: CNS TOTAL
##               Df  Sum Sq Mean Sq F value    Pr(>F)    
## `FFMQ TOTAL`   1  3608.6  3608.6  39.728 9.258e-10 ***
## Residuals    333 30247.5    90.8                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ggplot(survey, aes(x=`FFMQ TOTAL`, y=`CNS TOTAL`))+
  geom_point()+
  geom_abline(slope=mod2$coefficients[2], intercept=mod2$coefficients[1],
              color="blue", lty=2, lwd=1)+
  theme_bw()

PEBS as a function of CNS

mod3<-lm(`PEBS TOTAL`~`CNS TOTAL`)
summary(mod3)

## 
## Call:
## lm(formula = `PEBS TOTAL` ~ `CNS TOTAL`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -26.1445  -7.3916  -0.0411   6.9168  27.2770 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 26.89922    2.66078   10.11   <2e-16 ***
## `CNS TOTAL`  0.61686    0.05276   11.69   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.708 on 333 degrees of freedom
## Multiple R-squared:  0.291,  Adjusted R-squared:  0.2889 
## F-statistic: 136.7 on 1 and 333 DF,  p-value: < 2.2e-16

anova(mod3)

## Analysis of Variance Table
## 
## Response: PEBS TOTAL
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## `CNS TOTAL`   1  12883 12882.8  136.69 < 2.2e-16 ***
## Residuals   333  31384    94.2                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ggplot(survey, aes(x=`CNS TOTAL`, y=`PEBS TOTAL`))+
  geom_point()+
  geom_abline(slope=mod3$coefficients[2], intercept=mod3$coefficients[1],
              color="blue", lty=2, lwd=1)+
  theme_bw()

PEBS as a function of CNS and FFMQ to test for mediation

mod11<-lm(`PEBS TOTAL`~`CNS TOTAL`*`FFMQ TOTAL`)
summary(mod11)

## 
## Call:
## lm(formula = `PEBS TOTAL` ~ `CNS TOTAL` * `FFMQ TOTAL`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -25.6594  -7.6812  -0.1821   7.0754  27.4345 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              -5.726187  16.470246  -0.348 0.728310    
## `CNS TOTAL`               1.185073   0.320584   3.697 0.000256 ***
## `FFMQ TOTAL`              0.276634   0.135698   2.039 0.042284 *  
## `CNS TOTAL`:`FFMQ TOTAL` -0.004810   0.002587  -1.859 0.063853 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.671 on 331 degrees of freedom
## Multiple R-squared:  0.3006, Adjusted R-squared:  0.2943 
## F-statistic: 47.42 on 3 and 331 DF,  p-value: < 2.2e-16

anova(mod11)

## Analysis of Variance Table
## 
## Response: PEBS TOTAL
##                           Df  Sum Sq Mean Sq  F value  Pr(>F)    
## `CNS TOTAL`                1 12882.8 12882.8 137.7318 < 2e-16 ***
## `FFMQ TOTAL`               1   100.9   100.9   1.0785 0.29980    
## `CNS TOTAL`:`FFMQ TOTAL`   1   323.4   323.4   3.4575 0.06385 .  
## Residuals                331 30960.3    93.5                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Here we see that when FFMQ (Mindfulness) is controlled for, CNS still significantly predicts Pro-Environmental Behavior, while FFMQ (Mindfulness) no longer significantly predicts Pro-Environmental Behavior, suggesting a full mediation effect.

Here we attempt to use r to verify the mediation determined by causal steps

Causal Mediation Analysis: CMA, AVERAGE CAUSAL MEDIATION EFFECT: ACME, AVERAGE DIRECT EFFECT: ADE, TOTAL EFFECT

install.packages("mediation", repos = "http://cran.us.r-project.org")

## 
## The downloaded binary packages are in
##  /var/folders/8g/0gpmmj7127d6lzxd3cjv37940000gn/T//Rtmpb4ExhA/downloaded_packages

library(mediation)

