Familiarity Study - Pilot (N = 100; June 2023)
Sarah Coffin
2023-06-24
Dataset
#Data File
FS <- read.csv("fampilot.csv", header = T, na.strings=c(".", "", " ", "NA", "-99"))
#Sample Size: Number of participants (rows)
nrow(FS)## [1] 104Conditions
Control Condition
Biochar
Quiz Questions & Attention Check
## Time Spent Reading Instructions
describe(FS$BioCon_InstTime_Page.Submit)## FS$BioCon_InstTime_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 4.141 4.035 1.193 1.236
## .25 .50 .75 .90 .95
## 1.714 2.664 4.636 5.651 11.156
##
## lowest : 1.184 1.201 1.546 1.770 2.202, highest: 3.294 4.447 5.201 5.701 17.824
##
## Value 1.184 1.201 1.546 1.770 2.202 2.412 2.916 3.294 4.447
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 5.201 5.701 17.824
## Frequency 1 1 1
## Proportion 0.083 0.083 0.083INSTminutes_BIOC <- (mean(FS$BioCon_InstTime_Page.Submit, na.rm = TRUE)/60)
print(INSTminutes_BIOC)## [1] 0.069025## Time Spent Reading Technology Description (before proceeding to next page of survey)
### Raw Time Spent (Seconds)
describe(FS$BIO_Control_Time_Page.Submit)## FS$BIO_Control_Time_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 13.47 14.92 1.103 1.323
## .25 .50 .75 .90 .95
## 1.987 12.120 19.876 24.402 35.114
##
## lowest : 0.908 1.263 1.861 2.029 3.526, highest: 15.368 19.800 20.102 24.880 47.623
##
## Value 0.908 1.263 1.861 2.029 3.526 8.998 15.243 15.368 19.800
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 20.102 24.880 47.623
## Frequency 1 1 1
## Proportion 0.083 0.083 0.083 sd(FS$BIO_Control_Time_Page.Submit, na.rm = TRUE)## [1] 13.74699 range(FS$BIO_Control_Time_Page.Submit, na.rm = TRUE)## [1] 0.908 47.623 ### Convert to Minutes
TECHminutes_BIOC <- (mean(FS$BIO_Control_Time_Page.Submit, na.rm = TRUE)/60)
print(TECHminutes_BIOC)## [1] 0.2244458## Attention Checks
# Attention Check 1: What technology did you just read about?
FS$ATN_BIO_Control1 <- as.numeric(as.character(FS$BIO_Control_ATN))
FS$ATN_BIO_Control <- factor(FS$ATN_BIO_Control1, levels = c(1, 2, 3, 4),
labels = c("Biochar", "Biofuel", "Enhanced Weathering", "Wind Energy"))
describe(FS$ATN_BIO_Control)## FS$ATN_BIO_Control
## n missing distinct
## 12 92 4
##
## Value Biochar Biofuel Enhanced Weathering
## Frequency 6 2 1
## Proportion 0.500 0.167 0.083
##
## Value Wind Energy
## Frequency 3
## Proportion 0.250#### Time spent answering attention check #1:
##### (Seconds)
describe(FS$BIO_Control_ATN_TIME_Page.Submit)## FS$BIO_Control_ATN_TIME_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 7.102 4.23 2.914 3.575
## .25 .50 .75 .90 .95
## 5.002 5.793 9.001 13.373 13.799
##
## lowest : 2.232 3.472 4.501 5.169 5.608, highest: 6.338 8.648 10.059 13.741 13.869
##
## Value 2.232 3.472 4.501 5.169 5.608 5.666 5.921 6.338 8.648
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 10.059 13.741 13.869
## Frequency 1 1 1
## Proportion 0.083 0.083 0.083 sd(FS$BIO_Control_ATN_TIME_Page.Submit, na.rm = TRUE)## [1] 3.749796 range(FS$BIO_Control_ATN_TIME_Page.Submit, na.rm = TRUE)## [1] 2.232 13.869 ##### (Minutes)
ATN1_BIOC <- (mean(FS$BIO_Control_ATN_TIME_Page.Submit, na.rm = TRUE)/60)
print(ATN1_BIOC)## [1] 0.1183667### Attention Check 2: Describe as much as you can about the technology you read about.
describe(FS$BF_Control_ATN2)## FS$BF_Control_ATN2
## n missing distinct
## 11 93 11
##
## lowest : Bio fuel is created with use of corn’s ethonal and burns cleaner Climate change affects the environment in detrimental ways and we need to protect the planet i would suggest the alternative. It described a method of producing fuel from plant matter by heating it, then refining it so it can be used for fuel in cars and trucks, etc. it was very concerning
## highest: They take grass plants and grains a process them into oil later refine them more and they are made into biofuel and used in vehicles for oil. Unsure Using mostly organic materials they are processed into fuel Using plant life (grass etc) to ultimately create fuel for vehicles, industry etc. Wind energy to substitute energy used #### Time spent answering attention check #2
##### (Seconds)
describe(FS$BIO_Control_ATN2TIME_Page.Submit)## FS$BIO_Control_ATN2TIME_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 33.39 36.06 5.280 6.495
## .25 .50 .75 .90 .95
## 10.802 20.997 36.945 94.639 104.537
##
## lowest : 4.164 6.194 9.200 11.336 13.902
## highest: 29.821 36.800 37.379 101.001 108.858
##
## Value 4.164 6.194 9.200 11.336 13.902 19.612 22.382 29.821
## Frequency 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 36.800 37.379 101.001 108.858
## Frequency 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 sd(FS$BIO_Control_ATN2TIME_Page.Submit, na.rm = TRUE)## [1] 35.24338 range(FS$BIO_Control_ATN2TIME_Page.Submit, na.rm = TRUE)## [1] 4.164 108.858 ##### (Minutes)
ATN2_BIOC <- (mean(FS$BIO_Control_ATN2TIME_Page.Submit, na.rm = TRUE)/60)
print(ATN2_BIOC)## [1] 0.5564569Technology Ratings
### Naturalness
FS$N1_BioControl<- as.numeric(FS$Nat_BIO_Control_30)
FS$N2R_BioControl <- as.numeric(100 - FS$Nat_BIO_Control_31)
FS$N3R_BioControl <- as.numeric(100 - FS$Nat_BIO_Control_35)
FS$N4R_BioControl <- as.numeric(100- FS$Nat_BIO_Control_36)
hist(FS$N1_BioControl)
hist(FS$N2R_BioControl)
hist(FS$N3R_BioControl)
hist(FS$N4R_BioControl)
FS$NatScore_BioControl <- rowMeans(FS [, c( "N1_BioControl" , "N2R_BioControl", "N3R_BioControl", "N4R_BioControl")], na.rm=TRUE)
describe(FS$NatScore_BioControl)## FS$NatScore_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 11 0.997 45.85 13.35 29.43 33.48
## .25 .50 .75 .90 .95
## 40.38 45.25 55.12 58.12 61.06
##
## lowest : 24.75 33.25 35.50 42.00 44.00, highest: 46.00 54.50 57.00 58.25 64.50
##
## Value 24.75 33.25 35.50 42.00 44.00 45.25 46.00 54.50 57.00 58.25 64.50
## Frequency 1 1 1 1 1 2 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.167 0.083 0.083 0.083 0.083 0.083sd(FS$NatScore_BioControl, na.rm = TRUE)## [1] 11.40497FS$NatScale_BioControl <- data.frame(FS$N1_BioControl, FS$N2R_BioControl, FS$N3R_BioControl, FS$N4R_BioControl)
describe(FS$NatScale_BioControl)## FS$NatScale_BioControl
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.N1_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 11 0.997 72.58 25.92 39.70 46.90
## .25 .50 .75 .90 .95
## 61.75 70.00 95.75 98.90 99.45
##
## lowest : 32 46 55 64 67, highest: 75 95 98 99 100
##
## Value 32 46 55 64 67 73 75 95 98 99 100
## Frequency 1 1 1 1 2 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.167 0.083 0.083 0.083 0.083 0.083 0.083
## --------------------------------------------------------------------------------
## FS.N2R_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 36.58 27.68 6.60 12.50
## .25 .50 .75 .90 .95
## 20.00 33.00 52.25 65.90 69.60
##
## lowest : 0 12 17 21 26, highest: 44 48 65 66 74
##
## Value 0 12 17 21 26 32 34 44 48 65 66
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 74
## Frequency 1
## Proportion 0.083
## --------------------------------------------------------------------------------
## FS.N3R_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 11 0.997 31.5 26.52 0.00 1.50
## .25 .50 .75 .90 .95
## 15.75 30.00 44.75 62.30 66.25
##
## lowest : 0 15 16 24 27, highest: 39 44 47 64 69
##
## Value 0 15 16 24 27 33 39 44 47 64 69
## Frequency 2 1 1 1 1 1 1 1 1 1 1
## Proportion 0.167 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
## --------------------------------------------------------------------------------
## FS.N4R_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 42.75 30.65 3.30 7.80
## .25 .50 .75 .90 .95
## 28.50 43.50 63.75 69.60 77.20
##
## lowest : 0 6 24 30 34, highest: 47 63 66 70 86
##
## Value 0 6 24 30 34 42 45 47 63 66 70
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 86
## Frequency 1
## Proportion 0.083
## --------------------------------------------------------------------------------psych::alpha(FS$NatScale_BioControl)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$NatScale_BioControl): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( FS.N1_BioControl ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = FS$NatScale_BioControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.077 -0.18 0.5 -0.039 -0.15 0.14 46 11 -0.098
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.46 -0.08 0.23
## Duhachek -0.36 -0.08 0.20
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## FS.N1_BioControl 0.651 0.64694 0.64 0.37919 1.83238 0.06 0.079
## FS.N2R_BioControl -2.011 -2.40787 -0.56 -0.30808 -0.70656 0.46 0.170
## FS.N3R_BioControl 0.064 -0.00052 0.37 -0.00017 -0.00052 0.15 0.348
## FS.N4R_BioControl -1.185 -1.26490 -0.16 -0.22874 -0.55848 0.35 0.268
## med.r
## FS.N1_BioControl 0.33
## FS.N2R_BioControl -0.36
## FS.N3R_BioControl -0.32
## FS.N4R_BioControl -0.32
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.N1_BioControl 12 -0.22 -0.20 -0.61 -0.59 73 22
## FS.N2R_BioControl 12 0.90 0.90 0.93 0.67 37 23
## FS.N3R_BioControl 12 0.39 0.41 0.25 -0.10 32 22
## FS.N4R_BioControl 12 0.80 0.77 0.76 0.37 43 26cor(FS$NatScale_BioControl, use= "complete.obs")## FS.N1_BioControl FS.N2R_BioControl FS.N3R_BioControl
## FS.N1_BioControl 1.0000000 -0.3226682 -0.6926110
## FS.N2R_BioControl -0.3226682 1.0000000 0.3290523
## FS.N3R_BioControl -0.6926110 0.3290523 1.0000000
## FS.N4R_BioControl -0.3589965 0.6811426 0.1273688
## FS.N4R_BioControl
## FS.N1_BioControl -0.3589965
## FS.N2R_BioControl 0.6811426
## FS.N3R_BioControl 0.1273688
## FS.N4R_BioControl 1.0000000# Familiarity
FS$Fam_BioControl <- as.numeric(FS$Fam_BIO_Control_31)
hist(FS$Fam_BioControl)
describe(FS$Fam_BioControl)## FS$Fam_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 41.33 32.48 7.15 13.40
## .25 .50 .75 .90 .95
## 19.25 38.50 66.25 67.90 79.25
##
## lowest : 0 13 17 20 30, highest: 45 66 67 68 93
##
## Value 0 13 17 20 30 38 39 45 66 67 68
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 93
## Frequency 1
## Proportion 0.083sd(FS$Fam_BioControl, na.rm = TRUE)## [1] 27.57579### Understanding
FS$Und_BioControl <- as.numeric(FS$Fam_BIO_Control_33)
hist(FS$Und_BioControl)
describe(FS$Und_BioControl)## FS$Und_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 11 0.997 52 31.09 19.50 25.00
## .25 .50 .75 .90 .95
## 34.00 47.00 63.75 90.30 95.60
##
## lowest : 14 24 34 38 40, highest: 59 60 75 92 100
##
## Value 14 24 34 38 40 54 59 60 75 92 100
## Frequency 1 1 2 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.167 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083sd(FS$Und_BioControl, na.rm = TRUE)## [1] 26.57066### Fluency
FS$Fluency_BioControl <- as.numeric(FS$Fluency_BIO_Control_30)
hist(FS$Fluency_BioControl)
describe(FS$Fluency_BioControl)## FS$Fluency_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 10 0.986 71.58 32.35 30.50 35.80
## .25 .50 .75 .90 .95
## 49.00 76.00 99.25 100.00 100.00
##
## lowest : 25 35 43 51 63, highest: 75 77 91 99 100
##
## Value 25 35 43 51 63 75 77 91 99 100
## Frequency 1 1 1 1 1 1 1 1 1 3
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.250sd(FS$Fluency_BioControl, na.rm = TRUE)## [1] 27.6585### Risk
FS$R1_BioControl<- as.numeric(FS$Risk_BIO_Control_30)
FS$R2_BioControl <- as.numeric(FS$Risk_BIO_Control_31)
FS$R3_BioControl <- as.numeric(FS$Risk_BIO_Control_32)
hist(FS$R1_BioControl)
hist(FS$R2_BioControl)
hist(FS$R3_BioControl)
FS$RiskScore_BioControl <- rowMeans(FS [, c( "R1_BioControl" , "R2_BioControl", "R3_BioControl")], na.rm=TRUE)
describe(FS$RiskScore_BioControl)## FS$RiskScore_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 10 0.993 54.39 16.61 34.28 36.33
## .25 .50 .75 .90 .95
## 45.58 56.17 61.33 66.63 74.02
##
## lowest : 33.00000 35.33333 45.33333 45.66667 53.66667
## highest: 58.66667 59.66667 66.33333 66.66667 83.00000
##
## Value 33.00000 35.33333 45.33333 45.66667 53.66667 58.66667 59.66667
## Frequency 1 1 1 2 1 1 2
## Proportion 0.083 0.083 0.083 0.167 0.083 0.083 0.167
##
## Value 66.33333 66.66667 83.00000
## Frequency 1 1 1
## Proportion 0.083 0.083 0.083sd(FS$RiskScore_BioControl, na.rm = TRUE)## [1] 14.27425FS$RiskScale_BioControl <- data.frame(FS$R1_BioControl, FS$R2_BioControl, FS$R3_BioControl)
describe(FS$RiskScale_BioControl)## FS$RiskScale_BioControl
##
## 3 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.R1_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 10 0.993 50.58 26.5 18.15 33.00
## .25 .50 .75 .90 .95
## 35.25 57.00 66.25 76.60 77.00
##
## lowest : 0 33 36 37 53, highest: 61 63 64 73 77
##
## Value 0 33 36 37 53 61 63 64 73 77
## Frequency 1 2 1 1 1 1 1 1 1 2
## Proportion 0.083 0.167 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.167
## --------------------------------------------------------------------------------
## FS.R2_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 45.08 30.56 6.05 12.60
## .25 .50 .75 .90 .95
## 31.50 41.00 69.25 74.50 77.70
##
## lowest : 0 11 27 33 36, highest: 57 69 70 75 81
##
## Value 0 11 27 33 36 38 44 57 69 70 75
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 81
## Frequency 1
## Proportion 0.083
## --------------------------------------------------------------------------------
## FS.R3_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 10 0.993 67.5 26.39 33.00 35.00
## .25 .50 .75 .90 .95
## 58.25 62.00 90.50 94.70 96.80
##
## lowest : 33 53 60 61 63, highest: 71 90 92 95 99
##
## Value 33 53 60 61 63 71 90 92 95 99
## Frequency 2 1 2 1 1 1 1 1 1 1
## Proportion 0.167 0.083 0.167 0.083 0.083 0.083 0.083 0.083 0.083 0.083
## --------------------------------------------------------------------------------psych::alpha(FS$RiskScale_BioControl)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$RiskScale_BioControl): 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 ( FS.R3_BioControl ) 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 = FS$RiskScale_BioControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.096 0.06 0.44 0.021 0.063 0.15 54 14 -0.21
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.25 0.1 0.36
## Duhachek -0.20 0.1 0.39
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## FS.R1_BioControl -1.46 -1.49 -0.43 -0.43 -0.60 0.478 NA
## FS.R2_BioControl -0.53 -0.53 -0.21 -0.21 -0.35 0.301 NA
## FS.R3_BioControl 0.82 0.82 0.70 0.70 4.65 0.035 NA
## med.r
## FS.R1_BioControl -0.43
## FS.R2_BioControl -0.21
## FS.R3_BioControl 0.70
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.R1_BioControl 12 0.85 0.84 0.84 0.51 51 23
## FS.R2_BioControl 12 0.76 0.72 0.72 0.23 45 26
## FS.R3_BioControl 12 0.16 0.21 -0.37 -0.35 68 23cor(FS$RiskScale_BioControl, use= "complete.obs")## FS.R1_BioControl FS.R2_BioControl FS.R3_BioControl
## FS.R1_BioControl 1.0000000 0.6993546 -0.2109897
## FS.R2_BioControl 0.6993546 1.0000000 -0.4262864
## FS.R3_BioControl -0.2109897 -0.4262864 1.0000000### Benefit
FS$B1_BioControl<- as.numeric(FS$Ben_BIO_Control_40)
FS$B2_BioControl <- as.numeric(FS$Ben_BIO_Control_42)
FS$B3_BioControl <- as.numeric(FS$Ben_BIO_Control_43)
FS$B4_BioControl <- as.numeric(FS$Ben_BIO_Control_45)
hist(FS$B1_BioControl)
hist(FS$B2_BioControl)
hist(FS$B3_BioControl)
hist(FS$B4_BioControl)
FS$BenScore_BioControl <- rowMeans(FS [, c( "B1_BioControl" , "B2_BioControl", "B3_BioControl", "B4_BioControl")], na.rm=TRUE)
describe(FS$BenScore_BioControl)## FS$BenScore_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 67.92 21.37 44.14 51.03
## .25 .50 .75 .90 .95
## 58.56 64.75 82.44 88.70 93.95
##
## lowest : 35.75 51.00 51.25 61.00 62.75, highest: 67.50 81.25 86.00 89.00 100.00
##
## Value 35.75 51.00 51.25 61.00 62.75 63.25 66.25 67.50 81.25
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 86.00 89.00 100.00
## Frequency 1 1 1
## Proportion 0.083 0.083 0.083sd(FS$BenScore_BioControl, na.rm = TRUE)## [1] 18.28199FS$BenScale_BioControl <- data.frame(FS$B1_BioControl, FS$B2_BioControl, FS$B3_BioControl, FS$B4_BioControl)
describe(FS$BenScale_BioControl)## FS$BenScale_BioControl
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.B1_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 11 0.997 66.67 19.73 43.45 52.50
## .25 .50 .75 .90 .95
## 58.50 65.50 77.50 84.40 91.75
##
## lowest : 33 52 57 59 60, highest: 67 77 79 85 100
##
## Value 33 52 57 59 60 64 67 77 79 85 100
## Frequency 1 1 1 1 1 1 2 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.167 0.083 0.083 0.083 0.083
## --------------------------------------------------------------------------------
## FS.B2_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 69.58 21.98 43.80 51.70
## .25 .50 .75 .90 .95
## 58.75 68.00 84.25 94.00 97.25
##
## lowest : 35 51 58 59 62, highest: 70 84 85 95 100
##
## Value 35 51 58 59 62 67 69 70 84 85 95
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 100
## Frequency 1
## Proportion 0.083
## --------------------------------------------------------------------------------
## FS.B3_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 67 25.15 37.20 40.20
## .25 .50 .75 .90 .95
## 53.25 64.00 88.00 91.90 95.60
##
## lowest : 35 39 51 54 60, highest: 67 87 91 92 100
##
## Value 35 39 51 54 60 63 65 67 87 91 92
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 100
## Frequency 1
## Proportion 0.083
## --------------------------------------------------------------------------------
## FS.B4_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 11 0.997 68.42 24.38 36.00 37.50
## .25 .50 .75 .90 .95
## 59.25 69.50 78.00 94.80 97.25
##
## lowest : 36 51 62 64 69, highest: 72 73 93 95 100
##
## Value 36 51 62 64 69 70 72 73 93 95 100
## Frequency 2 1 1 1 1 1 1 1 1 1 1
## Proportion 0.167 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
## --------------------------------------------------------------------------------psych::alpha(FS$BenScale_BioControl)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$BenScale_BioControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.99 0.83 20 0.0082 68 18 0.84
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.95 0.96
## Duhachek 0.93 0.95 0.97
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## FS.B1_BioControl 0.94 0.94 0.93 0.84 16 0.010 0.0073
## FS.B2_BioControl 0.94 0.94 0.94 0.84 16 0.010 0.0077
## FS.B3_BioControl 0.93 0.93 0.91 0.82 14 0.012 0.0034
## FS.B4_BioControl 0.93 0.93 0.91 0.82 13 0.013 0.0033
## med.r
## FS.B1_BioControl 0.83
## FS.B2_BioControl 0.85
## FS.B3_BioControl 0.85
## FS.B4_BioControl 0.83
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.B1_BioControl 12 0.92 0.93 0.92 0.87 67 17
## FS.B2_BioControl 12 0.92 0.92 0.91 0.86 70 19
## FS.B3_BioControl 12 0.95 0.94 0.94 0.90 67 21
## FS.B4_BioControl 12 0.95 0.95 0.94 0.91 68 21cor(FS$BenScale_BioControl, use= "complete.obs")## FS.B1_BioControl FS.B2_BioControl FS.B3_BioControl
## FS.B1_BioControl 1.0000000 0.8652740 0.7532350
## FS.B2_BioControl 0.8652740 1.0000000 0.8332712
## FS.B3_BioControl 0.7532350 0.8332712 1.0000000
## FS.B4_BioControl 0.8482679 0.7575033 0.9280455
## FS.B4_BioControl
## FS.B1_BioControl 0.8482679
## FS.B2_BioControl 0.7575033
## FS.B3_BioControl 0.9280455
## FS.B4_BioControl 1.0000000### Support
FS$S1_BioControl<- as.numeric(FS$Sup_BIO_Control_40)
FS$S2_BioControl <- as.numeric(FS$Sup_BIO_Control_42)
FS$S3_BioControl <- as.numeric(FS$Sup_BIO_Control_43)
FS$S4_BioControl <- as.numeric(FS$Sup_BIO_Control_45)
hist(FS$S1_BioControl)
hist(FS$S2_BioControl)
hist(FS$S3_BioControl)
hist(FS$S4_BioControl)
FS$SupScore_BioControl <- rowMeans(FS [, c( "S1_BioControl" , "S2_BioControl", "S3_BioControl", "S4_BioControl")], na.rm=TRUE)
describe(FS$SupScore_BioControl)## FS$SupScore_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 64.83 24.31 36.23 41.80
## .25 .50 .75 .90 .95
## 54.62 60.00 83.06 92.03 95.79
##
## lowest : 31.00 40.50 53.50 55.00 55.75, highest: 62.25 82.25 85.50 92.75 99.50
##
## Value 31.00 40.50 53.50 55.00 55.75 59.50 60.50 62.25 82.25 85.50 92.75
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 99.50
## Frequency 1
## Proportion 0.083sd(FS$SupScore_BioControl, na.rm = TRUE)## [1] 20.91089FS$SupScale_BioControl <- data.frame(FS$S1_BioControl, FS$S2_BioControl, FS$S3_BioControl, FS$S4_BioControl)
describe(FS$SupScale_BioControl)## FS$SupScale_BioControl
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.S1_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 64.92 24.26 36.40 41.30
## .25 .50 .75 .90 .95
## 54.50 61.50 77.75 96.20 98.90
##
## lowest : 32 40 53 55 56, highest: 65 77 80 98 100
##
## Value 32 40 53 55 56 59 64 65 77 80 98
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 100
## Frequency 1
## Proportion 0.083
## --------------------------------------------------------------------------------
## FS.S2_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 10 0.993 66.25 26.74 35.85 40.30
## .25 .50 .75 .90 .95
## 52.00 61.50 82.25 99.50 100.00
##
## lowest : 32 39 52 56 61, highest: 62 68 78 95 100
##
## Value 32 39 52 56 61 62 68 78 95 100
## Frequency 1 1 2 1 1 1 1 1 1 2
## Proportion 0.083 0.083 0.167 0.083 0.083 0.083 0.083 0.083 0.083 0.167
## --------------------------------------------------------------------------------
## FS.S3_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 62.42 23.8 35.05 41.10
## .25 .50 .75 .90 .95
## 51.75 58.50 74.25 90.00 95.05
##
## lowest : 29 40 51 52 55, highest: 61 72 81 91 100
##
## Value 29 40 51 52 55 57 60 61 72 81 91
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 100
## Frequency 1
## Proportion 0.083
## --------------------------------------------------------------------------------
## FS.S4_BioControl
## n missing distinct Info Mean Gmd .05 .10
## 12 92 11 0.997 65.75 24.95 37.6 44.2
## .25 .50 .75 .90 .95
## 55.0 59.0 82.5 97.2 98.9
##
## lowest : 31 43 55 56 58, highest: 63 80 90 98 100
##
## Value 31 43 55 56 58 60 63 80 90 98 100
## Frequency 1 1 2 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.167 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
## --------------------------------------------------------------------------------psych::alpha(FS$SupScale_BioControl)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$SupScale_BioControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.98 0.98 0.98 0.93 57 0.0028 65 21 0.93
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.98 0.98 0.99
## Duhachek 0.98 0.98 0.99
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## FS.S1_BioControl 0.97 0.97 0.97 0.93 38 0.0046 0.00142
## FS.S2_BioControl 0.97 0.97 0.96 0.93 38 0.0044 0.00019
## FS.S3_BioControl 0.98 0.99 0.98 0.96 67 0.0025 0.00026
## FS.S4_BioControl 0.97 0.98 0.97 0.93 40 0.0044 0.00108
## med.r
## FS.S1_BioControl 0.91
## FS.S2_BioControl 0.93
## FS.S3_BioControl 0.96
## FS.S4_BioControl 0.93
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.S1_BioControl 12 0.98 0.98 0.98 0.97 65 21
## FS.S2_BioControl 12 0.98 0.98 0.98 0.97 66 23
## FS.S3_BioControl 12 0.96 0.96 0.94 0.93 62 20
## FS.S4_BioControl 12 0.98 0.98 0.97 0.96 66 22cor(FS$SupScale_BioControl, use= "complete.obs")## FS.S1_BioControl FS.S2_BioControl FS.S3_BioControl
## FS.S1_BioControl 1.0000000 0.9638984 0.9278995
## FS.S2_BioControl 0.9638984 1.0000000 0.8983275
## FS.S3_BioControl 0.9278995 0.8983275 1.0000000
## FS.S4_BioControl 0.9388624 0.9692017 0.9114829
## FS.S4_BioControl
## FS.S1_BioControl 0.9388624
## FS.S2_BioControl 0.9692017
## FS.S3_BioControl 0.9114829
## FS.S4_BioControl 1.0000000Biofuel
Quiz Questions & Attention Check
## Time Spent Reading Instructions
describe(FS$BFCon_InstTime_Page.Submit)## FS$BFCon_InstTime_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 5.493 6.819 1.100 1.296
## .25 .50 .75 .90 .95
## 1.445 2.126 5.391 7.897 18.242
##
## lowest : 0.904 1.296 1.331 1.559 1.891, highest: 4.051 4.682 6.099 7.897 28.588
##
## Value 0.904 1.296 1.331 1.559 1.891 2.126 4.051 4.682 6.099
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 7.897 28.588
## Frequency 1 1
## Proportion 0.091 0.091INSTminutes_BFC <- (mean(FS$BFCon_InstTime_Page.Submit, na.rm = TRUE)/60)
print(INSTminutes_BFC)## [1] 0.09155152## Time Spent Reading Technology Description (before proceeding to next page of survey)
### Raw Time Spent (Seconds)
describe(FS$BF_Control_Time_Page.Submit)## FS$BF_Control_Time_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 23.1 39.95 0.7865 0.9770
## .25 .50 .75 .90 .95
## 1.2000 2.0060 16.5030 26.7830 105.3970
##
## lowest : 0.596 0.977 1.110 1.290 1.871
## highest: 2.486 15.304 17.702 26.783 184.011
##
## Value 0.596 0.977 1.110 1.290 1.871 2.006 2.486 15.304
## Frequency 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 17.702 26.783 184.011
## Frequency 1 1 1
## Proportion 0.091 0.091 0.091 sd(FS$BF_Control_Time_Page.Submit, na.rm = TRUE)## [1] 54.10313 range(FS$BF_Control_Time_Page.Submit, na.rm = TRUE)## [1] 0.596 184.011 ### Convert to Minutes
TECHminutes_BFC <- (mean(FS$BF_Control_Time_Page.Submit, na.rm = TRUE)/60)
