Conditions
Biochar (Clear Condition)
#Time spent reading introductory study instructions (in seconds)
F$Instructions_Page_Submit
## [1] 1.084 0.869 1.047 7.554 3.597 0.820 0.978 2.335 1.594
## [10] 0.710 4.565 0.704 1.123 0.999 1.174 0.984 1.240 6.820
## [19] 1.549 22.875 2.854 3.607 8.761 18.619 1.898 51.922 527.461
## [28] 21.885 1.231 1.652 1.046 1.507 0.969 11.484 3.283 2.745
## [37] 1.265 1.277 1.323 4.152 4.689 3.576 4.476 4.071 1.101
## [46] 1.488 1.845 12.399 0.944 2.443
# Time spent reading biochar (clear) description (in seconds)
F$BIO_Clear_Page_Submit
## [1] 1.098 NA 1.643 NA 12.253 0.893 1.976 1.791 NA
## [10] 0.865 1.251 NA NA NA NA NA 1.417 18.495
## [19] 20.876 NA NA 24.701 NA 26.765 23.329 1.104 305.836
## [28] NA NA 1.099 4.917 NA NA 3.701 9.757 NA
## [37] NA NA NA NA 10.042 NA NA 14.179 NA
## [46] 36.961 NA 24.530 NA NA
# Attention Check #1: "The climate change method I just read about was: (1 = biochar, 2 = biofuel, 3 = enhanced weathering, 4 = wind energy)
table(F$BIO_Clear_ATN)
##
## 1 2 3 4
## 11 7 3 3
F$ATN_BioClear <- NA
F$ATN_BioClear[F$BIO_Clear_ATN == 1] <- 'Biochar'
F$ATN_BioClear[F$BIO_Clear_ATN == 2] <- 'Biofuel'
F$ATN_BioClear[F$BIO_Clear_ATN == 3] <- 'Enhanced Weathering'
F$ATN_BioClear[F$BIO_Clear_ATN == 4] <- 'Wind Energy'
describe(F$ATN_BioClear)
## F$ATN_BioClear
## n missing distinct
## 24 26 4
##
## Value Biochar Biofuel Enhanced Weathering
## Frequency 11 7 3
## Proportion 0.458 0.292 0.125
##
## Value Wind Energy
## Frequency 3
## Proportion 0.125
# Time spend responding to attention check #1
describe(F$BIO_Clear_ATN_TIME_Page.Submit, na.rm = TRUE)
##
## NULL
# Attention Check #2 (Qualitative, Open Text Response) "In your own words, please describe the climate change method you read about in this study. Include as many details as possible."
describe(F$BIO_Clear_ATN2)
## F$BIO_Clear_ATN2
## n missing distinct
## 24 26 24
##
## lowest : A solution that mixes animal and plant materials to break down into charcoal Animal and plant matter converted into oxygen Because it deacreases the carbon fuels Biochar Biochar is a form of fuel or charcoal that is obtained from burning plants and other organic materials in the absence of oxygen. This creates a more stable form of carbon that can be stored underground long-term.
## highest: It's about the turning tides if doing wat we wanna do about all of it. It's what we should be doing however our government isn't there yet Kwje lqej qlwne peiidi e uuue jqjhww Trying to make it with less energy as possible and doing it the safest way Wind study
## Electric study
## Gas study
## Solar panel study
# Time spend responding to attention check #2
describe(F$BIO_Clear_ATN2_TIME_Page.Submit)
## F$BIO_Clear_ATN2_TIME_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 26 24 26 1 39.22 50.87 5.835 6.839
## .25 .50 .75 .90 .95
## 11.337 18.707 24.971 62.774 80.337
##
## lowest : 4.252 5.753 6.081 7.597 8.057
## highest: 29.401 53.801 71.747 83.200 457.383
##
## Value 4 6 8 10 11 12 13 15 18 19 20
## Frequency 1 2 2 1 2 1 1 1 2 2 1
## Proportion 0.038 0.077 0.077 0.038 0.077 0.038 0.038 0.038 0.077 0.077 0.038
##
## Value 22 24 25 29 54 72 83 457
## Frequency 1 1 2 2 1 1 1 1
## Proportion 0.038 0.038 0.077 0.077 0.038 0.038 0.038 0.038
##
## For the frequency table, variable is rounded to the nearest 1
# Naturalness
F$N1_BioClear <- as.numeric(F$Naturalness_BIO_30)
F$N2R_BioClear <- as.numeric(100 - F$Naturalness_BIO_31)
F$N3R_BioClear <- as.numeric(100 - F$Naturalness_BIO_35)
F$N4R_BioClear <- as.numeric(100- F$Naturalness_BIO_36)
hist(F$N1_BioClear)
hist(F$N2R_BioClear)
hist(F$N3R_BioClear)
hist(F$N4R_BioClear)
F$NatScore_BioClear <- rowMeans(F [, c( "N1_BioClear" , "N2R_BioClear", "N3R_BioClear", "N4R_BioClear")], na.rm=TRUE)
describe(F$NatScore_BioClear)
## F$NatScore_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 21 0.998 41.91 15.83 25.00 26.27
## .25 .50 .75 .90 .95
## 31.38 36.38 52.19 64.15 65.39
##
## lowest : 25.00 29.25 31.00 31.50 32.50, highest: 57.75 62.75 64.75 65.50 66.00
sd(F$NatScore_BioClear, na.rm = TRUE)
## [1] 13.88106
F$NatScale_BioClear <- data.frame(F$N1_BioClear, F$N2R_BioClear, F$N3R_BioClear, F$N4R_BioClear)
describe(F$NatScale_BioClear)
## F$NatScale_BioClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.N1_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 19 0.995 70.25 29.77 23.30 26.50
## .25 .50 .75 .90 .95
## 61.50 73.00 92.25 100.00 100.00
##
## lowest : 11 23 25 30 50, highest: 89 91 96 99 100
##
## Value 11 23 25 30 50 57 63 65 68 69 73
## Frequency 1 1 1 1 1 1 1 1 2 1 2
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.083 0.042 0.083
##
## Value 75 79 82 89 91 96 99 100
## Frequency 1 1 1 1 1 1 1 4
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.167
## --------------------------------------------------------------------------------
## F.N2R_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 20 0.997 32.04 30.86 0.0 0.9
## .25 .50 .75 .90 .95
## 12.5 23.5 46.0 75.4 76.0
##
## lowest : 0 3 9 11 13, highest: 67 69 74 76 87
##
## Value 0 3 9 11 13 15 18 19 21 22 25
## Frequency 3 1 1 1 1 1 1 1 1 1 1
## Proportion 0.125 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 27 32 34 39 67 69 74 76 87
## Frequency 1 2 1 1 1 1 1 2 1
## Proportion 0.042 0.083 0.042 0.042 0.042 0.042 0.042 0.083 0.042
## --------------------------------------------------------------------------------
## F.N3R_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 16 0.983 25.62 26.5 0.00 0.00
## .25 .50 .75 .90 .95
## 8.25 21.00 35.00 64.20 72.80
##
## lowest : 0 11 13 17 20, highest: 48 60 66 74 75
##
## Value 0 11 13 17 20 21 22 26 30 34 38
## Frequency 6 1 2 1 1 2 1 2 1 1 1
## Proportion 0.250 0.042 0.083 0.042 0.042 0.083 0.042 0.083 0.042 0.042 0.042
##
## Value 48 60 66 74 75
## Frequency 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042
## --------------------------------------------------------------------------------
## F.N4R_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 21 0.998 39.71 32.68 0.00 3.30
## .25 .50 .75 .90 .95
## 17.75 36.00 59.25 82.00 88.40
##
## lowest : 0 11 15 17 18, highest: 68 75 85 89 93
## --------------------------------------------------------------------------------
# Fluency
F$Fluency_BioClear <- as.numeric(F$Fluency_BIO_30)
hist(F$Fluency_BioClear)
sd(F$Fluency_BioClear, na.rm = TRUE)
## [1] 27.38371
# Understanding
F$Und_BioClear <- as.numeric(F$Familiarity_BIO_33)
describe(F$Und_BioClear)
## F$Und_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 19 0.995 66.5 30.91 24.45 27.30
## .25 .50 .75 .90 .95
## 43.25 75.00 81.25 100.00 100.00
##
## lowest : 9 24 27 28 34, highest: 80 81 82 94 100
##
## Value 9 24 27 28 34 35 46 62 64 69 74
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 75 78 79 80 81 82 94 100
## Frequency 2 1 1 2 1 1 1 4
## Proportion 0.083 0.042 0.042 0.083 0.042 0.042 0.042 0.167
hist(F$Und_BioClear)
# Familiarity
F$Fam_BioClear <- as.numeric(F$Familiarity_BIO_31)
hist(F$Fam_BioClear)
# Risk
F$R1_BioClear <- as.numeric(F$Risk_BIO_30)
F$R2_BioClear <- as.numeric(F$Risk_BIO_31)
F$R3_BioClear <- as.numeric(F$Risk_BIO_32)
hist(F$R1_BioClear)
hist(F$R2_BioClear)
hist(F$R3_BioClear)
F$RiskScore_BioClear <- rowMeans(F [, c( "R1_BioClear" , "R2_BioClear", "R3_BioClear")], na.rm=TRUE)
describe(F$RiskScore_BioClear)
## F$RiskScore_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 22 0.999 61.81 28.61 25.97 33.33
## .25 .50 .75 .90 .95
## 42.67 61.67 82.50 92.13 99.15
##
## lowest : 13.33333 24.66667 33.33333 33.66667 40.66667
## highest: 83.00000 86.66667 87.00000 94.33333 100.00000
sd(F$RiskScore_BioClear, na.rm = TRUE)
## [1] 24.46765
F$RiskScale_BioClear <- data.frame(F$R1_BioClear, F$R2_BioClear, F$R3_BioClear)
describe(F$RiskScale_BioClear)
## F$RiskScale_BioClear
##
## 3 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.R1_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 18 0.997 61.46 35.55 1.8 13.5
## .25 .50 .75 .90 .95
## 46.5 71.5 86.0 95.1 99.4
##
## lowest : 0 12 17 34 36, highest: 85 86 93 96 100
##
## Value 0 12 17 34 36 50 51 64 70 73 76
## Frequency 2 1 1 1 1 2 2 1 1 1 1
## Proportion 0.083 0.042 0.042 0.042 0.042 0.083 0.083 0.042 0.042 0.042 0.042
##
## Value 78 81 85 86 93 96 100
## Frequency 1 1 1 3 1 1 2
## Proportion 0.042 0.042 0.042 0.125 0.042 0.042 0.083
## --------------------------------------------------------------------------------
## F.R2_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 20 0.998 56.08 40.13 0.90 8.70
## .25 .50 .75 .90 .95
## 26.00 71.00 85.25 94.70 99.25
##
## lowest : 0 6 15 17 29, highest: 86 91 94 95 100
##
## Value 0 6 15 17 29 30 32 49 51 68 74
## Frequency 2 1 2 1 1 1 1 1 1 1 1
## Proportion 0.083 0.042 0.083 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 75 78 81 85 86 91 94 95 100
## Frequency 2 1 1 1 1 1 1 1 2
## Proportion 0.083 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.083
## --------------------------------------------------------------------------------
## F.R3_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 17 0.994 67.88 31.33 20.00 22.10
## .25 .50 .75 .90 .95
## 56.25 73.00 86.00 100.00 100.00
##
## lowest : 13 20 27 33 36, highest: 82 84 92 96 100
##
## Value 13 20 27 33 36 63 67 69 71 73 80
## Frequency 1 2 1 1 1 1 1 2 1 2 2
## Proportion 0.042 0.083 0.042 0.042 0.042 0.042 0.042 0.083 0.042 0.083 0.083
##
## Value 81 82 84 92 96 100
## Frequency 1 1 1 1 1 4
## Proportion 0.042 0.042 0.042 0.042 0.042 0.167
## --------------------------------------------------------------------------------
# Benefit
F$B1_BioClear <- as.numeric(F$Benefit_BIO_40)
F$B2_BioClear <- as.numeric(F$Benefit_BIO_42)
F$B3_BioClear <- as.numeric(F$Benefit_BIO_43)
F$B4_BioClear <- as.numeric(F$Benefit_BIO_45)
hist(F$B1_BioClear)
hist(F$B2_BioClear)
hist(F$B3_BioClear)
hist(F$B4_BioClear)
F$BenScore_BioClear <- rowMeans(F [, c( "B1_BioClear" , "B2_BioClear", "B3_BioClear", "B4_BioClear")], na.rm=TRUE)
describe(F$BenScore_BioClear)
## F$BenScore_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 22 0.998 71.31 26.03 23.00 45.98
## .25 .50 .75 .90 .95
## 63.06 71.50 90.38 99.78 100.00
##
## lowest : 12.50 19.25 44.25 50.00 59.25, highest: 88.75 95.25 95.50 99.25 100.00
sd(F$BenScore_BioClear, na.rm = TRUE)
## [1] 23.49448
F$BenScale_BioClear <- data.frame(F$B1_BioClear, F$B2_BioClear, F$B3_BioClear, F$B4_BioClear)
describe(F$BenScale_BioClear)
## F$BenScale_BioClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.B1_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 17 0.99 67.5 32.27 16.90 23.50
## .25 .50 .75 .90 .95
## 50.00 76.50 85.25 100.00 100.00
##
## lowest : 12 16 22 27 31, highest: 81 84 85 86 100
##
## Value 12 16 22 27 31 50 55 61 71 72 74
## Frequency 1 1 1 1 1 2 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.083 0.042 0.042 0.042 0.042 0.042
##
## Value 79 81 84 85 86 100
## Frequency 2 1 1 2 1 5
## Proportion 0.083 0.042 0.042 0.083 0.042 0.208
## --------------------------------------------------------------------------------
## F.B2_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 18 0.984 71.83 32.38 15.90 24.90
## .25 .50 .75 .90 .95
## 50.75 82.50 94.75 100.00 100.00
##
## lowest : 11 15 21 34 35, highest: 85 90 91 93 100
##
## Value 11 15 21 34 35 50 51 65 71 81 82
## Frequency 1 1 1 1 1 1 1 1 1 1 2
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.083
##
## Value 83 84 85 90 91 93 100
## Frequency 1 1 1 1 1 1 6
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.250
## --------------------------------------------------------------------------------
## F.B3_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 16 0.983 74.08 27.72 18.70 38.80
## .25 .50 .75 .90 .95
## 69.00 79.00 94.75 100.00 100.00
##
## lowest : 9 16 34 50 51, highest: 82 84 85 93 100
##
## Value 9 16 34 50 51 63 71 75 77 78 80
## Frequency 1 1 1 1 1 1 2 1 2 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.083 0.042 0.083 0.042 0.042
##
## Value 82 84 85 93 100
## Frequency 2 1 1 1 6
## Proportion 0.083 0.042 0.042 0.042 0.250
## --------------------------------------------------------------------------------
## F.B4_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 18 0.994 71.83 29.04 24.00 25.80
## .25 .50 .75 .90 .95
## 65.00 76.00 96.25 100.00 100.00
##
## lowest : 18 24 30 48 50, highest: 85 96 97 99 100
##
## Value 18 24 30 48 50 70 71 73 74 75 77
## Frequency 1 2 1 1 1 1 1 2 1 1 1
## Proportion 0.042 0.083 0.042 0.042 0.042 0.042 0.042 0.083 0.042 0.042 0.042
##
## Value 79 82 85 96 97 99 100
## Frequency 2 1 1 1 1 1 4
## Proportion 0.083 0.042 0.042 0.042 0.042 0.042 0.167
## --------------------------------------------------------------------------------
# Support
F$S1_BioClear <- as.numeric(F$Support_BIO_40)
F$S2_BioClear <- as.numeric(F$Support_BIO_42)
F$S3_BioClear <- as.numeric(F$Support_BIO_43)
F$S4_BioClear <- as.numeric(F$Support_BIO_45)
hist(F$S1_BioClear)
hist(F$S2_BioClear)
hist(F$S3_BioClear)
hist(F$S4_BioClear)
F$SupScore_BioClear <- rowMeans(F [, c( "S1_BioClear" , "S2_BioClear", "S3_BioClear", "S4_BioClear")], na.rm=TRUE)
describe(F$SupScore_BioClear)
## F$SupScore_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 21 0.996 72.47 29.92 10.88 38.98
## .25 .50 .75 .90 .95
## 62.69 78.75 94.44 100.00 100.00
##
## lowest : 3.75 6.75 34.25 50.00 52.00, highest: 90.75 93.75 96.50 97.75 100.00
sd(F$SupScore_BioClear, na.rm = TRUE)
## [1] 27.54634
F$SupScale_BioClear <- data.frame(F$S1_BioClear, F$S2_BioClear, F$S3_BioClear, F$S4_BioClear)
describe(F$SupScale_BioClear)
## F$SupScale_BioClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.S1_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 17 0.984 70.92 32.57 10.20 35.50
## .25 .50 .75 .90 .95
## 48.25 81.00 97.75 100.00 100.00
##
## lowest : 1 6 34 39 43, highest: 83 84 88 97 100
##
## Value 1 6 34 39 43 50 68 70 74 78 81
## Frequency 1 1 1 1 2 1 1 1 1 1 2
## Proportion 0.042 0.042 0.042 0.042 0.083 0.042 0.042 0.042 0.042 0.042 0.083
##
## Value 82 83 84 88 97 100
## Frequency 1 1 1 1 1 6
## Proportion 0.042 0.042 0.042 0.042 0.042 0.250
## --------------------------------------------------------------------------------
## F.S2_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 16 0.99 74.62 26.92 13.25 45.10
## .25 .50 .75 .90 .95
## 74.25 80.50 89.00 100.00 100.00
##
## lowest : 1 8 43 50 57, highest: 82 85 88 92 100
##
## Value 1 8 43 50 57 72 75 76 77 80 81
## Frequency 1 1 1 1 1 1 2 2 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.083 0.083 0.042 0.042 0.042
##
## Value 82 85 88 92 100
## Frequency 1 2 2 1 5
## Proportion 0.042 0.083 0.083 0.042 0.208
## --------------------------------------------------------------------------------
## F.S3_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 15 0.963 72.88 33.71 5.65 20.70
## .25 .50 .75 .90 .95
## 59.25 82.00 100.00 100.00 100.00
##
## lowest : 4 15 34 50 51, highest: 83 87 91 94 100
##
## Value 4 15 34 50 51 62 71 72 78 81 83
## Frequency 2 1 1 1 1 1 1 2 1 1 1
## Proportion 0.083 0.042 0.042 0.042 0.042 0.042 0.042 0.083 0.042 0.042 0.042
##
## Value 87 91 94 100
## Frequency 1 1 1 8
## Proportion 0.042 0.042 0.042 0.333
## --------------------------------------------------------------------------------
## F.S4_BioClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 17 0.984 71.46 33.96 9.00 16.50
## .25 .50 .75 .90 .95
## 58.25 82.50 98.50 100.00 100.00
##
## lowest : 0 9 34 40 50, highest: 84 89 92 98 100
##
## Value 0 9 34 40 50 61 68 75 78 80 82
## Frequency 1 2 1 1 1 1 1 1 1 1 1
## Proportion 0.042 0.083 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 83 84 89 92 98 100
## Frequency 2 1 1 1 1 6
## Proportion 0.083 0.042 0.042 0.042 0.042 0.250
## --------------------------------------------------------------------------------
Biochar (Blurred Condition)
#Attention Checks & Timing
F$BIO_Blurred_Page.Submit
## [1] NA 6.786 NA 1.301 NA NA NA NA 4.770 NA
## [11] NA 10.201 13.219 23.802 3.597 23.801 NA NA NA 37.404
## [21] 49.877 NA 23.899 NA NA NA NA 38.664 6.085 NA
## [31] NA 12.100 14.820 NA NA 39.289 0.889 0.760 1.475 17.664
## [41] NA 11.300 3.079 NA 4.202 NA 1.588 NA 2.036 12.255
F$BIO_Blurred_ATN
## [1] NA 1 NA 4 NA NA NA NA 2 NA NA 1 2 1 2 1 NA NA NA 1 1 NA 4 NA NA
## [26] NA NA 1 3 NA NA 3 2 NA NA 1 2 4 4 1 NA 1 2 NA 1 NA 3 NA 4 1
F$BIO_Blurred_ATN_TIME_Page.Submit
## [1] NA 5.093 NA 8.233 NA NA NA NA 2.238 NA
## [11] NA 6.958 4.720 11.882 5.159 7.265 NA NA NA 2.602
## [21] 5.809 NA 8.680 NA NA NA NA 6.968 7.972 NA
## [31] NA 9.901 8.941 NA NA 7.994 6.272 4.184 6.753 7.250
## [41] NA 3.901 11.313 NA 7.508 NA 24.021 NA 20.544 3.499
F$BIO_Blurred_ATN2
## [1] NA
## [2] "Its getting bad "
## [3] NA
## [4] "The wind energy is a mindful"
## [5] NA
## [6] NA
## [7] NA
## [8] NA
## [9] "How are these still a lot as much in advance of my life since the first one of those days when you are a lot and"
## [10] NA
## [11] NA
## [12] "I could hardly see it it wouldn't let me enlarge it "
## [13] "I can not "
## [14] "Biochar changes from mass to energy"
## [15] "It was very hard to understand "
## [16] "Today's get hotter than usual in the summer and in the winter it don't last as long but some of the days are colder than usual"
## [17] NA
## [18] NA
## [19] NA
## [20] "They take organic matter from plants and animals and convert it to carbon."
## [21] "Taking the biomass from animals and plants, and burning away the non-carbon products, and then storing the rest underground in a form of Charcoal"