## Loading required package: MASS

## 
## Attaching package: 'MASS'

## The following object is masked _by_ '.GlobalEnv':
## 
##     survey

## The following object is masked from 'package:dplyr':
## 
##     select

## Loading required package: Matrix

## 
## Attaching package: 'Matrix'

## The following object is masked from 'package:tidyr':
## 
##     expand

## Loading required package: mvtnorm

## Loading required package: sandwich

## mediation: Causal Mediation Analysis
## Version: 4.5.0

?mediate
med= mediate(mod2, mod11, treat = "FFMQ TOTAL", mediator = "CNS TOTAL")
summary(med)

## 
## Causal Mediation Analysis 
## 
## Quasi-Bayesian Confidence Intervals
## 
##                Estimate 95% CI Lower 95% CI Upper p-value    
## ACME            0.20304      0.09418         0.35  <2e-16 ***
## ADE             0.14132      0.00382         0.28   0.038 *  
## Total Effect    0.34436      0.10988         0.62   0.002 ** 
## Prop. Mediated  0.59214      0.48653         0.95   0.002 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Sample Size Used: 335 
## 
## 
## Simulations: 1000

plot(med)

This suggests that there are significant average causal mediation effects and proportion mediated.

anova(mod1, mod11)

## Analysis of Variance Table
## 
## Model 1: `PEBS TOTAL` ~ `FFMQ TOTAL`
## Model 2: `PEBS TOTAL` ~ `CNS TOTAL` * `FFMQ TOTAL`
##   Res.Df   RSS Df Sum of Sq      F    Pr(>F)    
## 1    333 42101                                  
## 2    331 30960  2     11140 59.551 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Here we observe that the model controlling for Mindfulness has a significantly less Residual Sum of Squares, suggesting this is the better model that accounts for more variance and suggests that CNS mediates the relationship between FFMQ and PEBS.

Exploratory Analysis of FFMQ Facets with PEBS Facets

FFMQ - Observation & PEBS FACETS

cor(`FFMQ-O Total`, `PEBS-Conservation Total`) # 0.37        #MODEL

## [1] 0.3700544

cor(`FFMQ-O Total`, `PEBS-Envi Citizenship Total`) # 0.36    #MODEL

## [1] 0.3621746

cor(`FFMQ-O Total`, `PEBS-Food Total`) # 0.26                #MODEL

## [1] 0.2684055

cor(`FFMQ-O Total`, `PEBS-Transportation Total`) # 0.23      #MODEL

## [1] 0.2396257

FFMQ - Describe & PEBS FACETS

cor(`FFMQ-D Total`, `PEBS-Conservation Total`) # 0.2         #MODEL

## [1] 0.2068397

cor(`FFMQ-D Total`, `PEBS-Envi Citizenship Total`) # 0.1

## [1] 0.1052516

cor(`FFMQ-D Total`, `PEBS-Food Total`) # 0.11

## [1] 0.1111223

cor(`FFMQ-D Total`, `PEBS-Transportation Total`) # 0.06

## [1] 0.06291377

FFMQ - Awareness & PEBS FACETS

cor(`FFMQ-A Total`, `PEBS-Conservation Total`) # 0.13        #MODEL?

## [1] 0.138052

cor(`FFMQ-A Total`, `PEBS-Envi Citizenship Total`) # 0.02

## [1] 0.02283776

cor(`FFMQ-A Total`, `PEBS-Food Total`) # 0.0

## [1] 0.0007331314

cor(`FFMQ-A Total`, `PEBS-Transportation Total`) # 0.02

## [1] 0.02256094

FFMQ - Non-Judgement & PEBS FACETS

cor(`FFMQ-NJ Total`, `PEBS-Conservation Total`) # 0

## [1] -0.002143537

cor(`FFMQ-NJ Total`, `PEBS-Envi Citizenship Total`) # 0

## [1] -0.03088336

cor(`FFMQ-NJ Total`, `PEBS-Food Total`) # 0

## [1] 0.0250095

cor(`FFMQ-NJ Total`, `PEBS-Transportation Total`) # -0.06

## [1] -0.06572394

FFMQ - Non-Reactivity & PEBS FACETS

cor(`FFMQ-NR Total`, `PEBS-Conservation Total`) # 0.15       MODEL?