print(TECHminutes_BFC)## [1] 0.3850545## Attention Checks
# Attention Check 1: What technology did you just read about?
FS$ATN_BF_Control1 <- as.numeric(as.character(FS$BF_Control_ATN))
FS$ATN_BF_Control <- factor(FS$ATN_BF_Control1, levels = c(1, 2, 3, 4),
labels = c("Biochar", "Biofuel", "Enhanced Weathering", "Wind Energy"))
describe(FS$ATN_BF_Control)## FS$ATN_BF_Control
## n missing distinct
## 11 93 4
##
## Value Biochar Biofuel Enhanced Weathering
## Frequency 1 6 2
## Proportion 0.091 0.545 0.182
##
## Value Wind Energy
## Frequency 2
## Proportion 0.182#### Time spent answering attention check #1:
##### (Seconds)
describe(FS$BF_Control_ATN_TIME_Page.Submit)## FS$BF_Control_ATN_TIME_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 8.316 5.553 3.681 3.929
## .25 .50 .75 .90 .95
## 4.624 7.338 8.973 16.721 17.690
##
## lowest : 3.433 3.929 4.044 5.204 5.801, highest: 8.401 8.495 9.450 16.721 18.659
##
## Value 3.433 3.929 4.044 5.204 5.801 7.338 8.401 8.495 9.450
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 16.721 18.659
## Frequency 1 1
## Proportion 0.091 0.091 sd(FS$BF_Control_ATN_TIME_Page.Submit, na.rm = TRUE)## [1] 5.072007 range(FS$BF_Control_ATN_TIME_Page.Submit, na.rm = TRUE)## [1] 3.433 18.659 ##### (Minutes)
ATN1_BFC <- (mean(FS$BF_Control_ATN_TIME_Page.Submit, na.rm = TRUE)/60)
print(ATN1_BFC)## [1] 0.1385985### Attention Check 2: Describe as much as you can about the technology you read about.
describe(FS$BF_Control_ATN2)## FS$BF_Control_ATN2
## n missing distinct
## 11 93 11
##
## lowest : Bio fuel is created with use of corn’s ethonal and burns cleaner Climate change affects the environment in detrimental ways and we need to protect the planet i would suggest the alternative. It described a method of producing fuel from plant matter by heating it, then refining it so it can be used for fuel in cars and trucks, etc. it was very concerning
## highest: They take grass plants and grains a process them into oil later refine them more and they are made into biofuel and used in vehicles for oil. Unsure Using mostly organic materials they are processed into fuel Using plant life (grass etc) to ultimately create fuel for vehicles, industry etc. Wind energy to substitute energy used #### Time spent answering attention check #2
##### (Seconds)
describe(FS$BF_Control_ATN2_TIME_Page.Submit)## FS$BF_Control_ATN2_TIME_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 34.44 27.4 8.005 10.928
## .25 .50 .75 .90 .95
## 12.713 41.379 49.704 51.201 66.621
##
## lowest : 5.081 10.928 11.644 13.781 21.039, highest: 42.344 49.531 49.878 51.201 82.042
##
## Value 5.081 10.928 11.644 13.781 21.039 41.379 42.344 49.531 49.878
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 51.201 82.042
## Frequency 1 1
## Proportion 0.091 0.091 sd(FS$BF_Control_ATN2_TIME_Page.Submit, na.rm = TRUE)## [1] 23.79995 range(FS$BF_Control_ATN2_TIME_Page.Submit, na.rm = TRUE)## [1] 5.081 82.042 ##### (Minutes)
ATN2_BFC <- (mean(FS$BF_Control_ATN2_TIME_Page.Submit, na.rm = TRUE)/60)
print(ATN2_BFC)## [1] 0.5740121Technology Ratings
### Naturalness
FS$N1_BFControl <- as.numeric(FS$Nat_BF_Control_30)
FS$N2R_BFControl <- as.numeric(100 - FS$Nat_BF_Control_31)
FS$N3R_BFControl <- as.numeric(100 - FS$Nat_BF_Control_35)
FS$N4R_BFControl <- as.numeric(100- FS$Nat_BF_Control_36)
hist(FS$N1_BFControl)
hist(FS$N2R_BFControl)
hist(FS$N3R_BFControl)
hist(FS$N4R_BFControl)
FS$NatScore_BFControl <- rowMeans(FS [, c( "N1_BFControl" , "N2R_BFControl", "N3R_BFControl", "N4R_BFControl")], na.rm=TRUE)
describe(FS$NatScore_BFControl)## FS$NatScore_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 50.16 16.17 30.25 35.25
## .25 .50 .75 .90 .95
## 41.38 52.25 58.75 66.00 67.75
##
## lowest : 25.25 35.25 37.00 45.75 49.25, highest: 54.00 54.25 63.25 66.00 69.50
##
## Value 25.25 35.25 37.00 45.75 49.25 52.25 54.00 54.25 63.25 66.00 69.50
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091sd(FS$NatScore_BFControl, na.rm = TRUE)## [1] 13.66898FS$NatScale_BFControl <- data.frame(FS$N1_BFControl, FS$N2R_BFControl, FS$N3R_BFControl, FS$N4R_BFControl)
describe(FS$NatScale_BFControl)## FS$NatScale_BFControl
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.N1_BFControl
## n missing distinct Info Mean Gmd
## 11 93 9 0.991 72.73 25.53
##
## lowest : 42 43 51 58 69, highest: 69 81 84 91 100
##
## Value 42 43 51 58 69 81 84 91 100
## Frequency 1 1 1 1 1 2 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.182 0.091 0.091 0.182
## --------------------------------------------------------------------------------
## FS.N2R_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 44.18 26.04 12.0 24.0
## .25 .50 .75 .90 .95
## 33.0 48.0 56.0 74.0 75.5
##
## lowest : 0 24 32 34 36, highest: 49 54 58 74 77
##
## Value 0 24 32 34 36 48 49 54 58 74 77
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------
## FS.N3R_BFControl
## n missing distinct Info Mean Gmd
## 11 93 9 0.982 22.36 26.29
##
## lowest : 0 12 13 15 16, highest: 16 25 41 49 75
##
## Value 0 12 13 15 16 25 41 49 75
## Frequency 3 1 1 1 1 1 1 1 1
## Proportion 0.273 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------
## FS.N4R_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 61.36 35.02 14.0 27.0
## .25 .50 .75 .90 .95
## 45.5 65.0 84.5 89.0 94.5
##
## lowest : 1 27 43 48 56, highest: 77 83 86 89 100
##
## Value 1 27 43 48 56 65 77 83 86 89 100
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------psych::alpha(FS$NatScale_BFControl)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$NatScale_BFControl): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( FS.N1_BFControl ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = FS$NatScale_BFControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.26 0.2 0.27 0.059 0.25 0.11 50 14 0.029
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.01 0.26 0.46
## Duhachek 0.04 0.26 0.47
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## FS.N1_BFControl 0.5127 0.504 0.435 0.253 1.017 0.079 0.023
## FS.N2R_BFControl 0.0056 -0.082 0.049 -0.026 -0.076 0.156 0.083
## FS.N3R_BFControl 0.2189 0.196 0.221 0.075 0.243 0.126 0.070
## FS.N4R_BFControl -0.2193 -0.224 -0.096 -0.065 -0.183 0.206 0.030
## med.r
## FS.N1_BFControl 0.298
## FS.N2R_BFControl -0.123
## FS.N3R_BFControl -0.028
## FS.N4R_BFControl -0.028
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.N1_BFControl 11 0.20 0.27 -0.30 -0.19 73 21
## FS.N2R_BFControl 11 0.64 0.66 0.51 0.29 44 22
## FS.N3R_BFControl 11 0.53 0.52 0.24 0.12 22 24
## FS.N4R_BFControl 11 0.78 0.71 0.68 0.35 61 30cor(FS$NatScale_BFControl, use= "complete.obs")## FS.N1_BFControl FS.N2R_BFControl FS.N3R_BFControl
## FS.N1_BFControl 1.00000000 -0.02765433 -0.25275517
## FS.N2R_BFControl -0.02765433 1.00000000 0.08516666
## FS.N3R_BFControl -0.25275517 0.08516666 1.00000000
## FS.N4R_BFControl -0.12330003 0.37606837 0.29817082
## FS.N4R_BFControl
## FS.N1_BFControl -0.1233000
## FS.N2R_BFControl 0.3760684
## FS.N3R_BFControl 0.2981708
## FS.N4R_BFControl 1.0000000### Familiarity
FS$Fam_BFControl <- as.numeric(FS$Fam_BF_Control_32)
hist(FS$Fam_BFControl)
describe(FS$Fam_BFControl)## FS$Fam_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 59.09 33.45 14.0 15.0
## .25 .50 .75 .90 .95
## 40.5 69.0 77.5 86.0 93.0
##
## lowest : 13 15 35 46 61, highest: 70 76 79 86 100
##
## Value 13 15 35 46 61 69 70 76 79 86 100
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091sd(FS$Fam_BFControl, na.rm = TRUE)## [1] 28.49721### Understanding
FS$Und_BFControl <- as.numeric(FS$Fam_BF_Control_31)
hist(FS$Und_BFControl)
describe(FS$Und_BFControl)## FS$Und_BFControl
## n missing distinct Info Mean Gmd
## 11 93 9 0.991 77.45 22.4
##
## lowest : 43 52 65 69 73, highest: 73 83 90 94 100
##
## Value 43 52 65 69 73 83 90 94 100
## Frequency 1 1 1 1 1 2 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.182 0.091 0.091 0.182sd(FS$Und_BFControl, na.rm = TRUE)## [1] 18.94921### Fluency
FS$Fluency_BFControl <- as.numeric(FS$Fluency_BF_Control_30)
hist(FS$Fluency_BFControl)
describe(FS$Fluency_BFControl)## FS$Fluency_BFControl
## n missing distinct Info Mean Gmd
## 11 93 9 0.991 74.09 30.18
##
## lowest : 22 36 55 70 72, highest: 72 85 94 96 100
##
## Value 22 36 55 70 72 85 94 96 100
## Frequency 1 1 1 1 1 2 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.182 0.091 0.091 0.182sd(FS$Fluency_BFControl, na.rm = TRUE)## [1] 26.47057### Risk
FS$R1_BFControl<- as.numeric(FS$Risk_BF_Control_30)
FS$R2_BFControl <- as.numeric(FS$Risk_BF_Control_31)
FS$R3_BFControl <- as.numeric(FS$Risk_BF_Control_32)
length(FS$R1_BFControl)## [1] 104length(FS$R2_BFControl)## [1] 104length(FS$R3_BFControl)## [1] 104hist(FS$R1_BFControl)
hist(FS$R2_BFControl)
hist(FS$R3_BFControl)
FS$RiskScore_BFControl <- rowMeans(FS [, c( "R1_BFControl" , "R2_BFControl", "R3_BFControl")], na.rm=TRUE)
describe(FS$RiskScore_BFControl)## FS$RiskScore_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 55.06 20.15 36.17 39.00
## .25 .50 .75 .90 .95
## 44.17 47.33 65.33 70.67 83.00
##
## lowest : 33.33333 39.00000 43.00000 45.33333 47.33333
## highest: 53.66667 61.33333 69.33333 70.66667 95.33333
##
## Value 33.33333 39.00000 43.00000 45.33333 47.33333 53.66667 61.33333
## Frequency 1 1 1 1 2 1 1
## Proportion 0.091 0.091 0.091 0.091 0.182 0.091 0.091
##
## Value 69.33333 70.66667 95.33333
## Frequency 1 1 1
## Proportion 0.091 0.091 0.091sd(FS$RiskScore_BFControl, na.rm = TRUE)## [1] 17.88843FS$RiskScale_BFControl <- data.frame(FS$R1_BFControl, FS$R2_BFControl, FS$R3_BFControl)
describe(FS$RiskScale_BFControl)## FS$RiskScale_BFControl
##
## 3 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.R1_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 46.91 38.95 0.5 1.0
## .25 .50 .75 .90 .95
## 18.0 60.0 66.0 81.0 89.5
##
## lowest : 0 1 14 22 46, highest: 62 65 67 81 98
##
## Value 0 1 14 22 46 60 62 65 67 81 98
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------
## FS.R2_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 42.27 40.47 0.0 0.0
## .25 .50 .75 .90 .95
## 17.5 40.0 64.5 95.0 95.5
##
## lowest : 0 13 22 29 40, highest: 41 63 66 95 96
##
## Value 0 13 22 29 40 41 63 66 95 96
## Frequency 2 1 1 1 1 1 1 1 1 1
## Proportion 0.182 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------
## FS.R3_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 76 20.87 47.5 55.0
## .25 .50 .75 .90 .95
## 66.5 77.0 88.5 93.0 96.5
##
## lowest : 40 55 65 68 76, highest: 85 87 90 93 100
##
## Value 40 55 65 68 76 77 85 87 90 93 100
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------psych::alpha(FS$RiskScale_BFControl)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$RiskScale_BFControl): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( FS.R3_BFControl ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = FS$RiskScale_BFControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.16 -0.068 0.18 -0.022 -0.064 0.12 55 18 -0.0011
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.17 0.16 0.40
## Duhachek -0.07 0.16 0.38
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## FS.R1_BFControl -0.0019 -0.0023 -0.0011 -0.0011 -0.0023 0.161 NA
## FS.R2_BFControl -1.0631 -1.4185 -0.4150 -0.4150 -0.5865 0.328 NA
## FS.R3_BFControl 0.5191 0.5194 0.3508 0.3508 1.0806 0.094 NA
## med.r
## FS.R1_BFControl -0.0011
## FS.R2_BFControl -0.4150
## FS.R3_BFControl 0.3508
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.R1_BFControl 11 0.701 0.55 0.32 0.12 47 33
## FS.R2_BFControl 11 0.853 0.80 0.70 0.38 42 34
## FS.R3_BFControl 11 0.076 0.34 -0.31 -0.25 76 18cor(FS$RiskScale_BFControl, use= "complete.obs")## FS.R1_BFControl FS.R2_BFControl FS.R3_BFControl
## FS.R1_BFControl 1.0000000 0.350772493 -0.414955019
## FS.R2_BFControl 0.3507725 1.000000000 -0.001147297
## FS.R3_BFControl -0.4149550 -0.001147297 1.000000000### Benefit
FS$B1_BFControl<- as.numeric(FS$Ben_BF_Control_40)
FS$B2_BFControl <- as.numeric(FS$Ben_BF_Control_42)
FS$B3_BFControl <- as.numeric(FS$Ben_BF_Control_43)
FS$B4_BFControl <- as.numeric(FS$Ben_BF_Control_44)
hist(FS$B1_BFControl)
hist(FS$B2_BFControl)
hist(FS$B3_BFControl)
hist(FS$B4_BFControl)
FS$BenScore_BFControl <- rowMeans(FS [, c( "B1_BFControl" , "B2_BFControl", "B3_BFControl", "B4_BFControl")], na.rm=TRUE)
describe(FS$BenScore_BFControl)## FS$BenScore_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 77.45 22.42 51.50 57.75
## .25 .50 .75 .90 .95
## 63.00 75.75 93.88 99.50 99.75
##
## lowest : 45.25 57.75 60.50 65.50 70.75, highest: 89.25 93.50 94.25 99.50 100.00
##
## Value 45.25 57.75 60.50 65.50 70.75 75.75 89.25 93.50 94.25
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 99.50 100.00
## Frequency 1 1
## Proportion 0.091 0.091sd(FS$BenScore_BFControl, na.rm = TRUE)## [1] 18.91191FS$BenScale_BFControl <- data.frame(FS$B1_BFControl, FS$B2_BFControl, FS$B3_BFControl, FS$B4_BFControl)
describe(FS$BenScale_BFControl)## FS$BenScale_BFControl
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.B1_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 79 20.25 54.5 57.0
## .25 .50 .75 .90 .95
## 67.5 77.0 93.0 100.0 100.0
##
## lowest : 52 57 67 68 73, highest: 77 89 92 94 100
##
## Value 52 57 67 68 73 77 89 92 94 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182
## --------------------------------------------------------------------------------
## FS.B2_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 81.09 21.56 51.5 60.0
## .25 .50 .75 .90 .95
## 71.0 86.0 96.5 100.0 100.0
##
## lowest : 43 60 66 76 77, highest: 86 91 95 98 100
##
## Value 43 60 66 76 77 86 91 95 98 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182
## --------------------------------------------------------------------------------
## FS.B3_BFControl
## n missing distinct Info Mean Gmd
## 11 93 9 0.982 74.82 28.76
##
## lowest : 32 43 58 59 70, highest: 70 73 91 97 100
##
## Value 32 43 58 59 70 73 91 97 100
## Frequency 1 1 1 1 1 1 1 1 3
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.273
## --------------------------------------------------------------------------------
## FS.B4_BFControl
## n missing distinct Info Mean Gmd
## 11 93 8 0.977 74.91 29.56
##
## lowest : 18 43 66 69 81, highest: 69 81 88 93 100
##
## Value 18 43 66 69 81 88 93 100
## Frequency 1 1 2 1 1 1 1 3
## Proportion 0.091 0.091 0.182 0.091 0.091 0.091 0.091 0.273
## --------------------------------------------------------------------------------psych::alpha(FS$BenScale_BFControl)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$BenScale_BFControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.9 0.92 0.69 9 0.019 77 19 0.76
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.84 0.88 0.92
## Duhachek 0.85 0.88 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## FS.B1_BFControl 0.87 0.89 0.86 0.73 8.1 0.022 0.01008
## FS.B2_BFControl 0.80 0.82 0.82 0.60 4.5 0.033 0.04181
## FS.B3_BFControl 0.81 0.84 0.88 0.63 5.2 0.033 0.04956
## FS.B4_BFControl 0.91 0.92 0.89 0.80 12.3 0.013 0.00061
## med.r
## FS.B1_BFControl 0.73
## FS.B2_BFControl 0.63
## FS.B3_BFControl 0.73
## FS.B4_BFControl 0.80
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.B1_BFControl 11 0.81 0.84 0.81 0.70 79 17
## FS.B2_BFControl 11 0.95 0.96 0.95 0.91 81 19
## FS.B3_BFControl 11 0.93 0.93 0.89 0.85 75 25
## FS.B4_BFControl 11 0.82 0.78 0.71 0.63 75 26cor(FS$BenScale_BFControl, use= "complete.obs")## FS.B1_BFControl FS.B2_BFControl FS.B3_BFControl FS.B4_BFControl
## FS.B1_BFControl 1.0000000 0.7956334 0.7847937 0.3797788
## FS.B2_BFControl 0.7956334 1.0000000 0.8320682 0.7254958
## FS.B3_BFControl 0.7847937 0.8320682 1.0000000 0.6313735
## FS.B4_BFControl 0.3797788 0.7254958 0.6313735 1.0000000### Support
FS$S1_BFControl<- as.numeric(FS$Sup_BF_Control_40)
FS$S2_BFControl <- as.numeric(FS$Sup_BF_Control_42)
FS$S3_BFControl <- as.numeric(FS$Sup_BF_Control_43)
FS$S4_BFControl <- as.numeric(FS$Sup_BF_Control_45)
hist(FS$S1_BFControl)
hist(FS$S2_BFControl)
hist(FS$S3_BFControl)
hist(FS$S4_BFControl)
FS$SupScore_BFControl <- rowMeans(FS [, c( "S1_BFControl" , "S2_BFControl", "S3_BFControl", "S4_BFControl")], na.rm=TRUE)
describe(FS$SupScore_BFControl)## FS$SupScore_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 75.82 20.95 53.88 56.00
## .25 .50 .75 .90 .95
## 63.88 69.25 93.62 99.50 99.75
##
## lowest : 51.75 56.00 62.50 65.25 66.75, highest: 75.75 92.00 95.25 99.50 100.00
##
## Value 51.75 56.00 62.50 65.25 66.75 69.25 75.75 92.00 95.25
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 99.50 100.00
## Frequency 1 1
## Proportion 0.091 0.091sd(FS$SupScore_BFControl, na.rm = TRUE)## [1] 17.80874FS$SupScale_BFControl <- data.frame(FS$S1_BFControl, FS$S2_BFControl, FS$S3_BFControl, FS$S4_BFControl)
describe(FS$SupScale_BFControl)## FS$SupScale_BFControl
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.S1_BFControl
## n missing distinct Info Mean Gmd
## 11 93 9 0.982 78.18 25.56
##
## lowest : 41 52 61 62 73, highest: 73 74 98 99 100
##
## Value 41 52 61 62 73 74 98 99 100
## Frequency 1 1 1 1 1 1 1 1 3
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.273
## --------------------------------------------------------------------------------
## FS.S2_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 81.64 19.89 56.5 62.0
## .25 .50 .75 .90 .95
## 69.0 88.0 96.0 100.0 100.0
##
## lowest : 51 62 67 71 78, highest: 88 89 94 98 100
##
## Value 51 62 67 71 78 88 89 94 98 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182
## --------------------------------------------------------------------------------
## FS.S3_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 73.73 26.76 40.0 52.0
## .25 .50 .75 .90 .95
## 60.5 71.0 92.5 100.0 100.0
##
## lowest : 28 52 60 61 67, highest: 71 87 89 96 100
##
## Value 28 52 60 61 67 71 87 89 96 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182
## --------------------------------------------------------------------------------
## FS.S4_BFControl
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 69.73 29.38 33.5 52.0
## .25 .50 .75 .90 .95
## 57.0 70.0 92.5 100.0 100.0
##
## lowest : 15 52 56 58 60, highest: 70 71 91 94 100
##
## Value 15 52 56 58 60 70 71 91 94 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182
## --------------------------------------------------------------------------------psych::alpha(FS$SupScale_BFControl)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$SupScale_BFControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.84 0.85 0.56 5.2 0.031 76 18 0.52
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.75 0.82 0.87
## Duhachek 0.76 0.82 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.S1_BFControl 0.68 0.70 0.64 0.44 2.4 0.055 0.027 0.37
## FS.S2_BFControl 0.75 0.76 0.72 0.51 3.1 0.045 0.040 0.42
## FS.S3_BFControl 0.75 0.78 0.81 0.55 3.6 0.046 0.097 0.42
## FS.S4_BFControl 0.89 0.90 0.90 0.76 9.4 0.018 0.018 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.S1_BFControl 11 0.91 0.93 0.96 0.83 78 22
## FS.S2_BFControl 11 0.84 0.87 0.88 0.74 82 17
## FS.S3_BFControl 11 0.84 0.84 0.76 0.68 74 23
## FS.S4_BFControl 11 0.69 0.64 0.43 0.41 70 26cor(FS$SupScale_BFControl, use= "complete.obs")## FS.S1_BFControl FS.S2_BFControl FS.S3_BFControl FS.S4_BFControl
## FS.S1_BFControl 1.0000000 0.9012425 0.7396963 0.4154768
## FS.S2_BFControl 0.9012425 1.0000000 0.6314288 0.3226014
## FS.S3_BFControl 0.7396963 0.6314288 1.0000000 0.3731801
## FS.S4_BFControl 0.4154768 0.3226014 0.3731801 1.0000000Enhanced Weathering
Quiz Questions & Attention Check
## Time Spent Reading Instructions
describe(FS$EWCon_InstTime_Page.Submit)## FS$EWCon_InstTime_Page.Submit
## n missing distinct Info Mean Gmd
## 9 95 9 1 4.983 4.936
##
## lowest : 0.933 1.251 1.834 3.000 3.782, highest: 3.782 5.072 5.894 6.201 16.883
##
## Value 0.933 1.251 1.834 3.000 3.782 5.072 5.894 6.201 16.883
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111INSTminutes_EWC <- (mean(FS$EWCon_InstTime_Page.Submit, na.rm = TRUE)/60)
print(INSTminutes_EWC)## [1] 0.08305556## Time Spent Reading Technology Description (before proceeding to next page of survey)
### Raw Time Spent (Seconds)
describe(FS$EW_Control_Time_Page.Submit)## FS$EW_Control_Time_Page.Submit
## n missing distinct Info Mean Gmd
## 9 95 9 1 19.38 23.89
##
## lowest : 0.901 1.105 2.571 10.801 14.540, highest: 14.540 17.703 18.297 35.483 73.033
##
## Value 0.901 1.105 2.571 10.801 14.540 17.703 18.297 35.483 73.033
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 sd(FS$EW_Control_Time_Page.Submit, na.rm = TRUE)## [1] 22.90456 range(FS$EW_Control_Time_Page.Submit, na.rm = TRUE)## [1] 0.901 73.033 ### Convert to Minutes
TECHminutes_EWC <- (mean(FS$EW_Control_Time_Page.Submit, na.rm = TRUE)/60)