## [22] NA
## [23] "It was about wind energy which is a fairly new concept but makes sense that it would work this way."
## [24] NA
## [25] NA
## [26] NA
## [27] NA
## [28] "The breakdown and storage of carbon. I don't know how else to explain it "
## [29] "I don’t remember "
## [30] NA
## [31] NA
## [32] "I don't really know "
## [33] "Its about animal and carbon"
## [34] NA
## [35] NA
## [36] "None "
## [37] "Very cool "
## [38] "The climate of this communication and I am sure I could be of help in any time to talk to the person to work in and I am sure I could be of help "
## [39] "the climate change method that occurred in the study is a serious sign that things are changing drastically"
## [40] "It's very hard to explain "
## [41] NA
## [42] "It's innovative that will help materials being reused"
## [43] "Climate change is effecting the world drastically "
## [44] NA
## [45] "Everything from start to finish is good for me and it different from one side to another "
## [46] NA
## [47] "I didn’t catch the name of the company "
## [48] NA
## [49] "It cool to me"
## [50] "Sjeiejebfisnjfjsjsowkeowkketijsnd xjsifneovnxnvI Djd Diane Jens cjaisbcjsbdisjnc c"
F$BIO_Clear_ATN2_TIME_Page.Submit
## [1] NA 11.863 NA 7.597 NA NA NA NA 5.753
## [10] NA NA 24.630 8.057 29.401 15.351 20.158 NA NA
## [19] NA 25.085 29.265 NA 83.200 NA NA NA NA
## [28] 457.383 12.994 NA NA 11.288 18.465 NA NA 11.484
## [37] 6.081 10.233 71.747 23.700 NA 53.801 18.230 NA 21.505
## [46] NA 18.949 NA 4.252 19.200
# Naturalness
F$N1_BioBlur <- as.numeric(F$NaturalnessBLUR_BIO_30)
F$N2R_BioBlur <- as.numeric(100 - F$NaturalnessBLUR_BIO_31)
F$N3R_BioBlur <- as.numeric(100 - F$NaturalnessBLUR_BIO_35)
F$N4R_BioBlur <- as.numeric(100 - F$NaturalnessBLUR_BIO_36)
hist(F$N1_BioBlur)
hist(F$N2R_BioBlur)
hist(F$N3R_BioBlur)
hist(F$N4R_BioBlur)
F$NatScore_BioBlur <- rowMeans(F [, c( "N1_BioBlur" , "N2R_BioBlur", "N3R_BioBlur", "N4R_BioBlur")], na.rm=TRUE)
describe(F$NatScore_BioBlur)
## F$NatScore_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 23 0.999 40.53 16.2 25.00 25.38
## .25 .50 .75 .90 .95
## 29.00 40.50 48.88 54.00 61.12
##
## lowest : 7.00 25.00 25.75 27.25 27.50, highest: 50.50 51.00 57.00 62.50 80.50
sd(F$NatScore_BioBlur, na.rm = TRUE)
## [1] 14.63836
F$NatScale_BioBlur <- data.frame(F$N1_BioBlur, F$N2R_BioBlur, F$N3R_BioBlur, F$N4R_BioBlur)
describe(F$NatScale_BioBlur)
## F$NatScale_BioBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.N1_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.993 67.38 29.48 25.75 37.50
## .25 .50 .75 .90 .95
## 49.25 70.00 88.25 100.00 100.00
##
## lowest : 8 22 37 38 43, highest: 79 86 89 94 100
## --------------------------------------------------------------------------------
## F.N2R_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.998 28.88 30.51 0.00 0.50
## .25 .50 .75 .90 .95
## 8.50 21.50 43.50 61.00 89.25
##
## lowest : 0 1 3 8 10, highest: 50 53 69 96 100
## --------------------------------------------------------------------------------
## F.N3R_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 19 0.991 20.65 20.26 0.00 0.00
## .25 .50 .75 .90 .95
## 4.50 20.50 29.00 44.50 53.75
##
## lowest : 0 1 4 6 8, highest: 34 36 53 54 61
##
## Value 0 1 4 6 8 12 17 18 23 25 26
## Frequency 5 1 1 1 1 2 1 1 1 1 1
## Proportion 0.192 0.038 0.038 0.038 0.038 0.077 0.038 0.038 0.038 0.038 0.038
##
## Value 28 29 32 34 36 53 54 61
## Frequency 1 3 1 1 1 1 1 1
## Proportion 0.038 0.115 0.038 0.038 0.038 0.038 0.038 0.038
## --------------------------------------------------------------------------------
## F.N4R_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 24 0.999 45.19 34.82 0.00 1.50
## .25 .50 .75 .90 .95
## 27.25 42.50 70.75 78.00 89.75
##
## lowest : 0 3 8 16 27, highest: 75 76 80 93 99
## --------------------------------------------------------------------------------
# Fluency
F$Fluency_BioBlur <- as.numeric(F$FluencyBLUR_BIO_34)
describe(F$Fluency_BioBlur)
## F$Fluency_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 16 0.979 71.58 35.33 1.75 7.00
## .25 .50 .75 .90 .95
## 64.50 81.50 99.75 100.00 100.00
##
## lowest : 0 7 47 48 63, highest: 90 94 97 99 100
##
## Value 0 7 47 48 63 69 71 74 75 81 82
## Frequency 2 2 1 1 1 1 1 1 2 1 2
## Proportion 0.077 0.077 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.038 0.077
##
## Value 90 94 97 99 100
## Frequency 1 1 1 1 7
## Proportion 0.038 0.038 0.038 0.038 0.269
hist(F$Fluency_BioBlur)
sd(F$Fluency_BioBlur, na.rm = TRUE)
## [1] 33.50125
# Understanding
F$Und_BioBlur <- as.numeric(F$FamiliarityBLUR_BIO_33)
hist(F$Und_BioBlur)
# Familiarity
F$Fam_BioBlur <- as.numeric(F$FamiliarityBLUR_BIO_31)
# Risk
F$R1_BioBlur <- as.numeric(F$RiskBLUR_BIO_30)
F$R2_BioBlur <- as.numeric(F$RiskBLUR_BIO_31)
F$R3_BioBlur <- as.numeric(F$RiskBLUR_BIO_32)
hist(F$R1_BioBlur)
hist(F$R2_BioBlur)
hist(F$R3_BioBlur)
F$RiskScore_BioBlur <- rowMeans(F [, c( "R1_BioBlur" , "R2_BioBlur", "R3_BioBlur")], na.rm=TRUE)
describe(F$RiskScore_BioBlur)
## F$RiskScore_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 25 1 63.42 24.37 31.67 34.00
## .25 .50 .75 .90 .95
## 48.50 65.67 76.58 91.17 97.42
##
## lowest : 31.00000 31.33333 32.66667 35.33333 36.33333
## highest: 84.66667 88.66667 93.66667 98.66667 99.00000
sd(F$RiskScore_BioBlur, na.rm = TRUE)
## [1] 20.97587
F$RiskScale_BioBlur <- data.frame(F$R1_BioBlur, F$R2_BioBlur, F$R3_BioBlur)
describe(F$RiskScale_BioBlur)
## F$RiskScale_BioBlur
##
## 3 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.R1_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 23 0.999 57.88 38.32 0.50 2.50
## .25 .50 .75 .90 .95
## 30.25 68.00 83.50 96.00 99.75
##
## lowest : 0 2 3 24 27, highest: 85 87 93 99 100
## --------------------------------------------------------------------------------
## F.R2_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.996 62.04 38.3 1.25 8.00
## .25 .50 .75 .90 .95
## 33.25 69.00 88.75 100.00 100.00
##
## lowest : 0 5 11 29 31, highest: 86 88 89 97 100
## --------------------------------------------------------------------------------
## F.R3_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.996 70.35 31.58 24.00 31.00
## .25 .50 .75 .90 .95
## 54.25 77.00 93.00 100.00 100.00
##
## lowest : 1 22 30 32 35, highest: 90 93 96 98 100
## --------------------------------------------------------------------------------
# Benefit
F$B1_BioBlur <- as.numeric(F$BenefitBLUR_BIO_40)
F$B2_BioBlur <- as.numeric(F$BenefitBLUR_BIO_41)
F$B3_BioBlur <- as.numeric(F$BenefitBLUR_BIO_42)
F$B4_BioBlur <- as.numeric(F$BenefitBLUR_BIO_43)
hist(F$B1_BioBlur)
hist(F$B2_BioBlur)
hist(F$B3_BioBlur)
hist(F$B4_BioBlur)
F$BenScore_BioBlur <- rowMeans(F [, c( "B1_BioBlur" , "B2_BioBlur", "B3_BioBlur", "B4_BioBlur")], na.rm=TRUE)
describe(F$BenScore_BioBlur)
## F$BenScore_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 23 0.999 61.49 34.13 5.188 18.250
## .25 .50 .75 .90 .95
## 48.875 66.750 82.375 99.375 99.938
##
## lowest : 0.00 3.50 10.25 26.25 37.00, highest: 91.25 97.25 99.00 99.75 100.00
sd(F$BenScore_BioBlur, na.rm = TRUE)
## [1] 29.97203
F$BenScale_BioBlur <- data.frame(F$B1_BioBlur, F$B2_BioBlur, F$B3_BioBlur, F$B4_BioBlur)
describe(F$BenScale_BioBlur)
## F$BenScale_BioBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.B1_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 19 0.997 58.15 35.9 4.75 16.50
## .25 .50 .75 .90 .95
## 36.00 60.50 81.75 99.50 100.00
##
## lowest : 0 4 7 26 32, highest: 83 91 96 99 100
##
## Value 0 4 7 26 32 36 50 52 58 63 64
## Frequency 1 1 1 2 1 2 2 2 1 1 2
## Proportion 0.038 0.038 0.038 0.077 0.038 0.077 0.077 0.077 0.038 0.038 0.077
##
## Value 68 77 78 83 91 96 99 100
## Frequency 1 1 1 1 1 1 1 3
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.115
## --------------------------------------------------------------------------------
## F.B2_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 22 0.998 61.04 36.65 4.4 11.2
## .25 .50 .75 .90 .95
## 35.0 68.0 85.0 99.2 100.0
##
## lowest : 0 4 6 19 26, highest: 81 85 97 98 100
## --------------------------------------------------------------------------------
## F.B3_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 22 0.996 63.19 36.64 6.00 18.00
## .25 .50 .75 .90 .95
## 42.25 72.50 87.00 100.00 100.00
##
## lowest : 0 4 12 24 27, highest: 84 88 98 99 100
## --------------------------------------------------------------------------------
## F.B4_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.996 64 34.15 5.50 21.00
## .25 .50 .75 .90 .95
## 48.50 70.50 84.75 100.00 100.00
##
## lowest : 0 2 16 26 27, highest: 84 85 89 97 100
## --------------------------------------------------------------------------------
# Support
F$S1_BioBlur <- as.numeric(F$SupportBLUR_BIO_40)
F$S2_BioBlur <- as.numeric(F$SupportBLUR_BIO_42)
F$S3_BioBlur <- as.numeric(F$SupportBLUR_BIO_43)
F$S4_BioBlur <- as.numeric(F$SupportBLUR_BIO_45)
hist(F$S1_BioBlur)
hist(F$S2_BioBlur)
hist(F$S3_BioBlur)
hist(F$S4_BioBlur)
F$SupScore_BioBlur <- rowMeans(F [, c( "S1_BioBlur" , "S2_BioBlur", "S3_BioBlur", "S4_BioBlur")], na.rm=TRUE)
describe(F$SupScore_BioBlur)
## F$SupScore_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 23 0.998 64.44 30.44 21.88 30.00
## .25 .50 .75 .90 .95
## 51.25 66.62 86.81 99.75 100.00
##
## lowest : 5.75 21.25 23.75 36.25 36.50, highest: 88.75 91.00 95.75 99.50 100.00
sd(F$SupScore_BioBlur, na.rm = TRUE)
## [1] 26.37919
F$SupScale_BioBlur <- data.frame(F$S1_BioBlur, F$S2_BioBlur, F$S3_BioBlur, F$S4_BioBlur)
describe(F$SupScale_BioBlur)
## F$SupScale_BioBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.S1_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 20 0.996 63.73 33.18 14 24
## .25 .50 .75 .90 .95
## 52 67 89 100 100
##
## lowest : 0 11 23 25 32, highest: 79 83 91 94 100
##
## Value 0 11 23 25 32 37 52 53 54 59 66
## Frequency 1 1 1 1 1 1 2 1 1 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.038 0.038 0.038 0.038
##
## Value 67 70 74 77 79 83 91 94 100
## Frequency 2 1 1 1 1 1 2 1 4
## Proportion 0.077 0.038 0.038 0.038 0.038 0.038 0.077 0.038 0.154
## --------------------------------------------------------------------------------
## F.S2_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 19 0.995 66.27 31.02 22.0 29.0
## .25 .50 .75 .90 .95
## 52.0 67.5 89.5 100.0 100.0
##
## lowest : 8 21 25 33 50, highest: 85 91 92 96 100
##
## Value 8 21 25 33 50 52 54 64 66 67 68
## Frequency 1 1 1 2 1 3 1 1 1 1 1
## Proportion 0.038 0.038 0.038 0.077 0.038 0.115 0.038 0.038 0.038 0.038 0.038
##
## Value 75 78 83 85 91 92 96 100
## Frequency 1 2 1 1 1 1 1 4
## Proportion 0.038 0.077 0.038 0.038 0.038 0.038 0.038 0.154
## --------------------------------------------------------------------------------
## F.S3_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.996 63.54 33.06 23.00 28.00
## .25 .50 .75 .90 .95
## 46.50 65.00 88.75 100.00 100.00
##
## lowest : 0 23 33 34 36, highest: 82 91 98 99 100
## --------------------------------------------------------------------------------
## F.S4_BioBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 18 0.994 64.23 30.34 26.00 30.00
## .25 .50 .75 .90 .95
## 42.25 69.00 80.75 100.00 100.00
##
## lowest : 15 26 34 35 38, highest: 80 81 89 96 100
##
## Value 15 26 34 35 38 39 52 54 60 68 70
## Frequency 1 2 1 1 1 1 3 1 1 1 2
## Proportion 0.038 0.077 0.038 0.038 0.038 0.038 0.115 0.038 0.038 0.038 0.077
##
## Value 75 78 80 81 89 96 100
## Frequency 1 1 2 1 1 1 4
## Proportion 0.038 0.038 0.077 0.038 0.038 0.038 0.154
## --------------------------------------------------------------------------------
Biofuel (Clear Condition)
F$BF_Clear_Page.Submit
## [1] NA 8.922 NA NA NA NA 7.796 4.260 NA 0.732
## [11] NA 14.602 NA 14.977 9.343 NA NA 38.795 23.046 NA
## [21] 46.338 19.502 38.701 5.623 23.581 NA NA 43.987 NA NA
## [31] 16.316 NA 12.927 4.157 11.044 32.091 NA NA 5.526 NA
## [41] NA NA 34.825 NA NA 12.669 NA NA 1.314 2.599
F$BF_Clear_ATN
## [1] NA 2 NA NA NA NA 2 2 NA 1 NA 2 NA 2 2 NA NA 2 2 NA 2 2 2 2 2
## [26] NA NA 2 NA NA 2 NA 2 2 2 2 NA NA 2 NA NA NA 2 NA NA 2 NA NA 4 2
F$BF_Clear_ATN_TIME_Page.Submit
## [1] NA 1.889 NA NA NA NA 2.872 4.964 NA 2.411
## [11] NA 2.886 NA 1.573 3.136 NA NA 3.083 2.340 NA
## [21] 3.397 1.901 2.400 4.044 2.155 NA NA 6.880 NA NA
## [31] 1.253 NA 1.956 2.502 2.026 4.348 NA NA 3.250 NA
## [41] NA NA 3.434 NA NA 2.503 NA NA 15.846 3.753
F$BF_Clear_ATN2
## [1] NA
## [2] "Its about the fuel"
## [3] NA
## [4] NA
## [5] NA
## [6] NA
## [7] "They use plants to make fuel."
## [8] "I don't remember "
## [9] NA
## [10] "I’m not sure "
## [11] NA
## [12] "Uses plant to make fuel"
## [13] NA
## [14] "A proccess to created fuel liquid fuel"
## [15] "Taking plants and making energy "
## [16] NA
## [17] NA
## [18] "The process that uses plants and trees to produce fuel, to power many things including cars. "
## [19] "taking plants and making liquid out of them you can use as fuel"
## [20] NA
## [21] "biofuel is taking plants and either cooling or heating them at a biofuel facility in order to make a liquid biofuel that is then refined, and you can use it to run vehicles and other fuel dependent things"
## [22] "Biofuel is a liquid fuel obtained by burning various types of plants such as grass and trees. Once refined, it can be used in everyday transportation (cars, airplanes, etc.)"
## [23] "Biofuel is a fuel derived from plants. It is made to fuel cars and heat buildings. "
## [24] "Cut down on polution"
## [25] "biofuel is created by using agricultural waste, grass, grown crops. the products are converted into a crude 'oil' that can be used for vehicles, heating, etc."