## [1] 0.1510085

cor(`FFMQ-NR Total`, `PEBS-Envi Citizenship Total`) #0.04

## [1] 0.04861551

cor(`FFMQ-NR Total`, `PEBS-Food Total`) # 0.037

## [1] 0.03710116

cor(`FFMQ-NR Total`, `PEBS-Transportation Total`) # 0.04

## [1] 0.04091681

We will test for significance by performing ANOVA on Linear Models for FFMQ Observation with PEBS Conservation, Environmental Citizenship, Food, and Transportation, FFMQ Describe with PEBS Conservation, FFMQ Awareness with PEBS Conservation, and FFMQ Non-Reactivity with PEBS Conservation.

mod4<-lm(`PEBS-Conservation Total`~`FFMQ-O Total`)  # *** Significant
summary(mod4)

## 
## Call:
## lm(formula = `PEBS-Conservation Total` ~ `FFMQ-O Total`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.643  -2.202   0.298   2.636   9.886 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    19.02698    1.03665  18.354  < 2e-16 ***
## `FFMQ-O Total`  0.26466    0.03641   7.269  2.6e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.569 on 333 degrees of freedom
## Multiple R-squared:  0.1369, Adjusted R-squared:  0.1343 
## F-statistic: 52.84 on 1 and 333 DF,  p-value: 2.601e-12

anova(mod4)

## Analysis of Variance Table
## 
## Response: PEBS-Conservation Total
##                 Df Sum Sq Mean Sq F value    Pr(>F)    
## `FFMQ-O Total`   1  672.9  672.91  52.837 2.601e-12 ***
## Residuals      333 4241.0   12.74                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod5<-lm(`PEBS-Envi Citizenship Total`~`FFMQ-O Total`)  # *** Significant
summary(mod5)

## 
## Call:
## lm(formula = `PEBS-Envi Citizenship Total` ~ `FFMQ-O Total`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.7988  -3.7184  -0.4521   3.2615  12.8947 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      5.3563     1.3924   3.847 0.000143 ***
## `FFMQ-O Total`   0.3467     0.0489   7.090 8.05e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.793 on 333 degrees of freedom
## Multiple R-squared:  0.1312, Adjusted R-squared:  0.1286 
## F-statistic: 50.27 on 1 and 333 DF,  p-value: 8.052e-12

anova(mod5)

## Analysis of Variance Table
## 
## Response: PEBS-Envi Citizenship Total
##                 Df Sum Sq Mean Sq F value    Pr(>F)    
## `FFMQ-O Total`   1 1155.1 1155.06  50.274 8.052e-12 ***
## Residuals      333 7650.7   22.98                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod6<-lm(`PEBS-Food Total`~`FFMQ-O Total`)  # *** Significant
summary(mod6)

## 
## Call:
## lm(formula = `PEBS-Food Total` ~ `FFMQ-O Total`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.8493 -4.2128 -0.6977  3.9084  9.9690 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.15216    1.35753   0.849    0.397    
## `FFMQ-O Total`  0.24243    0.04768   5.085 6.16e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.673 on 333 degrees of freedom
## Multiple R-squared:  0.07204,    Adjusted R-squared:  0.06925 
## F-statistic: 25.85 on 1 and 333 DF,  p-value: 6.163e-07

anova(mod6)

## Analysis of Variance Table
## 
## Response: PEBS-Food Total
##                 Df Sum Sq Mean Sq F value    Pr(>F)    
## `FFMQ-O Total`   1  564.6  564.62  25.852 6.163e-07 ***
## Residuals      333 7272.8   21.84                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod7<-lm(`PEBS-Transportation Total`~`FFMQ-O Total`)  # *** Significant
summary(mod7)

## 
## Call:
## lm(formula = `PEBS-Transportation Total` ~ `FFMQ-O Total`)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.477 -2.090 -0.113  2.023  7.433 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     4.15729    0.86227   4.821 2.17e-06 ***
## `FFMQ-O Total`  0.13640    0.03029   4.504 9.24e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.968 on 333 degrees of freedom
## Multiple R-squared:  0.05742,    Adjusted R-squared:  0.05459 
## F-statistic: 20.29 on 1 and 333 DF,  p-value: 9.239e-06

anova(mod7)

## Analysis of Variance Table
## 
## Response: PEBS-Transportation Total
##                 Df  Sum Sq Mean Sq F value    Pr(>F)    
## `FFMQ-O Total`   1  178.75 178.749  20.286 9.239e-06 ***
## Residuals      333 2934.23   8.812                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod8<-lm(`PEBS-Conservation Total`~`FFMQ-D Total`)  # *** Significant
summary(mod8)