print(TECHminutes_EWC)## [1] 0.3230259## Attention Checks
### Attention Check 1: What technology did you just read about?
FS$ATN_EW_Control1 <- as.numeric(as.character(FS$EW_Control_ATN))
FS$ATN_EW_Control <- factor(FS$ATN_EW_Control1, levels = c(1, 2, 3, 4),
labels = c("Biochar", "Biofuel", "Enhanced Weathering", "Wind Energy"))
describe(FS$ATN_EW_Control)## FS$ATN_EW_Control
## n missing distinct
## 9 95 2
##
## Value Enhanced Weathering Wind Energy
## Frequency 7 2
## Proportion 0.778 0.222#### Time spent answering attention check #1:
##### (Seconds)
describe(FS$EW_Control_ATNTIME_Page.Submit)## FS$EW_Control_ATNTIME_Page.Submit
## n missing distinct Info Mean Gmd
## 9 95 9 1 22.42 33.66
##
## lowest : 3.504 3.588 3.900 6.321 7.912
## highest: 7.912 9.693 10.785 10.881 145.198
##
## Value 3.504 3.588 3.900 6.321 7.912 9.693 10.785 10.881
## Frequency 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
##
## Value 145.198
## Frequency 1
## Proportion 0.111 sd(FS$EW_Control_ATNTIME_Page.Submit, na.rm = TRUE)## [1] 46.13844 range(FS$EW_Control_ATNTIME_Page.Submit, na.rm = TRUE)## [1] 3.504 145.198 ##### (Minutes)
ATN1_EWC <- (mean(FS$EW_Control_ATNTIME_Page.Submit, na.rm = TRUE)/60)
print(ATN1_EWC)## [1] 0.3736704### Attention Check 2: Describe as much as you can about the technology you read about.
describe(FS$EW_Control_ATN2)## FS$EW_Control_ATN2
## n missing distinct
## 9 95 9
##
## lowest : drawing CO2 out of the atmosphere via reactions with minerals and water Enhanced weathering is a process that speeds up and removes co2 Enhanced weathering is when you're working with rocks in the ocean and using that for fuel I'm not sure I understood it. Iy is based off of fuel and energy stored.
## highest: Iy is based off of fuel and energy stored. The climate change method is sustainable. It uses natural processes to achieve a better use of technology. It aims to help the environment. UC is developing a new sensor to detect climate change Very new and exciting Weather changes because of man but also as well because of time. #### Time spent answering attention check #2
##### (Seconds)
describe(FS$EW_Control_ATN2TIME_Page.Submit)## FS$EW_Control_ATN2TIME_Page.Submit
## n missing distinct Info Mean Gmd
## 9 95 9 1 45.68 35.13
##
## lowest : 11.118 11.201 18.936 33.854 36.401, highest: 36.401 66.401 71.750 73.540 87.921
##
## Value 11.118 11.201 18.936 33.854 36.401 66.401 71.750 73.540 87.921
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 sd(FS$EW_Control_ATN2TIME_Page.Submit, na.rm = TRUE)## [1] 29.56775 range(FS$EW_Control_ATN2TIME_Page.Submit, na.rm = TRUE)## [1] 11.118 87.921 ##### (Minutes)
ATN2_EWC <- (mean(FS$EW_Control_ATN2TIME_Page.Submit, na.rm = TRUE)/60)
print(ATN2_EWC)## [1] 0.761337Technology Ratings
### Naturalness
FS$N1_EWControl <- as.numeric(FS$Nat_EW_Control_30)
FS$N2R_EWControl <- as.numeric(100 - FS$Nat_EW_Control_31)
FS$N3R_EWControl <- as.numeric(100 - FS$Nat_EW_Control_35)
FS$N4R_EWControl <- as.numeric(100- FS$Nat_EW_Control_36)
hist(FS$N1_EWControl)
hist(FS$N2R_EWControl)
hist(FS$N3R_EWControl)
hist(FS$N4R_EWControl)
FS$NatScore_EWControl <- rowMeans(FS [, c( "N1_EWControl" , "N2R_EWControl", "N3R_EWControl", "N4R_EWControl")], na.rm=TRUE)
describe(FS$NatScore_EWControl)## FS$NatScore_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 35.78 22.61
##
## lowest : 6.25 17.00 28.00 30.25 31.75, highest: 31.75 38.00 50.50 50.75 69.50
##
## Value 6.25 17.00 28.00 30.25 31.75 38.00 50.50 50.75 69.50
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111sd(FS$NatScore_EWControl, na.rm = TRUE)## [1] 19.08797FS$NatScale_EWControl <- data.frame(FS$N1_EWControl, FS$N2R_EWControl, FS$N3R_EWControl, FS$N4R_EWControl)
describe(FS$NatScale_EWControl)## FS$NatScale_EWControl
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.N1_EWControl
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 47.22 35.56
##
## lowest : 16 17 40 43 48, highest: 43 48 51 93 100
##
## Value 16 17 40 43 48 51 93 100
## Frequency 1 2 1 1 1 1 1 1
## Proportion 0.111 0.222 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.N2R_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 27.22 30.39
##
## lowest : 0 4 8 9 18, highest: 18 36 40 52 78
##
## Value 0 4 8 9 18 36 40 52 78
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.N3R_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 17.33 19.83
##
## lowest : 0 1 5 7 12, highest: 12 20 27 33 51
##
## Value 0 1 5 7 12 20 27 33 51
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.N4R_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 51.33 38.89
##
## lowest : 3 16 36 39 51, highest: 51 52 76 89 100
##
## Value 3 16 36 39 51 52 76 89 100
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------psych::alpha(FS$NatScale_EWControl)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$NatScale_EWControl): 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 ( FS.N3R_EWControl ) 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 = FS$NatScale_EWControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.65 0.58 0.78 0.25 1.4 0.051 36 19 0.36
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.52 0.65 0.75
## Duhachek 0.55 0.65 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## FS.N1_EWControl 0.39 0.39 0.55 0.17 0.63 0.097 0.15
## FS.N2R_EWControl 0.53 0.31 0.66 0.13 0.44 0.058 0.46
## FS.N3R_EWControl 0.79 0.78 0.81 0.54 3.56 0.036 0.10
## FS.N4R_EWControl 0.38 0.38 0.51 0.17 0.61 0.099 0.14
## med.r
## FS.N1_EWControl 0.38
## FS.N2R_EWControl -0.25
## FS.N3R_EWControl 0.38
## FS.N4R_EWControl 0.34
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.N1_EWControl 9 0.85 0.75 0.77 0.650 47 31
## FS.N2R_EWControl 9 0.74 0.81 0.67 0.503 27 26
## FS.N3R_EWControl 9 0.15 0.34 0.12 -0.076 17 17
## FS.N4R_EWControl 9 0.86 0.76 0.79 0.656 51 32cor(FS$NatScale_EWControl, use= "complete.obs")## FS.N1_EWControl FS.N2R_EWControl FS.N3R_EWControl
## FS.N1_EWControl 1.0000000 0.3434190 -0.2535695
## FS.N2R_EWControl 0.3434190 1.0000000 0.4170933
## FS.N3R_EWControl -0.2535695 0.4170933 1.0000000
## FS.N4R_EWControl 0.9071150 0.3769751 -0.2706262
## FS.N4R_EWControl
## FS.N1_EWControl 0.9071150
## FS.N2R_EWControl 0.3769751
## FS.N3R_EWControl -0.2706262
## FS.N4R_EWControl 1.0000000### Familiarity
FS$Fam_EWControl <- as.numeric(FS$Fam_EW_Control_34)
hist(FS$Fam_EWControl)
describe(FS$Fam_EWControl)## FS$Fam_EWControl
## n missing distinct Info Mean Gmd
## 9 95 7 0.983 39.22 35.83
##
## lowest : 0 28 39 40 52, highest: 39 40 52 79 87
##
## Value 0 28 39 40 52 79 87
## Frequency 2 2 1 1 1 1 1
## Proportion 0.222 0.222 0.111 0.111 0.111 0.111 0.111sd(FS$Fam_EWControl, na.rm = TRUE)## [1] 30.32646### Understanding
FS$Und_EWControl <- as.numeric(FS$Fam_EW_Control_33)
hist(FS$Und_EWControl)
describe(FS$Und_EWControl)## FS$Und_EWControl
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 46.33 37.61
##
## lowest : 0 22 28 39 63, highest: 39 63 70 80 93
##
## Value 0 22 28 39 63 70 80 93
## Frequency 1 2 1 1 1 1 1 1
## Proportion 0.111 0.222 0.111 0.111 0.111 0.111 0.111 0.111sd(FS$Und_EWControl, na.rm = TRUE)## [1] 31.36479### Fluency
FS$Fluency_EWControl <- as.numeric(FS$Fluency_EW_Control_30)
hist(FS$Fluency_EWControl)
describe(FS$Fluency_EWControl)## FS$Fluency_EWControl
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 68.78 37.67
##
## lowest : 0 37 51 72 82, highest: 72 82 88 91 99
##
## Value 0 37 51 72 82 88 91 99
## Frequency 1 1 1 1 1 1 1 2
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.222sd(FS$Fluency_EWControl, na.rm = TRUE)## [1] 33.41324### Risk
FS$R1_EWControl<- as.numeric(FS$Risk_EW_Control_32)
FS$R2_EWControl <- as.numeric(FS$Risk_EW_Control_33)
FS$R3_EWControl <- as.numeric(FS$Risk_EW_Control_34)
hist(FS$R1_EWControl)
hist(FS$R2_EWControl)
hist(FS$R3_EWControl)
FS$RiskScore_EWControl <- rowMeans(FS [, c( "R1_EWControl" , "R2_EWControl", "R3_EWControl")], na.rm=TRUE)
describe(FS$RiskScore_EWControl)## FS$RiskScore_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 55.11 28.06
##
## lowest : 17.33333 37.00000 41.66667 48.33333 50.00000
## highest: 50.00000 61.33333 61.66667 82.33333 96.33333
##
## Value 17.33333 37.00000 41.66667 48.33333 50.00000 61.33333 61.66667
## Frequency 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111
##
## Value 82.33333 96.33333
## Frequency 1 1
## Proportion 0.111 0.111sd(FS$RiskScore_EWControl, na.rm = TRUE)## [1] 23.79601FS$RiskScale_EWControl <- data.frame(FS$R1_EWControl, FS$R2_EWControl, FS$R3_EWControl)
describe(FS$RiskScale_EWControl)## FS$RiskScale_EWControl
##
## 3 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.R1_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 53.78 37.33
##
## lowest : 0 25 29 50 60, highest: 60 68 69 83 100
##
## Value 0 25 29 50 60 68 69 83 100
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.R2_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 43.56 39.28
##
## lowest : 0 12 19 24 49, highest: 49 50 58 81 99
##
## Value 0 12 19 24 49 50 58 81 99
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.R3_EWControl
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 68 17
##
## lowest : 50 52 58 70 75, highest: 70 75 76 83 90
##
## Value 50 52 58 70 75 76 83 90
## Frequency 1 1 2 1 1 1 1 1
## Proportion 0.111 0.111 0.222 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------psych::alpha(FS$RiskScale_EWControl)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$RiskScale_EWControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.86 0.85 0.67 6.1 0.018 55 24 0.61
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.77 0.84 0.88
## Duhachek 0.80 0.84 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.R1_EWControl 0.62 0.76 0.61 0.61 3.2 0.048 NA 0.61
## FS.R2_EWControl 0.56 0.69 0.52 0.52 2.2 0.060 NA 0.52
## FS.R3_EWControl 0.94 0.94 0.88 0.88 14.6 0.013 NA 0.88
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.R1_EWControl 9 0.95 0.91 0.89 0.84 54 31
## FS.R2_EWControl 9 0.97 0.94 0.94 0.89 44 33
## FS.R3_EWControl 9 0.71 0.81 0.62 0.59 68 14cor(FS$RiskScale_EWControl, use= "complete.obs")## FS.R1_EWControl FS.R2_EWControl FS.R3_EWControl
## FS.R1_EWControl 1.0000000 0.8796381 0.5231592
## FS.R2_EWControl 0.8796381 1.0000000 0.6128176
## FS.R3_EWControl 0.5231592 0.6128176 1.0000000### Benefit
FS$B1_EWControl<- as.numeric(FS$Ben_EW_Control_40)
FS$B2_EWControl <- as.numeric(FS$Ben_EW_Control_42)
FS$B3_EWControl <- as.numeric(FS$Ben_EW_Control_43)
FS$B4_EWControl <- as.numeric(FS$Ben_EW_Control_51)
hist(FS$B1_EWControl)
hist(FS$B2_EWControl)
hist(FS$B3_EWControl)
hist(FS$B4_EWControl)
FS$BenScore_EWControl <- rowMeans(FS [, c( "B1_EWControl" , "B2_EWControl", "B3_EWControl", "B4_EWControl")], na.rm=TRUE)
describe(FS$BenScore_EWControl)## FS$BenScore_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 65.25 24.86
##
## lowest : 32.50 50.00 52.00 54.50 64.00, highest: 64.00 69.25 76.00 91.25 97.75
##
## Value 32.50 50.00 52.00 54.50 64.00 69.25 76.00 91.25 97.75
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111sd(FS$BenScore_EWControl, na.rm = TRUE)## [1] 20.80152FS$BenScale_EWControl <- data.frame(FS$B1_EWControl, FS$B2_EWControl, FS$B3_EWControl, FS$B4_EWControl)
describe(FS$BenScale_EWControl)## FS$BenScale_EWControl
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.B1_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 61.78 29.56
##
## lowest : 23 36 50 52 58, highest: 58 73 76 93 95
##
## Value 23 36 50 52 58 73 76 93 95
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.B2_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 61 31
##
## lowest : 16 34 50 52 65, highest: 65 70 76 90 96
##
## Value 16 34 50 52 65 70 76 90 96
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.B3_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 67.78 25.17
##
## lowest : 40 50 52 55 61, highest: 61 76 82 94 100
##
## Value 40 50 52 55 61 76 82 94 100
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.B4_EWControl
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 70.44 22.06
##
## lowest : 50 51 52 62 67, highest: 62 67 76 88 100
##
## Value 50 51 52 62 67 76 88 100
## Frequency 1 1 1 1 1 1 2 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.222 0.111
## --------------------------------------------------------------------------------psych::alpha(FS$BenScale_EWControl)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$BenScale_EWControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.95 0.97 0.82 18 0.011 65 21 0.82
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.94 0.95
## Duhachek 0.92 0.94 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## FS.B1_EWControl 0.89 0.91 0.94 0.77 9.9 0.0204 0.0344
## FS.B2_EWControl 0.97 0.98 0.99 0.94 45.9 0.0048 0.0023
## FS.B3_EWControl 0.89 0.90 0.89 0.75 9.0 0.0187 0.0180
## FS.B4_EWControl 0.92 0.93 0.92 0.81 12.5 0.0146 0.0175
## med.r
## FS.B1_EWControl 0.71
## FS.B2_EWControl 0.96
## FS.B3_EWControl 0.75
## FS.B4_EWControl 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.B1_EWControl 9 0.97 0.97 0.97 0.93 62 25
## FS.B2_EWControl 9 0.85 0.83 0.74 0.71 61 26
## FS.B3_EWControl 9 0.97 0.98 1.00 0.95 68 21
## FS.B4_EWControl 9 0.92 0.94 0.95 0.87 70 19cor(FS$BenScale_EWControl, use= "complete.obs")## FS.B1_EWControl FS.B2_EWControl FS.B3_EWControl FS.B4_EWControl
## FS.B1_EWControl 1.0000000 0.7492446 0.9584845 0.8834541
## FS.B2_EWControl 0.7492446 1.0000000 0.7132660 0.6151490
## FS.B3_EWControl 0.9584845 0.7132660 1.0000000 0.9738896
## FS.B4_EWControl 0.8834541 0.6151490 0.9738896 1.0000000### Support
FS$S1_EWControl<- as.numeric(FS$Sup_EW_Control_40)
FS$S2_EWControl <- as.numeric(FS$Sup_EW_Control_42)
FS$S3_EWControl <- as.numeric(FS$Sup_EW_Control_43)
FS$S4_EWControl <- as.numeric(FS$Sup_EW_Control_45)
hist(FS$S1_EWControl)
hist(FS$S2_EWControl)
hist(FS$S3_EWControl)
hist(FS$S4_EWControl)
FS$SupScore_EWControl <- rowMeans(FS [, c( "S1_EWControl" , "S2_EWControl", "S3_EWControl", "S4_EWControl")], na.rm=TRUE)
describe(FS$SupScore_EWControl)## FS$SupScore_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 57.08 26.57
##
## lowest : 15.75 37.00 50.00 54.00 55.75, highest: 55.75 64.25 65.50 75.00 96.50
##
## Value 15.75 37.00 50.00 54.00 55.75 64.25 65.50 75.00 96.50
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111sd(FS$SupScore_EWControl, na.rm = TRUE)## [1] 22.83124FS$SupScale_EWControl <- data.frame(FS$S1_EWControl, FS$S2_EWControl, FS$S3_EWControl, FS$S4_EWControl)
describe(FS$SupScale_EWControl)## FS$SupScale_EWControl
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.S1_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 65.33 31.22
##
## lowest : 17 38 50 64 73, highest: 73 76 77 94 99
##
## Value 17 38 50 64 73 76 77 94 99
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.S2_EWControl
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 67.89 29.22
##
## lowest : 17 50 54 64 75, highest: 64 75 87 89 100
##
## Value 17 50 54 64 75 87 89 100
## Frequency 1 1 1 1 2 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.222 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.S3_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 46.67 37.78
##
## lowest : 0 18 23 30 50, highest: 50 62 63 74 100
##
## Value 0 18 23 30 50 62 63 74 100
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.S4_EWControl
## n missing distinct Info Mean Gmd
## 9 95 9 1 48.44 36.61
##
## lowest : 0 11 17 50 61, highest: 61 67 68 75 87
##
## Value 0 11 17 50 61 67 68 75 87
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------psych::alpha(FS$SupScale_EWControl)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$SupScale_EWControl)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.96 0.52 4.3 0.036 57 23 0.34
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.74 0.81 0.86
## Duhachek 0.74 0.81 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.S1_EWControl 0.78 0.77 0.80 0.52 3.3 0.037 0.10 0.39
## FS.S2_EWControl 0.75 0.74 0.78 0.48 2.8 0.043 0.13 0.30
## FS.S3_EWControl 0.76 0.79 0.87 0.55 3.7 0.046 0.13 0.39
## FS.S4_EWControl 0.73 0.76 0.83 0.51 3.1 0.053 0.16 0.29
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.S1_EWControl 9 0.75 0.79 0.79 0.57 65 26
## FS.S2_EWControl 9 0.79 0.83 0.83 0.64 68 25
## FS.S3_EWControl 9 0.81 0.77 0.73 0.62 47 32
## FS.S4_EWControl 9 0.84 0.81 0.78 0.68 48 31cor(FS$SupScale_EWControl, use= "complete.obs")## FS.S1_EWControl FS.S2_EWControl FS.S3_EWControl FS.S4_EWControl
## FS.S1_EWControl 1.0000000 0.9703386 0.2588807 0.2958704
## FS.S2_EWControl 0.9703386 1.0000000 0.2949075 0.3853197
## FS.S3_EWControl 0.2588807 0.2949075 1.0000000 0.8921764
## FS.S4_EWControl 0.2958704 0.3853197 0.8921764 1.0000000Less Familiar Condition
Biochar
Quiz Questions & Attention Check
## Time Spent Reading Instructions
describe(FS$BIOUF_InstTime_Page.Submit)## FS$BIOUF_InstTime_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 5.09 4.856 1.265 1.384
## .25 .50 .75 .90 .95
## 1.954 2.501 8.534 11.094 12.107
##
## lowest : 1.147 1.384 1.950 1.959 2.136, highest: 3.631 8.090 8.978 11.094 13.120
##
## Value 1.147 1.384 1.950 1.959 2.136 2.501 3.631 8.090 8.978
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 11.094 13.120
## Frequency 1 1
## Proportion 0.091 0.091INSTminutes_BIOUF <- (mean(FS$BIOUF_InstTime_Page.Submit, na.rm = TRUE)/60)
print(INSTminutes_BIOUF)## [1] 0.08483333## Time Spent Reading Technology Description (before proceeding to next page of survey)
### Raw Time Spent (Seconds)
describe(FS$BIO_UF_Time_Page.Submit)## FS$BIO_UF_Time_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 18.89 21.33 1.886 2.101
## .25 .50 .75 .90 .95
## 2.809 11.035 31.795 36.246 47.597
##
## lowest : 1.671 2.101 2.465 3.154 5.275, highest: 23.277 31.354 32.236 36.246 58.949
##
## Value 1.671 2.101 2.465 3.154 5.275 11.035 23.277 31.354 32.236
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 36.246 58.949
## Frequency 1 1
## Proportion 0.091 0.091 sd(FS$BIO_UF_Time_Page.Submit, na.rm = TRUE)## [1] 18.97763 range(FS$BIO_UF_Time_Page.Submit, na.rm = TRUE)## [1] 1.671 58.949 ### Convert to Minutes
TECHminutes_BIOUF <- (mean(FS$BIO_UF_Time_Page.Submit, na.rm = TRUE)/60)
print(TECHminutes_BIOUF)## [1] 0.3147924## Attention and Manipulation Checks
# Attention Check 1: What technology did you just read about?
FS$ATN_BIO_UF1 <- as.numeric(as.character(FS$BIO_UF_ATN))
FS$ATN_BIO_UF <- factor(FS$ATN_BIO_Control1, levels = c(1, 2, 3, 4),
labels = c("Biochar", "Biofuel", "Enhanced Weathering", "Wind Energy"))
describe(FS$ATN_BIO_UF)## FS$ATN_BIO_UF
## n missing distinct
## 12 92 4
##
## Value Biochar Biofuel Enhanced Weathering
## Frequency 6 2 1
## Proportion 0.500 0.167 0.083
##
## Value Wind Energy
## Frequency 3
## Proportion 0.250#### Time spent answering attention check #1:
##### (Seconds)
describe(FS$BIO_UF_ATNTime_Page.Submit)## FS$BIO_UF_ATNTime_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 8.449 4.454 4.335 4.368
## .25 .50 .75 .90 .95
## 5.155 8.736 10.329 11.501 14.451
##
## lowest : 4.301 4.368 4.808 5.502 6.810, highest: 8.859 10.136 10.521 11.501 17.402
##
## Value 4.301 4.368 4.808 5.502 6.810 8.736 8.859 10.136 10.521
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 11.501 17.402
## Frequency 1 1
## Proportion 0.091 0.091 sd(FS$BIO_UF_ATNTime_Page.Submit, na.rm = TRUE)## [1] 3.941434 range(FS$BIO_UF_ATNTime_Page.Submit, na.rm = TRUE)## [1] 4.301 17.402 ##### (Minutes)
ATN1_BIOUF <- (mean(FS$BIO_UF_ATNTime_Page.Submit, na.rm = TRUE)/60)
print(ATN1_BIOUF)## [1] 0.1408242### Attention Check 2: Describe as much as you can about the technology you read about.