## [26] NA
## [27] NA
## [28] "Taking natural things and breaking it all down to make biofuel to convert to crude oil."
## [29] NA
## [30] NA
## [31] "Using plants to create liquid fuel which would be used instead of gas. "
## [32] NA
## [33] "Biofuel made from like tress and all"
## [34] "Chemistry \nScientific facts\nSolar System\nSolar energy "
## [35] "Turning plants into fuel "
## [36] "None"
## [37] NA
## [38] NA
## [39] "it uses biofuel to operate or manage the energy crises"
## [40] NA
## [41] NA
## [42] NA
## [43] "It uses plants trees and other things to create crude oil"
## [44] NA
## [45] NA
## [46] "A prices that uses plants to produce natural gas"
## [47] NA
## [48] NA
## [49] "It cool to me"
## [50] "Fjeiengidhrbfhicjv did riv djdhsichdbeinwf n. Dis cow vid crown is jam sod cosmic. Jaosncb"
F$BF_Clear_ATN2_TIME_Page.Submit
## [1] NA 11.283 NA NA NA NA 14.218 7.490 NA
## [10] 5.500 NA 11.825 NA 27.092 15.687 NA NA 49.100
## [19] 26.960 NA 87.440 170.250 66.001 44.579 126.001 NA NA
## [28] 140.300 NA NA 15.973 NA 13.649 28.155 9.623 3.518
## [37] NA NA 35.430 NA NA NA 23.561 NA NA
## [46] 18.173 NA NA 3.802 9.571
# Naturalness
F$N1_BFClear <- as.numeric(F$Naturalness_BF_30)
F$N2R_BFClear <- as.numeric(100 - F$Naturalness_BF_31)
F$N3R_BFClear <- as.numeric(100 - F$Naturalness_BF_35)
F$N4R_BFClear <- as.numeric(100 - F$Naturalness_BF_36)
hist(F$N1_BFClear)
hist(F$N2R_BFClear)
hist(F$N3R_BFClear)
hist(F$N4R_BFClear)
F$NatScore_BFClear <- rowMeans(F [, c( "N1_BFClear" , "N2R_BFClear", "N3R_BFClear", "N4R_BFClear")], na.rm=TRUE)
describe(F$NatScore_BFClear)
## F$NatScore_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 24 1 43.68 24.44 4.75 21.45
## .25 .50 .75 .90 .95
## 28.75 44.25 58.00 72.05 73.55
##
## lowest : 0.00 0.75 20.75 22.50 26.00, highest: 65.00 71.00 72.75 73.75 75.00
sd(F$NatScore_BFClear, na.rm = TRUE)
## [1] 21.13073
F$NatScale_BFClear <- data.frame(F$N1_BFClear, F$N2R_BFClear, F$N3R_BFClear, F$N4R_BFClear)
describe(F$NatScale_BFClear)
## F$NatScale_BFClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.N1_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 17 0.967 59.84 42.27 0.4 2.8
## .25 .50 .75 .90 .95
## 32.0 66.0 100.0 100.0 100.0
##
## lowest : 0 2 4 15 31, highest: 71 76 77 81 100
##
## Value 0 2 4 15 31 32 33 43 50 51 64 66 71
## Frequency 2 1 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.08 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
##
## Value 76 77 81 100
## Frequency 1 1 1 8
## Proportion 0.04 0.04 0.04 0.32
## --------------------------------------------------------------------------------
## F.N2R_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 19 0.995 43.88 40.94 0.0 0.0
## .25 .50 .75 .90 .95
## 17.0 30.0 75.0 92.6 99.0
##
## lowest : 0 6 8 17 18, highest: 76 79 89 95 100
##
## Value 0 6 8 17 18 19 26 27 30 32 52 72 74
## Frequency 4 1 1 1 1 1 1 1 2 1 1 2 1
## Proportion 0.16 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.08 0.04
##
## Value 75 76 79 89 95 100
## Frequency 1 1 1 1 1 2
## Proportion 0.04 0.04 0.04 0.04 0.04 0.08
## --------------------------------------------------------------------------------
## F.N3R_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 16 0.977 20.92 25.29 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 16.0 30.0 50.0 59.6
##
## lowest : 0 1 3 11 12, highest: 35 39 50 62 99
##
## Value 0 1 3 11 12 15 16 20 22 26 30 35 39
## Frequency 7 1 1 1 1 1 3 1 1 1 1 1 1
## Proportion 0.28 0.04 0.04 0.04 0.04 0.04 0.12 0.04 0.04 0.04 0.04 0.04 0.04
##
## Value 50 62 99
## Frequency 2 1 1
## Proportion 0.08 0.04 0.04
## --------------------------------------------------------------------------------
## F.N4R_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 20 0.995 49.75 43.34 1.05 7.30
## .25 .50 .75 .90 .95
## 19.50 37.50 91.00 100.00 100.00
##
## lowest : 0 7 8 10 18, highest: 80 90 94 99 100
##
## Value 0 7 8 10 18 20 21 25 28 30 32
## Frequency 2 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 43 45 71 73 80 90 94 99 100
## Frequency 1 1 1 1 1 1 1 1 4
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.167
## --------------------------------------------------------------------------------
cor(F$NatScale_BFClear, use= "complete.obs")
## F.N1_BFClear F.N2R_BFClear F.N3R_BFClear F.N4R_BFClear
## F.N1_BFClear 1.0000000 0.1830883 -0.4643078 0.4025877
## F.N2R_BFClear 0.1830883 1.0000000 0.1945759 0.4566844
## F.N3R_BFClear -0.4643078 0.1945759 1.0000000 0.1458218
## F.N4R_BFClear 0.4025877 0.4566844 0.1458218 1.0000000
# Fluency
F$Fluency_BFClear <- as.numeric(F$Fluency_BF_30)
describe(F$Fluency_BFClear)
## F$Fluency_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 14 0.936 78.8 28.51 23.0 36.2
## .25 .50 .75 .90 .95
## 66.0 88.0 100.0 100.0 100.0
##
## lowest : 0 22 27 50 61, highest: 87 88 90 91 100
##
## Value 0 22 27 50 61 66 72 80 82 87 88 90 91
## Frequency 1 1 1 1 1 2 1 1 1 1 2 1 1
## Proportion 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.04 0.04 0.08 0.04 0.04
##
## Value 100
## Frequency 10
## Proportion 0.40
hist(F$Fluency_BFClear)
sd(F$Fluency_BFClear, na.rm = TRUE)
## [1] 27.8807
# Understanding
F$Und_BFClear <- as.numeric(F$Familiarity_BF_31)
hist(F$Und_BFClear, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...): "na.rm"
## is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, ...): "na.rm" is not a graphical parameter
# Familiarity
F$Fam_BFClear <- as.numeric(F$Familiarity_BF_32)
# Risk
F$R1_BFClear <- as.numeric(F$Risk_BF_30)
F$R2_BFClear <- as.numeric(F$Risk_BF_31)
F$R3_BFClear <- as.numeric(F$Risk_BF_32)
hist(F$R1_BFClear)
hist(F$R2_BFClear)
hist(F$R3_BFClear)
F$RiskScore_BFClear <- rowMeans(F [, c( "R1_BFClear" , "R2_BFClear", "R3_BFClear")], na.rm=TRUE)
describe(F$RiskScore_BFClear)
## F$RiskScore_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 20 0.995 49.03 28.48 14.07 20.20
## .25 .50 .75 .90 .95
## 33.33 38.33 66.33 82.47 96.73
##
## lowest : 10.33333 13.66667 15.66667 27.00000 33.33333
## highest: 69.33333 78.66667 85.00000 99.66667 100.00000
##
## 10.3333333333333 (1, 0.04), 13.6666666666667 (1, 0.04), 15.6666666666667 (1,
## 0.04), 27 (1, 0.04), 33.3333333333333 (4, 0.16), 33.6666666666667 (2, 0.08), 35
## (1, 0.04), 37.6666666666667 (1, 0.04), 38.3333333333333 (1, 0.04),
## 47.3333333333333 (1, 0.04), 56.3333333333333 (1, 0.04), 57.6666666666667 (1,
## 0.04), 58 (1, 0.04), 59.6666666666667 (1, 0.04), 66.3333333333333 (1, 0.04),
## 69.3333333333333 (2, 0.08), 78.6666666666667 (1, 0.04), 85 (1, 0.04),
## 99.6666666666667 (1, 0.04), 100 (1, 0.04)
sd(F$RiskScore_BFClear, na.rm = TRUE)
## [1] 25.02238
F$RiskScale_BFClear <- data.frame(F$R1_BFClear, F$R2_BFClear, F$R3_BFClear)
describe(F$RiskScale_BFClear)
## F$RiskScale_BFClear
##
## 3 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.R1_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 19 0.992 37.28 39.51 0.0 0.0
## .25 .50 .75 .90 .95
## 8.0 29.0 65.0 85.6 97.2
##
## lowest : 0 7 8 11 13, highest: 74 84 85 86 100
##
## Value 0 7 8 11 13 14 18 24 29 34 37 50 64
## Frequency 5 1 1 1 1 1 1 1 2 1 1 1 1
## Proportion 0.20 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.04 0.04
##
## Value 65 74 84 85 86 100
## Frequency 1 1 1 1 1 2
## Proportion 0.04 0.04 0.04 0.04 0.04 0.08
## --------------------------------------------------------------------------------
## F.R2_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 17 0.985 37.4 42.42 0.0 0.0
## .25 .50 .75 .90 .95
## 1.0 22.0 71.0 91.4 98.6
##
## lowest : 0 1 7 8 12, highest: 72 85 89 93 100
##
## Value 0 1 7 8 12 17 22 32 50 66 70 71 72
## Frequency 6 1 1 2 1 1 1 2 1 1 1 1 1
## Proportion 0.24 0.04 0.04 0.08 0.04 0.04 0.04 0.08 0.04 0.04 0.04 0.04 0.04
##
## Value 85 89 93 100
## Frequency 1 1 1 2
## Proportion 0.04 0.04 0.04 0.08
## --------------------------------------------------------------------------------
## F.R3_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 18 0.978 72.4 32.55 12.8 19.6
## .25 .50 .75 .90 .95
## 69.0 80.0 100.0 100.0 100.0
##
## lowest : 10 12 16 25 26, highest: 82 86 93 99 100
##
## Value 10 12 16 25 26 62 69 71 73 74 77 80 81
## Frequency 1 1 1 1 1 1 1 1 1 2 1 1 1
## Proportion 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.04
##
## Value 82 86 93 99 100
## Frequency 1 1 1 1 7
## Proportion 0.04 0.04 0.04 0.04 0.28
## --------------------------------------------------------------------------------
# Benefit
F$B1_BFClear <- as.numeric(F$Benefit_BF_40)
F$B2_BFClear <- as.numeric(F$Benefit_BF_42)
F$B3_BFClear <- as.numeric(F$Benefit_BF_43)
F$B4_BFClear <- as.numeric(F$Benefit_BF_44)
hist(F$B1_BFClear)
hist(F$B2_BFClear)
hist(F$B3_BFClear)
hist(F$B4_BFClear)
F$BenScore_BFClear <- rowMeans(F [, c( "B1_BFClear" , "B2_BFClear", "B3_BFClear", "B4_BFClear")], na.rm=TRUE)
describe(F$BenScore_BFClear)
## F$BenScore_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 19 0.992 70.56 34.43 1.50 9.10
## .25 .50 .75 .90 .95
## 66.25 79.25 96.00 100.00 100.00
##
## lowest : 0.00 7.50 11.50 51.00 54.75, highest: 88.50 89.75 96.00 99.25 100.00
##
## 0 (2, 0.08), 7.5 (1, 0.04), 11.5 (1, 0.04), 51 (1, 0.04), 54.75 (1, 0.04),
## 66.25 (1, 0.04), 69.5 (1, 0.04), 71 (1, 0.04), 72.25 (1, 0.04), 72.5 (1, 0.04),
## 73.75 (1, 0.04), 79.25 (1, 0.04), 85.25 (1, 0.04), 87.5 (1, 0.04), 88.5 (2,
## 0.08), 89.75 (1, 0.04), 96 (1, 0.04), 99.25 (1, 0.04), 100 (5, 0.20)
sd(F$BenScore_BFClear, na.rm = TRUE)
## [1] 32.58669
F$BenScale_BFClear <- data.frame(F$B1_BFClear, F$B2_BFClear, F$B3_BFClear, F$B4_BFClear)
describe(F$BenScale_BFClear)
## F$BenScale_BFClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.B1_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 17 0.978 68.52 37.78 1.0 6.6
## .25 .50 .75 .90 .95
## 51.0 73.0 100.0 100.0 100.0
##
## lowest : 0 5 9 27 50, highest: 88 90 93 99 100
##
## Value 0 5 9 27 50 51 66 68 69 71 73 81 88
## Frequency 2 1 1 1 1 1 1 1 1 1 2 1 1
## Proportion 0.08 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04
##
## Value 90 93 99 100
## Frequency 1 1 1 7
## Proportion 0.04 0.04 0.04 0.28
## --------------------------------------------------------------------------------
## F.B2_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 18 0.978 72.88 32.85 2.8 15.2
## .25 .50 .75 .90 .95
## 70.0 81.0 100.0 100.0 100.0
##
## lowest : 0 14 17 54 66, highest: 85 87 88 98 100
##
## Value 0 14 17 54 66 70 73 74 75 78 79 81 83
## Frequency 2 1 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.08 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
##
## Value 85 87 88 98 100
## Frequency 1 1 1 1 7
## Proportion 0.04 0.04 0.04 0.04 0.28
## --------------------------------------------------------------------------------
## F.B3_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 15 0.977 69.96 35.15 1.6 8.4
## .25 .50 .75 .90 .95
## 66.0 80.0 100.0 100.0 100.0
##
## lowest : 0 8 9 50 66, highest: 84 87 89 90 100
##
## Value 0 8 9 50 66 67 68 71 73 80 84 87 89
## Frequency 2 1 1 2 1 1 1 1 2 1 2 1 1
## Proportion 0.08 0.04 0.04 0.08 0.04 0.04 0.04 0.04 0.08 0.04 0.08 0.04 0.04
##
## Value 90 100
## Frequency 1 7
## Proportion 0.04 0.28
## --------------------------------------------------------------------------------
## F.B4_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 15 0.953 70.88 37.01 0.6 6.2
## .25 .50 .75 .90 .95
## 62.0 83.0 100.0 100.0 100.0
##
## lowest : 0 3 11 30 50, highest: 83 84 87 89 100
##
## Value 0 3 11 30 50 62 67 70 77 82 83 84 87
## Frequency 2 1 1 1 1 1 1 1 2 1 1 1 1
## Proportion 0.08 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.04 0.04
##
## Value 89 100
## Frequency 1 9
## Proportion 0.04 0.36
## --------------------------------------------------------------------------------
# Support
F$S1_BFClear <- as.numeric(F$Support_BF_40)
F$S2_BFClear <- as.numeric(F$Support_BF_42)
F$S3_BFClear <- as.numeric(F$Support_BF_43)
F$S4_BFClear <- as.numeric(F$Support_BF_45)
hist(F$S1_BFClear)
hist(F$S2_BFClear)
hist(F$S3_BFClear)
hist(F$S4_BFClear)
F$SupScore_BFClear <- rowMeans(F [, c( "S1_BFClear" , "S2_BFClear", "S3_BFClear", "S4_BFClear")], na.rm=TRUE)
describe(F$SupScore_BFClear)
## F$SupScore_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 24 1 64.59 34.45 5.05 7.45
## .25 .50 .75 .90 .95
## 57.50 70.00 85.25 99.15 99.95
##
## lowest : 0.00 4.75 6.25 9.25 34.50, highest: 89.00 96.75 98.25 99.75 100.00
sd(F$SupScore_BFClear, na.rm = TRUE)
## [1] 31.21847
F$SupScale_BFClear <- data.frame(F$S1_BFClear, F$S2_BFClear, F$S3_BFClear, F$S4_BFClear)
describe(F$SupScale_BFClear)
## F$SupScale_BFClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.S1_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 20 0.995 68.64 35.68 0.2 2.6
## .25 .50 .75 .90 .95
## 61.0 77.0 96.0 100.0 100.0
##
## lowest : 0 1 5 50 61, highest: 93 96 97 98 100
##
## Value 0 1 5 50 61 62 70 72 73 76 77 81 83
## Frequency 2 1 1 2 1 1 1 1 1 1 1 1 1
## Proportion 0.08 0.04 0.04 0.08 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
##
## Value 84 87 93 96 97 98 100
## Frequency 1 1 1 1 1 1 4
## Proportion 0.04 0.04 0.04 0.04 0.04 0.04 0.16
## --------------------------------------------------------------------------------
## F.S2_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 20 0.997 65.32 33.71 2.6 13.0
## .25 .50 .75 .90 .95
## 62.0 72.0 83.0 98.4 100.0
##
## lowest : 0 13 26 50 62, highest: 83 84 95 96 100
##
## Value 0 13 26 50 62 64 66 68 69 71 72 75 80
## Frequency 2 2 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.08 0.08 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
##
## Value 81 82 83 84 95 96 100
## Frequency 1 1 2 1 1 1 3
## Proportion 0.04 0.04 0.08 0.04 0.04 0.04 0.12
## --------------------------------------------------------------------------------
## F.S3_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 17 0.995 61.12 37.97 0.0 1.6
## .25 .50 .75 .90 .95
## 50.0 73.0 81.0 99.6 100.0
##
## lowest : 0 4 7 31 50, highest: 81 89 94 99 100
##
## Value 0 4 7 31 50 63 67 68 73 76 77 79 81
## Frequency 3 1 1 1 2 1 1 2 1 3 1 1 1
## Proportion 0.12 0.04 0.04 0.04 0.08 0.04 0.04 0.08 0.04 0.12 0.04 0.04 0.04
##
## Value 89 94 99 100
## Frequency 1 1 1 3
## Proportion 0.04 0.04 0.04 0.12
## --------------------------------------------------------------------------------
## F.S4_BFClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 17 0.991 63.28 37.67 1.4 9.0
## .25 .50 .75 .90 .95
## 50.0 74.0 88.0 100.0 100.0
##
## lowest : 0 7 12 19 30, highest: 84 86 88 89 100
##
## Value 0 7 12 19 30 50 60 65 69 74 75 80 84
## Frequency 2 1 1 1 1 2 1 1 2 1 2 1 1
## Proportion 0.08 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.08 0.04 0.08 0.04 0.04
##
## Value 86 88 89 100
## Frequency 1 1 1 5
## Proportion 0.04 0.04 0.04 0.20
## --------------------------------------------------------------------------------
Biofuel (Blurred Condition)
F$BF_Blurred_Page.Submit
## [1] 3.277 NA 0.957 2.101 14.101 0.714 NA NA 3.177 NA
## [11] 4.200 NA 5.210 NA NA 9.794 1.009 NA NA 12.951
## [21] NA NA NA NA NA 17.673 10.099 NA 12.404 2.378
## [31] NA 3.801 NA NA NA NA 10.272 0.623 NA 22.362
## [41] 36.739 11.301 NA 7.688 7.307 NA 23.510 35.302 NA NA
F$BF_Blurred_ATN
## [1] 2 NA 4 2 2 1 NA NA 2 NA 2 NA 1 NA NA 4 2 NA NA 2 NA NA NA NA NA
## [26] 2 2 NA 3 2 NA 3 NA NA NA NA 3 4 NA 2 4 2 NA 2 2 NA 2 2 NA NA
F$BF_Blurred_ATN_TIME_Page.Submit
## [1] 2.175 NA 4.876 2.186 1.593 1.254 NA NA 2.817 NA 2.177 NA
## [13] 2.232 NA NA 2.502 2.624 NA NA 1.104 NA NA NA NA
## [25] NA 6.207 1.601 NA 4.608 3.894 NA 3.546 NA NA NA NA
## [37] 3.214 1.668 NA 5.340 3.437 2.039 NA 4.001 3.993 NA 2.373 2.755
## [49] NA NA
F$BF_Blurred_ATN2
## [1] "Oahd pqid. Skei apeiej pausjs laisjs "
## [2] NA
## [3] "Interesting "
## [4] "We are coming tomorrow morning "
## [5] "They take trees and plants and they freeze them in that makes fuel."
## [6] "Htghhj"
## [7] NA
## [8] NA
## [9] "What do you mean the world to see the first time I was wondering how much you can even get the email exchange email to"
## [10] NA
## [11] "Goossjdjxjxjx"
## [12] NA
## [13] "I can not "
## [14] NA
## [15] NA
## [16] "Wild energy I believe it's just a force of nature and I don't think anybody really understand science"
## [17] "I'm interested"
## [18] NA
## [19] NA
## [20] "Plant matter only, no animal matter, is used to make fuel. The claim is that agricultural waste will be used, implying deforestation will not be considered."
## [21] NA
## [22] NA
## [23] NA
## [24] NA
## [25] NA
## [26] "It’s a way to change a plant based liquid into fuel for gas and trucks and other items"
## [27] "Biofuel is used just the same way as gasoline is used in trucks, cars, airplanes, etc. Except this method is made from plants. Examples would be trees or grass. "
## [28] NA
## [29] "Can’t even see the letters "
## [30] "I dont know"
## [31] NA
## [32] "I don't really know "
## [33] NA
## [34] NA
## [35] NA
## [36] NA
## [37] "Very cool "
## [38] "The only reason I have been a little busy with my work and then we can go on the road on the team gggg ytyt ytyyyg and then I can go to the Fair is the one thing I "
## [39] NA
## [40] "Biofuel is heating up plants to make biofuel "
## [41] "Everything it is good on my end but I just didn’t want to "
## [42] "The fuel can be recycled over again"
## [43] NA
## [44] "Fuel created from plants"
## [45] "Everything from start to finish is good for me and different from one side to another "
## [46] NA
## [47] "It cones from plants and trees so its more healthy for the environment "
## [48] "Biofuel can be used to fuel cars and planes"
## [49] NA
## [50] NA
F$BF_Blurred_ATN2_TIME_Page.Submit
## [1] 5.401 NA 6.469 4.554 18.165 2.568 NA NA 6.367 NA
## [11] 5.201 NA 6.435 NA NA 12.300 11.900 NA NA 47.565
## [21] NA NA NA NA NA 42.000 59.456 NA 8.701 10.451
## [31] NA 8.302 NA NA NA NA 3.481 16.661 NA 27.401
## [41] 5.702 39.801 NA 29.504 22.001 NA 29.322 21.396 NA NA
# Naturalness
F$N1_BFBlur <- as.numeric(F$NaturalnessBLUR_BF_30)
F$N2R_BFBlur <- as.numeric(100 - F$NaturalnessBLUR_BF_31)
F$N3R_BFBlur <- as.numeric(100 - F$NaturalnessBLUR_BF_35)
F$N4R_BFBlur <- as.numeric(100- F$NaturalnessBLUR_BF_36)
hist(F$N1_BFBlur)
hist(F$N2R_BFBlur)
hist(F$N3R_BFBlur)
hist(F$N4R_BFBlur)
F$NatScore_BFBlur <- rowMeans(F [, c( "N1_BFBlur" , "N2R_BFBlur", "N3R_BFBlur", "N4R_BFBlur")], na.rm=TRUE)
describe(F$NatScore_BFBlur)
## F$NatScore_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 24 1 38.28 15.61 21.9 24.7
## .25 .50 .75 .90 .95
## 25.5 36.0 49.0 55.8 60.0
##
## lowest : 18.25 21.25 24.50 25.00 25.25, highest: 52.50 55.50 56.00 61.00 62.00
sd(F$NatScore_BFBlur, na.rm = TRUE)
## [1] 13.4221
F$NatScale_BFBlur <- data.frame(F$N1_BFBlur, F$N2R_BFBlur, F$N3R_BFBlur, F$N4R_BFBlur)
describe(F$NatScale_BFBlur)
## F$NatScale_BFBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.N1_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 18 0.986 70.24 32.56 24.2 31.0
## .25 .50 .75 .90 .95
## 50.0 75.0 96.0 100.0 100.0
##
## lowest : 4 23 29 34 35, highest: 86 87 88 96 100
##
## Value 4 23 29 34 35 50 52 62 66 67 73 75 83
## Frequency 1 1 1 1 1 2 1 1 1 1 1 1 1
## Proportion 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.04 0.04 0.04 0.04 0.04
##
## Value 86 87 88 96 100
## Frequency 1 1 1 2 6
## Proportion 0.04 0.04 0.04 0.08 0.24
## --------------------------------------------------------------------------------
## F.N2R_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 17 0.994 24.5 27.24 0.00 0.00
## .25 .50 .75 .90 .95
## 1.75 22.50 32.00 50.00 68.70
##
## lowest : 0 1 2 3 7, highest: 32 48 50 72 100
##
## Value 0 1 2 3 7 17 22 23 26 27 28
## Frequency 4 2 1 1 1 2 1 1 1 1 1
## Proportion 0.167 0.083 0.042 0.042 0.042 0.083 0.042 0.042 0.042 0.042 0.042
##
## Value 30 32 48 50 72 100
## Frequency 1 2 1 2 1 1
## Proportion 0.042 0.083 0.042 0.083 0.042 0.042
## --------------------------------------------------------------------------------
## F.N3R_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 18 0.993 20.62 21.26 0.00 0.00
## .25 .50 .75 .90 .95
## 3.75 19.00 26.50 47.10 49.70
##
## lowest : 0 2 3 4 9, highest: 34 45 48 50 79
##
## Value 0 2 3 4 9 12 13 15 19 22 24
## Frequency 4 1 1 1 1 1 1 1 3 1 2
## Proportion 0.167 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.125 0.042 0.083
##
## Value 26 28 34 45 48 50 79
## Frequency 1 1 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042
## --------------------------------------------------------------------------------
## F.N4R_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 18 0.99 33.46 35.54 0.00 0.00
## .25 .50 .75 .90 .95
## 9.50 26.50 55.00 84.30 97.25
##
## lowest : 0 8 10 12 19, highest: 62 64 93 98 100
##
## Value 0 8 10 12 19 26 27 28 29 31 48
## Frequency 5 1 2 1 1 2 1 1 1 1 1
## Proportion 0.208 0.042 0.083 0.042 0.042 0.083 0.042 0.042 0.042 0.042 0.042
##
## Value 54 58 62 64 93 98 100
## Frequency 1 1 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042
## --------------------------------------------------------------------------------
# Fluency
F$Fluency_BFBlur <- as.numeric(F$FluencyBLUR_BF_34)