## 
## Call:
## lm(formula = `PEBS-Conservation Total` ~ `FFMQ-D Total`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.1395  -2.0738   0.1743   2.7291   9.3057 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    22.93534    0.92826  24.708  < 2e-16 ***
## `FFMQ-D Total`  0.13138    0.03405   3.858 0.000137 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.758 on 333 degrees of freedom
## Multiple R-squared:  0.04278,    Adjusted R-squared:  0.03991 
## F-statistic: 14.88 on 1 and 333 DF,  p-value: 0.0001373

anova(mod8)

## Analysis of Variance Table
## 
## Response: PEBS-Conservation Total
##                 Df Sum Sq Mean Sq F value    Pr(>F)    
## `FFMQ-D Total`   1  210.2 210.229  14.883 0.0001373 ***
## Residuals      333 4703.7  14.125                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod9<-lm(`PEBS-Conservation Total`~`FFMQ-A Total`)  # * Significant
summary(mod9)

## 
## Call:
## lm(formula = `PEBS-Conservation Total` ~ `FFMQ-A Total`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.4679  -2.1965   0.1702   2.5749   8.8035 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    24.20609    0.89784  26.960   <2e-16 ***
## `FFMQ-A Total`  0.09047    0.03557   2.544   0.0114 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.805 on 333 degrees of freedom
## Multiple R-squared:  0.01906,    Adjusted R-squared:  0.01611 
## F-statistic:  6.47 on 1 and 333 DF,  p-value: 0.01142

anova(mod9)

## Analysis of Variance Table
## 
## Response: PEBS-Conservation Total
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## `FFMQ-A Total`   1   93.7  93.651  6.4697 0.01142 *
## Residuals      333 4820.2  14.475                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod10<-lm(`PEBS-Conservation Total`~`FFMQ-NR Total`)  # ** Significant
summary(mod10)

## 
## Call:
## lm(formula = `PEBS-Conservation Total` ~ `FFMQ-NR Total`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.932  -2.258   0.372   2.560   8.952 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     23.95910    0.90956  26.341  < 2e-16 ***
## `FFMQ-NR Total`  0.11604    0.04163   2.788  0.00561 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.797 on 333 degrees of freedom
## Multiple R-squared:  0.0228, Adjusted R-squared:  0.01987 
## F-statistic: 7.771 on 1 and 333 DF,  p-value: 0.005615

anova(mod10)

## Analysis of Variance Table
## 
## Response: PEBS-Conservation Total
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## `FFMQ-NR Total`   1  112.1  112.05  7.7708 0.005615 **
## Residuals       333 4801.8   14.42                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod12=lm(`PEBS-Conservation Total`~`FFMQ-O Total`*`FFMQ-D Total`*`FFMQ-A Total`*`FFMQ-NR Total`)
anova(mod12)