describe(FS$BIO_UF_ATN2)## FS$BIO_UF_ATN2
## n missing distinct
## 11 93 11
##
## lowest : A long-term pattern of weather in a particular area. Weather is a particular region and time period. i really havnt done that It deals with how charcoal is made and then placed in the ground for later use It's changing 24/7 Needs attention now
## highest: Noithing I can remember That it’s very inconsistent Turning plant based materials into carbon to be stored underground for long periods. Using animal & plant remains to make by burning without oxygen a carbon product to be used later. You burn biomass with out oxygen which create coal which can used in the soil #### Time spent answering attention check #2
##### (Seconds)
describe(FS$BIO_UF_ATN2Time_Page.Submit)## FS$BIO_UF_ATN2Time_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 41.7 26.18 16.76 17.17
## .25 .50 .75 .90 .95
## 22.90 42.70 52.81 75.16 78.00
##
## lowest : 16.360 17.166 22.201 23.603 31.042, highest: 43.991 50.363 55.258 75.164 80.841
##
## Value 16.360 17.166 22.201 23.603 31.042 42.699 43.991 50.363 55.258
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 75.164 80.841
## Frequency 1 1
## Proportion 0.091 0.091 sd(FS$BIO_UF_ATN2Time_Page.Submit, na.rm = TRUE)## [1] 22.32109 range(FS$BIO_UF_ATN2Time_Page.Submit, na.rm = TRUE)## [1] 16.360 80.841 ##### (Minutes)
ATN2_BFUF <- (mean(FS$BIO_UF_ATN2Time_Page.Submit, na.rm = TRUE)/60)
print(ATN2_BFUF)## [1] 0.6949818### Quiz Questions
FS$BIO_UF_Quiz1## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] 4 4 1 4 2 4 4 4 3 4 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [101] NA NA NA NAFS$BIO_UF_QuizTime_Page.Submit## [1] NA NA NA NA NA NA NA NA NA NA
## [11] NA NA NA NA NA NA NA NA NA NA
## [21] NA NA NA NA NA 11.490 30.639 32.319 47.978 33.687
## [31] 6.460 10.392 39.268 8.401 19.280 34.151 NA NA NA NA
## [41] NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA
## [61] NA NA NA NA NA NA NA NA NA NA
## [71] NA NA NA NA NA NA NA NA NA NA
## [81] NA NA NA NA NA NA NA NA NA NA
## [91] NA NA NA NA NA NA NA NA NA NA
## [101] NA NA NA NATechnology Ratings
#Naturalness
FS$N1_BioUF<- as.numeric(FS$Nat_BIO_UF_30)
FS$N2R_BioUF <- as.numeric(100 - FS$Nat_BIO_UF_31)
FS$N3R_BioUF <- as.numeric(100 - FS$Nat_BIO_UF_35)
FS$N4R_BioUF <- as.numeric(100- FS$Nat_BIO_UF_36)
hist(FS$N1_BioUF)
hist(FS$N2R_BioUF)
hist(FS$N3R_BioUF)
hist(FS$N4R_BioUF)
FS$NatScore_BioUF <- rowMeans(FS [, c( "N1_BioUF" , "N2R_BioUF", "N3R_BioUF", "N4R_BioUF")], na.rm=TRUE)
describe(FS$NatScore_BioUF)## FS$NatScore_BioUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 44.2 24.2 14.75 25.00
## .25 .50 .75 .90 .95
## 28.25 49.75 59.75 63.50 68.25
##
## lowest : 4.50 25.00 25.75 30.75 44.50, highest: 50.00 56.25 63.25 63.50 73.00
##
## Value 4.50 25.00 25.75 30.75 44.50 49.75 50.00 56.25 63.25 63.50 73.00
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091sd(FS$NatScore_BioUF, na.rm = TRUE)## [1] 20.59272FS$NatScale_BioUF <- data.frame(FS$N1_BioUF, FS$N2R_BioUF, FS$N3R_BioUF, FS$N4R_BioUF)
describe(FS$NatScale_BioUF)## FS$NatScale_BioUF
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.N1_BioUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 60.27 40.87 8.0 16.0
## .25 .50 .75 .90 .95
## 38.0 60.0 88.5 100.0 100.0
##
## lowest : 0 16 26 50 57, highest: 60 77 85 92 100
##
## Value 0 16 26 50 57 60 77 85 92 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182
## --------------------------------------------------------------------------------
## FS.N2R_BioUF
## n missing distinct Info Mean Gmd
## 11 93 8 0.977 32.45 41.42
##
## lowest : 0 7 17 34 37, highest: 34 37 65 93 97
##
## Value 0 7 17 34 37 65 93 97
## Frequency 3 2 1 1 1 1 1 1
## Proportion 0.273 0.182 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------
## FS.N3R_BioUF
## n missing distinct Info Mean Gmd
## 11 93 8 0.955 22.82 31.09
##
## lowest : 0 2 19 23 28, highest: 23 28 38 41 100
##
## Value 0 2 19 23 28 38 41 100
## Frequency 4 1 1 1 1 1 1 1
## Proportion 0.364 0.091 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------
## FS.N4R_BioUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 61.27 44.87 0.5 1.0
## .25 .50 .75 .90 .95
## 33.0 65.0 94.0 97.0 98.5
##
## lowest : 0 1 16 50 65, highest: 92 93 95 97 100
##
## Value 0 1 16 50 65 92 93 95 97 100
## Frequency 1 1 1 1 2 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.182 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------psych::alpha(FS$NatScale_BioUF)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$NatScale_BioUF): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( FS.N1_BioUF ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = FS$NatScale_BioUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.35 0.33 0.73 0.11 0.49 0.098 44 21 0.25
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.12 0.35 0.53
## Duhachek 0.16 0.35 0.54
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## FS.N1_BioUF 0.789 0.8051 0.81 0.5792 4.1298 0.038 0.065
## FS.N2R_BioUF -0.026 -0.1249 0.44 -0.0384 -0.1110 0.161 0.270
## FS.N3R_BioUF 0.060 -0.0035 0.50 -0.0012 -0.0035 0.153 0.277
## FS.N4R_BioUF -0.445 -0.3936 0.55 -0.1039 -0.2824 0.235 0.713
## med.r
## FS.N1_BioUF 0.468
## FS.N2R_BioUF 0.099
## FS.N3R_BioUF 0.099
## FS.N4R_BioUF -0.570
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.N1_BioUF 11 -0.013 -0.037 -0.27 -0.40 60 35
## FS.N2R_BioUF 11 0.751 0.768 0.79 0.42 32 37
## FS.N3R_BioUF 11 0.689 0.719 0.73 0.41 23 30
## FS.N4R_BioUF 11 0.874 0.853 0.71 0.63 61 39cor(FS$NatScale_BioUF, use= "complete.obs")## FS.N1_BioUF FS.N2R_BioUF FS.N3R_BioUF FS.N4R_BioUF
## FS.N1_BioUF 1.00000000 -0.5700846 -0.6126885 0.09866055
## FS.N2R_BioUF -0.57008456 1.0000000 0.8710117 0.46794490
## FS.N3R_BioUF -0.61268851 0.8710117 1.0000000 0.39873696
## FS.N4R_BioUF 0.09866055 0.4679449 0.3987370 1.00000000### Familiarity
FS$Fam_BioUF <- as.numeric(FS$Fam_BIO_UF_31)
hist(FS$Fam_BioUF)
describe(FS$Fam_BioUF)## FS$Fam_BioUF
## n missing distinct Info Mean Gmd
## 11 93 9 0.982 33.64 42.51
##
## lowest : 0 2 19 20 23, highest: 23 35 81 94 96
##
## Value 0 2 19 20 23 35 81 94 96
## Frequency 3 1 1 1 1 1 1 1 1
## Proportion 0.273 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091sd(FS$Fam_BioUF, na.rm = TRUE)## [1] 38.32303### Understanding
FS$Und_BioUF <- as.numeric(FS$Fam_BIO_UF_33)
hist(FS$Und_BioUF)
describe(FS$Und_BioUF)## FS$Und_BioUF
## n missing distinct Info Mean Gmd
## 11 93 9 0.991 41.36 46.15
##
## lowest : 0 3 14 22 24, highest: 24 29 80 92 99
##
## Value 0 3 14 22 24 29 80 92 99
## Frequency 2 1 1 1 1 1 1 2 1
## Proportion 0.182 0.091 0.091 0.091 0.091 0.091 0.091 0.182 0.091sd(FS$Und_BioUF, na.rm = TRUE)## [1] 40.51487### Fluency
FS$Fluency_BioUF <- as.numeric(FS$Fluency_BIO_UF_34)
hist(FS$Fluency_BioUF)
describe(FS$Fluency_BioUF)## FS$Fluency_BioUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 74 33.38 13.5 25.0
## .25 .50 .75 .90 .95
## 72.5 85.0 95.5 100.0 100.0
##
## lowest : 2 25 72 73 77, highest: 85 89 95 96 100
##
## Value 2 25 72 73 77 85 89 95 96 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182sd(FS$Fluency_BioUF, na.rm = TRUE)## [1] 31.97186### Risk
FS$R1_BIOUF <- as.numeric(FS$Risk_BIO_UF_30)
FS$R2_BIOUF <- as.numeric(FS$Risk_BIO_UF_31)
FS$R3_BIOUF <- as.numeric(FS$Risk_BIO_UF_32)
hist(FS$R1_BIOUF)
hist(FS$R2_BIOUF)
hist(FS$R3_BIOUF)
FS$RiskScore_BIOUF <- rowMeans(FS [, c( "R1_BIOUF" , "R2_BIOUF", "R3_BIOUF")], na.rm=TRUE)
describe(FS$RiskScore_BIOUF)## FS$RiskScore_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 38.88 22.98 10.00 19.00
## .25 .50 .75 .90 .95
## 32.67 33.33 52.83 66.33 66.50
##
## lowest : 1.00000 19.00000 32.33333 33.00000 33.33333
## highest: 37.00000 43.33333 62.33333 66.33333 66.66667
##
## Value 1.00000 19.00000 32.33333 33.00000 33.33333 37.00000 43.33333
## Frequency 1 1 1 1 2 1 1
## Proportion 0.091 0.091 0.091 0.091 0.182 0.091 0.091
##
## Value 62.33333 66.33333 66.66667
## Frequency 1 1 1
## Proportion 0.091 0.091 0.091sd(FS$RiskScore_BIOUF, na.rm = TRUE)## [1] 20.16998FS$RiskScale_BIOUF <- data.frame(FS$R1_BIOUF, FS$R2_BIOUF, FS$R3_BIOUF)
describe(FS$RiskScale_BIOUF)## FS$RiskScale_BIOUF
##
## 3 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.R1_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 24.18 33.35 0.5 1.0
## .25 .50 .75 .90 .95
## 4.0 12.0 20.5 82.0 91.0
##
## lowest : 0 1 2 6 8, highest: 14 18 23 82 100
##
## Value 0 1 2 6 8 12 14 18 23 82 100
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------
## FS.R2_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 32.55 42.47 0.0 0.0
## .25 .50 .75 .90 .95
## 3.5 12.0 59.5 90.0 95.0
##
## lowest : 0 1 6 8 12, highest: 22 48 71 90 100
##
## Value 0 1 6 8 12 22 48 71 90 100
## Frequency 2 1 1 1 1 1 1 1 1 1
## Proportion 0.182 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------
## FS.R3_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 59.91 44.87 0.5 1.0
## .25 .50 .75 .90 .95
## 26.5 76.0 89.0 99.0 99.5
##
## lowest : 0 1 17 36 69, highest: 76 83 95 99 100
##
## Value 0 1 17 36 69 76 83 95 99 100
## Frequency 1 1 1 1 1 1 2 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.182 0.091 0.091 0.091
## --------------------------------------------------------------------------------psych::alpha(FS$RiskScale_BIOUF)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$RiskScale_BIOUF): 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 ( FS.R3_BIOUF ) 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 = FS$RiskScale_BIOUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.19 -0.13 0.33 -0.039 -0.11 0.21 39 20 -0.37
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.66 -0.19 0.15
## Duhachek -0.60 -0.19 0.21
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.R1_BIOUF -1.19 -1.19 -0.37 -0.37 -0.54 0.430 NA -0.37
## FS.R2_BIOUF -1.44 -1.47 -0.42 -0.42 -0.59 0.473 NA -0.42
## FS.R3_BIOUF 0.81 0.81 0.68 0.68 4.24 0.038 NA 0.68
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.R1_BIOUF 11 0.72 0.76 0.78 0.22 24 34
## FS.R2_BIOUF 11 0.77 0.79 0.81 0.22 33 38
## FS.R3_BIOUF 11 0.17 0.12 -0.63 -0.43 60 39cor(FS$RiskScale_BIOUF, use= "complete.obs")## FS.R1_BIOUF FS.R2_BIOUF FS.R3_BIOUF
## FS.R1_BIOUF 1.0000000 0.6792979 -0.4228377
## FS.R2_BIOUF 0.6792979 1.0000000 -0.3739449
## FS.R3_BIOUF -0.4228377 -0.3739449 1.0000000### Benefit
FS$B1_BIOUF <- as.numeric(FS$Ben_BIO_UF_40)
FS$B2_BIOUF <- as.numeric(FS$Ben_BIO_UF_41)
FS$B3_BIOUF <- as.numeric(FS$Ben_BIO_UF_42)
FS$B4_BIOUF <- as.numeric(FS$Ben_BIO_UF_43)
hist(FS$B1_BIOUF)
hist(FS$B2_BIOUF)
hist(FS$B3_BIOUF)
hist(FS$B4_BIOUF)
FS$BenScore_BIOUF <- rowMeans(FS [, c( "B1_BIOUF" , "B2_BIOUF", "B3_BIOUF", "B4_BIOUF")], na.rm=TRUE)
describe(FS$BenScore_BIOUF)## FS$BenScore_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 63.32 39.78 1.00 2.00
## .25 .50 .75 .90 .95
## 51.25 69.00 87.75 99.75 99.88
##
## lowest : 0.00 2.00 49.75 52.75 65.75, highest: 82.00 85.00 90.50 99.75 100.00
##
## Value 0.00 2.00 49.75 52.75 65.75 69.00 82.00 85.00 90.50
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 99.75 100.00
## Frequency 1 1
## Proportion 0.091 0.091sd(FS$BenScore_BIOUF, na.rm = TRUE)## [1] 35.11963FS$BenScale_BIOUF <- data.frame(FS$B1_BIOUF, FS$B2_BIOUF, FS$B3_BIOUF, FS$B4_BIOUF)
describe(FS$BenScale_BIOUF)## FS$BenScale_BIOUF
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.B1_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 56.55 45.2 1.0 2.0
## .25 .50 .75 .90 .95
## 24.0 65.0 87.5 100.0 100.0
##
## lowest : 0 2 23 25 50, highest: 65 82 87 88 100
##
## Value 0 2 23 25 50 65 82 87 88 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182
## --------------------------------------------------------------------------------
## FS.B2_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 64.18 42.07 1.0 2.0
## .25 .50 .75 .90 .95
## 45.0 76.0 90.5 100.0 100.0
##
## lowest : 0 2 27 63 70, highest: 76 87 88 93 100
##
## Value 0 2 27 63 70 76 87 88 93 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182
## --------------------------------------------------------------------------------
## FS.B3_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 66.55 40.55 0.5 1.0
## .25 .50 .75 .90 .95
## 53.0 80.0 91.5 100.0 100.0
##
## lowest : 0 1 31 75 79, highest: 80 83 91 92 100
##
## Value 0 1 31 75 79 80 83 91 92 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182
## --------------------------------------------------------------------------------
## FS.B4_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 66 39.09 1.5 3.0
## .25 .50 .75 .90 .95
## 54.0 82.0 86.5 99.0 99.5
##
## lowest : 0 3 33 75 78, highest: 83 84 89 99 100
##
## Value 0 3 33 75 78 82 83 84 89 99 100
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------psych::alpha(FS$BenScale_BIOUF)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$BenScale_BIOUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.99 0.84 21 0.0087 63 35 0.82
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.94 0.95 0.97
## Duhachek 0.94 0.95 0.97
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.B1_BIOUF 0.96 0.96 0.96 0.88 22 0.0085 0.010 0.82
## FS.B2_BIOUF 0.93 0.93 0.94 0.81 13 0.0140 0.025 0.73
## FS.B3_BIOUF 0.94 0.94 0.94 0.83 15 0.0116 0.012 0.82
## FS.B4_BIOUF 0.93 0.94 0.94 0.83 14 0.0120 0.013 0.82
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.B1_BIOUF 11 0.91 0.90 0.89 0.83 57 38
## FS.B2_BIOUF 11 0.96 0.96 0.95 0.92 64 37
## FS.B3_BIOUF 11 0.94 0.94 0.95 0.89 67 38
## FS.B4_BIOUF 11 0.94 0.95 0.95 0.90 66 36cor(FS$BenScale_BIOUF, use= "complete.obs")## FS.B1_BIOUF FS.B2_BIOUF FS.B3_BIOUF FS.B4_BIOUF
## FS.B1_BIOUF 1.0000000 0.9453717 0.7160649 0.7276280
## FS.B2_BIOUF 0.9453717 1.0000000 0.8214827 0.8196501
## FS.B3_BIOUF 0.7160649 0.8214827 1.0000000 0.9977035
## FS.B4_BIOUF 0.7276280 0.8196501 0.9977035 1.0000000### Support
FS$S1_BIOUF <- as.numeric(FS$Sup_BIO_UF_40)
FS$S2_BIOUF <- as.numeric(FS$Sup_BIO_UF_42)
FS$S3_BIOUF <- as.numeric(FS$Sup_BIO_UF_43)
FS$S4_BIOUF <- as.numeric(FS$Sup_BIO_UF_45)
hist(FS$S1_BIOUF)
hist(FS$S2_BIOUF)
hist(FS$S3_BIOUF)
hist(FS$S4_BIOUF)
FS$SupScore_BIOUF <- rowMeans(FS [, c( "S1_BIOUF" , "S2_BIOUF", "S3_BIOUF", "S4_BIOUF")], na.rm=TRUE)
describe(FS$SupScore_BIOUF)## FS$SupScore_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 61.11 39.6 1.125 2.250
## .25 .50 .75 .90 .95
## 47.000 70.000 83.875 99.500 99.750
##
## lowest : 0.00 2.25 46.75 47.25 62.75, highest: 76.00 81.75 86.00 99.50 100.00
##
## Value 0.00 2.25 46.75 47.25 62.75 70.00 76.00 81.75 86.00
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
##
## Value 99.50 100.00
## Frequency 1 1
## Proportion 0.091 0.091sd(FS$SupScore_BIOUF, na.rm = TRUE)## [1] 34.54334FS$SupScale_BIOUF <- data.frame(FS$S1_BIOUF, FS$S2_BIOUF, FS$S3_BIOUF, FS$S4_BIOUF)
describe(FS$SupScale_BIOUF)## FS$SupScale_BIOUF
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.S1_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 65.55 41.16 1 2
## .25 .50 .75 .90 .95
## 49 82 91 100 100
##
## lowest : 0 2 40 58 69, highest: 82 88 89 93 100
##
## Value 0 2 40 58 69 82 88 89 93 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182
## --------------------------------------------------------------------------------
## FS.S2_BIOUF
## n missing distinct Info Mean Gmd
## 11 93 9 0.991 65 39.64
##
## lowest : 0 3 45 58 72, highest: 72 76 85 88 100
##
## Value 0 3 45 58 72 76 85 88 100
## Frequency 1 1 1 1 1 1 1 2 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182 0.182
## --------------------------------------------------------------------------------
## FS.S3_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 11 1 51.73 45.67 1 2
## .25 .50 .75 .90 .95
## 15 64 82 98 99
##
## lowest : 0 2 9 21 41, highest: 70 80 84 98 100
##
## Value 0 2 9 21 41 64 70 80 84 98 100
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091
## --------------------------------------------------------------------------------
## FS.S4_BIOUF
## n missing distinct Info Mean Gmd .05 .10
## 11 93 10 0.995 62.18 41.45 1 2
## .25 .50 .75 .90 .95
## 44 71 87 100 100
##
## lowest : 0 2 25 63 69, highest: 71 80 84 90 100
##
## Value 0 2 25 63 69 71 80 84 90 100
## Frequency 1 1 1 1 1 1 1 1 1 2
## Proportion 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.091 0.182
## --------------------------------------------------------------------------------psych::alpha(FS$SupScale_BIOUF)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$SupScale_BIOUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.96 0.84 21 0.0081 61 35 0.84
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.94 0.95 0.97
## Duhachek 0.94 0.95 0.97
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.S1_BIOUF 0.93 0.93 0.91 0.81 13 0.013 0.0059 0.79
## FS.S2_BIOUF 0.92 0.92 0.91 0.80 12 0.013 0.0066 0.79
## FS.S3_BIOUF 0.97 0.97 0.97 0.93 38 0.005 0.0032 0.90
## FS.S4_BIOUF 0.93 0.93 0.94 0.82 14 0.013 0.0215 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.S1_BIOUF 11 0.96 0.96 0.98 0.93 66 37
## FS.S2_BIOUF 11 0.97 0.97 0.98 0.94 65 35
## FS.S3_BIOUF 11 0.87 0.87 0.79 0.78 52 39
## FS.S4_BIOUF 11 0.95 0.95 0.93 0.92 62 37cor(FS$SupScale_BIOUF, use= "complete.obs")## FS.S1_BIOUF FS.S2_BIOUF FS.S3_BIOUF FS.S4_BIOUF
## FS.S1_BIOUF 1.0000000 0.9916145 0.7297306 0.8908961
## FS.S2_BIOUF 0.9916145 1.0000000 0.7458561 0.8963154
## FS.S3_BIOUF 0.7297306 0.7458561 1.0000000 0.7917099
## FS.S4_BIOUF 0.8908961 0.8963154 0.7917099 1.0000000Biofuel
Quiz Questions & Attention Check
## Time Spent Reading Instructions
describe(FS$BFUF_InstTime_Page.Submit)## FS$BFUF_InstTime_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 6.974 6.64 0.8056 0.9380
## .25 .50 .75 .90 .95
## 2.1350 6.1010 10.8890 15.3906 16.9648
##
## lowest : 0.706 0.872 1.202 2.135 2.976, highest: 7.383 10.889 11.865 16.272 18.004
##
## Value 0.706 0.872 1.202 2.135 2.976 5.902 6.101 6.351 7.383
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 10.889 11.865 16.272 18.004
## Frequency 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077INSTminutes_BFUF <- (mean(FS$BFUF_InstTime_Page.Submit, na.rm = TRUE)/60)
print(INSTminutes_BFUF)## [1] 0.1162282## Time Spent Reading Technology Description (before proceeding to next page of survey)
### Raw Time Spent (Seconds)
describe(FS$BF_UF_Time_Page.Submit)## FS$BF_UF_Time_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 17.71 17.91 0.9346 1.2156
## .25 .50 .75 .90 .95
## 7.8830 12.7400 24.4870 41.6902 43.6104
##
## lowest : 0.682 1.103 1.666 7.883 8.612, highest: 20.280 24.487 38.307 42.536 45.222
##
## Value 0.682 1.103 1.666 7.883 8.612 11.072 12.740 15.606 20.280
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 24.487 38.307 42.536 45.222
## Frequency 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 sd(FS$BF_UF_Time_Page.Submit, na.rm = TRUE)## [1] 15.61436 range(FS$BF_UF_Time_Page.Submit, na.rm = TRUE)## [1] 0.682 45.222 ### Convert to Minutes
TECHminutes_BFUF <- (mean(FS$BF_UF_Time_Page.Submit, na.rm = TRUE)/60)
print(TECHminutes_BFUF)## [1] 0.2951231## Attention and Manipulation Checks
# Attention Check 1: What technology did you just read about?
FS$ATN_BF_UF1 <- as.numeric(as.character(FS$BF_UF_ATN))
FS$ATN_BF_UF <- factor(FS$ATN_BF_UF1, levels = c(1, 2, 3, 4),
labels = c("Biochar", "Biofuel", "Enhanced Weathering", "Wind Energy"))
describe(FS$ATN_BF_UF)## FS$ATN_BF_UF
## n missing distinct
## 13 91 2
##
## Value Biofuel Enhanced Weathering
## Frequency 12 1
## Proportion 0.923 0.077#### Time spent answering attention check #1:
##### (Seconds)
describe(FS$BF_UF_ATNTime_Page.Submit)## FS$BF_UF_ATNTime_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 5.512 2.741 2.834 3.373
## .25 .50 .75 .90 .95
## 3.882 4.811 7.160 8.299 9.375
##
## lowest : 2.170 3.276 3.762 3.882 4.224, highest: 5.342 7.160 7.876 8.405 10.830
##
## Value 2.170 3.276 3.762 3.882 4.224 4.584 4.811 5.338 5.342
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 7.160 7.876 8.405 10.830
## Frequency 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 sd(FS$BF_UF_ATNTime_Page.Submit, na.rm = TRUE)## [1] 2.413741 range(FS$BF_UF_ATNTime_Page.Submit, na.rm = TRUE)## [1] 2.17 10.83 ##### (Minutes)
ATN1_BFUF <- (mean(FS$BF_UF_ATNTime_Page.Submit, na.rm = TRUE)/60)
print(ATN1_BFUF)## [1] 0.09187179### Attention Check 2: Describe as much as you can about the technology you read about.
describe(FS$BF_UF_ATN2)## FS$BF_UF_ATN2
## n missing distinct
## 13 91 13
##
## lowest : BIOFUEL IS A COOL LIQUID THAT CAN BE MADE FROM WASTE FROM PLANTS TREES AND BE USED TO POWER CARS AND MANY OTHER THINGS Biofuel is made from plants, such as grass and agricultural waste. Then turned in to an oil that can be used as fuel biofuel is made using plants by heating or cooling them. this makes oil which can be made into biofuel as used to fuel cars etc. Biofuel process uses bio-degradable matter to produce fuel and thus save our environment from overuse and other problems. Biofuel processes plants like grasses and trees by heating them or cooling them until they become a type of oil; which is then refined to be used in cars for fuel.