describe(F$Fluency_BFBlur)
## F$Fluency_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 16 0.967 84.04 19.52 52.4 63.6
## .25 .50 .75 .90 .95
## 76.0 86.0 100.0 100.0 100.0
##
## lowest : 34 50 62 66 68, highest: 86 92 98 99 100
##
## Value 34 50 62 66 68 69 76 78 79 81 84 86 92
## Frequency 1 1 1 1 1 1 1 1 1 2 1 1 1
## Proportion 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.04
##
## Value 98 99 100
## Frequency 2 1 8
## Proportion 0.08 0.04 0.32
hist(F$Fluency_BFBlur)
sd(F$Fluency_BFBlur, na.rm = TRUE)
## [1] 18.05196
# Understanding
F$Und_BFBlur <- as.numeric(F$FamiliarityBLUR_BF_31)
describe(F$Und_BFBlur)
## F$Und_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 18 0.986 81.72 21.93 40.6 59.4
## .25 .50 .75 .90 .95
## 68.0 86.0 99.0 100.0 100.0
##
## lowest : 30 36 59 60 66, highest: 95 96 98 99 100
##
## Value 30 36 59 60 66 67 68 74 79 80 82 86 91
## Frequency 1 1 1 1 1 1 1 1 2 1 1 1 1
## Proportion 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.04 0.04
##
## Value 95 96 98 99 100
## Frequency 1 1 2 1 6
## Proportion 0.04 0.04 0.08 0.04 0.24
sd(F$Und_BFBlur, na.rm = TRUE)
## [1] 20.20833
hist(F$Und_BFBlur)
# Familiarity
F$Fam_BFBlur <- as.numeric(F$FamiliarityBLUR_BF_32)
# Risk
F$R1_BFBlur <- as.numeric(F$RiskBLUR_BF_30)
F$R2_BFBlur <- as.numeric(F$RiskBLUR_BF_31)
F$R3_BFBlur <- as.numeric(F$RiskBLUR_BF_32)
hist(F$R1_BFBlur)
hist(F$R2_BFBlur)
hist(F$R3_BFBlur)
F$RiskScore_BFBlur <- rowMeans(F [, c( "R1_BFBlur" , "R2_BFBlur", "R3_BFBlur")], na.rm=TRUE)
describe(F$RiskScore_BFBlur)
## F$RiskScore_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 21 0.998 65.17 23.5 34.07 39.40
## .25 .50 .75 .90 .95
## 52.00 65.00 78.33 96.20 99.67
##
## lowest : 32.33333 33.33333 37.00000 43.00000 47.33333
## highest: 78.33333 85.66667 93.00000 98.33333 100.00000
sd(F$RiskScore_BFBlur, na.rm = TRUE)
## [1] 20.20202
F$RiskScale_BFBlur <- data.frame(F$R1_BFBlur, F$R2_BFBlur, F$R3_BFBlur)
describe(F$RiskScale_BFBlur)
## F$RiskScale_BFBlur
##
## 3 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.R1_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 17 0.995 60.44 37.03 1.2 10.4
## .25 .50 .75 .90 .95
## 31.0 70.0 82.0 96.8 100.0
##
## lowest : 0 6 17 29 31, highest: 81 82 89 92 100
##
## Value 0 6 17 29 31 42 52 68 69 70 73 79 81
## Frequency 2 1 1 2 1 1 2 1 1 1 1 3 1
## Proportion 0.08 0.04 0.04 0.08 0.04 0.04 0.08 0.04 0.04 0.04 0.04 0.12 0.04
##
## Value 82 89 92 100
## Frequency 1 1 2 3
## Proportion 0.04 0.04 0.08 0.12
## --------------------------------------------------------------------------------
## F.R2_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 19 0.997 53.6 39.14 5.0 5.4
## .25 .50 .75 .90 .95
## 25.0 62.0 76.0 99.2 100.0
##
## lowest : 1 5 6 21 25, highest: 76 86 92 98 100
##
## Value 1 5 6 21 25 27 34 52 53 62 64 65 70
## Frequency 1 2 1 1 2 2 1 1 1 1 1 1 1
## Proportion 0.04 0.08 0.04 0.04 0.08 0.08 0.04 0.04 0.04 0.04 0.04 0.04 0.04
##
## Value 73 76 86 92 98 100
## Frequency 2 1 1 1 1 3
## Proportion 0.08 0.04 0.04 0.04 0.04 0.12
## --------------------------------------------------------------------------------
## F.R3_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 19 0.992 81.48 20.59 40.8 57.6
## .25 .50 .75 .90 .95
## 77.0 86.0 96.0 100.0 100.0
##
## lowest : 28 38 52 66 67, highest: 92 94 96 97 100
##
## Value 28 38 52 66 67 69 77 79 80 81 85 86 90
## Frequency 1 1 1 1 1 1 1 2 1 1 1 1 2
## Proportion 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.04 0.04 0.08
##
## Value 91 92 94 96 97 100
## Frequency 1 1 1 1 1 5
## Proportion 0.04 0.04 0.04 0.04 0.04 0.20
## --------------------------------------------------------------------------------
# Benefit
F$B1_BFBlur <- as.numeric(F$BenefitBLUR_BF_40)
F$B2_BFBlur <- as.numeric(F$BenefitBLUR_BF_42)
F$B3_BFBlur <- as.numeric(F$BenefitBLUR_BF_43)
F$B4_BFBlur <- as.numeric(F$BenefitBLUR_BF_44)
hist(F$B1_BFBlur)
hist(F$B2_BFBlur)
hist(F$B3_BFBlur)
hist(F$B4_BFBlur)
F$BenScore_BFBlur <- rowMeans(F [, c( "B1_BFBlur" , "B2_BFBlur", "B3_BFBlur", "B4_BFBlur")], na.rm=TRUE)
describe(F$BenScore_BFBlur)
## F$BenScore_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 22 0.998 75.53 21.79 47.10 55.30
## .25 .50 .75 .90 .95
## 61.00 70.25 93.00 99.70 100.00
##
## lowest : 41.50 45.50 53.50 58.00 59.50, highest: 93.00 98.25 98.75 99.25 100.00
sd(F$BenScore_BFBlur, na.rm = TRUE)
## [1] 18.82918
F$BenScale_BFBlur <- data.frame(F$B1_BFBlur, F$B2_BFBlur, F$B3_BFBlur, F$B4_BFBlur)
describe(F$BenScale_BFBlur)
## F$BenScale_BFBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.B1_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 21 0.996 74 29.28 33.2 34.8
## .25 .50 .75 .90 .95
## 65.0 80.0 98.0 100.0 100.0
##
## lowest : 3 33 34 36 45, highest: 91 93 98 99 100
## --------------------------------------------------------------------------------
## F.B2_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 19 0.986 74.4 29.89 29.6 32.8
## .25 .50 .75 .90 .95
## 58.0 87.0 97.0 100.0 100.0
##
## lowest : 20 29 32 34 39, highest: 93 94 95 97 100
##
## Value 20 29 32 34 39 42 58 66 69 70 79 80 87
## Frequency 1 1 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
##
## Value 88 93 94 95 97 100
## Frequency 2 1 1 1 1 6
## Proportion 0.08 0.04 0.04 0.04 0.04 0.24
## --------------------------------------------------------------------------------
## F.B3_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 19 0.978 75.44 28.15 29.4 35.0
## .25 .50 .75 .90 .95
## 63.0 81.0 100.0 100.0 100.0
##
## lowest : 16 29 31 41 52, highest: 85 89 91 99 100
##
## Value 16 29 31 41 52 55 63 68 72 74 78 80 81
## Frequency 1 1 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
##
## Value 82 85 89 91 99 100
## Frequency 1 1 1 1 1 7
## Proportion 0.04 0.04 0.04 0.04 0.04 0.28
## --------------------------------------------------------------------------------
## F.B4_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 19 0.992 78.28 21.99 41.0 47.8
## .25 .50 .75 .90 .95
## 72.0 81.0 97.0 100.0 100.0
##
## lowest : 38 40 45 52 57, highest: 89 91 97 99 100
##
## Value 38 40 45 52 57 66 72 74 75 78 80 81 82
## Frequency 1 1 1 1 1 1 1 2 1 1 1 2 1
## Proportion 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.04 0.08 0.04
##
## Value 86 89 91 97 99 100
## Frequency 1 1 1 1 1 5
## Proportion 0.04 0.04 0.04 0.04 0.04 0.20
## --------------------------------------------------------------------------------
# Support
F$S1_BFBlur <- as.numeric(F$SupportBLUR_BF_40)
F$S2_BFBlur <- as.numeric(F$SupportBLUR_BF_42)
F$S3_BFBlur <- as.numeric(F$SupportBLUR_BF_43)
F$S4_BFBlur <- as.numeric(F$SupportBLUR_BF_45)
hist(F$S1_BFBlur)
hist(F$S2_BFBlur)
hist(F$S3_BFBlur)
hist(F$S4_BFBlur)
F$SupScore_BFBlur <- rowMeans(F [, c( "S1_BFBlur" , "S2_BFBlur", "S3_BFBlur", "S4_BFBlur")], na.rm=TRUE)
describe(F$SupScore_BFBlur)
## F$SupScore_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 22 0.996 74.65 24.1 45.60 51.40
## .25 .50 .75 .90 .95
## 58.00 76.75 97.50 100.00 100.00
##
## lowest : 32.00 44.25 51.00 52.00 54.25, highest: 93.75 97.50 98.00 99.25 100.00
sd(F$SupScore_BFBlur, na.rm = TRUE)
## [1] 20.81115
F$SupScale_BFBlur <- data.frame(F$S1_BFBlur, F$S2_BFBlur, F$S3_BFBlur, F$S4_BFBlur)
describe(F$SupScale_BFBlur)
## F$SupScale_BFBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.S1_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 16 0.985 73.16 29.15 33.6 36.0
## .25 .50 .75 .90 .95
## 49.0 80.0 99.0 100.0 100.0
##
## lowest : 25 33 36 38 49, highest: 82 88 97 99 100
##
## Value 25 33 36 38 49 52 77 78 79 80 81 82 88
## Frequency 1 1 2 2 1 1 1 1 2 1 1 2 1
## Proportion 0.04 0.04 0.08 0.08 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.08 0.04
##
## Value 97 99 100
## Frequency 1 1 6
## Proportion 0.04 0.04 0.24
## --------------------------------------------------------------------------------
## F.S2_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 17 0.99 77.08 26.83 28.60 32.00
## .25 .50 .75 .90 .95
## 71.00 83.50 98.25 100.00 100.00
##
## lowest : 23 28 32 52 71, highest: 90 97 98 99 100
##
## Value 23 28 32 52 71 77 79 81 82 83 84
## Frequency 1 1 2 1 2 1 1 1 1 1 2
## Proportion 0.042 0.042 0.083 0.042 0.083 0.042 0.042 0.042 0.042 0.042 0.083
##
## Value 87 90 97 98 99 100
## Frequency 1 1 1 1 1 5
## Proportion 0.042 0.042 0.042 0.042 0.042 0.208
## --------------------------------------------------------------------------------
## F.S3_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 18 0.99 75.83 25.07 31.9 41.5
## .25 .50 .75 .90 .95
## 66.5 80.5 96.5 100.0 100.0
##
## lowest : 31 37 52 53 59, highest: 87 88 96 98 100
##
## Value 31 37 52 53 59 69 71 73 77 80 81
## Frequency 2 1 1 1 1 2 1 1 1 1 1
## Proportion 0.083 0.042 0.042 0.042 0.042 0.083 0.042 0.042 0.042 0.042 0.042
##
## Value 83 85 87 88 96 98 100
## Frequency 1 1 1 1 1 1 5
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.208
## --------------------------------------------------------------------------------
## F.S4_BFBlur
## n missing distinct Info Mean Gmd .05 .10
## 25 25 18 0.986 74 27.63 35.0 35.4
## .25 .50 .75 .90 .95
## 54.0 78.0 97.0 100.0 100.0
##
## lowest : 29 35 36 42 52, highest: 86 90 95 97 100
##
## Value 29 35 36 42 52 54 67 68 72 75 78 80 84
## Frequency 1 2 1 1 1 1 1 1 1 2 1 1 1
## Proportion 0.04 0.08 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.08 0.04 0.04 0.04
##
## Value 86 90 95 97 100
## Frequency 1 1 1 1 6
## Proportion 0.04 0.04 0.04 0.04 0.24
## --------------------------------------------------------------------------------
Enhanced Weathering (Clear Condition)
F$EW_Clear_Page.Submit
## [1] NA 1.478 1.937 NA NA NA NA 2.652 NA 0.870
## [11] 1.376 NA NA 69.565 NA 0.901 NA 37.983 NA 21.172
## [21] 88.520 NA NA 3.762 22.067 NA 19.378 45.657 NA NA
## [31] 23.935 4.101 NA 2.989 12.882 NA 1.013 NA NA 30.501
## [41] NA 8.684 35.346 17.406 5.079 48.032 NA 44.100 NA NA
F$EW_Clear_ATN
## [1] NA 1 3 NA NA NA NA 4 NA 4 3 NA NA 3 NA 3 NA 3 NA 3 3 NA NA 3 3
## [26] NA 3 3 NA NA 3 3 NA 3 3 NA 1 NA NA 3 NA 3 3 3 3 3 NA 3 NA NA
F$EW_Clear_ATN_TIME_Page.Submit
## [1] NA 4.939 1.811 NA NA NA NA 3.964 NA 1.783 2.200 NA
## [13] NA 1.788 NA 1.801 NA 2.152 NA 1.847 3.591 NA NA 2.580
## [25] 2.643 NA 2.001 4.574 NA NA 1.654 1.500 NA 1.218 2.719 NA
## [37] 6.535 NA NA 4.676 NA 1.400 2.200 3.870 1.802 2.258 NA 3.981
## [49] NA NA
F$EW_Clear_ATN2
## [1] NA
## [2] "Its dangerous "
## [3] "Yea"
## [4] NA
## [5] NA
## [6] NA
## [7] NA
## [8] "I don't remember "
## [9] NA
## [10] "I’m not sure "
## [11] "Cyhhzgbk"
## [12] NA
## [13] NA
## [14] "Been changed from rock form to powder form and goes into the ocean"
## [15] NA
## [16] "It's hard to believe in enhanced weather and I don't see how any human can make the weather happen"
## [17] NA
## [18] "It draws carbon out of the atmosphere and combined with other minerals "
## [19] NA
## [20] "This involves depositing calcium carbonate runoff into the water where they believe it will harmlessly settle on the ocean floor. "
## [21] "From what I understand enhanced weathering is the process of pulling carbon dioxide out of the atmosphere through powdered mineral rocks added to land, which flows into the ocean, and stores deposits at the bottom of the ocean"
## [22] NA
## [23] NA
## [24] "Was hard to read"
## [25] "using rocks to pull carbon out of the atmosphere and 'store' it in the deep ocean"
## [26] NA
## [27] "Pretty much taking the carbon from our atmosphere and mixing it with powdered rock minerals which eventually would run off into the ocean ultimately settling on the ocean floor "
## [28] "I really don't understand what this is or what it's main purpose is "
## [29] NA
## [30] NA
## [31] "Did not fully understand. It’s for weathering, but what does that even mean and how is it beneficial? "
## [32] "I don't really know "
## [33] NA
## [34] "Keeping elements from damage\nTokyo drift \nSolar System\nStars"
## [35] "No idea "
## [36] NA
## [37] "Very cool "
## [38] NA
## [39] NA
## [40] "Enhanced weathering is the process of using biofuels in our natural climate "
## [41] NA
## [42] "The weather is getting to the point where everything changes"
## [43] "It takes carbon dioxide out of the atmosphere"
## [44] "It takes carbon dioxide out of the atmosphere by using rocks"
## [45] "It help to know what going on in the world from start to finish is good "
## [46] "Mixing rocks and land to generate weather patterns "
## [47] NA
## [48] "Small scale field experiments "
## [49] NA
## [50] NA
F$EW_Clear_ATN2_TIME_Page.Submit
## [1] NA 7.179 2.790 NA NA NA NA 4.956 NA 3.648
## [11] 6.301 NA NA 38.174 NA 10.600 NA 22.561 NA 39.288
## [21] 37.024 NA NA 11.662 63.752 NA 65.672 62.572 NA NA
## [31] 26.761 8.303 NA 29.057 4.375 NA 3.123 NA NA 44.753
## [41] NA 34.087 13.158 78.481 24.405 18.775 NA 15.272 NA NA
# Naturalness
F$N1_EWClear <- as.numeric(F$Naturalness_EW_30)
F$N2R_EWClear <- as.numeric(100 - F$Naturalness_EW_31)
F$N3R_EWClear <- as.numeric(100 - F$Naturalness_EW_35)
F$N4R_EWClear <- as.numeric(100- F$Naturalness_EW_36)
hist(F$N1_EWClear)
hist(F$N2R_EWClear)
hist(F$N3R_EWClear)
hist(F$N4R_EWClear)
F$NatScore_EWClear <- rowMeans(F [, c( "N1_EWClear" , "N2R_EWClear", "N3R_EWClear", "N4R_EWClear")], na.rm=TRUE)
describe(F$NatScore_EWClear)
## F$NatScore_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 25 1 40.54 18.23 19.38 21.25
## .25 .50 .75 .90 .95
## 27.56 40.04 47.44 64.62 69.38
##
## lowest : 16.00 19.25 19.75 22.75 25.00, highest: 58.50 64.00 65.25 70.75 72.25
sd(F$NatScore_EWClear, na.rm = TRUE)
## [1] 15.85944
F$NatScale_EWClear <- data.frame(F$N1_EWClear, F$N2R_EWClear, F$N3R_EWClear, F$N4R_EWClear)
describe(F$NatScale_EWClear)
## F$NatScale_EWClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.N1_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 18 0.986 68.52 35.85 14.6 20.6
## .25 .50 .75 .90 .95
## 37.0 74.0 99.0 100.0 100.0
##
## lowest : 14 17 26 31 36, highest: 93 94 95 99 100
##
## Value 14 17 26 31 36 37 50 58 60 69 71 74 80
## Frequency 2 1 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.08 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
##
## Value 93 94 95 99 100
## Frequency 1 1 2 1 6
## Proportion 0.04 0.04 0.08 0.04 0.24
## --------------------------------------------------------------------------------
## F.N2R_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 23 0.998 31.19 32.63 0.00 1.00
## .25 .50 .75 .90 .95
## 5.25 21.00 47.75 74.00 81.25
##
## lowest : 0 2 3 4 5, highest: 66 69 79 82 100
## --------------------------------------------------------------------------------
## F.N3R_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 19 0.987 28.5 28.55 0.0 0.0
## .25 .50 .75 .90 .95
## 6.0 22.5 46.0 63.0 68.0
##
## lowest : 0 4 12 15 17, highest: 50 61 65 69 91
##
## Value 0 4 12 15 17 21 24 26 29 37 40
## Frequency 6 1 1 1 1 3 1 1 1 1 1
## Proportion 0.231 0.038 0.038 0.038 0.038 0.115 0.038 0.038 0.038 0.038 0.038
##
## Value 43 47 48 50 61 65 69 91
## Frequency 1 1 1 1 1 1 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038
## --------------------------------------------------------------------------------
## F.N4R_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.998 35.04 35.17 0.25 1.00
## .25 .50 .75 .90 .95
## 10.00 27.00 48.25 85.50 94.25
##
## lowest : 0 1 2 8 16, highest: 64 74 82 89 96
## --------------------------------------------------------------------------------
# Fluency
F$Fluency_EWClear <- as.numeric(F$Fluency_EW_30)
describe(F$Fluency_EWClear)
## F$Fluency_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 18 0.971 67.96 36.76 10.5 18.0
## .25 .50 .75 .90 .95
## 44.0 78.0 100.0 100.0 100.0
##
## lowest : 0 9 15 21 28, highest: 81 82 90 95 100
##
## Value 0 9 15 21 28 39 42 50 61 65 72
## Frequency 1 1 1 1 1 1 1 1 2 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.038 0.038
##
## Value 76 80 81 82 90 95 100
## Frequency 1 1 1 1 1 1 8
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.308
hist(F$Fluency_EWClear)
sd(F$Fluency_EWClear, na.rm = TRUE)
## [1] 32.65882
# Understanding
F$Und_EWClear <- as.numeric(F$Familiarity_EW_33)
describe(F$Und_EWClear)
## F$Und_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 18 0.992 64.65 38.64 2.00 11.00
## .25 .50 .75 .90 .95
## 35.75 73.00 97.00 100.00 100.00
##
## lowest : 0 8 14 18 20, highest: 84 94 98 99 100
##
## Value 0 8 14 18 20 27 62 67 69 73 77
## Frequency 2 1 1 1 1 1 1 2 2 2 1
## Proportion 0.077 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.077 0.077 0.038
##
## Value 79 83 84 94 98 99 100
## Frequency 1 1 1 1 1 1 5
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.192
hist(F$Und_EWClear)
sd(F$Und_EWClear, na.rm = TRUE)
## [1] 34.86424
# Familiarity
F$Fam_EWClear <- as.numeric(F$Familiarity_EW_34)
# Risk
F$R1_EWClear <- as.numeric(F$Risk_EW_32)
F$R2_EWClear <- as.numeric(F$Risk_EW_33)
F$R3_EWClear <- as.numeric(F$Risk_EW_34)
hist(F$R1_EWClear)
hist(F$R2_EWClear)
hist(F$R3_EWClear)
F$RiskScore_EWClear <- rowMeans(F [, c( "R1_EWClear" , "R2_EWClear", "R3_EWClear")], na.rm=TRUE)
describe(F$RiskScore_EWClear)
## F$RiskScore_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.998 59.22 29.55 18.92 33.67
## .25 .50 .75 .90 .95
## 42.75 56.33 79.17 93.67 99.58
##
## lowest : 2.666667 14.333333 32.666667 34.666667 35.000000
## highest: 82.000000 85.333333 89.000000 98.333333 100.000000
sd(F$RiskScore_EWClear, na.rm = TRUE)
## [1] 25.67869
F$RiskScale_EWClear <- data.frame(F$R1_EWClear, F$R2_EWClear, F$R3_EWClear)
describe(F$RiskScale_EWClear)
## F$RiskScale_EWClear
##
## 3 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.R1_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.997 59.35 38.04 0.50 3.50
## .25 .50 .75 .90 .95
## 50.00 63.50 85.75 99.50 100.00
##
## lowest : 0 2 5 13 30, highest: 87 92 96 99 100
## --------------------------------------------------------------------------------
## F.R2_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 18 0.991 51.23 44.1 0.5 5.0
## .25 .50 .75 .90 .95
## 13.0 50.0 91.5 100.0 100.0
##
## lowest : 0 2 8 9 12, highest: 81 90 92 99 100
##
## Value 0 2 8 9 12 16 22 24 38 50 52
## Frequency 2 1 2 1 1 1 1 1 1 3 1
## Proportion 0.077 0.038 0.077 0.038 0.038 0.038 0.038 0.038 0.038 0.115 0.038
##
## Value 62 67 81 90 92 99 100
## Frequency 1 1 1 1 1 1 5
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.192
## --------------------------------------------------------------------------------
## F.R3_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 19 0.991 67.08 33.38 18.5 21.5
## .25 .50 .75 .90 .95
## 50.0 75.0 92.0 100.0 100.0
##
## lowest : 0 18 20 23 42, highest: 83 89 93 98 100
##
## Value 0 18 20 23 42 44 50 62 66 67 75
## Frequency 1 1 1 1 1 1 3 1 1 1 2
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.115 0.038 0.038 0.038 0.077
##
## Value 78 79 82 83 89 93 98 100
## Frequency 1 1 1 1 1 1 1 5
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.192
## --------------------------------------------------------------------------------
# Benefit
F$B1_EWClear <- as.numeric(F$Benefit_EW_40)
F$B2_EWClear <- as.numeric(F$Benefit_EW_42)
F$B3_EWClear <- as.numeric(F$Benefit_EW_43)
F$B4_EWClear <- as.numeric(F$Benefit_EW_51)
hist(F$B1_EWClear)
hist(F$B2_EWClear)
hist(F$B3_EWClear)
hist(F$B4_EWClear)
F$BenScore_EWClear <- rowMeans(F [, c( "B1_EWClear" , "B2_EWClear", "B3_EWClear", "B4_EWClear")], na.rm=TRUE)
describe(F$BenScore_EWClear)
## F$BenScore_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 23 0.998 66.07 35.43 5.75 11.38
## .25 .50 .75 .90 .95
## 51.12 68.25 98.75 99.88 100.00
##
## lowest : 0.25 5.25 7.25 15.50 42.75, highest: 98.00 99.00 99.50 99.75 100.00
sd(F$BenScore_EWClear, na.rm = TRUE)
## [1] 31.66225
F$BenScale_EWClear <- data.frame(F$B1_EWClear, F$B2_EWClear, F$B3_EWClear, F$B4_EWClear)
describe(F$BenScale_EWClear)