## Analysis of Variance Table
## 
## Response: PEBS-Conservation Total
##                                                               Df Sum Sq
## `FFMQ-O Total`                                                 1  672.9
## `FFMQ-D Total`                                                 1   48.8
## `FFMQ-A Total`                                                 1   12.5
## `FFMQ-NR Total`                                                1    5.0
## `FFMQ-O Total`:`FFMQ-D Total`                                  1   37.5
## `FFMQ-O Total`:`FFMQ-A Total`                                  1   65.4
## `FFMQ-D Total`:`FFMQ-A Total`                                  1   13.0
## `FFMQ-O Total`:`FFMQ-NR Total`                                 1    7.3
## `FFMQ-D Total`:`FFMQ-NR Total`                                 1    0.5
## `FFMQ-A Total`:`FFMQ-NR Total`                                 1    1.6
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-A Total`                   1    7.4
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-NR Total`                  1    5.6
## `FFMQ-O Total`:`FFMQ-A Total`:`FFMQ-NR Total`                  1    7.7
## `FFMQ-D Total`:`FFMQ-A Total`:`FFMQ-NR Total`                  1    7.6
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-A Total`:`FFMQ-NR Total`   1    8.7
## Residuals                                                    319 4012.4
##                                                              Mean Sq
## `FFMQ-O Total`                                                672.91
## `FFMQ-D Total`                                                 48.76
## `FFMQ-A Total`                                                 12.51
## `FFMQ-NR Total`                                                 5.02
## `FFMQ-O Total`:`FFMQ-D Total`                                  37.47
## `FFMQ-O Total`:`FFMQ-A Total`                                  65.42
## `FFMQ-D Total`:`FFMQ-A Total`                                  12.99
## `FFMQ-O Total`:`FFMQ-NR Total`                                  7.27
## `FFMQ-D Total`:`FFMQ-NR Total`                                  0.46
## `FFMQ-A Total`:`FFMQ-NR Total`                                  1.57
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-A Total`                    7.44
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-NR Total`                   5.58
## `FFMQ-O Total`:`FFMQ-A Total`:`FFMQ-NR Total`                   7.70
## `FFMQ-D Total`:`FFMQ-A Total`:`FFMQ-NR Total`                   7.64
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-A Total`:`FFMQ-NR Total`    8.73
## Residuals                                                      12.58
##                                                              F value
## `FFMQ-O Total`                                               53.4986
## `FFMQ-D Total`                                                3.8765
## `FFMQ-A Total`                                                0.9948
## `FFMQ-NR Total`                                               0.3995
## `FFMQ-O Total`:`FFMQ-D Total`                                 2.9790
## `FFMQ-O Total`:`FFMQ-A Total`                                 5.2012
## `FFMQ-D Total`:`FFMQ-A Total`                                 1.0325
## `FFMQ-O Total`:`FFMQ-NR Total`                                0.5780
## `FFMQ-D Total`:`FFMQ-NR Total`                                0.0362
## `FFMQ-A Total`:`FFMQ-NR Total`                                0.1252
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-A Total`                  0.5918
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-NR Total`                 0.4439
## `FFMQ-O Total`:`FFMQ-A Total`:`FFMQ-NR Total`                 0.6121
## `FFMQ-D Total`:`FFMQ-A Total`:`FFMQ-NR Total`                 0.6074
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-A Total`:`FFMQ-NR Total`  0.6945
## Residuals                                                           
##                                                                 Pr(>F)    
## `FFMQ-O Total`                                               2.107e-12 ***
## `FFMQ-D Total`                                                 0.04983 *  
## `FFMQ-A Total`                                                 0.31932    
## `FFMQ-NR Total`                                                0.52782    
## `FFMQ-O Total`:`FFMQ-D Total`                                  0.08532 .  
## `FFMQ-O Total`:`FFMQ-A Total`                                  0.02323 *  
## `FFMQ-D Total`:`FFMQ-A Total`                                  0.31035    
## `FFMQ-O Total`:`FFMQ-NR Total`                                 0.44766    
## `FFMQ-D Total`:`FFMQ-NR Total`                                 0.84921    
## `FFMQ-A Total`:`FFMQ-NR Total`                                 0.72371    
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-A Total`                   0.44230    
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-NR Total`                  0.50573    
## `FFMQ-O Total`:`FFMQ-A Total`:`FFMQ-NR Total`                  0.43457    
## `FFMQ-D Total`:`FFMQ-A Total`:`FFMQ-NR Total`                  0.43634    
## `FFMQ-O Total`:`FFMQ-D Total`:`FFMQ-A Total`:`FFMQ-NR Total`   0.40527    
## Residuals                                                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

This suggests that the Observation facet of Mindfulness may be of greatest interest when trying to predcit Conservation based Pro-Environmental Behaviors

mod13=lm(`FFMQ-O Total`~`PEBS-Conservation Total`*`PEBS-Envi Citizenship Total`*`PEBS-Food Total`*`PEBS-Transportation Total`)
anova(mod13)