## highest: It takes energy from plants and other things to turn into energy. It's about biofuel and it's been derived from plants which is pretty awesome and then they use it to fuel cars Etc None not sure of it The introduction of plant based field, biofuels which can power automobiles, aircraft #### Time spent answering attention check #2
##### (Seconds)
describe(FS$BF_UF_ATN2Time_Page.Submit)## FS$BF_UF_ATN2Time_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 49.05 44.87 12.27 19.20
## .25 .50 .75 .90 .95
## 26.11 30.68 63.30 97.13 124.16
##
## lowest : 3.003 18.447 22.237 26.107 27.322
## highest: 36.905 63.301 87.920 99.427 161.258
##
## Value 3.003 18.447 22.237 26.107 27.322 29.405 30.684 31.600
## Frequency 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 36.905 63.301 87.920 99.427 161.258
## Frequency 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 sd(FS$BF_UF_ATN2Time_Page.Submit, na.rm = TRUE)## [1] 43.58132 range(FS$BF_UF_ATN2Time_Page.Submit, na.rm = TRUE)## [1] 3.003 161.258 ##### (Minutes)
ATN2_BFUF <- (mean(FS$BF_UF_ATN2Time_Page.Submit, na.rm = TRUE)/60)
print(ATN2_BFUF)## [1] 0.8174564### Quiz Questions
FS$BF_UF_Quiz1## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA 3 3 3 3 3 3 3 3 3 3 3 3 3 NA
## [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [101] NA NA NA NAFS$BF_UF_QuizTime_Page.Submit## [1] NA NA NA NA NA NA NA NA NA NA
## [11] NA NA NA NA NA NA NA NA NA NA
## [21] NA NA NA NA NA NA NA NA NA NA
## [31] NA NA NA NA NA NA NA NA NA NA
## [41] NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA
## [61] NA 8.466 33.980 9.169 9.954 5.601 8.724 22.104 35.010 26.897
## [71] 4.201 17.800 23.577 12.301 NA NA NA NA NA NA
## [81] NA NA NA NA NA NA NA NA NA NA
## [91] NA NA NA NA NA NA NA NA NA NA
## [101] NA NA NA NATechnology Ratings
#Naturalness
FS$N1_BFUF <- as.numeric(FS$Nat_BF_UF_30)
FS$N2R_BFUF <- as.numeric(100 - FS$Nat_BF_UF_31)
FS$N3R_BFUF <- as.numeric(100 - FS$Nat_BF_UF_35)
FS$N4R_BFUF <- as.numeric(100- FS$Nat_BF_UF_36)
hist(FS$N1_BFUF)
hist(FS$N2R_BFUF)
hist(FS$N3R_BFUF)
hist(FS$N4R_BFUF)
FS$NatScore_BFUF <- rowMeans(FS [, c( "N1_BFUF" , "N2R_BFUF", "N3R_BFUF", "N4R_BFUF")], na.rm=TRUE)
describe(FS$NatScore_BFUF)## FS$NatScore_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 57.13 19.02 36.30 37.55
## .25 .50 .75 .90 .95
## 45.00 56.75 72.00 76.10 79.10
##
## lowest : 34.50 37.50 37.75 45.00 49.00, highest: 65.75 72.00 72.50 77.00 82.25
##
## Value 34.50 37.50 37.75 45.00 49.00 54.25 56.75 58.50 65.75 72.00 72.50
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 77.00 82.25
## Frequency 1 1
## Proportion 0.077 0.077sd(FS$NatScore_BFUF, na.rm = TRUE)## [1] 15.95326FS$NatScale_BFUF <- data.frame(FS$N1_BFUF, FS$N2R_BFUF, FS$N3R_BFUF, FS$N4R_BFUF)
describe(FS$NatScale_BFUF)## FS$NatScale_BFUF
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.N1_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 10 0.973 84.46 19.05 56.8 61.0
## .25 .50 .75 .90 .95
## 78.0 88.0 100.0 100.0 100.0
##
## lowest : 52 60 65 78 79, highest: 83 88 96 97 100
##
## Value 52 60 65 78 79 83 88 96 97 100
## Frequency 1 1 1 1 1 1 1 1 1 4
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.308
## --------------------------------------------------------------------------------
## FS.N2R_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 51.31 39.62 1.2 6.2
## .25 .50 .75 .90 .95
## 33.0 44.0 88.0 92.4 95.8
##
## lowest : 0 2 23 33 40, highest: 65 88 90 93 100
##
## Value 0 2 23 33 40 41 44 48 65 88 90
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 93 100
## Frequency 1 1
## Proportion 0.077 0.077
## --------------------------------------------------------------------------------
## FS.N3R_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.989 25 20.49 0.0 0.0
## .25 .50 .75 .90 .95
## 14.0 26.0 36.0 45.6 47.2
##
## lowest : 0 14 20 25 26, highest: 33 36 44 46 49
##
## Value 0 14 20 25 26 32 33 36 44 46 49
## Frequency 3 1 1 1 1 1 1 1 1 1 1
## Proportion 0.231 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
## --------------------------------------------------------------------------------
## FS.N4R_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 12 0.997 67.77 29.28 28.0 38.4
## .25 .50 .75 .90 .95
## 50.0 73.0 86.0 96.8 98.8
##
## lowest : 16 36 48 50 59, highest: 77 86 92 98 100
##
## Value 16 36 48 50 59 69 73 77 86 92 98
## Frequency 1 1 1 1 1 1 1 2 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.154 0.077 0.077 0.077
##
## Value 100
## Frequency 1
## Proportion 0.077
## --------------------------------------------------------------------------------psych::alpha(FS$NatScale_BFUF)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$NatScale_BFUF): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( FS.N3R_BFUF ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = FS$NatScale_BFUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.56 0.42 0.79 0.15 0.71 0.045 57 16 0.24
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.41 0.56 0.69
## Duhachek 0.48 0.56 0.65
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.N1_BFUF 0.57 0.50 0.57 0.247 0.98 0.059 0.178 0.036
## FS.N2R_BFUF -0.11 -0.42 0.51 -0.109 -0.29 0.150 0.364 -0.027
## FS.N3R_BFUF 0.77 0.79 0.74 0.549 3.65 0.032 0.025 0.465
## FS.N4R_BFUF 0.11 -0.30 0.64 -0.083 -0.23 0.093 0.379 0.036
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.N1_BFUF 13 0.481 0.48 0.4562 0.24 84 17
## FS.N2R_BFUF 13 0.948 0.93 0.8824 0.80 51 34
## FS.N3R_BFUF 13 0.084 0.11 0.0066 -0.19 25 17
## FS.N4R_BFUF 13 0.891 0.89 0.8008 0.74 68 25cor(FS$NatScale_BFUF, use= "complete.obs")## FS.N1_BFUF FS.N2R_BFUF FS.N3R_BFUF FS.N4R_BFUF
## FS.N1_BFUF 1.0000000 0.46472581 -0.74888857 0.45025439
## FS.N2R_BFUF 0.4647258 1.00000000 0.03551076 0.73236668
## FS.N3R_BFUF -0.7488886 0.03551076 1.00000000 -0.02731077
## FS.N4R_BFUF 0.4502544 0.73236668 -0.02731077 1.00000000### Familiarity
FS$Fam_BFUF <- as.numeric(FS$Fam_BF_UF_32)
hist(FS$Fam_BFUF)
describe(FS$Fam_BFUF)## FS$Fam_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 42.15 30.85 2.4 7.4
## .25 .50 .75 .90 .95
## 25.0 38.0 65.0 68.4 75.0
##
## lowest : 0 4 21 25 32, highest: 60 65 66 69 84
##
## Value 0 4 21 25 32 33 38 51 60 65 66
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 69 84
## Frequency 1 1
## Proportion 0.077 0.077sd(FS$Fam_BFUF, na.rm = TRUE)## [1] 26.04114### Understanding
FS$Und_BFUF <- as.numeric(FS$Fam_BF_UF_31)
hist(FS$Und_BFUF)
describe(FS$Und_BFUF)## FS$Und_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 71 18.28 44.8 53.8
## .25 .50 .75 .90 .95
## 63.0 74.0 82.0 86.6 89.0
##
## lowest : 34 52 61 63 65, highest: 81 82 85 87 92
##
## Value 34 52 61 63 65 71 74 76 81 82 85
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 87 92
## Frequency 1 1
## Proportion 0.077 0.077sd(FS$Und_BFUF, na.rm = TRUE)## [1] 16.01562### Fluency
FS$Fluency_BFUF <- as.numeric(FS$Fluency_BF_UF_34)
hist(FS$Fluency_BFUF)
describe(FS$Fluency_BFUF)## FS$Fluency_BFUF
## n missing distinct Info Mean Gmd
## 13 91 8 0.967 88.08 14.82
##
## lowest : 65 68 71 83 84, highest: 83 84 88 99 100
##
## Value 65 68 71 83 84 88 99 100
## Frequency 1 1 1 1 1 2 2 4
## Proportion 0.077 0.077 0.077 0.077 0.077 0.154 0.154 0.308sd(FS$Fluency_BFUF, na.rm = TRUE)## [1] 13.15587### Risk
FS$R1_BFUF <- as.numeric(FS$Risk_BF_UF_30)
FS$R2_BFUF <- as.numeric(FS$Risk_BF_UF_31)
FS$R3_BFUF <- as.numeric(FS$Risk_BF_UF_32)
hist(FS$R1_BFUF)
hist(FS$R2_BFUF)
hist(FS$R3_BFUF)
FS$RiskScore_BFUF <- rowMeans(FS [, c( "R1_BFUF" , "R2_BFUF", "R3_BFUF")], na.rm=TRUE)
describe(FS$RiskScore_BFUF)## FS$RiskScore_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 12 0.997 46.51 16.9 29.47 33.33
## .25 .50 .75 .90 .95
## 34.00 47.67 50.67 62.13 70.67
##
## lowest : 23.66667 33.33333 34.00000 38.33333 44.66667
## highest: 50.00000 50.66667 54.66667 64.00000 80.66667
##
## Value 23.66667 33.33333 34.00000 38.33333 44.66667 47.66667 49.66667
## Frequency 1 2 1 1 1 1 1
## Proportion 0.077 0.154 0.077 0.077 0.077 0.077 0.077
##
## Value 50.00000 50.66667 54.66667 64.00000 80.66667
## Frequency 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077sd(FS$RiskScore_BFUF, na.rm = TRUE)## [1] 14.93233FS$RiskScale_BFUF <- data.frame(FS$R1_BFUF, FS$R2_BFUF, FS$R3_BFUF)
describe(FS$RiskScale_BFUF)## FS$RiskScale_BFUF
##
## 3 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.R1_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.989 34.92 34.85 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 40.0 52.0 74.0 77.8
##
## lowest : 0 7 11 31 40, highest: 44 52 70 75 82
##
## Value 0 7 11 31 40 42 44 52 70 75 82
## Frequency 3 1 1 1 1 1 1 1 1 1 1
## Proportion 0.231 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
## --------------------------------------------------------------------------------
## FS.R2_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 10 0.973 25.62 29.77 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 22.0 42.0 54.2 66.0
##
## lowest : 0 5 12 22 33, highest: 35 42 47 56 81
##
## Value 0 5 12 22 33 35 42 47 56 81
## Frequency 4 1 1 1 1 1 1 1 1 1
## Proportion 0.308 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
## --------------------------------------------------------------------------------
## FS.R3_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.989 79 21.13 49.4 53.6
## .25 .50 .75 .90 .95
## 70.0 79.0 92.0 100.0 100.0
##
## lowest : 47 51 64 70 71, highest: 79 88 90 92 100
##
## Value 47 51 64 70 71 75 79 88 90 92 100
## Frequency 1 1 1 1 1 1 1 1 1 1 3
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.231
## --------------------------------------------------------------------------------psych::alpha(FS$RiskScale_BFUF)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$RiskScale_BFUF): 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 ( FS.R3_BFUF ) 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 = FS$RiskScale_BFUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.097 -0.4 0.5 -0.1 -0.28 0.11 47 15 -0.47
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.25 0.1 0.36
## Duhachek -0.12 0.1 0.31
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.R1_BFUF -3.12 -3.73 -0.65 -0.65 -0.79 0.724 NA -0.65
## FS.R2_BFUF -1.41 -1.75 -0.47 -0.47 -0.64 0.408 NA -0.47
## FS.R3_BFUF 0.89 0.89 0.80 0.80 8.13 0.022 NA 0.80
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.R1_BFUF 13 0.94 0.868 0.90 0.63 35 30
## FS.R2_BFUF 13 0.85 0.748 0.82 0.45 26 26
## FS.R3_BFUF 13 -0.28 -0.076 -0.63 -0.58 79 18cor(FS$RiskScale_BFUF, use= "complete.obs")## FS.R1_BFUF FS.R2_BFUF FS.R3_BFUF
## FS.R1_BFUF 1.0000000 0.8025360 -0.4662278
## FS.R2_BFUF 0.8025360 1.0000000 -0.6506604
## FS.R3_BFUF -0.4662278 -0.6506604 1.0000000### Benefit
FS$B1_BFUF <- as.numeric(FS$Ben_BF_UF_40)
FS$B2_BFUF <- as.numeric(FS$Ben_BF_UF_42)
FS$B3_BFUF <- as.numeric(FS$Ben_BF_UF_43)
FS$B4_BFUF <- as.numeric(FS$Ben_BF_UF_44)
hist(FS$B1_BFUF)
hist(FS$B2_BFUF)
hist(FS$B3_BFUF)
hist(FS$B4_BFUF)
FS$BenScore_BFUF <- rowMeans(FS [, c( "B1_BFUF" , "B2_BFUF", "B3_BFUF", "B4_BFUF")], na.rm=TRUE)
describe(FS$BenScore_BFUF)## FS$BenScore_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 76.33 19.04 55.00 57.70
## .25 .50 .75 .90 .95
## 63.75 74.50 89.75 98.50 99.70
##
## lowest : 52.00 57.00 60.50 63.75 70.50, highest: 83.00 89.75 94.50 99.50 100.00
##
## Value 52.00 57.00 60.50 63.75 70.50 72.00 74.50 75.25 83.00
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 89.75 94.50 99.50 100.00
## Frequency 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077sd(FS$BenScore_BFUF, na.rm = TRUE)## [1] 16.03004FS$BenScale_BFUF <- data.frame(FS$B1_BFUF, FS$B2_BFUF, FS$B3_BFUF, FS$B4_BFUF)
describe(FS$BenScale_BFUF)## FS$BenScale_BFUF
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.B1_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 71.15 21.56 50.0 50.4
## .25 .50 .75 .90 .95
## 54.0 73.0 81.0 98.6 100.0
##
## lowest : 50 52 54 55 68, highest: 74 75 81 93 100
##
## Value 50 52 54 55 68 73 74 75 81 93 100
## Frequency 2 1 1 1 1 1 1 1 1 1 2
## Proportion 0.154 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.154
## --------------------------------------------------------------------------------
## FS.B2_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 78.54 18.67 53.8 57.6
## .25 .50 .75 .90 .95
## 69.0 75.0 94.0 97.8 98.8
##
## lowest : 52 55 68 69 73, highest: 88 94 97 98 100
##
## Value 52 55 68 69 73 74 75 78 88 94 97
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 98 100
## Frequency 1 1
## Proportion 0.077 0.077
## --------------------------------------------------------------------------------
## FS.B3_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 77.54 19.77 53.8 56.4
## .25 .50 .75 .90 .95
## 66.0 74.0 94.0 99.0 100.0
##
## lowest : 52 55 62 66 71, highest: 81 87 94 95 100
##
## Value 52 55 62 66 71 74 81 87 94 95 100
## Frequency 1 1 1 1 2 1 1 1 1 1 2
## Proportion 0.077 0.077 0.077 0.077 0.154 0.077 0.077 0.077 0.077 0.077 0.154
## --------------------------------------------------------------------------------
## FS.B4_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 78.08 18.51 57.4 61.4
## .25 .50 .75 .90 .95
## 67.0 75.0 94.0 99.0 100.0
##
## lowest : 52 61 63 67 70, highest: 81 82 94 95 100
##
## Value 52 61 63 67 70 75 81 82 94 95 100
## Frequency 1 1 1 1 1 2 1 1 1 1 2
## Proportion 0.077 0.077 0.077 0.077 0.077 0.154 0.077 0.077 0.077 0.077 0.154
## --------------------------------------------------------------------------------psych::alpha(FS$BenScale_BFUF)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$BenScale_BFUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.97 0.97 0.98 0.9 38 0.0049 76 16 0.92
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.96 0.97 0.98
## Duhachek 0.96 0.97 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.B1_BFUF 0.98 0.98 0.97 0.94 46 0.0038 0.00045 0.93
## FS.B2_BFUF 0.95 0.96 0.96 0.88 22 0.0085 0.00541 0.92
## FS.B3_BFUF 0.97 0.97 0.97 0.93 38 0.0051 0.00118 0.92
## FS.B4_BFUF 0.95 0.95 0.95 0.87 21 0.0089 0.00463 0.90
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.B1_BFUF 13 0.94 0.94 0.92 0.89 71 18
## FS.B2_BFUF 13 0.98 0.98 0.98 0.97 79 16
## FS.B3_BFUF 13 0.94 0.95 0.93 0.90 78 17
## FS.B4_BFUF 13 0.99 0.99 0.99 0.98 78 16cor(FS$BenScale_BFUF, use= "complete.obs")## FS.B1_BFUF FS.B2_BFUF FS.B3_BFUF FS.B4_BFUF
## FS.B1_BFUF 1.0000000 0.8955391 0.7963913 0.9205421
## FS.B2_BFUF 0.8955391 1.0000000 0.9266377 0.9633837
## FS.B3_BFUF 0.7963913 0.9266377 1.0000000 0.9267151
## FS.B4_BFUF 0.9205421 0.9633837 0.9267151 1.0000000### Support
FS$S1_BFUF <- as.numeric(FS$Sup_BF_UF_40)
FS$S2_BFUF <- as.numeric(FS$Sup_BF_UF_42)
FS$S3_BFUF <- as.numeric(FS$Sup_BF_UF_43)
FS$S4_BFUF <- as.numeric(FS$Sup_BF_UF_45)
hist(FS$S1_BFUF)
hist(FS$S2_BFUF)
hist(FS$S3_BFUF)
hist(FS$S4_BFUF)
FS$SupScore_BFUF <- rowMeans(FS [, c( "S1_BFUF" , "S2_BFUF", "S3_BFUF", "S4_BFUF")], na.rm=TRUE)
describe(FS$SupScore_BFUF)## FS$SupScore_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 12 0.997 70.62 23.18 49.20 50.60
## .25 .50 .75 .90 .95
## 54.75 63.50 88.25 99.30 99.85
##
## lowest : 48.00 50.00 53.00 54.75 56.75, highest: 85.25 88.25 97.50 99.75 100.00
##
## Value 48.00 50.00 53.00 54.75 56.75 63.50 64.50 85.25 88.25
## Frequency 1 1 1 1 2 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.154 0.077 0.077 0.077 0.077
##
## Value 97.50 99.75 100.00
## Frequency 1 1 1
## Proportion 0.077 0.077 0.077sd(FS$SupScore_BFUF, na.rm = TRUE)## [1] 20.27149FS$SupScale_BFUF <- data.frame(FS$S1_BFUF, FS$S2_BFUF, FS$S3_BFUF, FS$S4_BFUF)
describe(FS$SupScale_BFUF)## FS$SupScale_BFUF
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.S1_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.989 75.15 25.44 48.4 50.4
## .25 .50 .75 .90 .95
## 53.0 82.0 99.0 99.0 99.4
##
## lowest : 46 50 52 53 57, highest: 82 86 93 99 100
##
## Value 46 50 52 53 57 61 82 86 93 99 100
## Frequency 1 1 1 1 1 1 1 1 1 3 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.231 0.077
## --------------------------------------------------------------------------------
## FS.S2_BFUF
## n missing distinct Info Mean Gmd
## 13 91 8 0.981 79.23 22.23
##
## lowest : 56 57 65 71 82, highest: 71 82 88 99 100
##
## Value 56 57 65 71 82 88 99 100
## Frequency 2 2 1 1 1 1 2 3
## Proportion 0.154 0.154 0.077 0.077 0.077 0.077 0.154 0.231
## --------------------------------------------------------------------------------
## FS.S3_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 61.77 35.79 6.6 18.8
## .25 .50 .75 .90 .95
## 52.0 57.0 86.0 99.4 100.0
##
## lowest : 0 11 50 52 55, highest: 66 77 86 97 100
##
## Value 0 11 50 52 55 57 66 77 86 97 100
## Frequency 1 1 1 2 1 1 1 1 1 1 2
## Proportion 0.077 0.077 0.077 0.154 0.077 0.077 0.077 0.077 0.077 0.077 0.154
## --------------------------------------------------------------------------------
## FS.S4_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.989 66.31 33.87 25.2 43.2
## .25 .50 .75 .90 .95
## 50.0 62.0 95.0 100.0 100.0
##
## lowest : 0 42 48 50 52, highest: 62 63 93 95 100
##
## Value 0 42 48 50 52 57 62 63 93 95 100
## Frequency 1 1 1 1 1 1 1 1 1 1 3
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.231
## --------------------------------------------------------------------------------psych::alpha(FS$SupScale_BFUF)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$SupScale_BFUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.79 0.96 0.48 3.7 0.039 71 20 0.33
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.7 0.78 0.84
## Duhachek 0.7 0.78 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.S1_BFUF 0.74 0.71 0.80 0.45 2.4 0.038 0.17 0.28
## FS.S2_BFUF 0.77 0.75 0.82 0.50 2.9 0.038 0.14 0.37
## FS.S3_BFUF 0.72 0.78 0.86 0.54 3.5 0.053 0.14 0.37
## FS.S4_BFUF 0.62 0.70 0.82 0.44 2.4 0.073 0.21 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.S1_BFUF 13 0.72 0.82 0.82 0.54 75 22
## FS.S2_BFUF 13 0.66 0.77 0.77 0.49 79 19
## FS.S3_BFUF 13 0.81 0.72 0.69 0.59 62 31
## FS.S4_BFUF 13 0.89 0.82 0.79 0.75 66 30cor(FS$SupScale_BFUF, use= "complete.obs")## FS.S1_BFUF FS.S2_BFUF FS.S3_BFUF FS.S4_BFUF
## FS.S1_BFUF 1.0000000 0.9718411 0.2069772 0.3690695
## FS.S2_BFUF 0.9718411 1.0000000 0.1422023 0.2831316
## FS.S3_BFUF 0.2069772 0.1422023 1.0000000 0.9106395
## FS.S4_BFUF 0.3690695 0.2831316 0.9106395 1.0000000Enhanced Weathering
Quiz Questions & Attention Check
## Time Spent Reading Instructions
describe(FS$EWUF_InstTime_Page.Submit)## FS$EWUF_InstTime_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 10.92 13.02 0.6986 0.7045
## .25 .50 .75 .90 .95
## 1.1365 6.2980 16.7423 29.3580 30.4238
##
## lowest : 0.697 0.700 0.745 1.267 3.025, highest: 12.935 14.406 23.751 29.981 30.965
##
## Value 0.697 0.700 0.745 1.267 3.025 3.267 9.329 12.935 14.406
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 23.751 29.981 30.965
## Frequency 1 1 1
## Proportion 0.083 0.083 0.083INSTminutes_EWUF <- (mean(FS$EWUF_InstTime_Page.Submit, na.rm = TRUE)/60)
print(INSTminutes_EWUF)## [1] 0.1820389## Time Spent Reading Technology Description (before proceeding to next page of survey)
### Raw Time Spent (Seconds)
describe(FS$EW_UF_Time_Page.Submit)## FS$EW_UF_Time_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 19.37 22.51 1.490 1.780
## .25 .50 .75 .90 .95
## 2.572 7.582 36.560 48.597 49.466
##
## lowest : 1.193 1.733 2.201 2.696 3.259, highest: 31.295 35.131 40.846 49.458 49.476
##
## Value 1.193 1.733 2.201 2.696 3.259 3.498 11.665 31.295 35.131
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 40.846 49.458 49.476
## Frequency 1 1 1
## Proportion 0.083 0.083 0.083 sd(FS$EW_UF_Time_Page.Submit, na.rm = TRUE)## [1] 20.11017 range(FS$EW_UF_Time_Page.Submit, na.rm = TRUE)## [1] 1.193 49.476 ### Convert to Minutes
TECHminutes_EWUF <- (mean(FS$EW_UF_Time_Page.Submit, na.rm = TRUE)/60)
print(TECHminutes_EWUF)## [1] 0.3228486## Attention and Manipulation Checks
# Attention Check 1: What technology did you just read about?
FS$ATN_EW_UF1 <- as.numeric(as.character(FS$EW_UF_ATN))
FS$ATN_EW_UF <- factor(FS$ATN_EW_UF1, levels = c(1, 2, 3, 4),
labels = c("Biochar", "Biofuel", "Enhanced Weathering", "Wind Energy"))
describe(FS$ATN_EW_UF)## FS$ATN_EW_UF
## n missing distinct
## 12 92 3
##
## Value Biofuel Enhanced Weathering Wind Energy
## Frequency 4 7 1
## Proportion 0.333 0.583 0.083#### Time spent answering attention check #1:
##### (Seconds)
describe(FS$EW_UF_ATNTime_Page.Submit)## FS$EW_UF_ATNTime_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 7.979 6.498 2.767 3.660
## .25 .50 .75 .90 .95
## 3.821 5.734 10.072 13.306 18.065
##
## lowest : 1.688 3.649 3.761 3.841 3.959, highest: 9.466 9.751 11.037 13.558 23.574
##
## Value 1.688 3.649 3.761 3.841 3.959 4.749 6.720 9.466 9.751
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 11.037 13.558 23.574
## Frequency 1 1 1
## Proportion 0.083 0.083 0.083 sd(FS$EW_UF_ATNTime_Page.Submit, na.rm = TRUE)## [1] 6.108491 range(FS$EW_UF_ATNTime_Page.Submit, na.rm = TRUE)## [1] 1.688 23.574 ##### (Minutes)
ATN1_EWUF <- (mean(FS$EW_UF_ATNTime_Page.Submit, na.rm = TRUE)/60)
print(ATN1_EWUF)## [1] 0.1329903### Attention Check 2: Describe as much as you can about the technology you read about.