## F$BenScale_EWClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.B1_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 14 0.958 62.62 39.84 2.25 10.50
## .25 .50 .75 .90 .95
## 41.25 64.50 100.00 100.00 100.00
##
## lowest : 0 9 12 20 30, highest: 64 65 72 85 100
##
## Value 0 9 12 20 30 40 45 49 54 64 65
## Frequency 2 1 1 1 1 1 1 1 2 2 2
## Proportion 0.077 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.077 0.077
##
## Value 72 85 100
## Frequency 1 1 9
## Proportion 0.038 0.038 0.346
## --------------------------------------------------------------------------------
## F.B2_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 19 0.987 66 36.58 7.00 12.00
## .25 .50 .75 .90 .95
## 49.25 71.50 98.00 100.00 100.00
##
## lowest : 1 6 10 14 40, highest: 86 87 95 99 100
##
## Value 1 6 10 14 40 41 49 50 54 57 65
## Frequency 1 1 1 1 1 1 1 1 1 2 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.038
##
## Value 71 72 76 86 87 95 99 100
## Frequency 1 1 1 2 1 1 1 6
## Proportion 0.038 0.038 0.038 0.077 0.038 0.038 0.038 0.231
## --------------------------------------------------------------------------------
## F.B3_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 18 0.987 69.12 36.1 4.25 15.00
## .25 .50 .75 .90 .95
## 52.25 75.00 99.00 100.00 100.00
##
## lowest : 0 1 14 16 38, highest: 89 93 97 99 100
##
## Value 0 1 14 16 38 48 50 59 61 65 68
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038
##
## Value 73 77 89 93 97 99 100
## Frequency 2 2 1 1 1 2 6
## Proportion 0.077 0.077 0.038 0.038 0.038 0.077 0.231
## --------------------------------------------------------------------------------
## F.B4_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 25 25 22 0.996 65.28 36.36 2.6 13.4
## .25 .50 .75 .90 .95
## 50.0 72.0 97.0 100.0 100.0
##
## lowest : 0 1 9 20 35, highest: 88 97 98 99 100
## --------------------------------------------------------------------------------
# Support
F$S1_EWClear <- as.numeric(F$Support_EW_40)
F$S2_EWClear <- as.numeric(F$Support_EW_42)
F$S3_EWClear <- as.numeric(F$Support_EW_43)
F$S4_EWClear <- as.numeric(F$Support_EW_45)
hist(F$S1_EWClear)
hist(F$S2_EWClear)
hist(F$S3_EWClear)
hist(F$S4_EWClear)
F$SupScore_EWClear <- rowMeans(F [, c( "S1_EWClear" , "S2_EWClear", "S3_EWClear", "S4_EWClear")], na.rm=TRUE)
describe(F$SupScore_EWClear)
## F$SupScore_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 24 0.999 60.5 37.62 7.125 12.875
## .25 .50 .75 .90 .95
## 35.750 64.500 92.125 99.625 100.000
##
## lowest : 0.00 5.25 12.75 13.00 17.25, highest: 93.25 98.00 99.00 99.25 100.00
sd(F$SupScore_EWClear, na.rm = TRUE)
## [1] 32.83923
F$SupScale_EWClear <- data.frame(F$S1_EWClear, F$S2_EWClear, F$S3_EWClear, F$S4_EWClear)
describe(F$SupScale_EWClear)
## F$SupScale_EWClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.S1_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.995 59.81 38.3 6.5 9.0
## .25 .50 .75 .90 .95
## 33.5 63.0 89.0 100.0 100.0
##
## lowest : 0 6 8 10 19, highest: 83 91 97 98 100
## --------------------------------------------------------------------------------
## F.S2_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.993 61.42 38.94 6.00 12.50
## .25 .50 .75 .90 .95
## 34.25 66.00 95.50 100.00 100.00
##
## lowest : 0 4 12 13 18, highest: 83 91 97 99 100
## --------------------------------------------------------------------------------
## F.S3_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.993 59.65 38.96 3.00 9.00
## .25 .50 .75 .90 .95
## 36.25 61.50 92.75 100.00 100.00
##
## lowest : 0 2 6 12 15, highest: 75 92 93 97 100
## --------------------------------------------------------------------------------
## F.S4_EWClear
## n missing distinct Info Mean Gmd .05 .10
## 26 24 22 0.998 61.12 36.48 9.50 19.50
## .25 .50 .75 .90 .95
## 37.25 62.50 91.25 99.50 100.00
##
## lowest : 0 7 17 22 24, highest: 92 97 98 99 100
## --------------------------------------------------------------------------------
Enhanced Weathering (Blurred Condition)
F$EW_Blurred_Page.Submit
## [1] 1.902 NA NA 2.375 22.558 0.797 11.918 NA 3.438 NA
## [11] NA 26.701 0.881 NA 13.366 NA 0.717 NA 36.561 NA
## [21] NA 56.001 60.702 NA NA 55.011 NA NA 1.199 1.199
## [31] NA NA 16.758 NA NA 15.100 NA 0.686 4.355 NA
## [41] 0.818 NA NA NA NA NA 22.149 NA 0.636 2.072
F$EW_Blurred_ATN
## [1] 3 NA NA 3 3 4 3 NA 3 NA NA 3 3 NA 3 NA 2 NA 3 NA NA 3 3 NA NA
## [26] 3 NA NA 3 4 NA NA 3 NA NA 3 NA 4 3 NA 3 NA NA NA NA NA 3 NA 4 3
F$EW_Blurred_ATN_TIME_Page.Submit
## [1] 2.001 NA NA 1.700 1.792 3.886 1.588 NA 1.246 NA
## [11] NA 1.986 1.857 NA 2.928 NA 1.882 NA 1.700 NA
## [21] NA 2.201 3.603 NA NA 2.354 NA NA 2.752 2.256
## [31] NA NA 1.878 NA NA 2.394 NA 2.217 5.000 NA
## [41] 52.037 NA NA NA NA NA 8.229 NA 25.417 2.312
F$EW_Blurred_ATN2
## [1] "Lajdb. Aksibd aosi leuhsnqa pckabwnx oshs. pw ohdbo eobenwi"
## [2] NA
## [3] NA
## [4] "Ok boo I love you "
## [5] "They take carbon dioxide out of things."
## [6] "Hghjjj"
## [7] "Stealing resources from the atmosphere. "
## [8] NA
## [9] "How much is the best way of life and the rest is a good idea since it has to do it before the end result of a new hat"
## [10] NA
## [11] NA
## [12] "Rock weathering "
## [13] "I can not "
## [14] NA
## [15] "Taking energy from rocks "
## [16] NA
## [17] "I'm interested "
## [18] NA
## [19] "i wasn’t really sure but something about taking minerals out of rocks and adding them to the oceans and waterways "
## [20] NA
## [21] NA
## [22] "Enhanced weathering is a process where normal weathering is accelerated by using calcium and carbon."
## [23] "Enhanced weathering is a scientific approach to alter weather patterns around the world. "
## [24] NA
## [25] NA
## [26] "It’s a process of changing minareals to the ocean floor "
## [27] NA
## [28] NA
## [29] "Make the letters readable"
## [30] "I dont know"
## [31] NA
## [32] NA
## [33] "Not undersrand much"
## [34] NA
## [35] NA
## [36] "None"
## [37] NA
## [38] "The fact that you have a softball doubleheader and you want to do this for yourself as well and you are playing with your friends are a couple of things I need to be at Houghs and the baby will you have a "
## [39] "no specific reasoning"
## [40] NA
## [41] "Everything is good now I’m not sure if anything "
## [42] NA
## [43] NA
## [44] NA
## [45] NA
## [46] NA
## [47] "I dont like this idea to try to make fuel from the air"
## [48] NA
## [49] "It all good to me"
## [50] "Du diekigjenchebfijwfiwbijfbdiwnficnsodnfksnwicjsbfowbfinsificjdjeoxjsncosjfndowkfnoenfjfnd"
F$EW_Blurred_ATN2_TIME_Page.Submit
## [1] 13.166 NA NA 3.065 9.469 2.484 13.000 NA 5.843 NA
## [11] NA 8.831 4.362 NA 8.290 NA 4.972 NA 21.355 NA
## [21] NA 54.201 77.520 NA NA 25.439 NA NA 9.701 8.778
## [31] NA NA 9.748 NA NA 4.148 NA 10.487 18.503 NA
## [41] 5.078 NA NA NA NA NA 41.573 NA 5.690 9.787
# Naturalness
F$N1_EWBlur <- as.numeric(F$NaturalnessBLUR_EW_30)
F$N2R_EWBlur <- as.numeric(100 - F$NaturalnessBLUR_EW_31)
F$N3R_EWBlur <- as.numeric(100 - F$NaturalnessBLUR_EW_35)
F$N4R_EWBlur <- as.numeric(100- F$NaturalnessBLUR_EW_36)
hist(F$N1_EWBlur)
hist(F$N2R_EWBlur)
hist(F$N3R_EWBlur)
hist(F$N4R_EWBlur)
F$NatScore_EWBlur <- rowMeans(F [, c( "N1_EWBlur" , "N2R_EWBlur", "N3R_EWBlur", "N4R_EWBlur")], na.rm=TRUE)
describe(F$NatScore_EWBlur)
## F$NatScore_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 20 0.998 37.17 22.15 0.675 10.275
## .25 .50 .75 .90 .95
## 25.000 35.375 52.750 58.375 62.787
##
## lowest : 0.00 4.50 23.75 24.75 25.00, highest: 53.25 57.50 58.75 63.50 74.75
##
## Value 0.00 4.50 23.75 24.75 25.00 31.00 31.25 31.75 32.75 38.00 39.00
## Frequency 2 1 1 1 2 1 1 2 1 1 1
## Proportion 0.083 0.042 0.042 0.042 0.083 0.042 0.042 0.083 0.042 0.042 0.042
##
## Value 45.00 45.50 49.75 52.75 53.25 57.50 58.75 63.50 74.75
## Frequency 1 1 1 2 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.083 0.042 0.042 0.042 0.042 0.042
sd(F$NatScore_EWBlur, na.rm = TRUE)
## [1] 19.37511
F$NatScale_EWBlur <- data.frame(F$N1_EWBlur, F$N2R_EWBlur, F$N3R_EWBlur, F$N4R_EWBlur)
describe(F$NatScale_EWBlur)
## F$NatScale_EWBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.N1_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 20 0.997 52.54 39.88 0.00 0.90
## .25 .50 .75 .90 .95
## 25.25 57.00 82.00 95.10 99.85
##
## lowest : 0 3 17 23 26, highest: 82 85 86 99 100
##
## Value 0 3 17 23 26 35 36 39 43 52 62
## Frequency 3 1 1 1 1 1 1 1 1 1 1
## Proportion 0.125 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 64 72 77 78 82 85 86 99 100
## Frequency 1 1 1 1 2 1 1 1 2
## Proportion 0.042 0.042 0.042 0.042 0.083 0.042 0.042 0.042 0.083
## --------------------------------------------------------------------------------
## F.N2R_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 20 0.991 24.75 25.82 0.00 0.00
## .25 .50 .75 .90 .95
## 2.50 20.50 37.25 60.60 66.85
##
## lowest : 0 1 3 6 16, highest: 46 48 66 67 74
##
## Value 0 1 3 6 16 18 19 20 21 26 27
## Frequency 5 1 1 1 1 1 1 1 1 1 1
## Proportion 0.208 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 29 30 36 41 46 48 66 67 74
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
## --------------------------------------------------------------------------------
## F.N3R_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 18 0.99 32.79 35 0.00 0.00
## .25 .50 .75 .90 .95
## 7.75 25.50 60.50 73.70 94.85
##
## lowest : 0 7 8 12 15, highest: 63 66 77 98 100
##
## Value 0 7 8 12 15 20 22 29 30 32 39
## Frequency 5 1 1 1 2 1 1 1 1 2 1
## Proportion 0.208 0.042 0.042 0.042 0.083 0.042 0.042 0.042 0.042 0.083 0.042
##
## Value 60 62 63 66 77 98 100
## Frequency 1 1 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042
## --------------------------------------------------------------------------------
## F.N4R_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 20 0.995 38.58 37.49 0.00 0.00
## .25 .50 .75 .90 .95
## 15.50 28.00 68.75 82.90 96.05
##
## lowest : 0 8 14 16 18, highest: 76 78 85 98 99
##
## Value 0 8 14 16 18 19 23 24 32 34 35
## Frequency 4 1 1 1 2 1 1 1 1 1 1
## Proportion 0.167 0.042 0.042 0.042 0.083 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 48 60 67 74 76 78 85 98 99
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
## --------------------------------------------------------------------------------
# Fluency
F$Fluency_EWBlur <- as.numeric(F$FluencyBLUR_EW_34)
describe(F$Fluency_EWBlur)
## F$Fluency_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 18 0.99 75 28.2 18.85 37.10
## .25 .50 .75 .90 .95
## 71.50 83.00 97.25 100.00 100.00
##
## lowest : 11 16 35 42 44, highest: 86 87 97 98 100
##
## Value 11 16 35 42 44 64 74 78 79 80 83
## Frequency 1 1 1 1 1 1 2 1 1 1 2
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.083 0.042 0.042 0.042 0.083
##
## Value 84 85 86 87 97 98 100
## Frequency 1 1 1 1 1 1 5
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.208
hist(F$Fluency_EWBlur)
sd(F$Fluency_EWBlur, na.rm = TRUE)
## [1] 26.46737
# Understanding
F$Und_EWBlur <- as.numeric(F$FamiliarityBLUR_EW_33)
describe(F$Und_EWBlur)
## F$Und_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 22 0.999 55.71 36.47 8.45 12.80
## .25 .50 .75 .90 .95
## 28.25 66.50 81.75 89.70 98.50
##
## lowest : 5 8 11 17 20, highest: 84 85 89 90 100
hist(F$Und_EWBlur)
sd(F$Und_EWBlur, na.rm = TRUE)
## [1] 31.53118
# Familiarity
F$Fam_EWBlur <- as.numeric(F$FamiliarityBLUR_EW_34)
# Risk
F$R1_EWBlur <- as.numeric(F$RiskBLUR_EW_32)
F$R2_EWBlur <- as.numeric(F$RiskBLUR_EW_33)
F$R3_EWBlur <- as.numeric(F$RiskBLUR_EW_34)
hist(F$R1_EWBlur)
hist(F$R2_EWBlur)
hist(F$R3_EWBlur)
F$RiskScore_EWBlur <- rowMeans(F [, c( "R1_EWBlur" , "R2_EWBlur", "R3_EWBlur")], na.rm=TRUE)
describe(F$RiskScore_EWBlur)
## F$RiskScore_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 22 0.999 62.78 23.42 29.93 34.23
## .25 .50 .75 .90 .95
## 49.38 61.17 77.17 88.57 91.37
##
## lowest : 29.33333 33.33333 36.33333 47.33333 48.50000
## highest: 85.33333 86.00000 89.66667 91.66667 98.33333
sd(F$RiskScore_EWBlur, na.rm = TRUE)
## [1] 20.05623
F$RiskScale_EWBlur <- data.frame(F$R1_EWBlur, F$R2_EWBlur, F$R3_EWBlur)
describe(F$RiskScale_EWBlur)
## F$RiskScale_EWBlur
##
## 3 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.R1_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 23 27 16 0.996 67.17 29.57 2.2 23.6
## .25 .50 .75 .90 .95
## 63.0 76.0 84.5 89.0 98.9
##
## lowest : 0 22 30 54 60, highest: 84 85 87 89 100
##
## Value 0 22 30 54 60 66 72 74 76 77 80
## Frequency 2 1 1 1 1 2 1 2 1 1 3
## Proportion 0.087 0.043 0.043 0.043 0.043 0.087 0.043 0.087 0.043 0.043 0.130
##
## Value 84 85 87 89 100
## Frequency 1 1 1 2 2
## Proportion 0.043 0.043 0.043 0.087 0.087
## --------------------------------------------------------------------------------
## F.R2_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 19 0.998 59.71 34.92 1.65 14.60
## .25 .50 .75 .90 .95
## 32.00 72.00 82.75 90.00 90.85
##
## lowest : 0 11 23 30 32, highest: 85 87 90 91 100
##
## Value 0 11 23 30 32 40 44 69 71 73 75
## Frequency 2 1 1 1 2 1 1 2 1 1 1
## Proportion 0.083 0.042 0.042 0.042 0.083 0.042 0.042 0.083 0.042 0.042 0.042
##
## Value 76 81 82 85 87 90 91 100
## Frequency 1 1 2 1 1 2 1 1
## Proportion 0.042 0.042 0.083 0.042 0.042 0.083 0.042 0.042
## --------------------------------------------------------------------------------
## F.R3_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 22 0.999 62.25 32.96 15.35 23.90
## .25 .50 .75 .90 .95
## 35.75 67.00 85.00 94.10 99.25
##
## lowest : 9 14 23 26 30, highest: 88 90 92 95 100
## --------------------------------------------------------------------------------
# Benefit
F$B1_EWBlur <- as.numeric(F$BenefitBLUR_EW_40)
F$B2_EWBlur <- as.numeric(F$BenefitBLUR_EW_42)
F$B3_EWBlur <- as.numeric(F$BenefitBLUR_EW_43)
F$B4_EWBlur <- as.numeric(F$BenefitBLUR_EW_51)
hist(F$B1_EWBlur)
hist(F$B2_EWBlur)
hist(F$B3_EWBlur)
hist(F$B4_EWBlur)
F$BenScore_EWBlur <- rowMeans(F [, c( "B1_EWBlur" , "B2_EWBlur", "B3_EWBlur", "B4_EWBlur")], na.rm=TRUE)
describe(F$BenScore_EWBlur)
## F$BenScore_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 23 27 20 0.999 61.05 30.23 10.00 19.25
## .25 .50 .75 .90 .95
## 51.62 62.25 79.88 90.60 99.20
##
## lowest : 0.00 9.50 14.50 38.25 49.75, highest: 80.75 84.75 85.00 92.00 100.00
##
## 0 (1, 0.043), 9.5 (1, 0.043), 14.5 (1, 0.043), 38.25 (1, 0.043), 49.75 (1,
## 0.043), 51 (1, 0.043), 52.25 (2, 0.087), 54 (1, 0.043), 54.25 (1, 0.043), 60.25
## (1, 0.043), 62.25 (1, 0.043), 67 (2, 0.087), 73 (1, 0.043), 77.5 (1, 0.043), 79
## (1, 0.043), 80.75 (1, 0.043), 84.75 (1, 0.043), 85 (1, 0.043), 92 (1, 0.043),
## 100 (2, 0.087)
sd(F$BenScore_EWBlur, na.rm = TRUE)
## [1] 26.86223
F$BenScale_EWBlur <- data.frame(F$B1_EWBlur, F$B2_EWBlur, F$B3_EWBlur, F$B4_EWBlur)
describe(F$BenScale_EWBlur)
## F$BenScale_EWBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.B1_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 23 27 20 0.999 57.43 38.78 0.6 8.4
## .25 .50 .75 .90 .95
## 29.5 68.0 84.0 97.4 99.9
##
## lowest : 0 6 18 19 25, highest: 82 86 91 99 100
##
## Value 0 6 18 19 25 34 37 52 53 60 68
## Frequency 2 1 1 1 1 1 1 1 1 1 1
## Proportion 0.087 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043
##
## Value 70 76 78 81 82 86 91 99 100
## Frequency 1 1 1 1 1 2 1 1 2
## Proportion 0.043 0.043 0.043 0.043 0.043 0.087 0.043 0.043 0.087
## --------------------------------------------------------------------------------
## F.B2_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 23 27 19 0.997 62.96 32.44 9.0 19.2
## .25 .50 .75 .90 .95
## 52.0 72.0 82.5 89.6 99.0
##
## lowest : 0 8 18 24 32, highest: 84 87 88 90 100
##
## Value 0 8 18 24 32 52 61 65 69 72 75
## Frequency 1 1 1 1 1 3 1 1 1 1 1
## Proportion 0.043 0.043 0.043 0.043 0.043 0.130 0.043 0.043 0.043 0.043 0.043
##
## Value 78 79 81 84 87 88 90 100
## Frequency 1 1 2 1 1 1 1 2
## Proportion 0.043 0.043 0.087 0.043 0.043 0.043 0.043 0.087
## --------------------------------------------------------------------------------
## F.B3_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 23 27 17 0.997 61.61 34.13 2.0 21.6
## .25 .50 .75 .90 .95
## 42.5 71.0 84.0 92.4 99.3
##
## lowest : 0 20 28 33 52, highest: 85 86 90 93 100
##
## Value 0 20 28 33 52 61 70 71 72 75 82
## Frequency 2 1 2 1 2 2 1 1 1 2 1
## Proportion 0.087 0.043 0.087 0.043 0.087 0.087 0.043 0.043 0.043 0.087 0.043
##
## Value 83 85 86 90 93 100
## Frequency 1 1 1 1 1 2
## Proportion 0.043 0.043 0.043 0.043 0.043 0.087
## --------------------------------------------------------------------------------
## F.B4_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 23 27 20 0.999 62.22 31.58 5.6 11.6
## .25 .50 .75 .90 .95
## 56.5 70.0 81.0 87.6 98.8
##
## lowest : 0 5 11 14 36, highest: 82 84 86 88 100
##
## Value 0 5 11 14 36 52 61 65 68 69 70
## Frequency 1 1 1 1 1 1 1 1 1 2 1
## Proportion 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.087 0.043
##
## Value 71 73 76 80 82 84 86 88 100
## Frequency 2 1 1 1 1 1 1 1 2
## Proportion 0.087 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.087
## --------------------------------------------------------------------------------
# Support
F$S1_EWBlur <- as.numeric(F$SupportBLUR_EW_40)
F$S2_EWBlur <- as.numeric(F$SupportBLUR_EW_42)
F$S3_EWBlur <- as.numeric(F$SupportBLUR_EW_43)
F$S4_EWBlur <- as.numeric(F$SupportBLUR_EW_45)
hist(F$S1_EWBlur)
hist(F$S2_EWBlur)
hist(F$S3_EWBlur)
hist(F$S4_EWBlur)
F$SupScore_EWBlur <- rowMeans(F [, c( "S1_EWBlur" , "S2_EWBlur", "S3_EWBlur", "S4_EWBlur")], na.rm=TRUE)
describe(F$SupScore_EWBlur)
## F$SupScore_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 24 1 57.82 32.64 10.81 19.08
## .25 .50 .75 .90 .95
## 45.31 57.88 78.62 94.92 97.45
##
## lowest : 0.00 9.50 18.25 21.00 28.50, highest: 82.25 93.00 95.75 97.75 100.00
sd(F$SupScore_EWBlur, na.rm = TRUE)
## [1] 28.1679
F$SupScale_EWBlur <- data.frame(F$S1_EWBlur, F$S2_EWBlur, F$S3_EWBlur, F$S4_EWBlur)
describe(F$SupScale_EWBlur)
## F$SupScale_EWBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.S1_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 21 0.998 59 35.9 2.85 19.90
## .25 .50 .75 .90 .95
## 37.25 65.50 81.75 97.90 100.00
##
## lowest : 0 19 22 30 35, highest: 81 84 89 93 100
## --------------------------------------------------------------------------------
## F.S2_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 21 0.999 58.88 36.18 3.05 10.80
## .25 .50 .75 .90 .95
## 39.75 65.50 82.00 96.10 97.85
##
## lowest : 0 2 9 15 29, highest: 85 94 97 98 100
## --------------------------------------------------------------------------------
## F.S3_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 23 27 20 0.999 54.39 37.94 8.4 21.2
## .25 .50 .75 .90 .95
## 24.0 53.0 86.0 93.0 99.4
##
## lowest : 0 7 21 22 23, highest: 85 87 89 94 100
##
## Value 0 7 21 22 23 24 25 29 35 52 53
## Frequency 1 1 1 1 1 2 1 1 1 1 1
## Proportion 0.043 0.043 0.043 0.043 0.043 0.087 0.043 0.043 0.043 0.043 0.043
##
## Value 68 70 77 79 85 87 89 94 100
## Frequency 1 1 1 1 1 2 1 1 2
## Proportion 0.043 0.043 0.043 0.043 0.043 0.087 0.043 0.043 0.087
## --------------------------------------------------------------------------------
## F.S4_EWBlur
## n missing distinct Info Mean Gmd .05 .10
## 24 26 21 0.999 59.29 31.03 22.30 24.60
## .25 .50 .75 .90 .95
## 36.75 68.00 74.25 92.70 98.95
##
## lowest : 0 22 24 26 30, highest: 75 78 92 93 100
## --------------------------------------------------------------------------------
Wind Energy (Clear Condition)
F$WE_Clear_Page.Submit
## [1] NA NA 1.299 1.397 13.702 0.871 NA NA 1.440 0.684
## [11] 1.096 8.900 1.299 NA 14.149 NA NA 35.010 NA 12.603
## [21] NA NA 48.501 2.236 NA 12.766 7.134 NA NA 2.332
## [31] NA NA NA 0.932 NA NA NA 2.470 NA NA
## [41] 0.823 NA NA 67.273 NA NA 17.552 NA 0.722 1.100
F$WE_Clear_ATN
## [1] NA NA 4 4 4 1 NA NA 4 3 4 4 3 NA 4 NA NA 4 NA 4 NA NA 4 4 NA
## [26] 4 4 NA NA 2 NA NA NA 4 NA NA NA 4 NA NA 3 NA NA 4 NA NA 4 NA 4 4
F$WE_Clear_ATN_TIME_Page.Submit
## [1] NA NA 1.555 1.609 1.436 1.401 NA NA 1.369 1.532 1.501 2.567
## [13] 4.869 NA 1.782 NA NA 4.443 NA 1.401 NA NA 1.970 2.213
## [25] NA 1.696 5.863 NA NA 2.376 NA NA NA 1.691 NA NA
## [37] NA 1.136 NA NA 1.572 NA NA 7.659 NA NA 3.286 NA
## [49] 2.089 1.466
F$WE_Clear_ATN2
## [1] NA
## [2] NA
## [3] "Very good "
## [4] "Please post your links here "
## [5] "Turbines are used to produce energy from the wind which can then be connected to a generator and used as electricity."