## Analysis of Variance Table
## 
## Response: FFMQ-O Total
##                                                                                                        Df
## `PEBS-Conservation Total`                                                                               1
## `PEBS-Envi Citizenship Total`                                                                           1
## `PEBS-Food Total`                                                                                       1
## `PEBS-Transportation Total`                                                                             1
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`                                                 1
## `PEBS-Conservation Total`:`PEBS-Food Total`                                                             1
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`                                                         1
## `PEBS-Conservation Total`:`PEBS-Transportation Total`                                                   1
## `PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                                               1
## `PEBS-Food Total`:`PEBS-Transportation Total`                                                           1
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`                               1
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                     1
## `PEBS-Conservation Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                                 1
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                             1
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`   1
## Residuals                                                                                             319
##                                                                                                       Sum Sq
## `PEBS-Conservation Total`                                                                             1315.6
## `PEBS-Envi Citizenship Total`                                                                          553.5
## `PEBS-Food Total`                                                                                      131.5
## `PEBS-Transportation Total`                                                                            117.8
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`                                                 28.8
## `PEBS-Conservation Total`:`PEBS-Food Total`                                                              1.2
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`                                                          5.7
## `PEBS-Conservation Total`:`PEBS-Transportation Total`                                                    8.9
## `PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                                              107.7
## `PEBS-Food Total`:`PEBS-Transportation Total`                                                            1.0
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`                                1.2
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                     45.7
## `PEBS-Conservation Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                                  1.6
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                              4.1
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`    9.2
## Residuals                                                                                             7273.7
##                                                                                                       Mean Sq
## `PEBS-Conservation Total`                                                                             1315.60
## `PEBS-Envi Citizenship Total`                                                                          553.51
## `PEBS-Food Total`                                                                                      131.50
## `PEBS-Transportation Total`                                                                            117.80
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`                                                 28.79
## `PEBS-Conservation Total`:`PEBS-Food Total`                                                              1.24
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`                                                          5.68
## `PEBS-Conservation Total`:`PEBS-Transportation Total`                                                    8.92
## `PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                                              107.69
## `PEBS-Food Total`:`PEBS-Transportation Total`                                                            0.99
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`                                1.18
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                     45.72
## `PEBS-Conservation Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                                  1.56
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                              4.06
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`    9.16
## Residuals                                                                                               22.80
##                                                                                                       F value
## `PEBS-Conservation Total`                                                                             57.6976
## `PEBS-Envi Citizenship Total`                                                                         24.2751
## `PEBS-Food Total`                                                                                      5.7670
## `PEBS-Transportation Total`                                                                            5.1661
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`                                                1.2626
## `PEBS-Conservation Total`:`PEBS-Food Total`                                                            0.0546
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`                                                        0.2489
## `PEBS-Conservation Total`:`PEBS-Transportation Total`                                                  0.3910
## `PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                                              4.7229
## `PEBS-Food Total`:`PEBS-Transportation Total`                                                          0.0432
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`                              0.0519
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                    2.0053
## `PEBS-Conservation Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                                0.0682
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                            0.1781
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`  0.4016
## Residuals                                                                                                    
##                                                                                                          Pr(>F)
## `PEBS-Conservation Total`                                                                             3.417e-13
## `PEBS-Envi Citizenship Total`                                                                         1.344e-06
## `PEBS-Food Total`                                                                                        0.0169
## `PEBS-Transportation Total`                                                                              0.0237
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`                                                  0.2620
## `PEBS-Conservation Total`:`PEBS-Food Total`                                                              0.8154
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`                                                          0.6182
## `PEBS-Conservation Total`:`PEBS-Transportation Total`                                                    0.5322
## `PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                                                0.0305
## `PEBS-Food Total`:`PEBS-Transportation Total`                                                            0.8355
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`                                0.8199
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                      0.1577
## `PEBS-Conservation Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                                  0.7941
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                              0.6733
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`    0.5267
## Residuals                                                                                                      
##                                                                                                          
## `PEBS-Conservation Total`                                                                             ***
## `PEBS-Envi Citizenship Total`                                                                         ***
## `PEBS-Food Total`                                                                                     *  
## `PEBS-Transportation Total`                                                                           *  
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`                                                  
## `PEBS-Conservation Total`:`PEBS-Food Total`                                                              
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`                                                          
## `PEBS-Conservation Total`:`PEBS-Transportation Total`                                                    
## `PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                                             *  
## `PEBS-Food Total`:`PEBS-Transportation Total`                                                            
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`                                
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Transportation Total`                      
## `PEBS-Conservation Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                                  
## `PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`                              
## `PEBS-Conservation Total`:`PEBS-Envi Citizenship Total`:`PEBS-Food Total`:`PEBS-Transportation Total`    
## Residuals                                                                                                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

