Interaction Analyses
# interaction
InteractAnalyses <- subset(climate_labels, Condition != "Control")
InteractAnalyses$Condition <- as.factor(InteractAnalyses$Condition)
# Dummy coded variables
InteractAnalyses$DWS <- ifelse (InteractAnalyses$Condition=="Direct With Support", 1,0)
InteractAnalyses$IWS <- ifelse (InteractAnalyses$Condition== "Indirect With Support", 1,0)
# both conditions with support
InteractAnalyses$Support<-InteractAnalyses$DWS+InteractAnalyses$IWS
# both conditions with direct harm
InteractAnalyses$DNS <- ifelse(InteractAnalyses$Condition=="Direct Without Support", 1,0)
InteractAnalyses$DirectHarm<-InteractAnalyses$DNS+InteractAnalyses$DWS
#interaction
IN1_model <- lm(IN1 ~ Support*DirectHarm, data = InteractAnalyses)
summary(IN1_model)
##
## Call:
## lm(formula = IN1 ~ Support * DirectHarm, data = InteractAnalyses)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.03242 -0.95489 -0.03242 0.96758 2.45387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.54613 0.05350 47.594 < 2e-16 ***
## Support 0.40875 0.07575 5.396 7.84e-08 ***
## DirectHarm 0.01496 0.07566 0.198 0.843
## Support:DirectHarm 0.06257 0.10706 0.584 0.559
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.071 on 1598 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.04121, Adjusted R-squared: 0.03941
## F-statistic: 22.9 on 3 and 1598 DF, p-value: 1.635e-14
IN2_model <- lm(IN2 ~ Support*DirectHarm, data = InteractAnalyses)
summary(IN2_model)
##
## Call:
## lm(formula = IN2 ~ Support * DirectHarm, data = InteractAnalyses)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.15960 -0.79302 -0.09273 0.90727 2.23441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.76559 0.05577 49.588 < 2e-16 ***
## Support 0.32715 0.07897 4.143 3.61e-05 ***
## DirectHarm 0.02743 0.07887 0.348 0.728
## Support:DirectHarm 0.03944 0.11161 0.353 0.724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.117 on 1598 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.02412, Adjusted R-squared: 0.02228
## F-statistic: 13.16 on 3 and 1598 DF, p-value: 1.717e-08
DN1_model <- lm(DN1 ~ Support*DirectHarm, data = InteractAnalyses)
summary(DN1_model)
##
## Call:
## lm(formula = DN1 ~ Support * DirectHarm, data = InteractAnalyses)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5910 -0.5288 -0.3017 0.4913 2.6983
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.50873 0.05660 79.655 < 2e-16 ***
## Support 0.02009 0.08015 0.251 0.80207
## DirectHarm -0.20698 0.08005 -2.586 0.00981 **
## Support:DirectHarm 0.26918 0.11328 2.376 0.01760 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.133 on 1598 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.009149, Adjusted R-squared: 0.007289
## F-statistic: 4.918 on 3 and 1598 DF, p-value: 0.0021
DN2_model <- lm(DN2 ~ Support*DirectHarm, data = InteractAnalyses)
summary(DN2_model)
##
## Call:
## lm(formula = DN2 ~ Support * DirectHarm, data = InteractAnalyses)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4464 -1.2569 0.5536 0.7431 2.7431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.256858 0.069076 47.149 <2e-16 ***
## Support 0.104044 0.097810 1.064 0.288
## DirectHarm 0.082294 0.097688 0.842 0.400
## Support:DirectHarm 0.003188 0.138238 0.023 0.982
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 1598 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.002381, Adjusted R-squared: 0.0005082
## F-statistic: 1.271 on 3 and 1598 DF, p-value: 0.2826
DescN_model <- lm(DescN ~ Support*DirectHarm, data = InteractAnalyses)
summary(DescN_model)
##
## Call:
## lm(formula = DescN ~ Support * DirectHarm, data = InteractAnalyses)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.292 -18.292 2.048 17.048 58.584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.414 1.140 38.088 < 2e-16 ***
## Support 4.538 1.614 2.812 0.00498 **
## DirectHarm -1.998 1.612 -1.239 0.21547
## Support:DirectHarm 2.337 2.281 1.024 0.30577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.83 on 1598 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.01639, Adjusted R-squared: 0.01454
## F-statistic: 8.873 on 3 and 1598 DF, p-value: 7.832e-06
ReduceDrive_model <- lm(ReduceDrive ~ Support*DirectHarm, data = InteractAnalyses)
summary(ReduceDrive_model)
##
## Call:
## lm(formula = ReduceDrive ~ Support * DirectHarm, data = InteractAnalyses)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5711 -1.5062 -0.5062 1.4289 2.4938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.52120 0.06587 38.278 <2e-16 ***
## Support 0.02380 0.09321 0.255 0.798
## DirectHarm -0.01496 0.09315 -0.161 0.872
## Support:DirectHarm 0.04103 0.13177 0.311 0.756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.319 on 1599 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.0003481, Adjusted R-squared: -0.001527
## F-statistic: 0.1856 on 3 and 1599 DF, p-value: 0.9062
EV_model <- lm(EV ~ Support*DirectHarm, data = InteractAnalyses)
summary(EV_model)
##
## Call:
## lm(formula = EV ~ Support * DirectHarm, data = InteractAnalyses)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5700 -1.4888 -0.4888 1.4300 2.5112
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.52369 0.06640 38.006 <2e-16 ***
## Support 0.04631 0.09397 0.493 0.622
## DirectHarm -0.03491 0.09391 -0.372 0.710
## Support:DirectHarm 0.02102 0.13285 0.158 0.874
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.33 on 1599 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.0005573, Adjusted R-squared: -0.001318
## F-statistic: 0.2972 on 3 and 1599 DF, p-value: 0.8274
Culp1_model <- lm(Culp1 ~ Support*DirectHarm, data = InteractAnalyses)
summary(Culp1_model)
##
## Call:
## lm(formula = Culp1 ~ Support * DirectHarm, data = InteractAnalyses)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7905 -0.7175 0.2095 1.2095 2.3367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.667500 0.057940 46.039 <2e-16 ***
## Support 0.050000 0.081939 0.610 0.542
## DirectHarm -0.004158 0.081888 -0.051 0.960
## Support:DirectHarm 0.077182 0.115807 0.666 0.505
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.159 on 1598 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.001962, Adjusted R-squared: 8.816e-05
## F-statistic: 1.047 on 3 and 1598 DF, p-value: 0.3707
Culp2_model <- lm(Culp2 ~ Support*DirectHarm, data = InteractAnalyses)
summary(Culp2_model)
##
## Call:
## lm(formula = Culp2 ~ Support * DirectHarm, data = InteractAnalyses)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0474 -0.9175 0.0500 1.0500 2.1222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.91750 0.05760 50.655 <2e-16 ***
## Support 0.03250 0.08145 0.399 0.690
## DirectHarm -0.03969 0.08140 -0.488 0.626
## Support:DirectHarm 0.13708 0.11512 1.191 0.234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.152 on 1598 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.002967, Adjusted R-squared: 0.001095
## F-statistic: 1.585 on 3 and 1598 DF, p-value: 0.1911
Overall Summary