describe(FS$EW_UF_ATN2)## FS$EW_UF_ATN2
## n missing distinct
## 11 93 11
##
## lowest : Carbon dioxide is removed from the air by interacting with water and minerals. Enhanced weathering uses rocks to capture carbon from the atmosphere and then creates bicarbonate to then push it from the land to waterways and eventually enhance the ocean floor I do not remember what climate change method I read about It was many people in many life It's taking carbon dioxide out of the air
## highest: Making gas and groceries prices going up Minerals are scattered on land. Rain and air react with the minerals to form bicarbonate, which sequesters carbon from the atmosphere. It is eventually washed to the ocean floor. This process is similar to ocean alkinization. Nature idd Speeds up the process of weathering That the climate system has the most to do with climate change #### Time spent answering attention check #2
##### (Seconds)
describe(FS$EW_UF_ATN2Time_Page.Submit)## FS$EW_UF_ATN2Time_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 37.69 34.07 5.781 7.371
## .25 .50 .75 .90 .95
## 12.438 37.014 55.318 66.838 82.497
##
## lowest : 4.258 7.027 10.467 13.095 19.230
## highest: 38.071 51.801 65.867 66.946 101.504
##
## Value 4.258 7.027 10.467 13.095 19.230 36.202 37.827 38.071
## Frequency 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 51.801 65.867 66.946 101.504
## Frequency 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 sd(FS$EW_UF_ATN2Time_Page.Submit, na.rm = TRUE)## [1] 29.64683 range(FS$EW_UF_ATN2Time_Page.Submit, na.rm = TRUE)## [1] 4.258 101.504 ##### (Minutes)
ATN2_EWUF <- (mean(FS$EW_UF_ATN2Time_Page.Submit, na.rm = TRUE)/60)
print(ATN2_EWUF)## [1] 0.6281875### Quiz Questions
FS$EW_UF_Quiz1## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1 2 2 2 2 2 2 2
## [101] 2 3 2 2FS$EW_UF_QuizTime_Page.Submit## [1] NA NA NA NA NA NA NA NA NA NA
## [11] NA NA NA NA NA NA NA NA NA NA
## [21] NA NA NA NA NA NA NA NA NA NA
## [31] NA NA NA NA NA NA NA NA NA NA
## [41] NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA
## [61] NA NA NA NA NA NA NA NA NA NA
## [71] NA NA NA NA NA NA NA NA NA NA
## [81] NA NA NA NA NA NA NA NA NA NA
## [91] NA NA 13.088 9.601 2.764 23.357 27.529 23.733 1.887 29.460
## [101] 41.100 14.062 42.036 91.249Technology Ratings
### Naturalness
FS$N1_EWUF <- as.numeric(FS$Nat_EW_UF_30)
FS$N2R_EWUF <- as.numeric(100 - FS$Nat_EW_UF_31)
FS$N3R_EWUF <- as.numeric(100 - FS$Nat_EW_UF_35)
FS$N4R_EWUF <- as.numeric(100- FS$Nat_EW_UF_36)
hist(FS$N1_EWUF)
hist(FS$N2R_EWUF)
hist(FS$N3R_EWUF)
hist(FS$N4R_EWUF)
FS$NatScore_EWUF <- rowMeans(FS [, c( "N1_EWUF" , "N2R_EWUF", "N3R_EWUF", "N4R_EWUF")], na.rm=TRUE)
describe(FS$NatScore_EWUF)## FS$NatScore_EWUF
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 41.42 17.95 19.10 27.50
## .25 .50 .75 .90 .95
## 35.56 39.38 47.44 60.83 64.26
##
## lowest : 9.75 26.75 34.25 36.00 36.75, highest: 42.75 43.50 59.25 61.00 68.25
##
## Value 9.75 26.75 34.25 36.00 36.75 38.25 40.50 42.75 43.50 59.25 61.00
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 68.25
## Frequency 1
## Proportion 0.083sd(FS$NatScore_EWUF, na.rm = TRUE)## [1] 15.837FS$NatScale_EWUF <- data.frame(FS$N1_EWUF, FS$N2R_EWUF, FS$N3R_EWUF, FS$N4R_EWUF)
describe(FS$NatScale_EWUF)## FS$NatScale_EWUF
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.N1_EWUF
## n missing distinct Info Mean Gmd .05 .10
## 12 92 11 0.997 55.83 26.45 21.30 24.40
## .25 .50 .75 .90 .95
## 40.75 59.50 75.25 79.90 80.00
##
## lowest : 18 24 28 45 53, highest: 63 70 74 79 80
##
## Value 18 24 28 45 53 56 63 70 74 79 80
## Frequency 1 1 1 1 1 1 1 1 1 1 2
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.167
## --------------------------------------------------------------------------------
## FS.N2R_EWUF
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 32.92 29.05 3.85 7.70
## .25 .50 .75 .90 .95
## 15.50 27.00 49.75 67.90 71.70
##
## lowest : 0 7 14 16 18, highest: 37 47 58 69 75
##
## Value 0 7 14 16 18 20 34 37 47 58 69
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 75
## Frequency 1
## Proportion 0.083
## --------------------------------------------------------------------------------
## FS.N3R_EWUF
## n missing distinct Info Mean Gmd .05 .10
## 12 92 10 0.986 30.42 27.56 0.00 0.00
## .25 .50 .75 .90 .95
## 15.75 32.00 38.75 62.90 69.05
##
## lowest : 0 21 25 31 33, highest: 35 37 44 65 74
##
## Value 0 21 25 31 33 35 37 44 65 74
## Frequency 3 1 1 1 1 1 1 1 1 1
## Proportion 0.250 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
## --------------------------------------------------------------------------------
## FS.N4R_EWUF
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 46.5 30.12 15.65 17.50
## .25 .50 .75 .90 .95
## 22.75 45.00 68.50 79.00 80.90
##
## lowest : 14 17 22 23 28, highest: 64 68 70 80 82
##
## Value 14 17 22 23 28 43 47 64 68 70 80
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 82
## Frequency 1
## Proportion 0.083
## --------------------------------------------------------------------------------psych::alpha(FS$NatScale_EWUF)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$NatScale_EWUF): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( FS.N2R_EWUF ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = FS$NatScale_EWUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.56 0.56 0.58 0.24 1.3 0.071 41 16 0.2
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.40 0.56 0.68
## Duhachek 0.42 0.56 0.70
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.N1_EWUF 0.34 0.34 0.37 0.15 0.52 0.113 0.098 0.0066
## FS.N2R_EWUF 0.75 0.75 0.68 0.50 2.99 0.042 0.013 0.5079
## FS.N3R_EWUF 0.40 0.41 0.46 0.19 0.69 0.105 0.136 0.0210
## FS.N4R_EWUF 0.31 0.32 0.29 0.14 0.47 0.119 0.045 0.0210
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.N1_EWUF 12 0.75 0.76 0.693 0.517 56 23
## FS.N2R_EWUF 12 0.37 0.36 -0.014 -0.019 33 25
## FS.N3R_EWUF 12 0.72 0.72 0.578 0.442 30 24
## FS.N4R_EWUF 12 0.78 0.78 0.754 0.524 46 25cor(FS$NatScale_EWUF, use= "complete.obs")## FS.N1_EWUF FS.N2R_EWUF FS.N3R_EWUF FS.N4R_EWUF
## FS.N1_EWUF 1.00000000 0.02096275 0.38072385 0.60979735
## FS.N2R_EWUF 0.02096275 1.00000000 0.00655408 -0.06858307
## FS.N3R_EWUF 0.38072385 0.00655408 1.00000000 0.50785833
## FS.N4R_EWUF 0.60979735 -0.06858307 0.50785833 1.00000000### Familiarity
FS$Fam_EWUF <- as.numeric(FS$Fam_EW_UF_34)
hist(FS$Fam_EWUF)
describe(FS$Fam_EWUF)## FS$Fam_EWUF
## n missing distinct Info Mean Gmd .05 .10
## 12 92 11 0.997 40.92 39.83 0.00 1.60
## .25 .50 .75 .90 .95
## 16.75 25.50 73.50 80.40 89.55
##
## lowest : 0 16 17 22 23, highest: 56 73 75 81 100
##
## Value 0 16 17 22 23 28 56 73 75 81 100
## Frequency 2 1 1 1 1 1 1 1 1 1 1
## Proportion 0.167 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083sd(FS$Fam_EWUF, na.rm = TRUE)## [1] 34.24898### Understanding
FS$Und_EWUF <- as.numeric(FS$Fam_EW_UF_33)
hist(FS$Und_EWUF)
describe(FS$Und_EWUF)## FS$Und_EWUF
## n missing distinct Info Mean Gmd .05 .10
## 12 92 11 0.997 60.42 35.08 0.55 3.80
## .25 .50 .75 .90 .95
## 56.00 70.00 80.50 82.00 90.10
##
## lowest : 0 1 29 65 67, highest: 72 79 80 82 100
##
## Value 0 1 29 65 67 68 72 79 80 82 100
## Frequency 1 1 1 1 1 1 1 1 1 2 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.167 0.083sd(FS$Und_EWUF, na.rm = TRUE)## [1] 32.54496### Fluency
FS$Fluency_EWUF <- as.numeric(FS$Fluency_EW_UF_34)
hist(FS$Fluency_EWUF)
describe(FS$Fluency_EWUF)## FS$Fluency_EWUF
## n missing distinct Info Mean Gmd .05 .10
## 12 92 11 0.997 68.75 32.32 19.4 24.8
## .25 .50 .75 .90 .95
## 59.0 75.5 86.0 99.8 100.0
##
## lowest : 15 23 41 65 72, highest: 77 78 82 98 100
##
## Value 15 23 41 65 72 74 77 78 82 98 100
## Frequency 1 1 1 1 1 1 1 1 1 1 2
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.167sd(FS$Fluency_EWUF, na.rm = TRUE)## [1] 28.51196### Risk
FS$R1_EWUF <- as.numeric(FS$Risk_EW_UF_32)
FS$R2_EWUF <- as.numeric(FS$Risk_EW_UF_33)
FS$R3_EWUF <- as.numeric(FS$Risk_EW_UF_34)
hist(FS$R1_EWUF)
hist(FS$R2_EWUF)
hist(FS$R3_EWUF)
FS$RiskScore_EWUF <- rowMeans(FS [, c( "R1_EWUF" , "R2_EWUF", "R3_EWUF")], na.rm=TRUE)
describe(FS$RiskScore_EWUF)## FS$RiskScore_EWUF
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 47.76 25.79 16.77 19.23
## .25 .50 .75 .90 .95
## 34.58 44.67 67.12 77.05 78.30
##
## lowest : 15.66667 17.66667 33.33333 35.00000 36.66667
## highest: 53.66667 66.66667 68.50000 78.00000 78.66667
##
## Value 15.66667 17.66667 33.33333 35.00000 36.66667 39.33333 50.00000
## Frequency 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 53.66667 66.66667 68.50000 78.00000 78.66667
## Frequency 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083sd(FS$RiskScore_EWUF, na.rm = TRUE)## [1] 21.7377FS$RiskScale_EWUF <- data.frame(FS$R1_BFUF, FS$R2_BFUF, FS$R3_BFUF)
describe(FS$RiskScale_EWUF)## FS$RiskScale_EWUF
##
## 3 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.R1_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.989 34.92 34.85 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 40.0 52.0 74.0 77.8
##
## lowest : 0 7 11 31 40, highest: 44 52 70 75 82
##
## Value 0 7 11 31 40 42 44 52 70 75 82
## Frequency 3 1 1 1 1 1 1 1 1 1 1
## Proportion 0.231 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
## --------------------------------------------------------------------------------
## FS.R2_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 10 0.973 25.62 29.77 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 22.0 42.0 54.2 66.0
##
## lowest : 0 5 12 22 33, highest: 35 42 47 56 81
##
## Value 0 5 12 22 33 35 42 47 56 81
## Frequency 4 1 1 1 1 1 1 1 1 1
## Proportion 0.308 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
## --------------------------------------------------------------------------------
## FS.R3_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.989 79 21.13 49.4 53.6
## .25 .50 .75 .90 .95
## 70.0 79.0 92.0 100.0 100.0
##
## lowest : 47 51 64 70 71, highest: 79 88 90 92 100
##
## Value 47 51 64 70 71 75 79 88 90 92 100
## Frequency 1 1 1 1 1 1 1 1 1 1 3
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.231
## --------------------------------------------------------------------------------psych::alpha(FS$RiskScale_EWUF)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$RiskScale_EWUF): 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 ( FS.R3_BFUF ) 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 = FS$RiskScale_EWUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.097 -0.4 0.5 -0.1 -0.28 0.11 47 15 -0.47
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.25 0.1 0.36
## Duhachek -0.12 0.1 0.31
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.R1_BFUF -3.12 -3.73 -0.65 -0.65 -0.79 0.724 NA -0.65
## FS.R2_BFUF -1.41 -1.75 -0.47 -0.47 -0.64 0.408 NA -0.47
## FS.R3_BFUF 0.89 0.89 0.80 0.80 8.13 0.022 NA 0.80
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.R1_BFUF 13 0.94 0.868 0.90 0.63 35 30
## FS.R2_BFUF 13 0.85 0.748 0.82 0.45 26 26
## FS.R3_BFUF 13 -0.28 -0.076 -0.63 -0.58 79 18cor(FS$RiskScale_EWUF, use= "complete.obs")## FS.R1_BFUF FS.R2_BFUF FS.R3_BFUF
## FS.R1_BFUF 1.0000000 0.8025360 -0.4662278
## FS.R2_BFUF 0.8025360 1.0000000 -0.6506604
## FS.R3_BFUF -0.4662278 -0.6506604 1.0000000### Benefit
FS$B1_EWUF <- as.numeric(FS$Ben_EW_UF_40)
FS$B2_EWUF <- as.numeric(FS$Ben_EW_UF_42)
FS$B3_EWUF <- as.numeric(FS$Ben_EW_UF_43)
FS$B4_EWUF <- as.numeric(FS$Ben_EW_UF_51)
hist(FS$B1_EWUF)
hist(FS$B2_EWUF)
hist(FS$B3_EWUF)
hist(FS$B4_EWUF)
FS$BenScore_EWUF <- rowMeans(FS [, c( "B1_EWUF" , "B2_EWUF", "B3_EWUF", "B4_EWUF")], na.rm=TRUE)
describe(FS$BenScore_EWUF)## FS$BenScore_EWUF
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 65.9 25.17 29.75 50.50
## .25 .50 .75 .90 .95
## 58.56 71.50 80.31 81.85 90.10
##
## lowest : 5.00 50.00 55.00 59.75 60.25, highest: 75.00 80.25 80.50 82.00 100.00
##
## Value 5.00 50.00 55.00 59.75 60.25 70.00 73.00 75.00 80.25
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 80.50 82.00 100.00
## Frequency 1 1 1
## Proportion 0.083 0.083 0.083sd(FS$BenScore_EWUF, na.rm = TRUE)## [1] 23.62839FS$BenScale_EWUF <- data.frame(FS$B1_BFUF, FS$B2_BFUF, FS$B3_BFUF, FS$B4_BFUF)
describe(FS$BenScale_EWUF)## FS$BenScale_EWUF
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.B1_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 71.15 21.56 50.0 50.4
## .25 .50 .75 .90 .95
## 54.0 73.0 81.0 98.6 100.0
##
## lowest : 50 52 54 55 68, highest: 74 75 81 93 100
##
## Value 50 52 54 55 68 73 74 75 81 93 100
## Frequency 2 1 1 1 1 1 1 1 1 1 2
## Proportion 0.154 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.154
## --------------------------------------------------------------------------------
## FS.B2_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 78.54 18.67 53.8 57.6
## .25 .50 .75 .90 .95
## 69.0 75.0 94.0 97.8 98.8
##
## lowest : 52 55 68 69 73, highest: 88 94 97 98 100
##
## Value 52 55 68 69 73 74 75 78 88 94 97
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 98 100
## Frequency 1 1
## Proportion 0.077 0.077
## --------------------------------------------------------------------------------
## FS.B3_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 77.54 19.77 53.8 56.4
## .25 .50 .75 .90 .95
## 66.0 74.0 94.0 99.0 100.0
##
## lowest : 52 55 62 66 71, highest: 81 87 94 95 100
##
## Value 52 55 62 66 71 74 81 87 94 95 100
## Frequency 1 1 1 1 2 1 1 1 1 1 2
## Proportion 0.077 0.077 0.077 0.077 0.154 0.077 0.077 0.077 0.077 0.077 0.154
## --------------------------------------------------------------------------------
## FS.B4_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 78.08 18.51 57.4 61.4
## .25 .50 .75 .90 .95
## 67.0 75.0 94.0 99.0 100.0
##
## lowest : 52 61 63 67 70, highest: 81 82 94 95 100
##
## Value 52 61 63 67 70 75 81 82 94 95 100
## Frequency 1 1 1 1 1 2 1 1 1 1 2
## Proportion 0.077 0.077 0.077 0.077 0.077 0.154 0.077 0.077 0.077 0.077 0.154
## --------------------------------------------------------------------------------psych::alpha(FS$BenScale_EWUF)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$BenScale_EWUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.97 0.97 0.98 0.9 38 0.0049 76 16 0.92
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.96 0.97 0.98
## Duhachek 0.96 0.97 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.B1_BFUF 0.98 0.98 0.97 0.94 46 0.0038 0.00045 0.93
## FS.B2_BFUF 0.95 0.96 0.96 0.88 22 0.0085 0.00541 0.92
## FS.B3_BFUF 0.97 0.97 0.97 0.93 38 0.0051 0.00118 0.92
## FS.B4_BFUF 0.95 0.95 0.95 0.87 21 0.0089 0.00463 0.90
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.B1_BFUF 13 0.94 0.94 0.92 0.89 71 18
## FS.B2_BFUF 13 0.98 0.98 0.98 0.97 79 16
## FS.B3_BFUF 13 0.94 0.95 0.93 0.90 78 17
## FS.B4_BFUF 13 0.99 0.99 0.99 0.98 78 16cor(FS$BenScale_EWUF, use= "complete.obs")## FS.B1_BFUF FS.B2_BFUF FS.B3_BFUF FS.B4_BFUF
## FS.B1_BFUF 1.0000000 0.8955391 0.7963913 0.9205421
## FS.B2_BFUF 0.8955391 1.0000000 0.9266377 0.9633837
## FS.B3_BFUF 0.7963913 0.9266377 1.0000000 0.9267151
## FS.B4_BFUF 0.9205421 0.9633837 0.9267151 1.0000000### Support
FS$S1_EWUF <- as.numeric(FS$Sup_EW_UF_40)
FS$S2_EWUF <- as.numeric(FS$Sup_EW_UF_42)
FS$S3_EWUF <- as.numeric(FS$Sup_EW_UF_43)
FS$S4_EWUF <- as.numeric(FS$Sup_EW_UF_45)
hist(FS$S1_EWUF)
hist(FS$S2_EWUF)
hist(FS$S3_EWUF)
hist(FS$S4_EWUF)
FS$SupScore_EWUF <- rowMeans(FS [, c( "S1_EWUF" , "S2_EWUF", "S3_EWUF", "S4_EWUF")], na.rm=TRUE)
describe(FS$SupScore_EWUF)## FS$SupScore_EWUF
## n missing distinct Info Mean Gmd .05 .10
## 12 92 12 1 67.03 27.38 22.96 27.43
## .25 .50 .75 .90 .95
## 61.06 67.50 83.62 91.12 95.46
##
## lowest : 22.00000 23.75000 60.50000 61.25000 63.25000
## highest: 78.33333 83.00000 85.50000 91.75000 100.00000
##
## Value 22.00000 23.75000 60.50000 61.25000 63.25000 63.50000
## Frequency 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083
##
## Value 71.50000 78.33333 83.00000 85.50000 91.75000 100.00000
## Frequency 1 1 1 1 1 1
## Proportion 0.083 0.083 0.083 0.083 0.083 0.083sd(FS$SupScore_EWUF, na.rm = TRUE)## [1] 24.20685FS$SupScale_EWUF <- data.frame(FS$S1_BFUF, FS$S2_BFUF, FS$S3_BFUF, FS$S4_BFUF)
describe(FS$SupScale_EWUF)## FS$SupScale_EWUF
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.S1_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.989 75.15 25.44 48.4 50.4
## .25 .50 .75 .90 .95
## 53.0 82.0 99.0 99.0 99.4
##
## lowest : 46 50 52 53 57, highest: 82 86 93 99 100
##
## Value 46 50 52 53 57 61 82 86 93 99 100
## Frequency 1 1 1 1 1 1 1 1 1 3 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.231 0.077
## --------------------------------------------------------------------------------
## FS.S2_BFUF
## n missing distinct Info Mean Gmd
## 13 91 8 0.981 79.23 22.23
##
## lowest : 56 57 65 71 82, highest: 71 82 88 99 100
##
## Value 56 57 65 71 82 88 99 100
## Frequency 2 2 1 1 1 1 2 3
## Proportion 0.154 0.154 0.077 0.077 0.077 0.077 0.154 0.231
## --------------------------------------------------------------------------------
## FS.S3_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 61.77 35.79 6.6 18.8
## .25 .50 .75 .90 .95
## 52.0 57.0 86.0 99.4 100.0
##
## lowest : 0 11 50 52 55, highest: 66 77 86 97 100
##
## Value 0 11 50 52 55 57 66 77 86 97 100
## Frequency 1 1 1 2 1 1 1 1 1 1 2
## Proportion 0.077 0.077 0.077 0.154 0.077 0.077 0.077 0.077 0.077 0.077 0.154
## --------------------------------------------------------------------------------
## FS.S4_BFUF
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.989 66.31 33.87 25.2 43.2
## .25 .50 .75 .90 .95
## 50.0 62.0 95.0 100.0 100.0
##
## lowest : 0 42 48 50 52, highest: 62 63 93 95 100
##
## Value 0 42 48 50 52 57 62 63 93 95 100
## Frequency 1 1 1 1 1 1 1 1 1 1 3
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.231
## --------------------------------------------------------------------------------psych::alpha(FS$SupScale_EWUF)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$SupScale_EWUF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.79 0.96 0.48 3.7 0.039 71 20 0.33
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.7 0.78 0.84
## Duhachek 0.7 0.78 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.S1_BFUF 0.74 0.71 0.80 0.45 2.4 0.038 0.17 0.28
## FS.S2_BFUF 0.77 0.75 0.82 0.50 2.9 0.038 0.14 0.37
## FS.S3_BFUF 0.72 0.78 0.86 0.54 3.5 0.053 0.14 0.37
## FS.S4_BFUF 0.62 0.70 0.82 0.44 2.4 0.073 0.21 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.S1_BFUF 13 0.72 0.82 0.82 0.54 75 22
## FS.S2_BFUF 13 0.66 0.77 0.77 0.49 79 19
## FS.S3_BFUF 13 0.81 0.72 0.69 0.59 62 31
## FS.S4_BFUF 13 0.89 0.82 0.79 0.75 66 30cor(FS$SupScale_EWUF, use= "complete.obs")## FS.S1_BFUF FS.S2_BFUF FS.S3_BFUF FS.S4_BFUF
## FS.S1_BFUF 1.0000000 0.9718411 0.2069772 0.3690695
## FS.S2_BFUF 0.9718411 1.0000000 0.1422023 0.2831316
## FS.S3_BFUF 0.2069772 0.1422023 1.0000000 0.9106395
## FS.S4_BFUF 0.3690695 0.2831316 0.9106395 1.0000000More Familiar Condition
Biochar
Quiz Questions & Attention Check
### Minutes spent reading instructions before proceeding to next page in the survey
describe(FS$BioFam_InstTime_Page.Submit)## FS$BioFam_InstTime_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 6.564 5.393 1.502 1.598
## .25 .50 .75 .90 .95
## 2.409 5.752 8.365 9.974 14.099
##
## lowest : 1.496 1.506 1.967 2.409 3.814, highest: 8.189 8.365 9.045 10.206 19.938
##
## Value 1.496 1.506 1.967 2.409 3.814 5.702 5.752 6.949 8.189
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 8.365 9.045 10.206 19.938
## Frequency 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077INSTminutes_BIOFAM <- (mean(FS$BioFam_InstTime_Page.Submit, na.rm = TRUE)/60)
print(INSTminutes_BIOFAM)## [1] 0.1094077## Time Spent Reading Technology Description (before proceeding to next page of survey)
### Raw Time Spent (Seconds)
describe(FS$BIO_Fam_Time_Page.Submit)## FS$BIO_Fam_Time_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 22.25 21.99 1.387 2.112
## .25 .50 .75 .90 .95
## 5.309 19.901 31.701 49.423 50.874
##
## lowest : 0.795 1.781 3.435 5.309 5.784, highest: 27.245 31.701 48.759 49.589 52.802
##
## Value 0.795 1.781 3.435 5.309 5.784 19.554 19.901 22.552 27.245
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 31.701 48.759 49.589 52.802
## Frequency 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 sd(FS$BIO_Fam_Time_Page.Submit, na.rm = TRUE)## [1] 18.93442 range(FS$BIO_Fam_Time_Page.Submit, na.rm = TRUE)## [1] 0.795 52.802 ### Convert to Minutes
TECHminutes_BIOFAM <- (mean(FS$BIO_Fam_Time_Page.Submit, na.rm = TRUE)/60)
print(TECHminutes_BIOFAM)## [1] 0.3707782## Attention and Manipulation Checks
# Attention Check 1: What technology did you just read about?
FS$ATN_BIO_FAM1 <- as.numeric(as.character(FS$BIO_FAM_ATN))
FS$ATN_BIO_FAM <- factor(FS$ATN_BIO_FAM1, levels = c(1, 2, 3, 4),
labels = c("Biochar", "Biofuel", "Enhanced Weathering", "Wind Energy"))
describe(FS$ATN_BIO_FAM)## FS$ATN_BIO_FAM
## n missing distinct
## 13 91 4
##
## Value Biochar Biofuel Enhanced Weathering
## Frequency 8 1 1
## Proportion 0.615 0.077 0.077
##
## Value Wind Energy
## Frequency 3
## Proportion 0.231#### Time Spent answering attention check #1:
##### (Seconds)
describe(FS$BIO_Blurred_ATN_TIME_Page.Submit)## FS$BIO_Blurred_ATN_TIME_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 8.729 5.187 3.550 4.451
## .25 .50 .75 .90 .95
## 4.748 8.018 11.723 13.568 15.315
##
## lowest : 2.273 4.402 4.645 4.748 5.789, highest: 10.127 11.723 12.667 13.793 17.597
##
## Value 2.273 4.402 4.645 4.748 5.789 7.992 8.018 9.702 10.127
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 11.723 12.667 13.793 17.597
## Frequency 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 sd(FS$BIO_Blurred_ATN_TIME_Page.Submit, na.rm = TRUE)## [1] 4.418532 range(FS$BIO_Blurred_ATN_TIME_Page.Submit, na.rm = TRUE)## [1] 2.273 17.597 ##### (Minutes)
ATN1_BIOFAM <- (mean(FS$BIO_Blurred_ATN_TIME_Page.Submit, na.rm = TRUE)/60)
print(ATN1_BIOFAM)## [1] 0.1454821### Attention Check 2: Describe as much as you can about the technology you read about.