This suggests that the Observation facet of mindfulness best predicts Conservation and Environmental Citizenship

References

Amel, E. L., Manning, C. M., & Scott, B. A. (2009). Mindfulness and sustainable behavior: Pondering attention and awareness as means for increasing green behavior. Ecopsychology, 1(1), 14–25. https://doi.org/10.1089/eco.2008.0005

Baer, R. A., Smith, G. T., Lykins, E., Button, D., Krietemeyer, J., Sauer, S., … Williams, J. M. G. (2008). Construct validity of the Five Facet Mindfulness Questionnaire in meditating and nonmeditating samples. Assessment, 15(3), 329–342. https://doi.org/10.1177/1073191107313003

Barbaro, N., & Pickett, S. M. (2016). Mindfully green: Examining the effect of connectedness to nature on the relationship between mindfulness and engagement in pro-environmental behavior. Personality and Individual Differences, 93, 137–142. https://doi.org/10.1016/j.paid.2015.05.026

Black, D. S., Sussman, S., Johnson, C. A., & Milam, J. (2012). Trait mindfulness helps shield decision-making from translating into health-risk behavior. Journal of Adolescent Health, 51(6), 588–592. https://doi.org/10.1016/j.jadohealth.2012.03.011

Brower, M., Leon, W., & Schroeder, S. T. (2000). Consumer’s Guide to Effective Environmental Choices (Book). Electronic Green Journal, N.PAG. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=7435600

Hoot, R. E., & Friedman, H. (2011). Connectedness and environmental behavior: Sense of interconnectedness and pro-environmental behavior. International Journal of Transpersonal Studies, 30(1–2), 89–100. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-09637-009

Howell, A. J., Dopko, R. L., Passmore, H.-A., & Buro, K. (2011). Nature connectedness: Associations with well-being and mindfulness. Personality and Individual Differences, 51(2), 166–171. https://doi.org/10.1016/j.paid.2011.03.037

Kabat-Zinn, J. (2015). Mindfulness. Mindfulness, 6(6), 1481–1483.https://doi.org/10.1007/s12671-015-0456-x

Levesque, C., & Brown, K. W. (2007). Mindfulness as a moderator of the effect of implicit motivational self-concept on day-to-day behavioral motivation. Motivation & Emotion, 31(4), 284–299. https://doi.org/10.1007/s11031-007-9075-8

Markle, G. L. (2013). Pro-Environmental Behavior: Does It Matter How It’s Measured? Development and Validation of the Pro-Environmental Behavior Scale (PEBS). Human Ecology: An Interdisciplinary Journal, 41(6), 905–914. https://doi.org/10.1007/s10745-013-9614-8

Mayer, F. S., & Frantz, C. M. (2004). The connectedness to nature scale: A measure of individuals’ feeling in community with nature. Journal of Environmental Psychology, 24(4), 503–515. https://doi.org/10.1016/j.jenvp.2004.10.001

Tofighi, D., & MacKinnon, D. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43(3), 692–700. https://doi.org/10.3758/s13428-011-0076-x

Whitmarsh, L., & O’Neill, S. (2010). Green identity, green living? The role of pro-environmental self-identity in determining consistency across diverse pro-environmental behaviours. Journal of Environmental Psychology, 30(3), 305–314. https://doi.org/10.1016/j.jenvp.2010.01.003

M239ProjectDraft

Ross Enlow

11/5/2019

Introduction to your data set and questions of interest (this will help you draft your “Introduction” section of your

Summary

Connectedness to Nature as a Mediator in the Relationship Between Trait Mindfulness and Pro-Environmental Behavior

“Big Questions”

This study aims to answer the following questions:

Does Mindfulness significantly predict Pro-Environmental Behavior?

Does Connectedness to Nature significantly mediate the relationship between Mindfulness and Pro-Environment?

What facets of Mindfulness significantly predict facets of Pro-Environmental Behavior?

My Process:

Analyses

Distributions of Main Variables of Interest

PEBS as function of FFMQ

CNS as a function of FFMQ

PEBS as a function of CNS

PEBS as a function of CNS and FFMQ to test for mediation

Here we see that when FFMQ (Mindfulness) is controlled for, CNS still significantly predicts Pro-Environmental Behavior, while FFMQ (Mindfulness) no longer significantly predicts Pro-Environmental Behavior, suggesting a full mediation effect.