describe(climate_labels)
## vars n mean sd median trimmed mad min max range
## Duration 1 2012 245.35 327.86 177.0 195.48 85.99 16 8868 8852
## IN1 2 2003 2.73 1.08 3.0 2.71 1.48 1 5 4
## IN2 3 2003 2.92 1.12 3.0 2.92 1.48 1 5 4
## DN1 4 2003 4.49 1.11 4.0 4.52 1.48 1 7 6
## DN2 5 2002 3.36 1.38 4.0 3.37 1.48 1 6 5
## DescN 6 2003 43.93 23.48 46.0 44.01 28.17 0 100 100
## ReduceDrive 7 2005 2.55 1.30 2.0 2.44 1.48 1 5 4
## EV 8 2005 2.55 1.33 2.0 2.45 1.48 1 5 4
## Culp1 9 2004 2.74 1.16 3.0 2.71 1.48 1 5 4
## Culp2 10 2004 2.97 1.15 3.0 2.96 1.48 1 5 4
## Age 11 2000 41.13 13.56 38.0 40.10 13.34 18 88 70
## Gender 12 2001 1.52 0.53 2.0 1.50 1.48 1 3 2
## Gender_3_TEXT* 13 26 9.81 4.58 11.5 10.14 3.71 1 15 14
## Q48* 14 1998 3.01 4.05 1.0 2.05 0.00 1 14 13
## Race* 15 2001 21.35 6.75 25.0 22.32 0.00 1 29 28
## Race_7_TEXT* 16 22 8.09 4.30 8.5 8.00 5.19 1 16 15
## Politic 17 2000 3.36 1.71 3.0 3.27 1.48 1 7 6
## Income 18 1995 8.28 3.26 9.0 8.57 2.97 1 12 11
## Education 19 2001 4.55 1.24 5.0 4.66 1.48 1 6 5
## FinalComments* 20 585 198.61 91.20 197.0 201.36 81.54 1 375 374
## Condition* 21 2009 3.00 1.41 3.0 3.00 1.48 1 5 4
## skew kurtosis se
## Duration 14.76 331.70 7.31
## IN1 0.24 -0.65 0.02
## IN2 0.09 -0.77 0.03
## DN1 -0.28 0.45 0.02
## DN2 -0.06 -0.79 0.03
## DescN -0.06 -0.91 0.52
## ReduceDrive 0.40 -0.98 0.03
## EV 0.34 -1.09 0.03
## Culp1 0.21 -0.79 0.03
## Culp2 0.06 -0.83 0.03
## Age 0.62 -0.28 0.30
## Gender 0.21 -1.34 0.01
## Gender_3_TEXT* -0.62 -1.17 0.90
## Q48* 1.72 1.30 0.09
## Race* -1.28 0.10 0.15
## Race_7_TEXT* 0.15 -1.12 0.92
## Politic 0.37 -0.83 0.04
## Income -0.54 -0.96 0.07
## Education -0.72 -0.53 0.03
## FinalComments* -0.29 -0.52 3.77
## Condition* 0.00 -1.30 0.03
Time taken by participants
summary(climate_labels$Duration)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.0 129.0 177.0 245.3 260.0 8868.0
climate_labels$Duration_in_minutes <- climate_labels$Duration / 60
summary(climate_labels$Duration_in_minutes)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2667 2.1500 2.9500 4.0892 4.3333 147.8000
Outcome summary
describeBy(climate_labels$IN1, climate_labels$Condition)
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.55 1.01 2 2.52 1.48 1 5 4 0.35 -0.45 0.05
## ------------------------------------------------------------
## group: Direct With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 3.03 1.14 3 3.03 1.48 1 5 4 -0.04 -0.83 0.06
## ------------------------------------------------------------
## group: Direct Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.56 1.04 2 2.53 1.48 1 5 4 0.39 -0.45 0.05
## ------------------------------------------------------------
## group: Indirect With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 399 2.95 1.1 3 2.95 1.48 1 5 4 0.07 -0.8 0.05
## ------------------------------------------------------------
## group: Indirect Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.55 1.01 2 2.52 1.48 1 5 4 0.3 -0.41 0.05
describeBy(climate_labels$IN2, climate_labels$Condition)
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.8 1.08 3 2.79 1.48 1 5 4 0.15 -0.61 0.05
## ------------------------------------------------------------
## group: Direct With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 3.16 1.11 3 3.17 1.48 1 5 4 -0.15 -0.75 0.06
## ------------------------------------------------------------
## group: Direct Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.79 1.13 3 2.77 1.48 1 5 4 0.19 -0.74 0.06
## ------------------------------------------------------------
## group: Indirect With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 399 3.09 1.15 3 3.1 1.48 1 5 4 -0.06 -0.88 0.06
## ------------------------------------------------------------
## group: Indirect Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.77 1.08 3 2.74 1.48 1 5 4 0.32 -0.59 0.05
describeBy(climate_labels$DN1, climate_labels$Condition)
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 4.51 0.97 4 4.53 1.48 1 7 6 -0.25 0.92 0.05
## ------------------------------------------------------------
## group: Direct With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 4.59 1.16 5 4.63 1.48 1 7 6 -0.24 0.21 0.06
## ------------------------------------------------------------
## group: Direct Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 4.3 1.15 4 4.32 1.48 1 7 6 -0.2 0.29 0.06
## ------------------------------------------------------------
## group: Indirect With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 399 4.53 1.15 5 4.58 1.48 1 7 6 -0.35 0.22 0.06
## ------------------------------------------------------------
## group: Indirect Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 4.51 1.07 4 4.55 1.48 1 7 6 -0.33 0.65 0.05
describeBy(climate_labels$DN2, climate_labels$Condition)
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 400 3.38 1.38 4 3.4 1.48 1 6 5 -0.12 -0.75 0.07
## ------------------------------------------------------------
## group: Direct With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 3.45 1.39 4 3.47 1.48 1 6 5 -0.08 -0.82 0.07
## ------------------------------------------------------------
## group: Direct Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 3.34 1.44 4 3.35 1.48 1 6 5 -0.11 -0.94 0.07
## ------------------------------------------------------------
## group: Indirect With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 399 3.36 1.33 3 3.37 1.48 1 6 5 -0.02 -0.67 0.07
## ------------------------------------------------------------
## group: Indirect Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 3.26 1.37 3 3.26 1.48 1 6 5 0.02 -0.77 0.07
describeBy(climate_labels$DescN, climate_labels$Condition)
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 38.6 24.64 40 37.77 29.65 0 100 100 0.25 -0.95 1.23
## ------------------------------------------------------------
## group: Direct With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 48.29 22.36 51 48.89 22.24 0 100 100 -0.28 -0.76 1.12
## ------------------------------------------------------------
## group: Direct Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 41.42 23.8 40 41.02 29.65 0 100 100 0.09 -0.92 1.19
## ------------------------------------------------------------
## group: Indirect With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 399 47.95 21.58 50 48.62 22.24 0 100 100 -0.26 -0.65 1.08
## ------------------------------------------------------------
## group: Indirect Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 43.41 23.49 45 43.58 29.65 0 100 100 -0.02 -0.91 1.17
describeBy(climate_labels$ReduceDrive, climate_labels$Condition)