## [6] "Ytguyy"
## [7] NA
## [8] NA
## [9] "What does this mean for the next few days to go through the years and years old when the first to know that"
## [10] "I’m not sure "
## [11] "such jdfjufdjjssjjsjsjsjdjdj"
## [12] "Energy my through wind turbines"
## [13] "I can not "
## [14] NA
## [15] "Wind energy from wind turbines in fields "
## [16] NA
## [17] NA
## [18] "It’s extracting energy from wind to use for power "
## [19] NA
## [20] "This is anout using turbines to generate wond energy. This is already being done."
## [21] NA
## [22] NA
## [23] "Wind energy is generated through turbines and makes electricity for generators to be used on farms."
## [24] "Wind energy is natural and very sustainable and would be very good for environment and human existence "
## [25] NA
## [26] "It’s a way to take wind and turn it into energy to power things and use it as a genaratior"
## [27] "Wind energy takes the wind and by the force generated from that wind, through windmills or turbines it creates energy. The energy then creates electricity which in turn we can use. Powering everything we power now."
## [28] NA
## [29] NA
## [30] "Dont know"
## [31] NA
## [32] NA
## [33] NA
## [34] "Ways to promote this possibility.\nScientific Research\nWind farts"
## [35] NA
## [36] NA
## [37] NA
## [38] "The only way to make it work and we can still get it to you at all times if we need it to be more efficient to do this is to be a good friend to you "
## [39] NA
## [40] NA
## [41] "Everything is okay and everything it was just happened "
## [42] NA
## [43] NA
## [44] "Power generated through the use of wind"
## [45] NA
## [46] NA
## [47] "Generating energy from spinning wind power"
## [48] NA
## [49] "It cool to me"
## [50] "Eirjjxkfnxiwnfjiwkdjdbsknfnxnc. Oencn jd cksnckdiwnsndo. Xenografts ficndnck cnsisnc"
F$WE_Clear_ATN2_TIME_Page.Submit
## [1] NA NA 6.438 2.801 29.650 2.271 NA NA 4.642 3.727
## [11] 5.301 13.872 5.225 NA 14.390 NA NA 14.584 NA 15.844
## [21] NA NA 66.463 75.020 NA 33.873 77.500 NA NA 8.194
## [31] NA NA NA 33.258 NA NA NA 9.276 NA NA
## [41] 5.114 NA NA 40.979 NA NA 24.713 NA 3.629 8.841
# Naturalness
F$N1_WEClear <- as.numeric(F$Naturalness_WE_30)
F$N2R_WEClear <- as.numeric(100 - F$Naturalness_WE_31)
F$N3R_WEClear <- as.numeric(100 - F$Naturalness_WE_35)
F$N4R_WEClear <- as.numeric(100- F$Naturalness_WE_36)
hist(F$N1_WEClear)
hist(F$N2R_WEClear)
hist(F$N3R_WEClear)
hist(F$N4R_WEClear)
F$NatScore_WEClear <- rowMeans(F [, c( "N1_WEClear" , "N2R_WEClear", "N3R_WEClear", "N4R_WEClear")], na.rm=TRUE)
describe(F$NatScore_WEClear)
## F$NatScore_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 21 0.999 38.95 17.83 14.38 25.00
## .25 .50 .75 .90 .95
## 29.69 39.38 46.69 56.42 63.85
##
## lowest : 0.00 12.50 25.00 25.50 28.75, highest: 50.00 51.00 58.75 64.75 75.00
sd(F$NatScore_WEClear, na.rm = TRUE)
## [1] 15.99541
F$NatScale_WEClear <- data.frame(F$N1_WEClear, F$N2R_WEClear, F$N3R_WEClear, F$N4R_WEClear)
describe(F$NatScale_WEClear)
## F$NatScale_WEClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.N1_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 15 0.974 76.04 30.87 4.65 32.50
## .25 .50 .75 .90 .95
## 70.00 85.50 100.00 100.00 100.00
##
## lowest : 0 31 36 52 70, highest: 87 90 91 99 100
##
## Value 0 31 36 52 70 81 82 84 85 86 87
## Frequency 2 1 1 1 2 2 1 1 1 1 1
## Proportion 0.083 0.042 0.042 0.042 0.083 0.083 0.042 0.042 0.042 0.042 0.042
##
## Value 90 91 99 100
## Frequency 1 1 1 7
## Proportion 0.042 0.042 0.042 0.292
## --------------------------------------------------------------------------------
## F.N2R_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 13 0.962 19.21 22.83 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 16.0 30.5 48.0 56.5
##
## lowest : 0 6 13 16 18, highest: 32 33 48 58 75
##
## Value 0 6 13 16 18 20 24 30 32 33 48
## Frequency 8 2 1 2 2 1 1 1 1 1 2
## Proportion 0.333 0.083 0.042 0.083 0.083 0.042 0.042 0.042 0.042 0.042 0.083
##
## Value 58 75
## Frequency 1 1
## Proportion 0.042 0.042
## --------------------------------------------------------------------------------
## F.N3R_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 17 0.99 22.62 25.66 0.00 0.00
## .25 .50 .75 .90 .95
## 2.75 19.00 25.25 62.00 68.00
##
## lowest : 0 2 3 5 12, highest: 26 27 48 68 100
##
## Value 0 2 3 5 12 15 18 19 20 21 22
## Frequency 5 1 1 1 1 1 1 2 1 1 1
## Proportion 0.208 0.042 0.042 0.042 0.042 0.042 0.042 0.083 0.042 0.042 0.042
##
## Value 25 26 27 48 68 100
## Frequency 2 1 1 1 2 1
## Proportion 0.083 0.042 0.042 0.042 0.083 0.042
## --------------------------------------------------------------------------------
## F.N4R_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 19 0.995 37.92 38.28 0.00 0.00
## .25 .50 .75 .90 .95
## 12.00 27.50 68.75 88.10 98.80
##
## lowest : 0 10 12 13 16, highest: 77 78 79 92 100
##
## Value 0 10 12 13 16 18 23 25 30 34 36
## Frequency 4 1 2 1 1 1 1 1 1 1 1
## Proportion 0.167 0.042 0.083 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 42 47 66 77 78 79 92 100
## Frequency 1 1 1 1 1 1 1 2
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.083
## --------------------------------------------------------------------------------
# Fluency
F$Fluency_WEClear <- as.numeric(F$Fluency_WE_30)
describe(F$Fluency_WEClear)
## F$Fluency_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 15 0.974 84.12 22.25 34.9 50.8
## .25 .50 .75 .90 .95
## 81.0 93.0 100.0 100.0 100.0
##
## lowest : 11 34 40 76 78, highest: 95 97 98 99 100
##
## Value 11 34 40 76 78 82 83 85 90 91 95
## Frequency 1 1 1 1 2 1 2 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.083 0.042 0.083 0.042 0.042 0.042 0.042
##
## Value 97 98 99 100
## Frequency 1 1 2 7
## Proportion 0.042 0.042 0.083 0.292
hist(F$Fluency_WEClear)
sd(F$Fluency_WEClear, na.rm = TRUE)
## [1] 23.51745
# Understanding
F$Und_WEClear <- as.numeric(F$Familiarity_WE_30)
describe(F$Und_WEClear)
## F$Und_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 14 0.928 85.17 19.28 54.25 68.80
## .25 .50 .75 .90 .95
## 76.75 92.00 100.00 100.00 100.00
##
## lowest : 22 52 67 73 75, highest: 85 91 93 97 100
##
## Value 22 52 67 73 75 76 77 78 81 85 91
## Frequency 1 1 1 1 1 1 2 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.083 0.042 0.042 0.042 0.042
##
## Value 93 97 100
## Frequency 1 1 10
## Proportion 0.042 0.042 0.417
hist(F$Und_WEClear)
sd(F$Und_WEClear , na.rm = TRUE)
## [1] 19.10308
# Familiarity
F$Fam_WEClear <- as.numeric(F$Familiarity_WE_31)
# Risk
F$R1_WEClear <- as.numeric(F$Risk_WE_32)
F$R2_WEClear <- as.numeric(F$Risk_WE_33)
F$R3_WEClear <- as.numeric(F$Risk_WE_34)
hist(F$R1_WEClear)
hist(F$R2_WEClear)
hist(F$R3_WEClear)
F$RiskScore_WEClear <- rowMeans(F [, c( "R1_WEClear" , "R2_WEClear", "R3_WEClear")], na.rm=TRUE)
describe(F$RiskScore_WEClear)
## F$RiskScore_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 22 0.999 58.79 24.05 33.33 33.53
## .25 .50 .75 .90 .95
## 39.25 64.00 71.33 85.33 88.88
##
## lowest : 27.00000 33.33333 34.00000 36.33333 38.00000
## highest: 76.33333 83.00000 86.33333 89.33333 100.00000
sd(F$RiskScore_WEClear, na.rm = TRUE)
## [1] 20.73663
F$RiskScale_WEClear <- data.frame(F$R1_WEClear, F$R2_WEClear, F$R3_WEClear)
describe(F$RiskScale_WEClear)
## F$RiskScale_WEClear
##
## 3 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.R1_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 18 0.996 53.71 42.62 0.00 0.60
## .25 .50 .75 .90 .95
## 13.25 69.00 82.00 97.00 100.00
##
## lowest : 0 2 4 11 14, highest: 81 82 86 90 100
##
## Value 0 2 4 11 14 27 35 46 52 62 76
## Frequency 3 1 1 1 1 1 1 1 1 1 1
## Proportion 0.125 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 79 80 81 82 86 90 100
## Frequency 1 2 1 2 1 1 3
## Proportion 0.042 0.083 0.042 0.083 0.042 0.042 0.125
## --------------------------------------------------------------------------------
## F.R2_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 19 0.995 48.75 41.86 0.00 0.00
## .25 .50 .75 .90 .95
## 14.50 53.50 81.25 94.10 99.25
##
## lowest : 0 8 10 16 22, highest: 81 82 92 95 100
##
## Value 0 8 10 16 22 27 33 41 52 55 60
## Frequency 4 1 1 1 1 1 1 1 1 1 1
## Proportion 0.167 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 68 70 76 81 82 92 95 100
## Frequency 1 1 1 1 2 1 1 2
## Proportion 0.042 0.042 0.042 0.042 0.083 0.042 0.042 0.083
## --------------------------------------------------------------------------------
## F.R3_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 16 0.975 73.92 29.28 26.2 34.2
## .25 .50 .75 .90 .95
## 69.0 79.0 100.0 100.0 100.0
##
## lowest : 0 25 33 37 52, highest: 81 82 87 92 100
##
## Value 0 25 33 37 52 63 71 72 73 75 79
## Frequency 1 1 1 1 1 1 1 1 2 1 2
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.083 0.042 0.083
##
## Value 81 82 87 92 100
## Frequency 1 1 1 1 7
## Proportion 0.042 0.042 0.042 0.042 0.292
## --------------------------------------------------------------------------------
# Benefit
F$B1_WEClear <- as.numeric(F$Benefit_WE_19)
F$B2_WEClear <- as.numeric(F$Benefit_WE_27)
F$B3_WEClear <- as.numeric(F$Benefit_WE_28)
F$B4_WEClear <- as.numeric(F$Benefit_WE_29)
hist(F$B1_WEClear)
hist(F$B2_WEClear)
hist(F$B3_WEClear)
hist(F$B4_WEClear)
F$BenScore_WEClear <- rowMeans(F [, c( "B1_WEClear" , "B2_WEClear", "B3_WEClear", "B4_WEClear")], na.rm=TRUE)
describe(F$BenScore_WEClear)
## F$BenScore_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 20 0.995 78.92 22.74 44.75 49.90
## .25 .50 .75 .90 .95
## 65.00 83.62 95.06 100.00 100.00
##
## lowest : 32.75 44.00 49.00 52.00 55.00, highest: 93.50 94.25 97.50 99.50 100.00
##
## 32.75 (1, 0.042), 44 (1, 0.042), 49 (1, 0.042), 52 (1, 0.042), 55 (1, 0.042),
## 62 (1, 0.042), 66 (1, 0.042), 75.25 (1, 0.042), 76 (1, 0.042), 81.5 (1, 0.042),
## 81.75 (1, 0.042), 83.5 (1, 0.042), 83.75 (1, 0.042), 86.5 (1, 0.042), 86.75 (1,
## 0.042), 93.5 (2, 0.083), 94.25 (1, 0.042), 97.5 (1, 0.042), 99.5 (1, 0.042),
## 100 (4, 0.167)
sd(F$BenScore_WEClear, na.rm = TRUE)
## [1] 20.21887
F$BenScale_WEClear <- data.frame(F$B1_WEClear, F$B2_WEClear, F$B3_WEClear, F$B4_WEClear)
describe(F$BenScale_WEClear)
## F$BenScale_WEClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.B1_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 16 0.975 75 29.26 24.15 27.10
## .25 .50 .75 .90 .95
## 70.25 79.50 100.00 100.00 100.00
##
## lowest : 7 24 25 32 52, highest: 80 82 84 93 100
##
## Value 7 24 25 32 52 68 71 76 77 78 79
## Frequency 1 1 1 1 1 1 1 1 1 1 2
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.083
##
## Value 80 82 84 93 100
## Frequency 1 1 1 2 7
## Proportion 0.042 0.042 0.042 0.083 0.292
## --------------------------------------------------------------------------------
## F.B2_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 23 27 15 0.981 84.61 17.11 64 66
## .25 .50 .75 .90 .95
## 79 86 99 100 100
##
## lowest : 29 64 74 78 80, highest: 90 91 94 98 100
##
## Value 29 64 74 78 80 81 84 85 86 89 90
## Frequency 1 2 2 1 1 1 1 2 1 1 1
## Proportion 0.043 0.087 0.087 0.043 0.043 0.043 0.043 0.087 0.043 0.043 0.043
##
## Value 91 94 98 100
## Frequency 1 1 1 6
## Proportion 0.043 0.043 0.043 0.261
## --------------------------------------------------------------------------------
## F.B3_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 16 0.983 79.83 19.67 53.35 62.50
## .25 .50 .75 .90 .95
## 72.75 80.00 92.50 100.00 100.00
##
## lowest : 19 52 61 66 72, highest: 84 85 86 90 100
##
## Value 19 52 61 66 72 73 74 76 78 79 81
## Frequency 1 1 1 1 2 1 1 1 2 1 1
## Proportion 0.042 0.042 0.042 0.042 0.083 0.042 0.042 0.042 0.083 0.042 0.042
##
## Value 84 85 86 90 100
## Frequency 1 1 1 2 6
## Proportion 0.042 0.042 0.042 0.083 0.250
## --------------------------------------------------------------------------------
## F.B4_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 14 0.928 77.58 30.18 24.6 31.3
## .25 .50 .75 .90 .95
## 68.5 89.0 100.0 100.0 100.0
##
## lowest : 9 24 28 39 41, highest: 84 86 88 90 100
##
## Value 9 24 28 39 41 52 74 77 80 84 86
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 88 90 100
## Frequency 1 2 10
## Proportion 0.042 0.083 0.417
## --------------------------------------------------------------------------------
# Support
F$S1_WEClear <- as.numeric(F$Support_WE_40)
F$S2_WEClear <- as.numeric(F$Support_WE_42)
F$S3_WEClear <- as.numeric(F$Support_WE_43)
F$S4_WEClear <- as.numeric(F$Support_WE_45)
hist(F$S1_WEClear)
hist(F$S2_WEClear)
hist(F$S3_WEClear)
hist(F$S4_WEClear)
F$SupScore_WEClear <- rowMeans(F [, c( "S1_WEClear" , "S2_WEClear", "S3_WEClear", "S4_WEClear")], na.rm=TRUE)
describe(F$SupScore_WEClear)
## F$SupScore_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 20 0.997 74.2 26.23 37.09 45.88
## .25 .50 .75 .90 .95
## 62.50 76.25 96.25 99.78 100.00
##
## lowest : 11.75 36.00 43.25 52.00 52.50, highest: 96.00 97.00 97.75 99.25 100.00
##
## 11.75 (1, 0.042), 36 (1, 0.042), 43.25 (1, 0.042), 52 (1, 0.042), 52.5 (1,
## 0.042), 61 (1, 0.042), 63 (1, 0.042), 67 (1, 0.042), 67.5 (1, 0.042), 68.5 (1,
## 0.042), 69.5 (2, 0.083), 83 (1, 0.042), 84.75 (1, 0.042), 86.75 (2, 0.083), 88
## (1, 0.042), 96 (1, 0.042), 97 (1, 0.042), 97.75 (1, 0.042), 99.25 (1, 0.042),
## 100 (3, 0.125)
sd(F$SupScore_WEClear, na.rm = TRUE)
## [1] 23.42634
F$SupScale_WEClear <- data.frame(F$S1_WEClear, F$S2_WEClear, F$S3_WEClear, F$S4_WEClear)
describe(F$SupScale_WEClear)
## F$SupScale_WEClear
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.S1_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 19 0.995 75.83 28.08 25.90 31.90
## .25 .50 .75 .90 .95
## 65.75 85.50 95.50 100.00 100.00
##
## lowest : 15 25 31 34 52, highest: 91 95 97 99 100
##
## Value 15 25 31 34 52 65 66 74 76 79 80
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 82 89 90 91 95 97 99 100
## Frequency 1 2 1 2 1 1 1 4
## Proportion 0.042 0.083 0.042 0.083 0.042 0.042 0.042 0.167
## --------------------------------------------------------------------------------
## F.S2_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 18 0.994 79.71 21.99 37.55 56.20
## .25 .50 .75 .90 .95
## 76.25 85.00 93.50 100.00 100.00
##
## lowest : 8 35 52 66 67, highest: 91 93 95 96 100
##
## Value 8 35 52 66 67 71 78 80 82 84 86
## Frequency 1 1 1 1 1 1 1 1 2 2 2
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.083 0.083 0.083
##
## Value 88 89 91 93 95 96 100
## Frequency 1 1 1 1 1 1 4
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.167
## --------------------------------------------------------------------------------
## F.S3_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 19 0.995 71.29 33 23.45 28.40
## .25 .50 .75 .90 .95
## 49.50 82.50 98.00 100.00 100.00
##
## lowest : 5 23 26 34 35, highest: 90 96 98 99 100
##
## Value 5 23 26 34 35 42 52 63 64 71 78
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 82 83 89 90 96 98 99 100
## Frequency 1 2 1 1 1 2 1 4
## Proportion 0.042 0.083 0.042 0.042 0.042 0.083 0.042 0.167
## --------------------------------------------------------------------------------
## F.S4_WEClear
## n missing distinct Info Mean Gmd .05 .10
## 24 26 18 0.99 69.96 32.97 21.15 23.20
## .25 .50 .75 .90 .95
## 43.75 81.00 97.50 100.00 100.00
##
## lowest : 9 21 22 26 40, highest: 84 85 97 99 100
##
## Value 9 21 22 26 40 43 44 52 75 76 80
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042
##
## Value 81 83 84 85 97 99 100
## Frequency 3 1 1 1 1 1 5
## Proportion 0.125 0.042 0.042 0.042 0.042 0.042 0.208
## --------------------------------------------------------------------------------
Wind Energy (Blurred Condition)
F$WE_Blurred_Page.Submit
## [1] 1.147 2.385 NA NA NA NA 2.001 0.701 NA NA
## [11] NA NA NA 48.389 NA 8.573 0.712 NA 10.254 NA
## [21] 22.425 54.905 NA NA 22.788 NA NA 41.601 0.621 NA
## [31] 7.614 3.101 7.426 NA 4.072 7.375 0.879 NA 1.500 3.555
## [41] NA 4.903 20.008 NA 1.708 14.652 NA 27.324 NA NA
F$WE_Blurred_ATN
## [1] 4 4 NA NA NA NA 4 4 NA NA NA NA NA 4 NA 4 2 NA 4 NA 4 4 NA NA 4
## [26] NA NA 4 3 NA 4 4 4 NA 4 4 3 NA 4 4 NA 4 4 NA 4 4 NA 4 NA NA
F$WE_Blurred_ATN_TIME_Page.Submit
## [1] 1.202 1.563 NA NA NA NA 1.482 3.701 NA NA NA NA
## [13] NA 3.201 NA 1.201 3.858 NA 2.270 NA 2.661 2.099 NA NA
## [25] 3.015 NA NA 3.851 1.291 NA 2.138 1.701 2.129 NA 2.115 2.176
## [37] 4.828 NA 2.663 2.948 NA 3.401 1.845 NA 1.602 1.942 NA 2.482
## [49] NA NA
F$WE_Blurred_ATN2
## [1] "Laid oais paisb isna poshe posi eon sidb "
## [2] "Its when.the entertainment is bad "
## [3] NA
## [4] NA
## [5] NA
## [6] NA
## [7] "Combustion wind through energy "
## [8] "I don't remember "
## [9] NA
## [10] NA
## [11] NA
## [12] NA
## [13] NA
## [14] "Wind blows tubines to create energy"
## [15] NA
## [16] "Wild energy is just something people are trying to make"
## [17] "I'm interested "
## [18] NA
## [19] "the use of windmills and technology to make wind be energy"
## [20] NA
## [21] "Wind energy is using the wind to turn turbines, which then can be produced into energy and distributed through generators"
## [22] "Through the turning of wind turbines, electricity is produced, therefore providing wind energy. These turbines are often found in areas such as farmland."