Here we attempt to use r to verify the mediation determined by causal steps

Causal Mediation Analysis: CMA, AVERAGE CAUSAL MEDIATION EFFECT: ACME, AVERAGE DIRECT EFFECT: ADE, TOTAL EFFECT

This suggests that there are significant average causal mediation effects and proportion mediated.

Here we observe that the model controlling for Mindfulness has a significantly less Residual Sum of Squares, suggesting this is the better model that accounts for more variance and suggests that CNS mediates the relationship between FFMQ and PEBS.

Exploratory Analysis of FFMQ Facets with PEBS Facets

FFMQ - Observation & PEBS FACETS

FFMQ - Describe & PEBS FACETS

FFMQ - Awareness & PEBS FACETS

FFMQ - Non-Judgement & PEBS FACETS

FFMQ - Non-Reactivity & PEBS FACETS

We will test for significance by performing ANOVA on Linear Models for FFMQ Observation with PEBS Conservation, Environmental Citizenship, Food, and Transportation, FFMQ Describe with PEBS Conservation, FFMQ Awareness with PEBS Conservation, and FFMQ Non-Reactivity with PEBS Conservation.

This suggests that the Observation facet of Mindfulness may be of greatest interest when trying to predcit Conservation based Pro-Environmental Behaviors

This suggests that the Observation facet of mindfulness best predicts Conservation and Environmental Citizenship

References

Amel, E. L., Manning, C. M., & Scott, B. A. (2009). Mindfulness and sustainable behavior: Pondering attention and awareness as means for increasing green behavior. Ecopsychology, 1(1), 14–25. https://doi.org/10.1089/eco.2008.0005

Aron, A., Aron, E. N., Tudor, M., & Nelson, G. (1991). Close relationships as including other in the self. Journal of Personality and Social Psychology, 60(2), 241–253. https://doi.org/10.1037/0022-3514.60.2.241

Baer, R. A., Smith, G. T., Hopkins, J., Krietemeyer, J., & Toney, L. (2006). Using self-report assessment methods to explore facets of mindfulness. Assessment, 13(1), 27–45. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=cmedm&AN=16443717

Baer, R. A., Smith, G. T., Lykins, E., Button, D., Krietemeyer, J., Sauer, S., … Williams, J. M. G. (2008). Construct validity of the Five Facet Mindfulness Questionnaire in meditating and nonmeditating samples. Assessment, 15(3), 329–342. https://doi.org/10.1177/1073191107313003

Barbaro, N., & Pickett, S. M. (2016). Mindfully green: Examining the effect of connectedness to nature on the relationship between mindfulness and engagement in pro-environmental behavior. Personality and Individual Differences, 93, 137–142. https://doi.org/10.1016/j.paid.2015.05.026

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

Black, D. S., Sussman, S., Johnson, C. A., & Milam, J. (2012). Trait mindfulness helps shield decision-making from translating into health-risk behavior. Journal of Adolescent Health, 51(6), 588–592. https://doi.org/10.1016/j.jadohealth.2012.03.011

Brower, M., Leon, W., & Schroeder, S. T. (2000). Consumer’s Guide to Effective Environmental Choices (Book). Electronic Green Journal, N.PAG. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=7435600

Cialdini, R. B., Brown, S. L., Lewis, B. P., Luce, C., & Neuberg, S. L. (1997). Reinterpreting the empathy–altruism relationship: When one into one equals oneness. Journal of Personality and Social Psychology, 73(3), 481–494. https://doi.org/10.1037/0022-3514.73.3.481

Hoot, R. E., & Friedman, H. (2011). Connectedness and environmental behavior: Sense of interconnectedness and pro-environmental behavior. International Journal of Transpersonal Studies, 30(1–2), 89–100. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-09637-009

Howell, A. J., Dopko, R. L., Passmore, H.-A., & Buro, K. (2011). Nature connectedness: Associations with well-being and mindfulness. Personality and Individual Differences, 51(2), 166–171. https://doi.org/10.1016/j.paid.2011.03.037

Kabat-Zinn, J. (2015). Mindfulness. Mindfulness, 6(6), 1481–1483.https://doi.org/10.1007/s12671-015-0456-x

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