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 402 2.59 1.25 3 2.51 1.48 1 5 4 0.33 -0.9 0.06
## ------------------------------------------------------------
## group: Direct With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.57 1.33 2 2.46 1.48 1 5 4 0.4 -1.01 0.07
## ------------------------------------------------------------
## group: Direct Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.51 1.36 2 2.38 1.48 1 5 4 0.43 -1.1 0.07
## ------------------------------------------------------------
## group: Indirect With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 400 2.54 1.3 2 2.43 1.48 1 5 4 0.45 -0.89 0.07
## ------------------------------------------------------------
## group: Indirect Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.52 1.28 2 2.42 1.48 1 5 4 0.38 -1 0.06
describeBy(climate_labels$EV, climate_labels$Condition)
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 402 2.61 1.31 3 2.52 1.48 1 5 4 0.27 -1.11 0.07
## ------------------------------------------------------------
## group: Direct With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.56 1.37 2 2.45 1.48 1 5 4 0.35 -1.16 0.07
## ------------------------------------------------------------
## group: Direct Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.49 1.29 2 2.39 1.48 1 5 4 0.35 -1.06 0.06
## ------------------------------------------------------------
## group: Indirect With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 400 2.57 1.31 2 2.47 1.48 1 5 4 0.36 -1.02 0.07
## ------------------------------------------------------------
## group: Indirect Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.52 1.35 2 2.41 1.48 1 5 4 0.38 -1.13 0.07
describeBy(climate_labels$Culp1, climate_labels$Condition)
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 402 2.88 1.16 3 2.86 1.48 1 5 4 0.09 -0.84 0.06
## ------------------------------------------------------------
## group: Direct With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.79 1.14 3 2.78 1.48 1 5 4 0.11 -0.86 0.06
## ------------------------------------------------------------
## group: Direct Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.66 1.2 2 2.6 1.48 1 5 4 0.34 -0.83 0.06
## ------------------------------------------------------------
## group: Indirect With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 400 2.72 1.14 3 2.69 1.48 1 5 4 0.2 -0.75 0.06
## ------------------------------------------------------------
## group: Indirect Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 400 2.67 1.15 3 2.61 1.48 1 5 4 0.31 -0.63 0.06
describeBy(climate_labels$Culp2, climate_labels$Condition)
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 402 3.03 1.16 3 3.03 1.48 1 5 4 0.06 -0.86 0.06
## ------------------------------------------------------------
## group: Direct With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 3.05 1.16 3 3.06 1.48 1 5 4 -0.08 -0.82 0.06
## ------------------------------------------------------------
## group: Direct Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.88 1.17 3 2.86 1.48 1 5 4 0.11 -0.88 0.06
## ------------------------------------------------------------
## group: Indirect With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 400 2.95 1.11 3 2.95 1.48 1 5 4 0.07 -0.77 0.06
## ------------------------------------------------------------
## group: Indirect Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 400 2.92 1.17 3 2.9 1.48 1 5 4 0.15 -0.83 0.06
Condition-wise summary
#1.control
control<-filter(climate_labels,Condition == "Control")
describe(control)
## vars n mean sd median trimmed mad min max
## Duration 1 402 255.35 479.17 173.50 192.45 80.80 54.0 8868.0
## IN1 2 401 2.55 1.01 2.00 2.52 1.48 1.0 5.0
## IN2 3 401 2.80 1.08 3.00 2.79 1.48 1.0 5.0
## DN1 4 401 4.51 0.97 4.00 4.53 1.48 1.0 7.0
## DN2 5 400 3.38 1.38 4.00 3.40 1.48 1.0 6.0
## DescN 6 401 38.60 24.64 40.00 37.77 29.65 0.0 100.0
## ReduceDrive 7 402 2.59 1.25 3.00 2.51 1.48 1.0 5.0
## EV 8 402 2.61 1.31 3.00 2.52 1.48 1.0 5.0
## Culp1 9 402 2.88 1.16 3.00 2.86 1.48 1.0 5.0
## Culp2 10 402 3.03 1.16 3.00 3.03 1.48 1.0 5.0
## Age 11 400 40.25 12.52 38.50 39.29 14.08 19.0 86.0
## Gender 12 401 1.51 0.52 2.00 1.50 1.48 1.0 3.0
## Gender_3_TEXT* 13 5 3.00 1.58 3.00 3.00 1.48 1.0 5.0
## Q48* 14 401 2.25 2.59 1.00 1.65 0.00 1.0 10.0
## Race* 15 401 11.74 4.48 14.00 12.26 0.00 1.0 18.0
## Race_7_TEXT* 16 7 3.14 1.95 3.00 3.14 2.97 1.0 6.0
## Politic 17 400 3.29 1.71 3.00 3.19 1.48 1.0 7.0
## Income 18 398 8.25 3.25 9.00 8.51 4.45 1.0 12.0
## Education 19 401 4.54 1.21 5.00 4.64 1.48 1.0 6.0
## FinalComments* 20 106 43.56 21.46 45.00 44.15 22.98 1.0 81.0
## Condition* 21 402 1.00 0.00 1.00 1.00 0.00 1.0 1.0
## Duration_in_minutes 22 402 4.26 7.99 2.89 3.21 1.35 0.9 147.8
## range skew kurtosis se
## Duration 8814.0 14.77 257.48 23.90
## IN1 4.0 0.35 -0.45 0.05
## IN2 4.0 0.15 -0.61 0.05
## DN1 6.0 -0.25 0.92 0.05
## DN2 5.0 -0.12 -0.75 0.07
## DescN 100.0 0.25 -0.95 1.23
## ReduceDrive 4.0 0.33 -0.90 0.06
## EV 4.0 0.27 -1.11 0.07
## Culp1 4.0 0.09 -0.84 0.06
## Culp2 4.0 0.06 -0.86 0.06
## Age 67.0 0.64 -0.01 0.63
## Gender 2.0 0.20 -1.38 0.03
## Gender_3_TEXT* 4.0 0.00 -1.91 0.71
## Q48* 9.0 1.76 1.41 0.13
## Race* 17.0 -1.14 -0.19 0.22
## Race_7_TEXT* 5.0 0.18 -1.79 0.74
## Politic 6.0 0.42 -0.84 0.09
## Income 11.0 -0.45 -1.14 0.16
## Education 5.0 -0.70 -0.54 0.06
## FinalComments* 80.0 -0.27 -0.93 2.08
## Condition* 0.0 NaN NaN 0.00
## Duration_in_minutes 146.9 14.77 257.48 0.40
describeBy(climate_labels$Culp1, climate_labels$Condition)
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 402 2.88 1.16 3 2.86 1.48 1 5 4 0.09 -0.84 0.06
## ------------------------------------------------------------
## group: Direct With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.79 1.14 3 2.78 1.48 1 5 4 0.11 -0.86 0.06
## ------------------------------------------------------------
## group: Direct Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 401 2.66 1.2 2 2.6 1.48 1 5 4 0.34 -0.83 0.06
## ------------------------------------------------------------
## group: Indirect With Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 400 2.72 1.14 3 2.69 1.48 1 5 4 0.2 -0.75 0.06
## ------------------------------------------------------------
## group: Indirect Without Support
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 400 2.67 1.15 3 2.61 1.48 1 5 4 0.31 -0.63 0.06
#2. direct harm with support
direct_with_support<-filter(climate_labels,Condition == "Direct With Support")
describe(direct_with_support)