## [23] NA
## [24] NA
## [25] "wind energy is created by using turbines that generate energy by spinning. It is used in areas with enough wind to support the devices"
## [26] NA
## [27] NA
## [28] "The process of using wind to produce electricity."
## [29] "Too small"
## [30] NA
## [31] "Uses windmills to create energy. "
## [32] "I don't know "
## [33] "Generate electricity from spinning"
## [34] NA
## [35] "Natural wind into energy "
## [36] "None"
## [37] "Very cool "
## [38] NA
## [39] "i think that this is the most logical of all the four energy sources"
## [40] "Wind energy is accumulated by wind turbines "
## [41] NA
## [42] "The wind helps make energy better and does different things"
## [43] "Produces natural energy by utilizing the wind "
## [44] NA
## [45] "Easy to know what happened in the world from the past to finish "
## [46] "Using wind turbines to produce electricity "
## [47] NA
## [48] "Turbines generate energy from spinning "
## [49] NA
## [50] NA
F$WE_Blurred_ATN2_TIME_Page.Submit
## [1] 7.053 19.969 NA NA NA NA 10.498 13.501 NA NA
## [11] NA NA NA 19.001 NA 8.297 3.608 NA 11.931 NA
## [21] 20.501 29.884 NA NA 92.765 NA NA 37.901 5.338 NA
## [31] 8.998 6.158 9.928 NA 11.029 3.690 9.136 NA 42.367 20.501
## [41] NA 35.350 12.930 NA 26.401 15.773 NA 19.750 NA NA
# Naturalness
F$N1_WEBlur <- as.numeric(F$NaturalnessBLUR_WE_30)
F$N2R_WEBlur <- as.numeric(100 - F$NaturalnessBLUR_WE_31)
F$N3R_WEBlur <- as.numeric(100 - F$NaturalnessBLUR_WE_35)
F$N4R_WEBlur <- as.numeric(100- F$NaturalnessBLUR_WE_36)
hist(F$N1_WEBlur)
hist(F$N2R_WEBlur)
hist(F$N3R_WEBlur)
hist(F$N4R_WEBlur)
F$NatScore_WEBlur <- rowMeans(F [, c( "N1_WEBlur" , "N2R_WEBlur", "N3R_WEBlur", "N4R_WEBlur")], na.rm=TRUE)
describe(F$NatScore_WEBlur)
## F$NatScore_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 25 1 50.21 27.55 20.50 25.38
## .25 .50 .75 .90 .95
## 32.69 44.50 72.56 79.75 87.12
##
## lowest : 0.75 19.00 25.00 25.75 26.50, highest: 75.00 77.50 79.50 80.00 89.50
sd(F$NatScore_WEBlur, na.rm = TRUE)
## [1] 23.77916
F$NatScale_WEBlur <- data.frame(F$N1_WEBlur, F$N2R_WEBlur, F$N3R_WEBlur, F$N4R_WEBlur)
describe(F$NatScale_WEBlur)
## F$NatScale_WEBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.N1_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 18 0.971 72.23 33.9 9.75 19.00
## .25 .50 .75 .90 .95
## 64.50 80.50 100.00 100.00 100.00
##
## lowest : 3 7 18 20 25, highest: 82 87 95 98 100
##
## Value 3 7 18 20 25 62 64 66 73 74 77
## Frequency 1 1 1 1 1 1 1 2 1 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.038 0.038 0.038
##
## Value 80 81 82 87 95 98 100
## Frequency 1 1 1 1 1 1 8
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.308
## --------------------------------------------------------------------------------
## F.N2R_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 17 0.986 51.19 44.22 0.00 0.50
## .25 .50 .75 .90 .95
## 20.25 37.00 89.50 100.00 100.00
##
## lowest : 0 1 7 15 20, highest: 76 82 88 90 100
##
## Value 0 1 7 15 20 21 28 31 35 37 57
## Frequency 3 1 1 1 1 1 1 2 1 2 1
## Proportion 0.115 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.038 0.077 0.038
##
## Value 75 76 82 88 90 100
## Frequency 1 1 1 1 1 6
## Proportion 0.038 0.038 0.038 0.038 0.038 0.231
## --------------------------------------------------------------------------------
## F.N3R_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.993 30.73 31.39 0.00 0.00
## .25 .50 .75 .90 .95
## 8.50 23.50 38.75 73.50 76.25
##
## lowest : 0 2 7 13 18, highest: 70 73 74 77 94
## --------------------------------------------------------------------------------
## F.N4R_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 19 0.995 46.69 40.56 0.00 3.00
## .25 .50 .75 .90 .95
## 19.75 32.50 82.25 100.00 100.00
##
## lowest : 0 6 12 18 19, highest: 77 84 85 88 100
##
## Value 0 6 12 18 19 22 28 29 32 33 37
## Frequency 3 1 1 1 1 1 1 2 2 1 1
## Proportion 0.115 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.077 0.038 0.038
##
## Value 48 61 74 77 84 85 88 100
## Frequency 1 1 1 1 1 1 1 4
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.154
## --------------------------------------------------------------------------------
# Fluency
F$Fluency_WEBlur <- as.numeric(F$FluencyBLUR_WE_34)
describe(F$Fluency_WEBlur)
## F$Fluency_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 15 0.958 78.77 27.02 22.5 41.0
## .25 .50 .75 .90 .95
## 69.5 87.0 100.0 100.0 100.0
##
## lowest : 13 22 24 58 63, highest: 86 88 94 99 100
##
## Value 13 22 24 58 63 69 71 73 74 77 86
## Frequency 1 1 1 1 2 1 1 1 1 1 2
## Proportion 0.038 0.038 0.038 0.038 0.077 0.038 0.038 0.038 0.038 0.038 0.077
##
## Value 88 94 99 100
## Frequency 2 1 1 9
## Proportion 0.077 0.038 0.038 0.346
hist(F$Fluency_WEBlur)
sd(F$Fluency_WEBlur, na.rm = TRUE)
## [1] 25.8183
# Understanding
F$Und_WEBlur <- as.numeric(F$FamiliarityBLUR_WE_30)
describe(F$Und_WEBlur)
## F$Und_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 17 0.971 79.46 26.86 23.25 44.00
## .25 .50 .75 .90 .95
## 71.50 89.50 100.00 100.00 100.00
##
## lowest : 4 22 27 61 62, highest: 90 95 97 98 100
##
## Value 4 22 27 61 62 67 71 73 76 79 81
## Frequency 1 1 1 1 1 1 1 1 1 2 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.038
##
## Value 89 90 95 97 98 100
## Frequency 1 1 2 1 1 8
## Proportion 0.038 0.038 0.077 0.038 0.038 0.308
hist(F$Und_WEBlur)
sd(F$Und_WEBlur, na.rm = TRUE)
## [1] 26.37306
# Familiarity
F$Fam_WEBlur <- as.numeric(F$FamiliarityBLUR_WE_31)
# Risk
F$R1_WEBlur <- as.numeric(F$RiskBLUR_WE_32)
F$R2_WEBlur <- as.numeric(F$RiskBLUR_WE_33)
F$R3_WEBlur <- as.numeric(F$RiskBLUR_WE_34)
hist(F$R1_WEBlur)
hist(F$R2_WEBlur)
hist(F$R3_WEBlur)
F$RiskScore_WEBlur <- rowMeans(F [, c( "R1_WEBlur" , "R2_WEBlur", "R3_WEBlur")], na.rm=TRUE)
describe(F$RiskScore_WEBlur)
## F$RiskScore_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 21 0.993 49.85 29.53 18.58 25.33
## .25 .50 .75 .90 .95
## 33.33 37.83 71.67 83.00 94.50
##
## lowest : 8.00000 16.66667 24.33333 26.33333 29.66667
## highest: 80.66667 82.00000 84.00000 98.00000 100.00000
sd(F$RiskScore_WEBlur, na.rm = TRUE)
## [1] 26.15921
F$RiskScale_WEBlur <- data.frame(F$R1_WEBlur, F$R2_WEBlur, F$R3_WEBlur)
describe(F$RiskScale_WEBlur)
## F$RiskScale_WEBlur
##
## 3 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.R1_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 18 0.979 36.12 42.95 0.00 0.00
## .25 .50 .75 .90 .95
## 0.75 17.50 76.00 91.00 95.75
##
## lowest : 0 3 9 15 17, highest: 89 90 92 97 100
##
## Value 0 3 9 15 17 18 20 29 53 67 73
## Frequency 7 3 1 1 1 1 1 1 1 1 1
## Proportion 0.269 0.115 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038
##
## Value 77 84 89 90 92 97 100
## Frequency 1 1 1 1 1 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038
## --------------------------------------------------------------------------------
## F.R2_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 16 0.97 35.62 43.06 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 17.0 80.5 92.0 99.5
##
## lowest : 0 2 6 7 16, highest: 81 85 86 98 100
##
## Value 0 2 6 7 16 17 18 26 41 66 79
## Frequency 8 1 1 1 1 2 1 1 1 1 1
## Proportion 0.308 0.038 0.038 0.038 0.038 0.077 0.038 0.038 0.038 0.038 0.038
##
## Value 81 85 86 98 100
## Frequency 2 1 1 1 2
## Proportion 0.077 0.038 0.038 0.038 0.077
## --------------------------------------------------------------------------------
## F.R3_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 15 0.97 77.81 24.43 35.75 56.50
## .25 .50 .75 .90 .95
## 69.25 80.50 100.00 100.00 100.00
##
## lowest : 4 31 50 63 67, highest: 81 82 83 99 100
##
## Value 4 31 50 63 67 68 73 74 75 80 81
## Frequency 1 1 1 2 1 1 2 2 1 1 1
## Proportion 0.038 0.038 0.038 0.077 0.038 0.038 0.077 0.077 0.038 0.038 0.038
##
## Value 82 83 99 100
## Frequency 1 2 1 8
## Proportion 0.038 0.077 0.038 0.308
## --------------------------------------------------------------------------------
# Benefit
F$B1_WEBlur <- as.numeric(F$BenefitBLUR_WE_19)
F$B2_WEBlur <- as.numeric(F$BenefitBLUR_WE_27)
F$B3_WEBlur <- as.numeric(F$BenefitBLUR_WE_28)
F$B4_WEBlur <- as.numeric(F$BenefitBLUR_WE_29)
hist(F$B1_WEBlur)
hist(F$B2_WEBlur)
hist(F$B3_WEBlur)
hist(F$B4_WEBlur)
F$BenScore_WEBlur <- rowMeans(F [, c( "B1_WEBlur" , "B2_WEBlur", "B3_WEBlur", "B4_WEBlur")], na.rm=TRUE)
describe(F$BenScore_WEBlur)
## F$BenScore_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 18 0.971 77.38 26.53 37.56 44.62
## .25 .50 .75 .90 .95
## 65.12 82.50 100.00 100.00 100.00
##
## lowest : 9.75 35.50 43.75 45.50 57.50, highest: 86.25 86.75 97.75 98.75 100.00
##
## Value 9.75 35.50 43.75 45.50 57.50 59.50 63.75 69.25 71.25
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038
##
## Value 71.75 74.00 75.75 82.50 86.25 86.75 97.75 98.75 100.00
## Frequency 1 1 1 2 1 1 1 1 8
## Proportion 0.038 0.038 0.038 0.077 0.038 0.038 0.038 0.038 0.308
sd(F$BenScore_WEBlur, na.rm = TRUE)
## [1] 24.25616
F$BenScale_WEBlur <- data.frame(F$B1_WEBlur, F$B2_WEBlur, F$B3_WEBlur, F$B4_WEBlur)
describe(F$BenScale_WEBlur)
## F$BenScale_WEBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.B1_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 18 0.971 78.69 25.4 37.50 46.50
## .25 .50 .75 .90 .95
## 70.25 83.00 100.00 100.00 100.00
##
## lowest : 11 37 39 54 59, highest: 85 88 97 98 100
##
## Value 11 37 39 54 59 63 70 71 73 74 78
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038
##
## Value 80 81 85 88 97 98 100
## Frequency 1 1 1 2 1 1 8
## Proportion 0.038 0.038 0.038 0.077 0.038 0.038 0.308
## --------------------------------------------------------------------------------
## F.B2_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 15 0.957 79.15 25.2 34.25 49.00
## .25 .50 .75 .90 .95
## 72.25 85.00 100.00 100.00 100.00
##
## lowest : 6 32 41 57 61, highest: 86 87 88 97 100
##
## Value 6 32 41 57 61 72 73 75 77 84 86
## Frequency 1 1 1 1 1 2 1 3 1 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.077 0.038 0.115 0.038 0.038 0.038
##
## Value 87 88 97 100
## Frequency 1 1 1 9
## Proportion 0.038 0.038 0.038 0.346
## --------------------------------------------------------------------------------
## F.B3_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 16 0.958 73.08 32.61 13.50 31.00
## .25 .50 .75 .90 .95
## 60.25 79.00 100.00 100.00 100.00
##
## lowest : 8 9 27 35 37, highest: 79 82 86 98 100
##
## Value 8 9 27 35 37 57 70 71 73 76 78
## Frequency 1 1 1 2 1 1 1 1 1 1 1
## Proportion 0.038 0.038 0.038 0.077 0.038 0.038 0.038 0.038 0.038 0.038 0.038
##
## Value 79 82 86 98 100
## Frequency 2 1 1 1 9
## Proportion 0.077 0.038 0.038 0.038 0.346
## --------------------------------------------------------------------------------
## F.B4_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 15 0.97 78.58 25.98 31.5 45.5
## .25 .50 .75 .90 .95
## 69.0 88.5 100.0 100.0 100.0
##
## lowest : 14 30 36 55 61, highest: 87 90 92 98 100
##
## Value 14 30 36 55 61 68 69 70 71 74 87
## Frequency 1 1 1 1 1 1 2 1 2 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.038 0.077 0.038 0.038
##
## Value 90 92 98 100
## Frequency 2 1 2 8
## Proportion 0.077 0.038 0.077 0.308
## --------------------------------------------------------------------------------
# Support
F$S1_WEBlur <- as.numeric(F$SupportBLUR_WE_40)
F$S2_WEBlur <- as.numeric(F$SupportBLUR_WE_42)
F$S3_WEBlur <- as.numeric(F$SupportBLUR_WE_43)
F$S4_WEBlur <- as.numeric(F$SupportBLUR_WE_45)
hist(F$S1_WEBlur)
hist(F$S2_WEBlur)
hist(F$S3_WEBlur)
hist(F$S4_WEBlur)
F$SupScore_WEBlur <- rowMeans(F [, c( "S1_WEBlur" , "S2_WEBlur", "S3_WEBlur", "S4_WEBlur")], na.rm=TRUE)
describe(F$SupScore_WEBlur)
## F$SupScore_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 19 0.971 74.62 30.56 21.06 30.50
## .25 .50 .75 .90 .95
## 59.56 83.38 100.00 100.00 100.00
##
## lowest : 5.00 19.25 26.50 34.50 57.25, highest: 85.50 90.50 96.00 97.50 100.00
##
## 5 (1, 0.038), 19.25 (1, 0.038), 26.5 (1, 0.038), 34.5 (1, 0.038), 57.25 (1,
## 0.038), 58.5 (1, 0.038), 59 (1, 0.038), 61.25 (1, 0.038), 64 (1, 0.038), 69.75
## (1, 0.038), 74 (1, 0.038), 74.75 (1, 0.038), 82.5 (1, 0.038), 84.25 (1, 0.038),
## 85.5 (1, 0.038), 90.5 (1, 0.038), 96 (1, 0.038), 97.5 (1, 0.038), 100 (8,
## 0.308)
sd(F$SupScore_WEBlur, na.rm = TRUE)
## [1] 28.01305
F$SupScale_WEBlur <- data.frame(F$S1_WEBlur, F$S2_WEBlur, F$S3_WEBlur, F$S4_WEBlur)
describe(F$SupScale_WEBlur)
## F$SupScale_WEBlur
##
## 4 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.S1_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 17 0.97 77.92 27.25 24.75 36.00