describe(FS$BIO_Fam_ATN2)## FS$BIO_Fam_ATN2
## n missing distinct
## 13 91 13
##
## lowest : Burn in absence of oxygen her coal Burn away all leaving behind the carbon which is stored underground until it is needed. Burning off non-carbon remains Carbon dioxide is burned off. Leaves charcoal for further use changes plant matter by removing the oxygen from it
## highest: I would need to read over the subject once more, short term memory loss I would say the process of turning biomass into biocar It burns plant parts into carbon It's something to know about and learn from it None #### Time spent answering attention check #2
##### (Seconds)
describe(FS$BIO_Fam_ATN2_TIME_Page.Submit)## FS$BIO_Fam_ATN2_TIME_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 47.06 42.79 9.156 11.899
## .25 .50 .75 .90 .95
## 24.193 31.101 57.230 73.625 117.923
##
## lowest : 8.488 9.602 21.085 24.193 28.265
## highest: 53.155 57.230 60.200 76.981 179.335
##
## Value 8.488 9.602 21.085 24.193 28.265 30.301 31.101 31.808
## Frequency 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 53.155 57.230 60.200 76.981 179.335
## Frequency 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 sd(FS$BIO_Fam_ATN2_TIME_Page.Submit, na.rm = TRUE)## [1] 44.6422 range(FS$BIO_Fam_ATN2_TIME_Page.Submit, na.rm = TRUE)## [1] 8.488 179.335 ##### (Minutes)
ATN2_BIOFAM <- (mean(FS$BIO_Fam_ATN2_TIME_Page.Submit, na.rm = TRUE)/60)
print(ATN2_BIOFAM)## [1] 0.7842872## Quiz Questions
FS$BIO_Fam_Quiz1## [1] NA NA NA NA NA NA NA NA NA NA NA NA 3 3 4 1 4 1 4 4 4 4 1 4 4
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [101] NA NA NA NAFS$BIO_Fam_Quiz2## [1] NA NA NA NA NA NA NA NA NA NA NA NA 3 2 4 3 3 3 4 3 3 4 3 3 4
## [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
## [51] 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
## [76] 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
## [101] NA NA NA NAFS$BIO_Fam_Quiz3## [1] NA NA NA NA NA NA NA NA NA NA NA NA 2 1 2 2 2 2 1 2 2 1 1 2 1
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [101] NA NA NA NAFS$BIO_Fam_Quiz4## [1] NA NA NA NA NA NA NA NA NA NA NA NA 2 1 1 1 1 1 1 1 1 1 1 1 1
## [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
## [51] 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
## [76] 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
## [101] NA NA NA NAFS$Bio_Fam_QuizTime_Page.Submit## [1] NA NA NA NA NA NA NA NA NA
## [10] NA NA NA 13.099 8.256 41.237 79.747 95.574 46.119
## [19] 129.020 37.639 25.607 50.288 25.400 53.307 13.062 NA NA
## [28] NA NA NA NA NA NA NA NA NA
## [37] NA NA NA NA NA NA NA NA NA
## [46] NA NA NA NA NA NA NA NA NA
## [55] NA NA NA NA NA NA NA NA NA
## [64] NA NA NA NA NA NA NA NA NA
## [73] NA NA NA NA NA NA NA NA NA
## [82] NA NA NA NA NA NA NA NA NA
## [91] NA NA NA NA NA NA NA NA NA
## [100] NA NA NA NA NATechnology Ratings
### Naturalness
FS$N1_BioFAM<- as.numeric(FS$Nat_BIO_Fam_30)
FS$N2R_BioFAM <- as.numeric(100 - FS$Nat_BIO_Fam_31)
FS$N3R_BioFAM <- as.numeric(100 - FS$Nat_BIO_Fam_35)
FS$N4R_BioFAM <- as.numeric(100- FS$Nat_BIO_Fam_36)
hist(FS$N1_BioFAM)
hist(FS$N2R_BioFAM)
hist(FS$N3R_BioFAM)
hist(FS$N4R_BioFAM)
FS$NatScore_BioFAM <- rowMeans(FS [, c( "N1_BioFAM" , "N2R_BioFAM", "N3R_BioFAM", "N4R_BioFAM")], na.rm=TRUE)
describe(FS$NatScore_BioFAM)## FS$NatScore_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 47.15 15.66 29.05 33.05
## .25 .50 .75 .90 .95
## 39.25 45.50 51.50 67.50 72.20
##
## lowest : 25.00 31.75 38.25 39.25 42.00, highest: 47.25 51.50 55.50 70.50 74.75
##
## Value 25.00 31.75 38.25 39.25 42.00 44.75 45.50 47.00 47.25 51.50 55.50
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 70.50 74.75
## Frequency 1 1
## Proportion 0.077 0.077sd(FS$NatScore_BioFAM, na.rm = TRUE)## [1] 13.84458FS$NatScale_BioFAM <- data.frame(FS$N1_BioFAM, FS$N2R_BioFAM, FS$N3R_BioFAM, FS$N4R_BioFAM)
describe(FS$NatScale_BioFAM)## FS$NatScale_BioFAM
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.N1_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 10 0.986 80 22.9 42.8 52.6
## .25 .50 .75 .90 .95
## 76.0 87.0 95.0 100.0 100.0
##
## lowest : 32 50 63 76 84, highest: 87 88 89 95 100
##
## Value 32 50 63 76 84 87 88 89 95 100
## Frequency 1 1 1 2 1 1 1 1 1 3
## Proportion 0.077 0.077 0.077 0.154 0.077 0.077 0.077 0.077 0.077 0.231
## --------------------------------------------------------------------------------
## FS.N2R_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 29.92 29.15 0.0 2.0
## .25 .50 .75 .90 .95
## 15.0 23.0 48.0 62.8 74.0
##
## lowest : 0 10 15 22 23, highest: 30 48 50 66 86
##
## Value 0 10 15 22 23 24 30 48 50 66 86
## Frequency 2 1 2 1 1 1 1 1 1 1 1
## Proportion 0.154 0.077 0.154 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
## --------------------------------------------------------------------------------
## FS.N3R_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.989 25.31 32.1 0.0 0.0
## .25 .50 .75 .90 .95
## 6.0 17.0 24.0 76.8 92.8
##
## lowest : 0 6 10 12 17, highest: 22 24 32 88 100
##
## Value 0 6 10 12 17 18 22 24 32 88 100
## Frequency 3 1 1 1 1 1 1 1 1 1 1
## Proportion 0.231 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
## --------------------------------------------------------------------------------
## FS.N4R_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 53.38 36.74 7.2 14.2
## .25 .50 .75 .90 .95
## 33.0 59.0 79.0 83.4 90.4
##
## lowest : 0 12 23 33 34, highest: 76 79 81 84 100
##
## Value 0 12 23 33 34 47 59 66 76 79 81
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 84 100
## Frequency 1 1
## Proportion 0.077 0.077
## --------------------------------------------------------------------------------psych::alpha(FS$NatScale_BioFAM)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$NatScale_BioFAM): 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 ( FS.N1_BioFAM ) 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 = FS$NatScale_BioFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.0093 -0.3 0.48 -0.061 -0.23 0.14 47 14 -0.12
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.37 -0.01 0.27
## Duhachek -0.28 -0.01 0.26
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.N1_BioFAM 0.53 0.55 0.66 0.290 1.22 0.085 0.226 0.019
## FS.N2R_BioFAM -0.68 -0.96 -0.43 -0.195 -0.49 0.259 0.037 -0.250
## FS.N3R_BioFAM -1.36 -2.07 -0.61 -0.290 -0.67 0.340 0.104 -0.250
## FS.N4R_BioFAM 0.16 -0.16 0.66 -0.048 -0.14 0.112 0.609 -0.353
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.N1_BioFAM 13 -0.26 -0.13 -0.536 -0.552 80 21
## FS.N2R_BioFAM 13 0.72 0.67 0.849 0.348 30 26
## FS.N3R_BioFAM 13 0.85 0.83 1.014 0.453 25 32
## FS.N4R_BioFAM 13 0.48 0.43 -0.077 -0.088 53 31cor(FS$NatScale_BioFAM, use= "complete.obs")## FS.N1_BioFAM FS.N2R_BioFAM FS.N3R_BioFAM FS.N4R_BioFAM
## FS.N1_BioFAM 1.0000000 -0.62995324 -0.3528685 -0.25026991
## FS.N2R_BioFAM -0.6299532 1.00000000 0.8388256 0.01120342
## FS.N3R_BioFAM -0.3528685 0.83882563 1.0000000 0.01876070
## FS.N4R_BioFAM -0.2502699 0.01120342 0.0187607 1.00000000### Familiarity
FS$Fam_BioFAM <- as.numeric(FS$Fam_BIO_Fam_31)
hist(FS$Fam_BioFAM)
describe(FS$Fam_BioFAM)## FS$Fam_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 12 0.997 45.54 33.59 9.6 16.6
## .25 .50 .75 .90 .95
## 26.0 46.0 64.0 78.8 89.2
##
## lowest : 0 16 19 26 31, highest: 60 64 66 82 100
##
## Value 0 16 19 26 31 46 51 60 64 66 82
## Frequency 1 1 1 1 2 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.154 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 100
## Frequency 1
## Proportion 0.077sd(FS$Fam_BioFAM, na.rm = TRUE)## [1] 28.50326### Understanding
FS$Und_BioFAM <- as.numeric(FS$Fam_BIO_Fam_33)
hist(FS$Und_BioFAM)
describe(FS$Und_BioFAM)## FS$Und_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 60.54 30.46 30.8 32.4
## .25 .50 .75 .90 .95
## 36.0 54.0 84.0 93.0 97.0
##
## lowest : 29 32 34 36 40, highest: 83 84 85 95 100
##
## Value 29 32 34 36 40 51 54 64 83 84 85
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 95 100
## Frequency 1 1
## Proportion 0.077 0.077sd(FS$Und_BioFAM, na.rm = TRUE)## [1] 25.95064### Fluency
FS$Fluency_BioFAM <- as.numeric(FS$Fluency_BIO_Fam_34)
hist(FS$Fluency_BioFAM)
describe(FS$Fluency_BioFAM)## FS$Fluency_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 10 0.973 75.08 30.95 21.6 29.2
## .25 .50 .75 .90 .95
## 65.0 80.0 100.0 100.0 100.0
##
## lowest : 21 22 58 65 70, highest: 73 80 91 96 100
##
## Value 21 22 58 65 70 73 80 91 96 100
## Frequency 1 1 1 1 1 1 1 1 1 4
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.308sd(FS$Fluency_BioFAM, na.rm = TRUE)## [1] 27.93582### Risk
FS$R1_BioFAM <- as.numeric(FS$Risk_BIO_Fam_30)
FS$R2_BioFAM <- as.numeric(FS$Risk_BIO_Fam_31)
FS$R3_BioFAM <- as.numeric(FS$Risk_BIO_Fam_32)
hist(FS$R1_BioFAM)
hist(FS$R2_BioFAM)
hist(FS$R3_BioFAM)
FS$RiskScore_BioFAM <- rowMeans(FS [, c( "R1_BioFAM" , "R2_BioFAM", "R3_BioFAM")], na.rm=TRUE)
describe(FS$RiskScore_BioFAM)## FS$RiskScore_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 46.21 16.7 28.47 34.73
## .25 .50 .75 .90 .95
## 36.67 44.00 53.67 58.27 69.07
##
## lowest : 19.66667 34.33333 36.33333 36.66667 40.33333
## highest: 48.66667 53.66667 54.00000 59.33333 83.66667
##
## Value 19.66667 34.33333 36.33333 36.66667 40.33333 43.00000 44.00000
## Frequency 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 47.00000 48.66667 53.66667 54.00000 59.33333 83.66667
## Frequency 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077sd(FS$RiskScore_BioFAM, na.rm = TRUE)## [1] 15.2264FS$RiskScale_BioFAM <- data.frame(FS$R1_BioFAM, FS$R2_BioFAM, FS$R3_BioFAM)
describe(FS$RiskScale_BioFAM)## FS$RiskScale_BioFAM
##
## 3 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.R1_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 42.38 32.72 4.8 7.6
## .25 .50 .75 .90 .95
## 23.0 43.0 62.0 77.4 85.4
##
## lowest : 3 6 14 23 28, highest: 52 62 63 81 92
##
## Value 3 6 14 23 28 39 43 45 52 62 63
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 81 92
## Frequency 1 1
## Proportion 0.077 0.077
## --------------------------------------------------------------------------------
## FS.R2_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 32.62 25.97 1.8 5.2
## .25 .50 .75 .90 .95
## 22.0 33.0 42.0 59.6 70.2
##
## lowest : 0 3 14 22 23, highest: 38 42 46 63 81
##
## Value 0 3 14 22 23 25 33 34 38 42 46
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 63 81
## Frequency 1 1
## Proportion 0.077 0.077
## --------------------------------------------------------------------------------
## FS.R3_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 12 0.997 63.62 33.38 19.2 24.2
## .25 .50 .75 .90 .95
## 44.0 70.0 82.0 97.8 100.0
##
## lowest : 15 22 33 44 53, highest: 73 81 82 89 100
##
## Value 15 22 33 44 53 65 70 73 81 82 89
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 100
## Frequency 2
## Proportion 0.154
## --------------------------------------------------------------------------------psych::alpha(FS$RiskScale_BioFAM)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$RiskScale_BioFAM): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( FS.R3_BioFAM ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = FS$RiskScale_BioFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.012 0.13 0.69 0.049 0.16 0.18 46 15 -0.068
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.37 0.01 0.30
## Duhachek -0.34 0.01 0.37
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.R1_BioFAM -0.14 -0.15 -0.068 -0.068 -0.13 0.22 NA -0.068
## FS.R2_BioFAM -2.21 -2.21 -0.525 -0.525 -0.69 0.63 NA -0.525
## FS.R3_BioFAM 0.84 0.85 0.741 0.741 5.71 0.03 NA 0.741
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.R1_BioFAM 13 0.64 0.67 0.658 0.053 42 28
## FS.R2_BioFAM 13 0.90 0.92 0.906 0.680 33 23
## FS.R3_BioFAM 13 0.27 0.22 -0.059 -0.342 64 28cor(FS$RiskScale_BioFAM, use= "complete.obs")## FS.R1_BioFAM FS.R2_BioFAM FS.R3_BioFAM
## FS.R1_BioFAM 1.0000000 0.74059030 -0.52530643
## FS.R2_BioFAM 0.7405903 1.00000000 -0.06776323
## FS.R3_BioFAM -0.5253064 -0.06776323 1.00000000### Benefit
FS$B1_BioFAM <- as.numeric(FS$Ben_BIO_Fam_40)
FS$B2_BioFAM <- as.numeric(FS$Ben_BIO_Fam_41)
FS$B3_BioFAM <- as.numeric(FS$Ben_BIO_Fam_42)
FS$B4_BioFAM <- as.numeric(FS$Ben_BIO_Fam_43)
hist(FS$B1_BioFAM)
hist(FS$B2_BioFAM)
hist(FS$B3_BioFAM)
hist(FS$B4_BioFAM)
FS$BenScore_BioFAM <- rowMeans(FS [, c( "B1_BioFAM" , "B2_BioFAM", "B3_BioFAM", "B4_BioFAM")], na.rm=TRUE)
describe(FS$BenScore_BioFAM)## FS$BenScore_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 70.42 27.82 33.30 45.20
## .25 .50 .75 .90 .95
## 56.50 75.75 87.75 96.25 98.80
##
## lowest : 17.25 44.00 50.00 56.50 61.25, highest: 85.50 87.75 89.25 98.00 100.00
##
## Value 17.25 44.00 50.00 56.50 61.25 65.50 75.75 84.75 85.50
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 87.75 89.25 98.00 100.00
## Frequency 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077sd(FS$BenScore_BioFAM, na.rm = TRUE)## [1] 24.14504FS$BenScale_BioFAM <- data.frame(FS$B1_BioFAM, FS$B2_BioFAM, FS$B3_BioFAM, FS$B4_BioFAM)
describe(FS$BenScale_BioFAM)## FS$BenScale_BioFAM
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.B1_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 63.92 34.03 17.6 26.2
## .25 .50 .75 .90 .95
## 43.0 59.0 87.0 97.8 100.0
##
## lowest : 11 22 43 54 56, highest: 83 84 87 89 100
##
## Value 11 22 43 54 56 59 83 84 87 89 100
## Frequency 1 1 2 1 1 1 1 1 1 1 2
## Proportion 0.077 0.077 0.154 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.154
## --------------------------------------------------------------------------------
## FS.B2_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 12 0.997 73.77 27.18 32.0 47.4
## .25 .50 .75 .90 .95
## 62.0 85.0 88.0 96.4 98.8
##
## lowest : 11 46 53 62 69, highest: 87 88 90 98 100
##
## Value 11 46 53 62 69 83 85 87 88 90 98
## Frequency 1 1 1 1 1 1 1 2 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.154 0.077 0.077 0.077
##
## Value 100
## Frequency 1
## Proportion 0.077
## --------------------------------------------------------------------------------
## FS.B3_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 76.62 25.56 39.8 49.6
## .25 .50 .75 .90 .95
## 66.0 83.0 90.0 99.6 100.0
##
## lowest : 26 49 52 66 72, highest: 83 88 90 98 100
##
## Value 26 49 52 66 72 82 83 88 90 98 100
## Frequency 1 1 1 1 1 1 1 1 2 1 2
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.154 0.077 0.154
## --------------------------------------------------------------------------------
## FS.B4_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 67.38 32.21 22.0 31.6
## .25 .50 .75 .90 .95
## 52.0 81.0 89.0 93.2 96.4
##
## lowest : 10 30 38 52 55, highest: 86 89 90 94 100
##
## Value 10 30 38 52 55 68 81 83 86 89 90
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 94 100
## Frequency 1 1
## Proportion 0.077 0.077
## --------------------------------------------------------------------------------psych::alpha(FS$BenScale_BioFAM)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$BenScale_BioFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.99 0.79 15 0.012 70 24 0.79
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.91 0.94 0.95
## Duhachek 0.91 0.94 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.B1_BioFAM 0.94 0.94 0.97 0.85 17.0 0.011 0.014 0.83
## FS.B2_BioFAM 0.90 0.90 0.92 0.76 9.3 0.016 0.027 0.74
## FS.B3_BioFAM 0.93 0.93 0.95 0.81 13.2 0.013 0.015 0.83
## FS.B4_BioFAM 0.89 0.90 0.93 0.75 9.1 0.021 0.039 0.68
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.B1_BioFAM 13 0.89 0.87 0.85 0.79 64 29
## FS.B2_BioFAM 13 0.94 0.95 0.95 0.89 74 25
## FS.B3_BioFAM 13 0.89 0.90 0.90 0.81 77 23
## FS.B4_BioFAM 13 0.96 0.95 0.95 0.93 67 28cor(FS$BenScale_BioFAM, use= "complete.obs")## FS.B1_BioFAM FS.B2_BioFAM FS.B3_BioFAM FS.B4_BioFAM
## FS.B1_BioFAM 1.0000000 0.6812088 0.6005103 0.9276168
## FS.B2_BioFAM 0.6812088 1.0000000 0.9761429 0.8341729
## FS.B3_BioFAM 0.6005103 0.9761429 1.0000000 0.7404802
## FS.B4_BioFAM 0.9276168 0.8341729 0.7404802 1.0000000### Support
FS$S1_BioFAM <- as.numeric(FS$Sup_BIO_Fam_40)
FS$S2_BioFAM <- as.numeric(FS$Sup_BIO_Fam_42)
FS$S3_BioFAM <- as.numeric(FS$Sup_BIO_Fam_43)
FS$S4_BioFAM <- as.numeric(FS$Sup_BIO_Fam_45)
hist(FS$S1_BioFAM)
hist(FS$S2_BioFAM)
hist(FS$S3_BioFAM)
hist(FS$S4_BioFAM)
FS$SupScore_BioFAM <- rowMeans(FS [, c( "S1_BioFAM" , "S2_BioFAM", "S3_BioFAM", "S4_BioFAM")], na.rm=TRUE)
describe(FS$SupScore_BioFAM)## FS$SupScore_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 12 0.997 70.87 30.54 26.70 37.15
## .25 .50 .75 .90 .95
## 56.50 82.75 91.50 98.95 100.00
##
## lowest : 16.50 33.50 51.75 56.50 60.25, highest: 84.00 89.00 91.50 94.75 100.00
##
## Value 16.50 33.50 51.75 56.50 60.25 60.75 82.75 84.00 89.00
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 91.50 94.75 100.00
## Frequency 1 1 2
## Proportion 0.077 0.077 0.154sd(FS$SupScore_BioFAM, na.rm = TRUE)## [1] 26.57549FS$SupScale_BioFAM <- data.frame(FS$S1_BioFAM, FS$S2_BioFAM, FS$S3_BioFAM, FS$S4_BioFAM)
describe(FS$SupScale_BioFAM)## FS$SupScale_BioFAM
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.S1_BioFAM
## n missing distinct Info Mean Gmd
## 13 91 7 0.959 73.46 29.97
##
## lowest : 16 41 53 61 83, highest: 53 61 83 99 100
##
## Value 16 41 53 61 83 99 100
## Frequency 1 1 2 1 4 1 3
## Proportion 0.077 0.077 0.154 0.077 0.308 0.077 0.231
## --------------------------------------------------------------------------------
## FS.S2_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 12 0.997 68.92 33.97 23.0 28.0
## .25 .50 .75 .90 .95
## 50.0 85.0 91.0 99.4 100.0
##
## lowest : 17 27 32 50 60, highest: 86 88 91 97 100
##
## Value 17 27 32 50 60 63 85 86 88 91 97
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 100
## Frequency 2
## Proportion 0.154
## --------------------------------------------------------------------------------
## FS.S3_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.995 73.62 30.23 27.0 38.4
## .25 .50 .75 .90 .95
## 60.0 83.0 92.0 99.8 100.0
##
## lowest : 15 35 52 60 62, highest: 85 91 92 99 100
##
## Value 15 35 52 60 62 83 85 91 92 99 100
## Frequency 1 1 1 1 1 2 1 1 1 1 2
## Proportion 0.077 0.077 0.077 0.077 0.077 0.154 0.077 0.077 0.077 0.077 0.154
## --------------------------------------------------------------------------------
## FS.S4_BioFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 12 0.997 67.46 37.51 14.4 19.6
## .25 .50 .75 .90 .95
## 52.0 78.0 98.0 99.8 100.0
##
## lowest : 9 18 26 52 60, highest: 82 90 98 99 100
##
## Value 9 18 26 52 60 65 78 82 90 98 99
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 100
## Frequency 2
## Proportion 0.154
## --------------------------------------------------------------------------------psych::alpha(FS$SupScale_BioFAM)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$SupScale_BioFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.99 0.79 15 0.012 71 27 0.75
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.91 0.94 0.95
## Duhachek 0.91 0.94 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.S1_BioFAM 0.93 0.93 0.96 0.82 13.8 0.012 0.020 0.78
## FS.S2_BioFAM 0.89 0.90 0.92 0.76 9.5 0.020 0.030 0.70
## FS.S3_BioFAM 0.91 0.91 0.96 0.78 10.4 0.016 0.034 0.73
## FS.S4_BioFAM 0.93 0.93 0.93 0.82 13.7 0.013 0.014 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.S1_BioFAM 13 0.88 0.90 0.89 0.80 73 27
## FS.S2_BioFAM 13 0.96 0.95 0.95 0.92 69 29
## FS.S3_BioFAM 13 0.92 0.93 0.92 0.87 74 27
## FS.S4_BioFAM 13 0.91 0.90 0.90 0.83 67 33cor(FS$SupScale_BioFAM, use= "complete.obs")## FS.S1_BioFAM FS.S2_BioFAM FS.S3_BioFAM FS.S4_BioFAM
## FS.S1_BioFAM 1.0000000 0.7274995 0.9526996 0.6215522
## FS.S2_BioFAM 0.7274995 1.0000000 0.7823678 0.9802210
## FS.S3_BioFAM 0.9526996 0.7823678 1.0000000 0.7031719
## FS.S4_BioFAM 0.6215522 0.9802210 0.7031719 1.0000000Biofuel
Quiz Questions & Attention Check
### Minutes spent reading instructions before proceeding to next page in the survey
describe(FS$BF_Fam_InstTime_Page.Submit)## FS$BF_Fam_InstTime_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 14 90 14 1 9.801 11.86 0.7672 0.7933
## .25 .50 .75 .90 .95
## 1.8975 4.9950 15.8738 22.4891 28.2868
##
## lowest : 0.725 0.790 0.801 1.858 2.016, highest: 14.802 16.231 20.289 23.432 37.303
##
## Value 0.725 0.790 0.801 1.858 2.016 2.116 4.895 5.095 6.867
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071
##
## Value 14.802 16.231 20.289 23.432 37.303
## Frequency 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071INSTminutes_BFFAM <- (mean(FS$BF_Fam_InstTime_Page.Submit, na.rm = TRUE)/60)
print(INSTminutes_BFFAM)## [1] 0.1633571## Time Spent Reading Technology Description (before proceeding to next page of survey)
### Raw Time Spent (Seconds)
describe(FS$BF_Fam_Time_Page.Submit)## FS$BF_Fam_Time_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 14 90 14 1 16.43 18.27 1.071 1.353
## .25 .50 .75 .90 .95
## 3.576 11.518 23.448 37.799 45.031
##
## lowest : 0.701 1.270 1.547 3.036 5.197, highest: 15.056 26.245 34.599 39.171 55.915
##
## Value 0.701 1.270 1.547 3.036 5.197 9.558 10.007 13.028 14.656
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071
##
## Value 15.056 26.245 34.599 39.171 55.915
## Frequency 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071 sd(FS$BF_Fam_Time_Page.Submit, na.rm = TRUE)## [1] 16.66886 range(FS$BF_Fam_Time_Page.Submit, na.rm = TRUE)## [1] 0.701 55.915 ### Convert to Minutes
TECHminutes_BFFAM <- (mean(FS$BF_Fam_Time_Page.Submit, na.rm = TRUE)/60)