## vars n mean sd median trimmed mad min max
## Duration 1 402 232.64 192.29 179.00 194.56 91.92 50.00 1723.00
## IN1 2 401 3.03 1.14 3.00 3.03 1.48 1.00 5.00
## IN2 3 401 3.16 1.11 3.00 3.17 1.48 1.00 5.00
## DN1 4 401 4.59 1.16 5.00 4.63 1.48 1.00 7.00
## DN2 5 401 3.45 1.39 4.00 3.47 1.48 1.00 6.00
## DescN 6 401 48.29 22.36 51.00 48.89 22.24 0.00 100.00
## ReduceDrive 7 401 2.57 1.33 2.00 2.46 1.48 1.00 5.00
## EV 8 401 2.56 1.37 2.00 2.45 1.48 1.00 5.00
## Culp1 9 401 2.79 1.14 3.00 2.78 1.48 1.00 5.00
## Culp2 10 401 3.05 1.16 3.00 3.06 1.48 1.00 5.00
## Age 11 401 41.30 14.18 38.00 40.15 14.83 18.00 88.00
## Gender 12 401 1.51 0.52 1.00 1.50 0.00 1.00 3.00
## Gender_3_TEXT* 13 4 2.25 0.96 2.50 2.25 0.74 1.00 3.00
## Q48* 14 401 2.34 2.87 1.00 1.61 0.00 1.00 11.00
## Race* 15 401 12.90 4.06 15.00 13.42 0.00 1.00 19.00
## Race_7_TEXT* 16 2 1.50 0.71 1.50 1.50 0.74 1.00 2.00
## Politic 17 401 3.45 1.70 3.00 3.39 1.48 1.00 7.00
## Income 18 401 8.34 3.23 9.00 8.65 2.97 1.00 12.00
## Education 19 401 4.58 1.26 5.00 4.71 1.48 1.00 6.00
## FinalComments* 20 134 50.06 23.98 49.00 50.44 21.50 1.00 96.00
## Condition* 21 402 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## Duration_in_minutes 22 402 3.88 3.20 2.98 3.24 1.53 0.83 28.72
## range skew kurtosis se
## Duration 1673.00 3.63 18.93 9.59
## IN1 4.00 -0.04 -0.83 0.06
## IN2 4.00 -0.15 -0.75 0.06
## DN1 6.00 -0.24 0.21 0.06
## DN2 5.00 -0.08 -0.82 0.07
## DescN 100.00 -0.28 -0.76 1.12
## ReduceDrive 4.00 0.40 -1.01 0.07
## EV 4.00 0.35 -1.16 0.07
## Culp1 4.00 0.11 -0.86 0.06
## Culp2 4.00 -0.08 -0.82 0.06
## Age 70.00 0.66 -0.29 0.71
## Gender 2.00 0.19 -1.48 0.03
## Gender_3_TEXT* 2.00 -0.32 -2.08 0.48
## Q48* 10.00 1.90 2.02 0.14
## Race* 18.00 -1.24 0.15 0.20
## Race_7_TEXT* 1.00 0.00 -2.75 0.50
## Politic 6.00 0.28 -0.88 0.08
## Income 11.00 -0.58 -0.87 0.16
## Education 5.00 -0.74 -0.53 0.06
## FinalComments* 95.00 -0.17 -0.67 2.07
## Condition* 0.00 NaN NaN 0.00
## Duration_in_minutes 27.88 3.63 18.93 0.16
#3. indirect with support
indirect_with_support<-filter(climate_labels,Condition == "Indirect With Support")
describe(indirect_with_support)
## vars n mean sd median trimmed mad min max
## Duration 1 402 260.76 412.02 181.00 199.98 90.44 16.00 6998.00
## IN1 2 399 2.95 1.10 3.00 2.95 1.48 1.00 5.00
## IN2 3 399 3.09 1.15 3.00 3.10 1.48 1.00 5.00
## DN1 4 399 4.53 1.15 5.00 4.58 1.48 1.00 7.00
## DN2 5 399 3.36 1.33 3.00 3.37 1.48 1.00 6.00
## DescN 6 399 47.95 21.58 50.00 48.62 22.24 0.00 100.00
## ReduceDrive 7 400 2.54 1.30 2.00 2.43 1.48 1.00 5.00
## EV 8 400 2.57 1.31 2.00 2.47 1.48 1.00 5.00
## Culp1 9 400 2.72 1.14 3.00 2.69 1.48 1.00 5.00
## Culp2 10 400 2.95 1.11 3.00 2.95 1.48 1.00 5.00
## Age 11 398 41.59 13.39 40.00 40.81 14.83 19.00 81.00
## Gender 12 398 1.53 0.55 2.00 1.51 1.48 1.00 3.00
## Gender_3_TEXT* 13 9 4.89 2.57 5.00 4.89 2.97 1.00 8.00
## Q48* 14 398 2.84 3.37 1.00 2.14 0.00 1.00 11.00
## Race* 15 398 11.96 3.77 14.00 12.43 0.00 1.00 17.00
## Race_7_TEXT* 16 2 1.50 0.71 1.50 1.50 0.74 1.00 2.00
## Politic 17 398 3.28 1.69 3.00 3.18 1.48 1.00 7.00
## Income 18 398 8.32 3.32 9.00 8.64 2.97 1.00 12.00
## Education 19 398 4.48 1.24 5.00 4.60 1.48 1.00 6.00
## FinalComments* 20 100 36.60 18.14 35.50 36.75 18.53 1.00 71.00
## Condition* 21 402 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## Duration_in_minutes 22 402 4.35 6.87 3.02 3.33 1.51 0.27 116.63
## range skew kurtosis se
## Duration 6982.00 11.85 179.85 20.55
## IN1 4.00 0.07 -0.80 0.05
## IN2 4.00 -0.06 -0.88 0.06
## DN1 6.00 -0.35 0.22 0.06
## DN2 5.00 -0.02 -0.67 0.07
## DescN 100.00 -0.26 -0.65 1.08
## ReduceDrive 4.00 0.45 -0.89 0.07
## EV 4.00 0.36 -1.02 0.07
## Culp1 4.00 0.20 -0.75 0.06
## Culp2 4.00 0.07 -0.77 0.06
## Age 62.00 0.52 -0.38 0.67
## Gender 2.00 0.34 -1.00 0.03
## Gender_3_TEXT* 7.00 -0.13 -1.68 0.86
## Q48* 10.00 1.49 0.52 0.17
## Race* 16.00 -1.16 -0.12 0.19
## Race_7_TEXT* 1.00 0.00 -2.75 0.50
## Politic 6.00 0.47 -0.78 0.08
## Income 11.00 -0.59 -0.91 0.17
## Education 5.00 -0.73 -0.50 0.06
## FinalComments* 70.00 -0.05 -0.81 1.81
## Condition* 0.00 NaN NaN 0.00
## Duration_in_minutes 116.37 11.85 179.85 0.34
#4. direct without support
direct_without_support<-filter(climate_labels, Condition == "Direct Without Support")
describe(direct_without_support)
## vars n mean sd median trimmed mad min max
## Duration 1 401 235.83 185.79 179.00 199.03 90.44 34.00 1286.00
## IN1 2 401 2.56 1.04 2.00 2.53 1.48 1.00 5.00
## IN2 3 401 2.79 1.13 3.00 2.77 1.48 1.00 5.00
## DN1 4 401 4.30 1.15 4.00 4.32 1.48 1.00 7.00
## DN2 5 401 3.34 1.44 4.00 3.35 1.48 1.00 6.00
## DescN 6 401 41.42 23.80 40.00 41.02 29.65 0.00 100.00
## ReduceDrive 7 401 2.51 1.36 2.00 2.38 1.48 1.00 5.00
## EV 8 401 2.49 1.29 2.00 2.39 1.48 1.00 5.00
## Culp1 9 401 2.66 1.20 2.00 2.60 1.48 1.00 5.00
## Culp2 10 401 2.88 1.17 3.00 2.86 1.48 1.00 5.00
## Age 11 401 41.42 13.43 38.00 40.43 14.83 19.00 82.00
## Gender 12 401 1.51 0.53 1.00 1.49 0.00 1.00 3.00
## Gender_3_TEXT* 13 6 3.33 1.63 3.50 3.33 2.22 1.00 5.00
## Q48* 14 400 2.06 2.10 1.00 1.57 0.00 1.00 8.00
## Race* 15 401 11.99 3.82 14.00 12.41 0.00 1.00 17.00
## Race_7_TEXT* 16 7 3.29 1.80 3.00 3.29 1.48 1.00 6.00
## Politic 17 401 3.40 1.74 3.00 3.31 1.48 1.00 7.00
## Income 18 399 8.27 3.29 9.00 8.57 2.97 1.00 12.00
## Education 19 401 4.55 1.24 5.00 4.66 1.48 1.00 6.00
## FinalComments* 20 134 53.81 26.43 53.00 54.49 28.17 1.00 101.00
## Condition* 21 401 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## Duration_in_minutes 22 401 3.93 3.10 2.98 3.32 1.51 0.57 21.43
## range skew kurtosis se
## Duration 1252.00 2.69 8.97 9.28
## IN1 4.00 0.39 -0.45 0.05
## IN2 4.00 0.19 -0.74 0.06
## DN1 6.00 -0.20 0.29 0.06
## DN2 5.00 -0.11 -0.94 0.07
## DescN 100.00 0.09 -0.92 1.19
## ReduceDrive 4.00 0.43 -1.10 0.07
## EV 4.00 0.35 -1.06 0.06
## Culp1 4.00 0.34 -0.83 0.06
## Culp2 4.00 0.11 -0.88 0.06
## Age 63.00 0.58 -0.41 0.67
## Gender 2.00 0.27 -1.27 0.03
## Gender_3_TEXT* 4.00 -0.21 -1.86 0.67
## Q48* 7.00 1.75 1.56 0.11
## Race* 16.00 -1.09 -0.28 0.19
## Race_7_TEXT* 5.00 0.23 -1.69 0.68
## Politic 6.00 0.30 -0.84 0.09
## Income 11.00 -0.59 -0.92 0.16
## Education 5.00 -0.70 -0.61 0.06
## FinalComments* 100.00 -0.24 -0.88 2.28
## Condition* 0.00 NaN NaN 0.00
## Duration_in_minutes 20.87 2.69 8.97 0.15
#5. indirect without support
indirect_without_support<-filter(climate_labels, Condition == "Indirect Without Support")
describe(indirect_without_support)