## .25 .50 .75 .90 .95
## 73.50 83.50 100.00 100.00 100.00
##
## lowest : 7 22 33 39 61, highest: 87 89 95 99 100
##
## Value 7 22 33 39 61 70 73 75 79 80 81
## Frequency 1 1 1 1 1 1 1 3 1 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.115 0.038 0.038 0.038
##
## Value 86 87 89 95 99 100
## Frequency 1 1 1 1 1 8
## Proportion 0.038 0.038 0.038 0.038 0.038 0.308
## --------------------------------------------------------------------------------
## F.S2_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 19 0.971 75.42 29.53 29.5 35.5
## .25 .50 .75 .90 .95
## 64.0 82.5 100.0 100.0 100.0
##
## lowest : 3 29 31 40 44, highest: 84 93 95 96 100
##
## Value 3 29 31 40 44 58 63 67 69 70 75
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038
##
## Value 79 82 83 84 93 95 96 100
## Frequency 1 1 1 1 1 1 1 8
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.308
## --------------------------------------------------------------------------------
## F.S3_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 17 0.971 74.31 31.89 12.00 28.00
## .25 .50 .75 .90 .95
## 63.25 84.00 100.00 100.00 100.00
##
## lowest : 0 7 27 29 55, highest: 86 87 96 99 100
##
## Value 0 7 27 29 55 63 64 69 70 74 83
## Frequency 1 1 1 1 2 1 1 1 1 1 1
## Proportion 0.038 0.038 0.038 0.038 0.077 0.038 0.038 0.038 0.038 0.038 0.038
##
## Value 84 86 87 96 99 100
## Frequency 2 1 1 1 1 8
## Proportion 0.077 0.038 0.038 0.038 0.038 0.308
## --------------------------------------------------------------------------------
## F.S4_WEBlur
## n missing distinct Info Mean Gmd .05 .10
## 26 24 17 0.971 70.81 36.61 6.25 21.00
## .25 .50 .75 .90 .95
## 45.75 84.00 100.00 100.00 100.00
##
## lowest : 0 3 16 26 33, highest: 85 89 95 99 100
##
## Value 0 3 16 26 33 36 41 60 62 75 81
## Frequency 1 1 1 1 1 1 1 1 2 1 1
## Proportion 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.077 0.038 0.038
##
## Value 83 85 89 95 99 100
## Frequency 1 1 1 2 1 8
## Proportion 0.038 0.038 0.038 0.077 0.038 0.308
## --------------------------------------------------------------------------------
Individual Differences
Aversion to Tampering with Nature
# Aversion to Tampering with Nature: How much do you agree or disagree with the following statements?
## Item 1: People who push for technological fixes to environmental problems are underestimating the risks.
## Item 2: People who say we shouldn’t tamper with nature are just being naïve.
## Item 3: Human beings have no right to meddle with the natural environment.
## Item 4: I would prefer to live in a world where humans leave nature alone.
## Item 5: Altering nature will be our downfall as a species.
F$ATNS1 <- as.numeric(F$ATNS_36)
F$ATNS2 <- as.numeric(F$ATNS_37)
F$ATNS3 <- as.numeric(F$ATNS_38)
F$ATNS4 <- as.numeric(F$ATNS_39)
F$ATNS5 <- as.numeric(F$ATNS_40)
# Reverse Code Item 2
F$ATNS2R <- (100- F$ATNS2)
describe(F$ATNS2R)
## F$ATNS2R
## n missing distinct Info Mean Gmd .05 .10
## 50 0 33 0.998 50.64 39.43 0.00 0.00
## .25 .50 .75 .90 .95
## 13.50 61.00 75.75 94.20 99.10
##
## lowest : 0 5 8 11 12, highest: 93 94 96 98 100
describe(F$ATNS1)
## F$ATNS1
## n missing distinct Info Mean Gmd .05 .10
## 50 0 37 0.999 63.48 30.64 4.45 15.80
## .25 .50 .75 .90 .95
## 50.50 72.00 83.50 91.60 100.00
##
## lowest : 0 4 5 14 16, highest: 87 89 91 97 100
describe(F$ATNS2R)
## F$ATNS2R
## n missing distinct Info Mean Gmd .05 .10
## 50 0 33 0.998 50.64 39.43 0.00 0.00
## .25 .50 .75 .90 .95
## 13.50 61.00 75.75 94.20 99.10
##
## lowest : 0 5 8 11 12, highest: 93 94 96 98 100
describe(F$ATNS3)
## F$ATNS3
## n missing distinct Info Mean Gmd .05 .10
## 50 0 38 0.998 60.84 34.51 6.25 16.90
## .25 .50 .75 .90 .95
## 37.75 68.50 85.00 100.00 100.00
##
## lowest : 0 4 9 16 17, highest: 90 91 92 97 100
describe(F$ATNS4)
## F$ATNS4
## n missing distinct Info Mean Gmd .05 .10
## 50 0 30 0.991 73.9 28.52 15.95 37.50
## .25 .50 .75 .90 .95
## 64.25 79.00 96.50 100.00 100.00
##
## lowest : 2 9 11 22 24, highest: 92 95 97 99 100
describe(F$ATNS5)
## F$ATNS5
## n missing distinct Info Mean Gmd .05 .10
## 50 0 36 0.997 71.4 28.34 22.5 34.9
## .25 .50 .75 .90 .95
## 56.5 78.0 91.5 100.0 100.0
##
## lowest : 0 2 18 28 34, highest: 92 93 94 95 100
range(F$ATNS1, na.rm=TRUE)
## [1] 0 100
range(F$ATNS2R, na.rm=TRUE)
## [1] 0 100
range(F$ATNS3, na.rm=TRUE)
## [1] 0 100
range(F$ATNS4, na.rm=TRUE)
## [1] 2 100
range(F$ATNS5, na.rm=TRUE)
## [1] 0 100
hist(F$ATNS1, main = 'ATNS #1: People who push for technological fixes to environmental problems are underestimating the risks.')
hist(F$ATNS2R, main = 'ATNS #2: People who say we shouldn’t tamper with nature are just being naïve.')
hist(F$ATNS3, main = 'ATNS #3: Human beings have no right to meddle with the natural environment.')
hist(F$ATNS4, main = 'ATNS #4: I would prefer to live in a world where humans leave nature alone.')
hist(F$ATNS5, main = 'ATNS #5: Altering nature will be our downfall as a species.')
F$ATNS_Scale <- data.frame(F$ATNS1, F$ATNS2R, F$ATNS3, F$ATNS4, F$ATNS5)
psych::alpha(F$ATNS_Scale)
## Number of categories should be increased in order to count frequencies.
## Warning in psych::alpha(F$ATNS_Scale): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( F.ATNS2R ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
##
## Reliability analysis
## Call: psych::alpha(x = F$ATNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.48 0.53 0.6 0.19 1.1 0.12 64 17 0.26
##
## lower alpha upper 95% confidence boundaries
## 0.25 0.48 0.71
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## F.ATNS1 0.45 0.51 0.56 0.21 1.04 0.133 0.096 0.29
## F.ATNS2R 0.71 0.71 0.67 0.38 2.48 0.066 0.014 0.40
## F.ATNS3 0.31 0.37 0.43 0.13 0.60 0.165 0.071 0.16
## F.ATNS4 0.25 0.33 0.43 0.11 0.50 0.179 0.116 0.14
## F.ATNS5 0.25 0.32 0.42 0.10 0.46 0.177 0.090 0.16
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F.ATNS1 50 0.52 0.55 0.37 0.22 63 28
## F.ATNS2R 50 0.26 0.19 -0.11 -0.15 51 34
## F.ATNS3 50 0.69 0.71 0.65 0.41 61 30
## F.ATNS4 50 0.74 0.75 0.67 0.53 74 27
## F.ATNS5 50 0.73 0.76 0.70 0.52 71 26
describe(F$ATNS_Scale)
## F$ATNS_Scale
##
## 5 Variables 50 Observations
## --------------------------------------------------------------------------------
## F.ATNS1
## n missing distinct Info Mean Gmd .05 .10
## 50 0 37 0.999 63.48 30.64 4.45 15.80
## .25 .50 .75 .90 .95
## 50.50 72.00 83.50 91.60 100.00
##
## lowest : 0 4 5 14 16, highest: 87 89 91 97 100
## --------------------------------------------------------------------------------
## F.ATNS2R
## n missing distinct Info Mean Gmd .05 .10
## 50 0 33 0.998 50.64 39.43 0.00 0.00
## .25 .50 .75 .90 .95
## 13.50 61.00 75.75 94.20 99.10
##
## lowest : 0 5 8 11 12, highest: 93 94 96 98 100
## --------------------------------------------------------------------------------
## F.ATNS3
## n missing distinct Info Mean Gmd .05 .10
## 50 0 38 0.998 60.84 34.51 6.25 16.90
## .25 .50 .75 .90 .95
## 37.75 68.50 85.00 100.00 100.00
##
## lowest : 0 4 9 16 17, highest: 90 91 92 97 100
## --------------------------------------------------------------------------------
## F.ATNS4
## n missing distinct Info Mean Gmd .05 .10
## 50 0 30 0.991 73.9 28.52 15.95 37.50
## .25 .50 .75 .90 .95
## 64.25 79.00 96.50 100.00 100.00
##
## lowest : 2 9 11 22 24, highest: 92 95 97 99 100
## --------------------------------------------------------------------------------
## F.ATNS5
## n missing distinct Info Mean Gmd .05 .10
## 50 0 36 0.997 71.4 28.34 22.5 34.9
## .25 .50 .75 .90 .95
## 56.5 78.0 91.5 100.0 100.0
##
## lowest : 0 2 18 28 34, highest: 92 93 94 95 100
## --------------------------------------------------------------------------------
F$ATNS_Score <- rowMeans(F [, c("ATNS1", "ATNS2R", "ATNS3", "ATNS4", "ATNS5")], na.rm=TRUE)
describe(F$ATNS_Score)
## F$ATNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 50 0 45 0.999 64.05 17.83 44.07 46.76
## .25 .50 .75 .90 .95
## 54.25 67.40 76.90 80.00 82.23
##
## lowest : 1.8 24.0 43.8 44.4 46.4, highest: 80.0 80.8 83.4 85.4 92.8
Climate Change Belief
# Climate Change Belief: How much do you agree or disagree with the following statements?
## Item #1: Climate change is happening.
## Item #2: Climate change poses a risk to human health, safety, and prosperity.
## Item #3: Human activity is largely responsible for recent climate change.
## Item #4: Reducing greenhouse gas emissions will reduce global warming and climate change.
F$CCB1 <- F$CCB_48
F$CCB2 <- F$CCB_49
F$CCB3 <- F$CCB_50
F$CCB4 <- F$CCB_51
#Climate Change Belief Descriptives
describe(F$CCB1)
## F$CCB1
## n missing distinct Info Mean Gmd .05 .10
## 50 0 22 0.972 84.14 19.35 43.2 68.7
## .25 .50 .75 .90 .95
## 77.0 86.0 100.0 100.0 100.0
##
## lowest : 7 21 36 52 66, highest: 90 93 97 99 100
range(F$CCB1, na.rm=TRUE)
## [1] 7 100
describe(F$CCB2)
## F$CCB2
## n missing distinct Info Mean Gmd .05 .10
## 50 0 28 0.978 74.66 30.81 15.00 23.10
## .25 .50 .75 .90 .95
## 65.25 83.00 100.00 100.00 100.00
##
## lowest : 2 4 15 24 29, highest: 92 94 96 99 100
range(F$CCB2, na.rm=TRUE)
## [1] 2 100
describe(F$CCB3)
## F$CCB3
## n missing distinct Info Mean Gmd .05 .10
## 50 0 30 0.982 76.02 28.87 17.45 25.20
## .25 .50 .75 .90 .95
## 68.00 84.50 99.75 100.00 100.00
##
## lowest : 3 14 17 18 26, highest: 90 96 97 99 100
range(F$CCB3, na.rm=TRUE)
## [1] 3 100
describe(F$CCB4)
## F$CCB4
## n missing distinct Info Mean Gmd .05 .10
## 50 0 34 0.989 73.64 28.43 16.60 33.30
## .25 .50 .75 .90 .95
## 64.50 79.50 93.75 100.00 100.00
##
## lowest : 0 10 13 21 27, highest: 91 93 94 99 100
range(F$CCB4, na.rm=TRUE)
## [1] 0 100
#Climate Change Belief Histograms
hist(F$CCB1, main = 'Climate Change Belief #1: Climate change is happening."')
hist(F$CCB2, main = 'Climate Change Belief #2:Climate change poses a risk to human health, safety, and prosperity."')
hist(F$CCB3, main = 'Climate Change Belief #3:Human activity is largely responsible for recent climate change."')
hist(F$CCB4, main = 'Climate Change Belief #4: Reducing greenhouse gas emissions will reduce global warming and climate change."')
F$CCB_Score <- rowMeans(F[, c('CCB1', 'CCB2', 'CCB3','CCB4')], na.rm=T)
describe(F$CCB_Score)
## F$CCB_Score
## n missing distinct Info Mean Gmd .05 .10
## 50 0 40 0.997 77.11 22.52 34.60 51.98
## .25 .50 .75 .90 .95
## 66.81 79.88 96.88 100.00 100.00
##
## lowest : 17.25 22.25 28.75 41.75 51.75, highest: 97.00 98.00 98.50 99.75 100.00
F$CCB_Scale <- data.frame(F$CCB1, F$CCB2, F$CCB3, F$CCB4)
psych::alpha(F$CCB_Scale)
## Number of categories should be increased in order to count frequencies.
##
## Reliability analysis
## Call: psych::alpha(x = F$CCB_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.77 0.51 4.1 0.044 77 21 0.48
##
## lower alpha upper 95% confidence boundaries
## 0.71 0.8 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## F.CCB1 0.78 0.78 0.73 0.53 3.4 0.055 0.02466 0.47
## F.CCB2 0.70 0.72 0.63 0.46 2.5 0.071 0.00251 0.44
## F.CCB3 0.71 0.72 0.63 0.46 2.6 0.068 0.00057 0.47
## F.CCB4 0.80 0.80 0.75 0.57 4.0 0.046 0.01571 0.51
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F.CCB1 50 0.73 0.77 0.63 0.57 84 20
## F.CCB2 50 0.86 0.84 0.80 0.71 75 29
## F.CCB3 50 0.85 0.83 0.79 0.69 76 28
## F.CCB4 50 0.73 0.73 0.58 0.52 74 27
cor(F$CCB_Scale, use= "complete.obs")
## F.CCB1 F.CCB2 F.CCB3 F.CCB4
## F.CCB1 1.0000000 0.4827780 0.5138911 0.4365292
## F.CCB2 0.4827780 1.0000000 0.7137441 0.4706329
## F.CCB3 0.5138911 0.7137441 1.0000000 0.4199783
## F.CCB4 0.4365292 0.4706329 0.4199783 1.0000000
Demographics
## Gender ("What is your gender identity?" [ 1 = woman, 2 = man, 3 = prefer to self-describe, 4 = non-binary])
F$Dem_Gender <- as.numeric(as.character(F$Dem_Gen))
F$Gender <- factor(F$Dem_Gender, levels = c(1, 2, 3, 4),
labels = c("Woman", "Man", "Prefer to self-describe", "Non-binary"))
table(F$Gender)
##
## Woman Man Prefer to self-describe
## 31 18 1
## Non-binary
## 0
## Age ("How old are you?")
range(F$Dem_Age, na.rm = T)
## [1] 18 62
describe(F$Dem_Age, na.rm = T)
## F$Dem_Age
## n missing distinct Info Mean Gmd .05 .10
## 49 1 24 0.998 34.9 12.42 20.8 22.0
## .25 .50 .75 .90 .95
## 26.0 34.0 40.0 49.8 56.8
##
## lowest : 18 20 22 24 25, highest: 49 53 55 58 62
sd(F$Dem_Age, na.rm = T)
## [1] 10.97961
# Education: "Please indicate the highest level of education you have completed" (1 = Elementary/Grammar School, 2 = Middle School, 3 = High School or Equivalent, 4 = Vocational/Technical School (2 years), 5 = Some College, 6 = College or University (4 years), 7 = Master's Degree (MS, MA, MBA, etc.), 8 = Doctoral Degree (PhD), 9 = Professional Degree (MD, JD, etc.).
F$EdNum <- as.numeric(as.character(F$Dem_Edu))
hist(F$EdNum)
F$EDU <- factor(F$EdNum, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
labels = c("Elementary/Grammar School", "Middle School", "High School or Equivalent", "Vocational/Technical School (2 years)", "Some College", "College or University (4 years)", "Master's Degree (MS, MA, MBA, etc.)", "Doctoral Degree (PhD)", "Doctoral Degree (PhD)", "Other"))
table(F$EDU)
##
## Elementary/Grammar School Middle School
## 0 0
## High School or Equivalent Vocational/Technical School (2 years)
## 21 3
## Some College College or University (4 years)
## 17 6
## Master's Degree (MS, MA, MBA, etc.) Doctoral Degree (PhD)
## 2 1
## Other
## 0
# Living Environment: "Which of the following best describes the area you live in?" (1 = Urban, 2 = Suburban, 3 = Rural)
F$LivNum <- as.numeric(as.character(F$Dem_Living))
F$LIVING <- factor(F$LivNum, levels = c(1, 2, 3),
labels = c("Urban", "Suburban", "Rural"))
table(F$LIVING)
##
## Urban Suburban Rural
## 17 18 15
# Primary Language Spoken
F$Dem_Lang
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [39] 1 1 1 1 1 1 1 1 1 1 1 1
F$Dem_Lang_10_TEXT
## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
# Ethnicity: "Which racial or ethnic group best describes you?" (1 = Asian, Asian-American, 2 = Black, Black American, 3 = Hispanic/Latino-American, 4 = Native American, 5 = Native Pacific Islander, 6 = White/Caucasian-American, 7 = Other)
F$Ethnicity <- NA
F$Ethnicity[F$Dem_Ethnicity == 1] <- 'Asian'
F$Ethnicity[F$Dem_Ethnicity == 2] <- 'Black'
F$Ethnicity[F$Dem_Ethnicity == 3] <- 'Hispanic'
F$Ethnicity[F$Dem_Ethnicity == 4] <- 'Nat Amer'
F$Ethnicity[F$Dem_Ethnicity == 5] <- 'Nat Pac'
F$Ethnicity[F$Dem_Ethnicity == 6] <- 'White'
F$Ethnicity[F$Dem_Ethnicity == 7] <- 'Other'
table(F$Ethnicity)
##
## Asian Black Hispanic Other White
## 1 12 4 3 30
print(F$Dem_Ethnicity_7_TEXT)
## [1] NA NA "Mixed" NA NA NA
## [7] NA NA NA NA NA NA
## [13] NA NA NA NA NA NA
## [19] NA NA NA NA NA NA
## [25] NA NA NA NA "Hispanic " NA
## [31] NA NA NA NA NA NA
## [37] NA NA NA NA NA NA
## [43] NA NA NA NA NA NA
## [49] NA "Mixed"
# Subjective Social Status
F$SSS <- F$SSS_US
describe(F$SSS)
## F$SSS
## n missing distinct Info Mean Gmd .05 .10
## 50 0 10 0.977 5.06 2.848 1.00 1.00
## .25 .50 .75 .90 .95
## 3.25 5.00 7.00 8.10 9.00
##
## lowest : 1 2 3 4 5, highest: 6 7 8 9 10
##
## Value 1 2 3 4 5 6 7 8 9 10
## Frequency 8 1 4 4 11 8 7 2 3 2
## Proportion 0.16 0.02 0.08 0.08 0.22 0.16 0.14 0.04 0.06 0.04
range(F$SSS)
## [1] 1 10
hist(F$SSS)
sd(F$SSS)
## [1] 2.510468
Political Ideology
# Political Party
##Generally speaking, do you usually think of yourself as a Republican, a Democrat, an Independent, or what? (1 = Republican, 2 = Democrat, 3 = Independent, 4 = Other (write-in), 5 = No Preference)
F$Party <- F$PolParty
F$DemStrength <- F$PolDemStrength
F$RepStrength <- F$PolRepStrength
F$PartyClose <- F$PolCloserTo
describe(F$Party)
## F$Party
## n missing distinct Info Mean Gmd
## 50 0 4 0.887 1.94 0.9984
##
## Value 1 2 3 5
## Frequency 20 15 14 1
## Proportion 0.40 0.30 0.28 0.02
describe(F$DemStrength)
## F$DemStrength
## n missing distinct Info Mean Gmd
## 15 35 1 0 1 0
##
## Value 1
## Frequency 15
## Proportion 1
describe(F$RepStrength)
## F$RepStrength
## n missing distinct Info Mean Gmd
## 20 30 2 0.383 1.15 0.2684
##
## Value 1 2
## Frequency 17 3
## Proportion 0.85 0.15
describe(F$PartyClose)
## F$PartyClose
## n missing distinct Info Mean Gmd
## 15 35 3 0.696 2.533 0.7238
##
## Value 1 2 3
## Frequency 2 3 10
## Proportion 0.133 0.200 0.667
F$PartyFull <- NA
F$PartyFull[F$DemStrength == 1] <- -3
F$PartyFull[F$DemStrength == 2] <- -2
F$PartyFull[F$PartyClose == 1] <- -1
F$PartyFull[F$PartyClose == 3] <- 0
F$PartyFull[F$PartyClose == 2] <- 1
F$PartyFull[F$RepStrength == 2] <- 2
F$PartyFull[F$RepStrength == 1] <- 3
describe(F$PartyFull)
## F$PartyFull
## n missing distinct Info Mean Gmd
## 50 0 6 0.926 0.26 2.77
##
## lowest : -3 -1 0 1 2, highest: -1 0 1 2 3
##
## Value -3 -1 0 1 2 3
## Frequency 15 2 10 3 3 17
## Proportion 0.30 0.04 0.20 0.06 0.06 0.34
hist(F$PartyFull , main = 'Party Identification')
describe(F$PolImportance)
## F$PolImportance
## n missing distinct Info Mean Gmd
## 50 0 6 0.948 4.22 2.615
##
## lowest : 1 3 4 5 6, highest: 3 4 5 6 7
##
## Value 1 3 4 5 6 7
## Frequency 13 7 2 12 3 13
## Proportion 0.26 0.14 0.04 0.24 0.06 0.26
# Political Orientation: Which of the following describes your political orientation? (1 = Strongly Conservative, 2 = Moderately Conservative, 3 = Slightly Conservative, 4 = Neither Conservative Nor Liberal, 5 = Slightly Liberal, 6 = Moderately Liberal, 7 = Strongly Liberal)
F$polOR <- factor(F$PolOrientation, levels = c(1, 2, 3, 4, 5, 6, 7),
labels = c("Strongly Conservative", "Moderately Conservative", "Slightly Conservative", "Neither Conservative Nor Liberal", "Slightly Liberal", "Moderately Liberal", "Strongly Liberal"))
table(F$polOR)
##
## Strongly Conservative Moderately Conservative
## 4 8
## Slightly Conservative Neither Conservative Nor Liberal
## 6 18
## Slightly Liberal Moderately Liberal
## 5 5
## Strongly Liberal
## 4