## vars n mean sd median trimmed mad min max
## Duration 1 402 233.71 225.79 175.00 191.43 80.06 56.00 2792.00
## IN1 2 401 2.55 1.01 2.00 2.52 1.48 1.00 5.00
## IN2 3 401 2.77 1.08 3.00 2.74 1.48 1.00 5.00
## DN1 4 401 4.51 1.07 4.00 4.55 1.48 1.00 7.00
## DN2 5 401 3.26 1.37 3.00 3.26 1.48 1.00 6.00
## DescN 6 401 43.41 23.49 45.00 43.58 29.65 0.00 100.00
## ReduceDrive 7 401 2.52 1.28 2.00 2.42 1.48 1.00 5.00
## EV 8 401 2.52 1.35 2.00 2.41 1.48 1.00 5.00
## Culp1 9 400 2.67 1.15 3.00 2.61 1.48 1.00 5.00
## Culp2 10 400 2.92 1.17 3.00 2.90 1.48 1.00 5.00
## Age 11 400 41.08 14.21 38.00 39.93 13.34 18.00 82.00
## Gender 12 400 1.53 0.51 2.00 1.53 0.00 1.00 3.00
## Gender_3_TEXT* 13 2 1.50 0.71 1.50 1.50 0.74 1.00 2.00
## Q48* 14 398 2.60 3.32 1.00 1.80 0.00 1.00 12.00
## Race* 15 400 8.36 3.29 10.00 8.67 0.00 1.00 14.00
## Race_7_TEXT* 16 4 2.50 1.29 2.50 2.50 1.48 1.00 4.00
## Politic 17 400 3.36 1.74 3.00 3.27 1.48 1.00 7.00
## Income 18 399 8.22 3.21 9.00 8.48 2.97 1.00 12.00
## Education 19 400 4.58 1.24 5.00 4.70 1.48 1.00 6.00
## FinalComments* 20 111 42.86 22.31 39.00 42.71 23.72 1.00 86.00
## Condition* 21 402 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## Duration_in_minutes 22 402 3.90 3.76 2.92 3.19 1.33 0.93 46.53
## range skew kurtosis se
## Duration 2736.0 5.55 46.78 11.26
## IN1 4.0 0.30 -0.41 0.05
## IN2 4.0 0.32 -0.59 0.05
## DN1 6.0 -0.33 0.65 0.05
## DN2 5.0 0.02 -0.77 0.07
## DescN 100.0 -0.02 -0.91 1.17
## ReduceDrive 4.0 0.38 -1.00 0.06
## EV 4.0 0.38 -1.13 0.07
## Culp1 4.0 0.31 -0.63 0.06
## Culp2 4.0 0.15 -0.83 0.06
## Age 64.0 0.67 -0.41 0.71
## Gender 2.0 0.00 -1.72 0.03
## Gender_3_TEXT* 1.0 0.00 -2.75 0.50
## Q48* 11.0 1.80 1.61 0.17
## Race* 13.0 -0.96 -0.31 0.16
## Race_7_TEXT* 3.0 0.00 -2.08 0.65
## Politic 6.0 0.40 -0.84 0.09
## Income 11.0 -0.49 -1.00 0.16
## Education 5.0 -0.75 -0.51 0.06
## FinalComments* 85.0 0.09 -0.84 2.12
## Condition* 0.0 NaN NaN 0.00
## Duration_in_minutes 45.6 5.55 46.78 0.19
Linear models
climate_labels$Condition <- factor(climate_labels$Condition,
levels = c("Control",
"Direct With Support",
"Indirect With Support",
"Direct Without Support",
"Indirect Without Support"))
# Linear models
# here is the outcome for the measure: IN1
# to what extent if at all would you think people in your community disapprove of using fossil fuels for personal vehicles?
describe(climate_labels$IN1)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2003 2.73 1.08 3 2.71 1.48 1 5 4 0.24 -0.65 0.02
tapply(climate_labels$IN1, climate_labels$Condition, summary)
## $Control
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 2.000 2.551 3.000 5.000 1
##
## $`Direct With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 3.032 4.000 5.000 1
##
## $`Indirect With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 2.955 4.000 5.000 3
##
## $`Direct Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.000 2.561 3.000 5.000
##
## $`Indirect Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 2.000 2.546 3.000 5.000 1
model_in1 <- lm(IN1 ~ Condition, data = climate_labels)
summary(model_in1)
##
## Call:
## lm(formula = IN1 ~ Condition, data = climate_labels)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.03242 -0.56110 -0.03242 0.45387 2.45387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.551122 0.052936 48.193 < 2e-16 ***
## ConditionDirect With Support 0.481297 0.074863 6.429 1.60e-10 ***
## ConditionIndirect With Support 0.403765 0.074956 5.387 8.02e-08 ***
## ConditionDirect Without Support 0.009975 0.074863 0.133 0.894
## ConditionIndirect Without Support -0.004988 0.074863 -0.067 0.947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.06 on 1998 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.04046, Adjusted R-squared: 0.03854
## F-statistic: 21.06 on 4 and 1998 DF, p-value: < 2.2e-16
# here is the outcome for the measure: IN2
# to what extent would you think people in your community feel that people should transition away from using fossil fuels?
describe(climate_labels$IN2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2003 2.92 1.12 3 2.92 1.48 1 5 4 0.09 -0.77 0.03
tapply(climate_labels$IN2, climate_labels$Condition, summary)
## $Control
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 2.798 4.000 5.000 1
##
## $`Direct With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 2.00 3.00 3.16 4.00 5.00 1
##
## $`Indirect With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 3.093 4.000 5.000 3
##
## $`Direct Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.793 4.000 5.000
##
## $`Indirect Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 2.766 3.000 5.000 1
model_in2 <- lm(IN2 ~ Condition, data = climate_labels)
summary(model_in2)
##
## Call:
## lm(formula = IN2 ~ Condition, data = climate_labels)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1596 -0.7980 0.2020 0.9073 2.2344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.798005 0.055431 50.477 < 2e-16 ***
## ConditionDirect With Support 0.361596 0.078392 4.613 4.23e-06 ***
## ConditionIndirect With Support 0.294727 0.078490 3.755 0.000178 ***
## ConditionDirect Without Support -0.004988 0.078392 -0.064 0.949277
## ConditionIndirect Without Support -0.032419 0.078392 -0.414 0.679247
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.11 on 1998 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.0226, Adjusted R-squared: 0.02064
## F-statistic: 11.55 on 4 and 1998 DF, p-value: 2.857e-09
# here is the outcome for the measure: DN1
# would you think the number of people in your community who disapprove of using fossil fuels for personal vehicles will increase, decrease or stay the same in the near future?
describe(climate_labels$DN1)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2003 4.49 1.11 4 4.52 1.48 1 7 6 -0.28 0.45 0.02
tapply(climate_labels$DN1, climate_labels$Condition, summary)
## $Control
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 4.000 4.000 4.506 5.000 7.000 1
##
## $`Direct With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 4.000 5.000 4.591 5.000 7.000 1
##
## $`Indirect With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 4.000 5.000 4.529 5.000 7.000 3
##
## $`Direct Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 4.000 4.302 5.000 7.000
##
## $`Indirect Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 4.000 4.000 4.509 5.000 7.000 1
model_dn1 <- lm(DN1 ~ Condition, data = climate_labels)
summary(model_dn1)
##
## Call:
## lm(formula = DN1 ~ Condition, data = climate_labels)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5910 -0.5288 -0.3017 0.4938 2.6983
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.506234 0.055064 81.836 < 2e-16 ***
## ConditionDirect With Support 0.084788 0.077873 1.089 0.27637
## ConditionIndirect With Support 0.022588 0.077970 0.290 0.77208
## ConditionDirect Without Support -0.204489 0.077873 -2.626 0.00871 **
## ConditionIndirect Without Support 0.002494 0.077873 0.032 0.97446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 1998 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.007816, Adjusted R-squared: 0.005829
## F-statistic: 3.935 on 4 and 1998 DF, p-value: 0.003472
# here is the outcome for the measure: DN2
# how likely or unlikely do you think using fossil fuels for personal vehicles will become “a thing of the past” over the next 10-15 years?
describe(climate_labels$DN2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2002 3.36 1.38 4 3.37 1.48 1 6 5 -0.06 -0.79 0.03
tapply(climate_labels$DN2, climate_labels$Condition, summary)
## $Control
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 2.00 4.00 3.38 4.00 6.00 2
##
## $`Direct With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 4.000 3.446 4.000 6.000 1
##
## $`Indirect With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 3.361 4.000 6.000 3
##
## $`Direct Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 4.000 3.339 4.000 6.000
##
## $`Indirect Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 3.257 4.000 6.000 1
model_dn2 <- lm(DN2 ~ Condition, data = climate_labels)
summary(model_dn2)
##
## Call:
## lm(formula = DN2 ~ Condition, data = climate_labels)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4464 -1.3392 0.5536 0.7431 2.7431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.38000 0.06913 48.891 <2e-16 ***
## ConditionDirect With Support 0.06638 0.09771 0.679 0.497
## ConditionIndirect With Support -0.01910 0.09783 -0.195 0.845
## ConditionDirect Without Support -0.04085 0.09771 -0.418 0.676
## ConditionIndirect Without Support -0.12314 0.09771 -1.260 0.208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 1997 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.001979, Adjusted R-squared: -2.005e-05
## F-statistic: 0.99 on 4 and 1997 DF, p-value: 0.4117
# here is the outcome for the measure: DescN
# what percent of voters from your municipality would you think believe that the use of fossil fuels causes childhood asthma in your area and harms future generations due to climate change?
describe(climate_labels$DescN)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2003 43.93 23.48 46 44.01 28.17 0 100 100 -0.06 -0.91 0.52
tapply(climate_labels$DescN, climate_labels$Condition, summary)
## $Control
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 20.0 40.0 38.6 60.0 100.0 1
##
## $`Direct With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 30.00 51.00 48.29 65.00 100.00 1
##
## $`Indirect With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 30.00 50.00 47.95 65.00 100.00 3
##
## $`Direct Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 20.00 40.00 41.42 60.00 100.00
##
## $`Indirect Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 25.00 45.00 43.41 60.00 100.00 1
model_descn <- lm(DescN ~ Condition, data = climate_labels)
summary(model_descn)
##
## Call:
## lm(formula = DescN ~ Condition, data = climate_labels)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.292 -18.603 1.708 17.048 61.397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.603 1.159 33.319 < 2e-16 ***
## ConditionDirect With Support 9.688 1.638 5.913 3.94e-09 ***
## ConditionIndirect With Support 9.349 1.641 5.699 1.39e-08 ***
## ConditionDirect Without Support 2.813 1.638 1.717 0.08617 .
## ConditionIndirect Without Support 4.810 1.638 2.936 0.00336 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.2 on 1998 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.02546, Adjusted R-squared: 0.02351
## F-statistic: 13.05 on 4 and 1998 DF, p-value: 1.703e-10
# here is the outcome for the measure: ReduceDrive
# to what extent if at all would you like to reduce your use of fossil fuels for driving?
describe(climate_labels$ReduceDrive)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2005 2.55 1.3 2 2.44 1.48 1 5 4 0.4 -0.98 0.03
tapply(climate_labels$ReduceDrive, climate_labels$Condition, summary)
## $Control
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.592 3.000 5.000
##
## $`Direct With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 2.000 2.571 4.000 5.000 1
##
## $`Indirect With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 2.000 2.545 3.000 5.000 2
##
## $`Direct Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.506 4.000 5.000
##
## $`Indirect Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 2.000 2.521 4.000 5.000 1
model_reducedrive<- lm(ReduceDrive ~ Condition, data = climate_labels)
summary(model_reducedrive)
##
## Call:
## lm(formula = ReduceDrive ~ Condition, data = climate_labels)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5920 -1.5062 -0.5062 1.4080 2.4938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.59204 0.06511 39.808 <2e-16 ***
## ConditionDirect With Support -0.02097 0.09214 -0.228 0.820
## ConditionIndirect With Support -0.04704 0.09220 -0.510 0.610
## ConditionDirect Without Support -0.08581 0.09214 -0.931 0.352
## ConditionIndirect Without Support -0.07084 0.09214 -0.769 0.442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.306 on 2000 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.0005813, Adjusted R-squared: -0.001418
## F-statistic: 0.2908 on 4 and 2000 DF, p-value: 0.8841
# here is the outcome for the measure: EV
# to what extent if at all would you be interested in switching to a fully electric vehicle?
describe(climate_labels$EV)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2005 2.55 1.33 2 2.45 1.48 1 5 4 0.34 -1.09 0.03
tapply(climate_labels$EV, climate_labels$Condition, summary)
## $Control
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 3.000 2.612 4.000 5.000
##
## $`Direct With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 2.000 2.556 4.000 5.000 1
##
## $`Indirect With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 1.00 2.00 2.57 4.00 5.00 2
##
## $`Direct Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.489 3.000 5.000
##
## $`Indirect Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 2.000 2.524 4.000 5.000 1
model_ev<- lm(EV ~ Condition, data = climate_labels)
summary(model_ev)
##
## Call:
## lm(formula = EV ~ Condition, data = climate_labels)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6119 -1.4888 -0.4888 1.3881 2.5112
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.61194 0.06616 39.482 <2e-16 ***
## ConditionDirect With Support -0.05583 0.09362 -0.596 0.551
## ConditionIndirect With Support -0.04194 0.09367 -0.448 0.654
## ConditionDirect Without Support -0.12316 0.09362 -1.316 0.188
## ConditionIndirect Without Support -0.08825 0.09362 -0.943 0.346
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.326 on 2000 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.0009931, Adjusted R-squared: -0.001005
## F-statistic: 0.497 on 4 and 2000 DF, p-value: 0.7379
# here is the outcome for the measure: Culp1
# to what extent if at all would you think drivers (including yourself) are responsible for any harms that come from using fossil fuels?
describe(climate_labels$Culp1)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2004 2.74 1.16 3 2.71 1.48 1 5 4 0.21 -0.79 0.03
tapply(climate_labels$Culp1, climate_labels$Condition, summary)
## $Control
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.876 4.000 5.000
##
## $`Direct With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 2.791 4.000 5.000 1
##
## $`Indirect With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 2.717 4.000 5.000 2
##
## $`Direct Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.000 2.663 4.000 5.000
##
## $`Indirect Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 2.667 3.000 5.000 2
model_culp1<- lm(Culp1 ~ Condition, data = climate_labels)
summary(model_culp1)
##
## Call:
## lm(formula = Culp1 ~ Condition, data = climate_labels)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8756 -0.7905 0.1244 1.1244 2.3367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.87562 0.05777 49.780 < 2e-16 ***
## ConditionDirect With Support -0.08510 0.08175 -1.041 0.29799
## ConditionIndirect With Support -0.15812 0.08180 -1.933 0.05336 .
## ConditionDirect Without Support -0.21228 0.08175 -2.597 0.00948 **
## ConditionIndirect Without Support -0.20812 0.08180 -2.544 0.01102 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.158 on 1999 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.004847, Adjusted R-squared: 0.002856
## F-statistic: 2.434 on 4 and 1999 DF, p-value: 0.04546
# here is the outcome for the measure: Culp2
# to what extent if at all would you think driving a fossil fuel vehicle harms others?
describe(climate_labels$Culp2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2004 2.97 1.15 3 2.96 1.48 1 5 4 0.06 -0.83 0.03
tapply(climate_labels$Culp2, climate_labels$Condition, summary)
## $Control
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.035 4.000 5.000
##
## $`Direct With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 3.047 4.000 5.000 1
##
## $`Indirect With Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 2.00 3.00 2.95 4.00 5.00 2
##
## $`Direct Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.878 4.000 5.000
##
## $`Indirect Without Support`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 2.917 4.000 5.000 2
model_culp2<- lm(Culp2 ~ Condition, data = climate_labels)
summary(model_culp2)
##
## Call:
## lm(formula = Culp2 ~ Condition, data = climate_labels)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0474 -0.9500 0.0500 0.9652 2.1222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.03483 0.05751 52.770 <2e-16 ***
## ConditionDirect With Support 0.01256 0.08138 0.154 0.8774
## ConditionIndirect With Support -0.08483 0.08143 -1.042 0.2977
## ConditionDirect Without Support -0.15702 0.08138 -1.929 0.0538 .
## ConditionIndirect Without Support -0.11733 0.08143 -1.441 0.1498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.153 on 1999 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.003271, Adjusted R-squared: 0.001276
## F-statistic: 1.64 on 4 and 1999 DF, p-value: 0.1615
Pairwise comparisons
#here are the pair-wise comparisons for IN1
pairwise_in1 <- emmeans(model_in1, pairwise ~ Condition)
summary(pairwise_in1)
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## Control 2.55 0.0529 1998 2.45 2.65
## Direct With Support 3.03 0.0529 1998 2.93 3.14
## Indirect With Support 2.95 0.0531 1998 2.85 3.06
## Direct Without Support 2.56 0.0529 1998 2.46 2.66
## Indirect Without Support 2.55 0.0529 1998 2.44 2.65
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Control - Direct With Support -0.48130 0.0749 1998 -6.429
## Control - Indirect With Support -0.40376 0.0750 1998 -5.387
## Control - Direct Without Support -0.00998 0.0749 1998 -0.133
## Control - Indirect Without Support 0.00499 0.0749 1998 0.067
## Direct With Support - Indirect With Support 0.07753 0.0750 1998 1.034
## Direct With Support - Direct Without Support 0.47132 0.0749 1998 6.296
## Direct With Support - Indirect Without Support 0.48628 0.0749 1998 6.496
## Indirect With Support - Direct Without Support 0.39379 0.0750 1998 5.254
## Indirect With Support - Indirect Without Support 0.40875 0.0750 1998 5.453
## Direct Without Support - Indirect Without Support 0.01496 0.0749 1998 0.200
## p.value
## <.0001
## <.0001
## 0.9999
## 1.0000
## 0.8394
## <.0001
## <.0001
## <.0001
## <.0001
## 0.9996
##
## P value adjustment: tukey method for comparing a family of 5 estimates
#here are the pair-wise comparisons for IN2
pairwise_in2 <- emmeans(model_in2, pairwise ~ Condition)
summary(pairwise_in2)
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## Control 2.80 0.0554 1998 2.69 2.91
## Direct With Support 3.16 0.0554 1998 3.05 3.27
## Indirect With Support 3.09 0.0556 1998 2.98 3.20
## Direct Without Support 2.79 0.0554 1998 2.68 2.90
## Indirect Without Support 2.77 0.0554 1998 2.66 2.87
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Control - Direct With Support -0.36160 0.0784 1998 -4.613
## Control - Indirect With Support -0.29473 0.0785 1998 -3.755
## Control - Direct Without Support 0.00499 0.0784 1998 0.064
## Control - Indirect Without Support 0.03242 0.0784 1998 0.414
## Direct With Support - Indirect With Support 0.06687 0.0785 1998 0.852
## Direct With Support - Direct Without Support 0.36658 0.0784 1998 4.676
## Direct With Support - Indirect Without Support 0.39402 0.0784 1998 5.026
## Indirect With Support - Direct Without Support 0.29971 0.0785 1998 3.819
## Indirect With Support - Indirect Without Support 0.32715 0.0785 1998 4.168
## Direct Without Support - Indirect Without Support 0.02743 0.0784 1998 0.350
## p.value
## <.0001
## 0.0017
## 1.0000
## 0.9939
## 0.9141
## <.0001
## <.0001
## 0.0013
## 0.0003
## 0.9968
##
## P value adjustment: tukey method for comparing a family of 5 estimates
#here are the pair-wise comparisons for DN1
pairwise_dn1 <- emmeans(model_dn1, pairwise ~ Condition)
summary(pairwise_dn1)
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## Control 4.51 0.0551 1998 4.40 4.61
## Direct With Support 4.59 0.0551 1998 4.48 4.70
## Indirect With Support 4.53 0.0552 1998 4.42 4.64
## Direct Without Support 4.30 0.0551 1998 4.19 4.41
## Indirect Without Support 4.51 0.0551 1998 4.40 4.62
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Control - Direct With Support -0.08479 0.0779 1998 -1.089
## Control - Indirect With Support -0.02259 0.0780 1998 -0.290
## Control - Direct Without Support 0.20449 0.0779 1998 2.626
## Control - Indirect Without Support -0.00249 0.0779 1998 -0.032
## Direct With Support - Indirect With Support 0.06220 0.0780 1998 0.798
## Direct With Support - Direct Without Support 0.28928 0.0779 1998 3.715
## Direct With Support - Indirect Without Support 0.08229 0.0779 1998 1.057
## Indirect With Support - Direct Without Support 0.22708 0.0780 1998 2.912
## Indirect With Support - Indirect Without Support 0.02009 0.0780 1998 0.258
## Direct Without Support - Indirect Without Support -0.20698 0.0779 1998 -2.658
## p.value
## 0.8124
## 0.9985
## 0.0661
## 1.0000
## 0.9313
## 0.0020
## 0.8286
## 0.0298
## 0.9990
## 0.0608
##
## P value adjustment: tukey method for comparing a family of 5 estimates
#here are the pair-wise comparisons for DN2
pairwise_dn2 <- emmeans(model_dn2, pairwise ~ Condition)
summary(pairwise_dn2)
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## Control 3.38 0.0691 1997 3.24 3.52
## Direct With Support 3.45 0.0690 1997 3.31 3.58
## Indirect With Support 3.36 0.0692 1997 3.23 3.50
## Direct Without Support 3.34 0.0690 1997 3.20 3.47
## Indirect Without Support 3.26 0.0690 1997 3.12 3.39
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Control - Direct With Support -0.0664 0.0977 1997 -0.679
## Control - Indirect With Support 0.0191 0.0978 1997 0.195
## Control - Direct Without Support 0.0408 0.0977 1997 0.418
## Control - Indirect Without Support 0.1231 0.0977 1997 1.260
## Direct With Support - Indirect With Support 0.0855 0.0978 1997 0.874
## Direct With Support - Direct Without Support 0.1072 0.0976 1997 1.098
## Direct With Support - Indirect Without Support 0.1895 0.0976 1997 1.941
## Indirect With Support - Direct Without Support 0.0218 0.0978 1997 0.222
## Indirect With Support - Indirect Without Support 0.1040 0.0978 1997 1.064
## Direct Without Support - Indirect Without Support 0.0823 0.0976 1997 0.843
## p.value
## 0.9609
## 0.9997
## 0.9936
## 0.7156
## 0.9064
## 0.8076
## 0.2960
## 0.9995
## 0.8249
## 0.9172
##
## P value adjustment: tukey method for comparing a family of 5 estimates
#here are the pair-wise comparisons for DescN
pairwise_descn <- emmeans(model_descn, pairwise ~ Condition)
summary(pairwise_descn)
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## Control 38.6 1.16 1998 36.3 40.9
## Direct With Support 48.3 1.16 1998 46.0 50.6
## Indirect With Support 48.0 1.16 1998 45.7 50.2
## Direct Without Support 41.4 1.16 1998 39.1 43.7
## Indirect Without Support 43.4 1.16 1998 41.1 45.7
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Control - Direct With Support -9.688 1.64 1998 -5.913
## Control - Indirect With Support -9.349 1.64 1998 -5.699
## Control - Direct Without Support -2.813 1.64 1998 -1.717
## Control - Indirect Without Support -4.810 1.64 1998 -2.936
## Direct With Support - Indirect With Support 0.339 1.64 1998 0.207
## Direct With Support - Direct Without Support 6.875 1.64 1998 4.196
## Direct With Support - Indirect Without Support 4.878 1.64 1998 2.977
## Indirect With Support - Direct Without Support 6.536 1.64 1998 3.984
## Indirect With Support - Indirect Without Support 4.538 1.64 1998 2.766
## Direct Without Support - Indirect Without Support -1.998 1.64 1998 -1.219
## p.value
## <.0001
## <.0001
## 0.4237
## 0.0278
## 0.9996
## 0.0003
## 0.0246
## 0.0007
## 0.0453
## 0.7403
##
## P value adjustment: tukey method for comparing a family of 5 estimates
#here are the pair-wise comparisons for reducedrive
pairwise_reducedrive <- emmeans(model_reducedrive, pairwise ~ Condition)
summary(pairwise_reducedrive)
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## Control 2.59 0.0651 2000 2.46 2.72
## Direct With Support 2.57 0.0652 2000 2.44 2.70
## Indirect With Support 2.54 0.0653 2000 2.42 2.67
## Direct Without Support 2.51 0.0652 2000 2.38 2.63
## Indirect Without Support 2.52 0.0652 2000 2.39 2.65
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Control - Direct With Support 0.0210 0.0921 2000 0.228
## Control - Indirect With Support 0.0470 0.0922 2000 0.510
## Control - Direct Without Support 0.0858 0.0921 2000 0.931
## Control - Indirect Without Support 0.0708 0.0921 2000 0.769
## Direct With Support - Indirect With Support 0.0261 0.0923 2000 0.283
## Direct With Support - Direct Without Support 0.0648 0.0922 2000 0.703
## Direct With Support - Indirect Without Support 0.0499 0.0922 2000 0.541
## Indirect With Support - Direct Without Support 0.0388 0.0923 2000 0.420
## Indirect With Support - Indirect Without Support 0.0238 0.0923 2000 0.258
## Direct Without Support - Indirect Without Support -0.0150 0.0922 2000 -0.162
## p.value
## 0.9994
## 0.9864
## 0.8848
## 0.9395
## 0.9986
## 0.9558
## 0.9830
## 0.9935
## 0.9990
## 0.9998
##
## P value adjustment: tukey method for comparing a family of 5 estimates
#here are the pair-wise comparisons for ev
pairwise_ev <- emmeans(model_ev, pairwise ~ Condition)
summary(pairwise_ev)
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## Control 2.61 0.0662 2000 2.48 2.74
## Direct With Support 2.56 0.0662 2000 2.43 2.69
## Indirect With Support 2.57 0.0663 2000 2.44 2.70
## Direct Without Support 2.49 0.0662 2000 2.36 2.62
## Indirect Without Support 2.52 0.0662 2000 2.39 2.65
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Control - Direct With Support 0.0558 0.0936 2000 0.596
## Control - Indirect With Support 0.0419 0.0937 2000 0.448
## Control - Direct Without Support 0.1232 0.0936 2000 1.316
## Control - Indirect Without Support 0.0882 0.0936 2000 0.943
## Direct With Support - Indirect With Support -0.0139 0.0937 2000 -0.148
## Direct With Support - Direct Without Support 0.0673 0.0937 2000 0.719
## Direct With Support - Indirect Without Support 0.0324 0.0937 2000 0.346
## Indirect With Support - Direct Without Support 0.0812 0.0937 2000 0.867
## Indirect With Support - Indirect Without Support 0.0463 0.0937 2000 0.494
## Direct Without Support - Indirect Without Support -0.0349 0.0937 2000 -0.373
## p.value
## 0.9757
## 0.9917
## 0.6815
## 0.8802
## 0.9999
## 0.9522
## 0.9969
## 0.9091
## 0.9879
## 0.9959
##
## P value adjustment: tukey method for comparing a family of 5 estimates
#here are the pair-wise comparisons for Culp1
pairwise_culp1 <- emmeans(model_culp1, pairwise ~ Condition)
summary(pairwise_culp1)
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## Control 2.88 0.0578 1999 2.76 2.99
## Direct With Support 2.79 0.0578 1999 2.68 2.90
## Indirect With Support 2.72 0.0579 1999 2.60 2.83
## Direct Without Support 2.66 0.0578 1999 2.55 2.78
## Indirect Without Support 2.67 0.0579 1999 2.55 2.78
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Control - Direct With Support 0.08510 0.0817 1999 1.041
## Control - Indirect With Support 0.15812 0.0818 1999 1.933
## Control - Direct Without Support 0.21228 0.0817 1999 2.597
## Control - Indirect Without Support 0.20812 0.0818 1999 2.544
## Direct With Support - Indirect With Support 0.07302 0.0818 1999 0.892
## Direct With Support - Direct Without Support 0.12718 0.0818 1999 1.555
## Direct With Support - Indirect Without Support 0.12302 0.0818 1999 1.503
## Indirect With Support - Direct Without Support 0.05416 0.0818 1999 0.662
## Indirect With Support - Indirect Without Support 0.05000 0.0819 1999 0.611
## Direct Without Support - Indirect Without Support -0.00416 0.0818 1999 -0.051
## p.value
## 0.8362
## 0.3000
## 0.0713
## 0.0815
## 0.8999
## 0.5268
## 0.5606
## 0.9645
## 0.9735
## 1.0000
##
## P value adjustment: tukey method for comparing a family of 5 estimates
#here are the pair-wise comparisons for Culp2
pairwise_culp2 <- emmeans(model_culp2, pairwise ~ Condition)
summary(pairwise_culp2)
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## Control 3.03 0.0575 1999 2.92 3.15
## Direct With Support 3.05 0.0576 1999 2.93 3.16
## Indirect With Support 2.95 0.0577 1999 2.84 3.06
## Direct Without Support 2.88 0.0576 1999 2.76 2.99
## Indirect Without Support 2.92 0.0577 1999 2.80 3.03
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Control - Direct With Support -0.0126 0.0814 1999 -0.154
## Control - Indirect With Support 0.0848 0.0814 1999 1.042
## Control - Direct Without Support 0.1570 0.0814 1999 1.929
## Control - Indirect Without Support 0.1173 0.0814 1999 1.441
## Direct With Support - Indirect With Support 0.0974 0.0815 1999 1.195
## Direct With Support - Direct Without Support 0.1696 0.0814 1999 2.082
## Direct With Support - Indirect Without Support 0.1299 0.0815 1999 1.594
## Indirect With Support - Direct Without Support 0.0722 0.0815 1999 0.886
## Indirect With Support - Indirect Without Support 0.0325 0.0815 1999 0.399
## Direct Without Support - Indirect Without Support -0.0397 0.0815 1999 -0.487
## p.value
## 0.9999
## 0.8359
## 0.3020
## 0.6013
## 0.7543
## 0.2281
## 0.5014
## 0.9022
## 0.9947
## 0.9886
##
## P value adjustment: tukey method for comparing a family of 5 estimates
Correlation matrix
cor_matrix <- cor(climate_labels[, c("IN1", "IN2", "DN1","DN2","ReduceDrive", "EV", "Culp1", "Culp2")], use = "pairwise.complete.obs")
print(cor_matrix)
## IN1 IN2 DN1 DN2 ReduceDrive EV
## IN1 1.0000000 0.7330038 0.3403338 0.4634391 0.4565967 0.4519696
## IN2 0.7330038 1.0000000 0.3340303 0.5058141 0.5105959 0.5093250
## DN1 0.3403338 0.3340303 1.0000000 0.2999791 0.2483679 0.2509037
## DN2 0.4634391 0.5058141 0.2999791 1.0000000 0.4994589 0.5320793
## ReduceDrive 0.4565967 0.5105959 0.2483679 0.4994589 1.0000000 0.7358545
## EV 0.4519696 0.5093250 0.2509037 0.5320793 0.7358545 1.0000000
## Culp1 0.4211577 0.4570611 0.1857102 0.4533616 0.6061753 0.5600057
## Culp2 0.4313957 0.4680638 0.2390210 0.4723404 0.6428699 0.6085404
## Culp1 Culp2
## IN1 0.4211577 0.4313957
## IN2 0.4570611 0.4680638
## DN1 0.1857102 0.2390210
## DN2 0.4533616 0.4723404
## ReduceDrive 0.6061753 0.6428699
## EV 0.5600057 0.6085404
## Culp1 1.0000000 0.7488125
## Culp2 0.7488125 1.0000000
# correlation visualization
ggcorrplot(cor_matrix,
method = "circle",
type = "lower",
lab = TRUE,
lab_size = 4,
colors = c("#6D9EC1", "white", "#E46726"),
title = "Correlation Matrix",
ggtheme = ggplot2::theme_minimal()
)