print(TECHminutes_BFFAM)## [1] 0.2737929## Attention and Manipulation Checks
# Attention Check 1: What technology did you just read about?
FS$ATN_BF_FAM1 <- as.numeric(as.character(FS$BF_Fam_ATN))
FS$ATN_BF_FAM <- factor(FS$ATN_BF_FAM1, levels = c(1, 2, 3, 4),
labels = c("Biochar", "Biofuel", "Enhanced Weathering", "Wind Energy"))
describe(FS$ATN_BF_FAM)## FS$ATN_BF_FAM
## n missing distinct
## 14 90 2
##
## Value Biofuel Wind Energy
## Frequency 10 4
## Proportion 0.714 0.286#### Time Spent answering attention check #1:
##### (Seconds)
describe(FS$BF_Fam_ATN_TIME_Page.Submit)## FS$BF_Fam_ATN_TIME_Page.Submit
## n missing distinct Info Mean Gmd .05 .10
## 14 90 14 1 29.37 46.6 4.465 5.437
## .25 .50 .75 .90 .95
## 5.920 6.436 7.929 28.032 127.930
##
## lowest : 2.768 5.379 5.572 5.899 5.983
## highest: 7.475 8.080 9.268 36.074 298.519
##
## Value 2.768 5.379 5.572 5.899 5.983 6.318 6.394 6.477
## Frequency 1 1 1 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071
##
## Value 7.043 7.475 8.080 9.268 36.074 298.519
## Frequency 1 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 sd(FS$BF_Fam_ATN_TIME_Page.Submit, na.rm = TRUE)## [1] 77.88165 range(FS$BF_Fam_ATN_TIME_Page.Submit, na.rm = TRUE)## [1] 2.768 298.519 ##### (Minutes)
ATN1_BFFAM <- (mean(FS$BF_Fam_ATN_TIME_Page.Submit, na.rm = TRUE)/60)
print(ATN1_BFFAM)## [1] 0.4895821### Attention Check 2: Describe as much as you can about the technology you read about.
FS$ATN2_BFFAM <- FS$BF_Blurred_ATN2
FS$ATN2_BFFAM_Time <- FS$BF_Blurred_ATN2_TIME_Page.Submit
describe(FS$ATN2_BFFAM)## FS$ATN2_BFFAM
## n missing distinct
## 14 90 14
##
## lowest : Biofuel can be used instead of natural fossil fuel Biofuel is created by using things that would go to the dump, leaves, brush, etc and instead it is made in to biofuel. Biofuel takes grass etc. & cools or heats it to make a fuel that is then refined so it can be used for cars, planes etc. biofuel uses plants to create oil Fuel derived from plants
## highest: The climate change goes at far as the governments control to the society To help our country stop burning oil which causes climate change, biofuels have been created from plants such as grass, trees and agricultural waste by changing their properties and using them as fuels for cars, etc. Turning plants into fuel Very all quality creative in nature Wind #### Time spent answering attention check #2
##### (Seconds)
describe(FS$ATN2_BFFAM_Time)## FS$ATN2_BFFAM_Time
## n missing distinct Info Mean Gmd .05 .10
## 14 90 14 1 52.27 59.56 7.306 7.744
## .25 .50 .75 .90 .95
## 14.905 26.352 68.183 146.175 154.258
##
## lowest : 7.001 7.470 8.385 14.856 15.053
## highest: 52.863 73.290 139.251 149.143 163.758
##
## Value 7.001 7.470 8.385 14.856 15.053 17.358 22.493 30.211
## Frequency 1 1 1 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071
##
## Value 30.701 52.863 73.290 139.251 149.143 163.758
## Frequency 1 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 sd(FS$ATN2_BFFAM_Time, na.rm = TRUE)## [1] 56.60002 range(FS$ATN2_BFFAM_Time, na.rm = TRUE)## [1] 7.001 163.758 ##### (Minutes)
ATN2_BFFAM <- (mean(FS$ATN2_BFFAM_Time, na.rm = TRUE)/60)
print(ATN2_BFFAM)## [1] 0.8712298## Quiz Questions
FS$BF_Fam_Quiz1## [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 3 3 2
## [51] 3 3 3 2 3 3 3 3 3 2 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] 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
## [101] NA NA NA NAFS$BF_Fam_Quiz2## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1 1
## [51] 1 2 1 1 1 1 1 1 1 1 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [101] NA NA NA NAFS$BF_Fam_Quiz3## [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 1 1 1
## [51] 1 1 1 1 1 1 1 1 1 1 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] 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
## [101] NA NA NA NAFS$BF_Fam_Quiz4## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 4 1 2
## [51] 4 4 1 4 5 5 4 4 5 2 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [101] NA NA NA NAFS$Q737_Page.Submit## [1] NA NA NA NA NA NA NA NA NA NA
## [11] NA NA NA NA NA NA NA NA NA NA
## [21] NA NA NA NA NA NA NA NA NA NA
## [31] NA NA NA NA NA NA NA NA NA NA
## [41] NA NA NA NA NA NA NA 57.789 37.704 18.791
## [51] 41.303 11.869 71.067 55.527 65.245 51.460 46.319 45.250 45.689 13.550
## [61] 14.495 NA NA NA NA NA NA NA NA NA
## [71] NA NA NA NA NA NA NA NA NA NA
## [81] NA NA NA NA NA NA NA NA NA NA
## [91] NA NA NA NA NA NA NA NA NA NA
## [101] NA NA NA NATechnology Ratings
### Naturalness
FS$N1_BFFAM<- as.numeric(FS$Nat_BF_Fam_30)
FS$N2R_BFFAM <- as.numeric(100 - FS$Nat_BF_Fam_31)
FS$N3R_BFFAM <- as.numeric(100 - FS$Nat_BF_Fam_35)
FS$N4R_BFFAM <- as.numeric(100- FS$Nat_BF_Fam_36)
hist(FS$N1_BFFAM)
hist(FS$N2R_BFFAM)
hist(FS$N3R_BFFAM)
hist(FS$N4R_BFFAM)
FS$NatScore_BFFAM <- rowMeans(FS [, c( "N1_BFFAM" , "N2R_BFFAM", "N3R_BFFAM", "N4R_BioFAM")], na.rm=TRUE)
describe(FS$NatScore_BFFAM)## FS$NatScore_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 27 77 24 0.998 48.91 25.93 15.30 27.60
## .25 .50 .75 .90 .95
## 33.33 47.00 62.50 79.80 83.10
##
## lowest : 0.00000 12.00000 23.00000 30.66667 33.00000
## highest: 76.00000 79.00000 81.00000 84.00000 100.00000sd(FS$NatScore_BFFAM, na.rm = TRUE)## [1] 22.81973FS$NatScale_BFFAM <- data.frame(FS$N1_BFFAM, FS$N2R_BFFAM, FS$N3R_BFFAM, FS$N4R_BFFAM)
describe(FS$NatScale_BFFAM)## FS$NatScale_BFFAM
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.N1_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 10 0.976 76.64 25.88 39.9 51.4
## .25 .50 .75 .90 .95
## 66.5 79.5 97.5 100.0 100.0
##
## lowest : 23 49 57 66 68, highest: 71 79 80 90 100
##
## Value 23 49 57 66 68 71 79 80 90 100
## Frequency 1 1 1 1 1 1 1 1 2 4
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.143 0.286
## --------------------------------------------------------------------------------
## FS.N2R_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 10 0.976 29.07 30.69 0.0 0.0
## .25 .50 .75 .90 .95
## 5.0 24.5 41.5 62.7 77.4
##
## lowest : 0 20 23 24 25, highest: 37 43 48 69 93
##
## Value 0 20 23 24 25 37 43 48 69 93
## Frequency 4 1 1 1 2 1 1 1 1 1
## Proportion 0.286 0.071 0.071 0.071 0.143 0.071 0.071 0.071 0.071 0.071
## --------------------------------------------------------------------------------
## FS.N3R_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 11 0.989 28.57 30.02 0.00 0.00
## .25 .50 .75 .90 .95
## 9.25 27.50 45.00 62.20 69.60
##
## lowest : 0 9 10 25 30, highest: 33 49 58 64 80
##
## Value 0 9 10 25 30 32 33 49 58 64 80
## Frequency 3 1 2 1 1 1 1 1 1 1 1
## Proportion 0.214 0.071 0.143 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071
## --------------------------------------------------------------------------------
## FS.N4R_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 13 0.998 69.21 34.45 19.50 26.00
## .25 .50 .75 .90 .95
## 54.50 73.00 95.75 99.70 100.00
##
## lowest : 13 23 33 54 56, highest: 91 92 97 99 100
##
## Value 13 23 33 54 56 65 71 75 91 92 97
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071
##
## Value 99 100
## Frequency 1 2
## Proportion 0.071 0.143
## --------------------------------------------------------------------------------psych::alpha(FS$NatScale_BFFAM)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$NatScale_BFFAM): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( FS.N1_BFFAM FS.N4R_BFFAM ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Warning in sqrt(Vtc): NaNs produced##
## Reliability analysis
## Call: psych::alpha(x = FS$NatScale_BFFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -1.2 -1.6 -0.25 -0.18 -0.61 0.36 51 9.6 -0.28
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -2.04 -1.25 -0.62
## Duhachek -1.95 -1.25 -0.54
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.N1_BFFAM -0.12 -0.05 0.200 -0.016 -0.047 0.20 0.21 -0.23
## FS.N2R_BFFAM -0.85 -1.05 -0.053 -0.206 -0.513 0.28 0.27 -0.23
## FS.N3R_BFFAM -1.06 -1.12 -0.184 -0.214 -0.529 0.33 0.24 -0.33
## FS.N4R_BFFAM -1.34 -1.92 -0.116 -0.281 -0.658 0.31 0.47 -0.64
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.N1_BFFAM 14 -0.092 -0.02 NaN -0.57 77 23
## FS.N2R_BFFAM 14 0.426 0.40 NaN -0.31 29 28
## FS.N3R_BFFAM 14 0.438 0.42 NaN -0.26 29 26
## FS.N4R_BFFAM 14 0.582 0.56 NaN -0.24 69 30cor(FS$NatScale_BFFAM, use= "complete.obs")## FS.N1_BFFAM FS.N2R_BFFAM FS.N3R_BFFAM FS.N4R_BFFAM
## FS.N1_BFFAM 1.0000000 -0.6364889 -0.7117792 0.3206001
## FS.N2R_BFFAM -0.6364889 1.0000000 0.5060330 -0.3269525
## FS.N3R_BFFAM -0.7117792 0.5060330 1.0000000 -0.2270946
## FS.N4R_BFFAM 0.3206001 -0.3269525 -0.2270946 1.0000000### Familiarity
FS$Fam_BFFAM <- as.numeric(FS$Fam_BF_Fam_32)
hist(FS$Fam_BFFAM)
describe(FS$Fam_BFFAM)## FS$Fam_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 14 1 55.86 33.01 14.30 23.20
## .25 .50 .75 .90 .95
## 31.50 64.00 74.75 85.80 89.10
##
## lowest : 0 22 26 28 42, highest: 74 75 83 87 93
##
## Value 0 22 26 28 42 51 58 70 73 74 75
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071
##
## Value 83 87 93
## Frequency 1 1 1
## Proportion 0.071 0.071 0.071sd(FS$Fam_BFFAM, na.rm = TRUE)## [1] 28.32465### Understanding
FS$Und_BFFAM <- as.numeric(FS$Fam_BF_Fam_31)
hist(FS$Und_BFFAM)
describe(FS$Und_BFFAM)## FS$Und_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 13 1 67.69 34.44 18.6 25.4
## .25 .50 .75 .90 .95
## 55.0 76.0 88.0 97.8 99.4
##
## lowest : 9 25 27 55 61, highest: 87 88 93 99 100
##
## Value 9 25 27 55 61 74 76 86 87 88 93
## Frequency 1 1 1 1 1 1 1 1 1 1 1
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 99 100
## Frequency 1 1
## Proportion 0.077 0.077sd(FS$Und_BFFAM, na.rm = TRUE)## [1] 30.30782### Fluency
FS$Fluency_BFFAM <- as.numeric(FS$Fluency_BF_Fam_34)
hist(FS$Fluency_BFFAM)
describe(FS$Fluency_BFFAM)## FS$Fluency_BFFAM
## n missing distinct Info Mean Gmd
## 14 90 9 0.969 76.93 30.36
##
## lowest : 11 20 54 70 80, highest: 80 83 87 98 100
##
## Value 11 20 54 70 80 83 87 98 100
## Frequency 1 1 1 1 1 1 3 1 4
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.214 0.071 0.286sd(FS$Fluency_BFFAM, na.rm = TRUE)## [1] 29.2008### Risk
FS$R1_BFFAM <- as.numeric(FS$Risk_BF_Fam_30)
FS$R2_BFFAM <- as.numeric(FS$Risk_BF_Fam_31)
FS$R3_BFFAM <- as.numeric(FS$Risk_BF_Fam_32)
hist(FS$R1_BFFAM)
hist(FS$R2_BFFAM)
hist(FS$R3_BFFAM)
FS$RiskScore_BFFAM <- rowMeans(FS [, c( "R1_BFFAM" , "R2_BFFAM", "R3_BFFAM")], na.rm=TRUE)
describe(FS$RiskScore_BFFAM)## FS$RiskScore_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 12 0.991 48.6 24.11 29.30 31.00
## .25 .50 .75 .90 .95
## 33.33 35.00 70.33 80.93 84.13
##
## lowest : 28.00000 30.00000 33.33333 33.66667 34.00000
## highest: 48.33333 77.66667 80.00000 81.33333 89.33333
##
## Value 28.00000 30.00000 33.33333 33.66667 34.00000 36.00000 42.00000
## Frequency 1 1 3 1 1 1 1
## Proportion 0.071 0.071 0.214 0.071 0.071 0.071 0.071
##
## Value 48.33333 77.66667 80.00000 81.33333 89.33333
## Frequency 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071sd(FS$RiskScore_BFFAM, na.rm = TRUE)## [1] 22.65051FS$RiskScale_BFFAM <- data.frame(FS$R1_BFFAM, FS$R2_BFFAM, FS$R3_BFFAM)
describe(FS$RiskScale_BFFAM)## FS$RiskScale_BFFAM
##
## 3 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.R1_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 11 0.978 41.14 45.58 0.00 0.00
## .25 .50 .75 .90 .95
## 1.25 36.00 80.75 90.70 94.15
##
## lowest : 0 5 14 31 41, highest: 77 82 90 91 100
##
## Value 0 5 14 31 41 45 77 82 90 91 100
## Frequency 4 1 1 1 1 1 1 1 1 1 1
## Proportion 0.286 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071
## --------------------------------------------------------------------------------
## FS.R2_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 12 0.991 31.29 36.31 0.00 0.00
## .25 .50 .75 .90 .95
## 5.25 20.50 50.00 77.80 82.50
##
## lowest : 0 5 6 11 16, highest: 44 52 75 79 89
##
## Value 0 5 6 11 16 25 36 44 52 75 79
## Frequency 3 1 1 1 1 1 1 1 1 1 1
## Proportion 0.214 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071
##
## Value 89
## Frequency 1
## Proportion 0.071
## --------------------------------------------------------------------------------
## FS.R3_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 13 91 11 0.989 76.85 27.9 27.8 48.0
## .25 .50 .75 .90 .95
## 76.0 86.0 90.0 100.0 100.0
##
## lowest : 2 45 60 76 80, highest: 86 88 89 90 100
##
## Value 2 45 60 76 80 83 86 88 89 90 100
## Frequency 1 1 1 1 1 1 1 1 1 1 3
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.231
## --------------------------------------------------------------------------------psych::alpha(FS$RiskScale_BFFAM)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$RiskScale_BFFAM): 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 ( FS.R3_BFFAM ) 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 = FS$RiskScale_BFFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.35 0.29 0.78 0.12 0.42 0.11 49 23 0.088
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.09 0.35 0.54
## Duhachek 0.14 0.35 0.56
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.R1_BFFAM 0.16 0.16 0.088 0.088 0.19 0.163 NA 0.088
## FS.R2_BFFAM -1.50 -1.68 -0.457 -0.457 -0.63 0.453 NA -0.457
## FS.R3_BFFAM 0.83 0.85 0.734 0.734 5.51 0.031 NA 0.734
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.R1_BFFAM 14 0.77 0.66 0.64 0.24 41 39
## FS.R2_BFFAM 14 0.94 0.94 0.93 0.86 31 32
## FS.R3_BFFAM 13 0.18 0.33 0.13 -0.23 77 28cor(FS$RiskScale_BFFAM, use= "complete.obs")## FS.R1_BFFAM FS.R2_BFFAM FS.R3_BFFAM
## FS.R1_BFFAM 1.0000000 0.7325566 -0.440161
## FS.R2_BFFAM 0.7325566 1.0000000 0.084938
## FS.R3_BFFAM -0.4401610 0.0849380 1.000000### Benefit
FS$B1_BFFAM <- as.numeric(FS$Ben_BF_Fam_40)
FS$B2_BFFAM <- as.numeric(FS$Ben_BF_Fam_42)
FS$B3_BFFAM <- as.numeric(FS$Ben_BF_Fam_43)
FS$B4_BFFAM <- as.numeric(FS$Ben_BF_Fam_44)
hist(FS$B1_BFFAM)
hist(FS$B2_BFFAM)
hist(FS$B3_BFFAM)
hist(FS$B4_BFFAM)
FS$BenScore_BFFAM <- rowMeans(FS [, c( "B1_BFFAM" , "B2_BFFAM", "B3_BFFAM", "B4_BFFAM")], na.rm=TRUE)
describe(FS$BenScore_BFFAM)## FS$BenScore_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 10 0.976 76 30.26 20.75 33.40
## .25 .50 .75 .90 .95
## 77.88 86.38 97.00 100.00 100.00
##
## lowest : 4.50 29.50 42.50 77.50 79.00, highest: 82.75 85.25 87.50 88.00 100.00
##
## Value 4.50 29.50 42.50 77.50 79.00 82.75 85.25 87.50 88.00
## Frequency 1 1 1 1 1 1 1 2 1
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.143 0.071
##
## Value 100.00
## Frequency 4
## Proportion 0.286sd(FS$BenScore_BFFAM, na.rm = TRUE)## [1] 29.41954FS$BenScale_BFFAM <- data.frame(FS$B1_BFFAM, FS$B2_BFFAM, FS$B3_BFFAM, FS$B4_BFFAM)
describe(FS$BenScale_BFFAM)## FS$BenScale_BFFAM
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.B1_BFFAM
## n missing distinct Info Mean Gmd
## 14 90 9 0.974 75.43 30.04
##
## lowest : 0 34 45 79 80, highest: 80 81 83 91 100
##
## Value 0 34 45 79 80 81 83 91 100
## Frequency 1 1 1 1 2 1 2 1 4
## Proportion 0.071 0.071 0.071 0.071 0.143 0.071 0.143 0.071 0.286
## --------------------------------------------------------------------------------
## FS.B2_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 11 0.978 76.64 30.12 26.60 35.60
## .25 .50 .75 .90 .95
## 75.25 85.50 98.75 100.00 100.00
##
## lowest : 11 35 37 75 76, highest: 83 88 91 95 100
##
## Value 11 35 37 75 76 82 83 88 91 95 100
## Frequency 1 1 1 1 1 1 1 1 1 1 4
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.286
## --------------------------------------------------------------------------------
## FS.B3_BFFAM
## n missing distinct Info Mean Gmd
## 14 90 9 0.974 76.14 30.86
##
## lowest : 7 19 49 76 80, highest: 80 87 88 90 100
##
## Value 7 19 49 76 80 87 88 90 100
## Frequency 1 1 1 1 2 1 1 2 4
## Proportion 0.071 0.071 0.071 0.071 0.143 0.071 0.071 0.143 0.286
## --------------------------------------------------------------------------------
## FS.B4_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 10 0.976 75.79 31.79 19.50 32.70
## .25 .50 .75 .90 .95
## 75.25 86.00 98.75 100.00 100.00
##
## lowest : 0 30 39 74 79, highest: 84 86 88 95 100
##
## Value 0 30 39 74 79 84 86 88 95 100
## Frequency 1 1 1 1 1 1 2 1 1 4
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.143 0.071 0.071 0.286
## --------------------------------------------------------------------------------psych::alpha(FS$BenScale_BFFAM)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$BenScale_BFFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.99 0.99 1 0.98 175 0.00096 76 29 0.98
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.99 0.99 1
## Duhachek 0.99 0.99 1
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.B1_BFFAM 0.99 0.99 0.99 0.98 120 0.00152 2.2e-04 0.97
## FS.B2_BFFAM 0.99 0.99 0.99 0.98 138 0.00129 7.7e-05 0.98
## FS.B3_BFFAM 0.99 0.99 1.00 0.98 185 0.00097 1.0e-04 0.99
## FS.B4_BFFAM 0.99 0.99 0.99 0.97 106 0.00162 5.5e-05 0.97
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.B1_BFFAM 14 0.99 0.99 0.99 0.99 75 29
## FS.B2_BFFAM 14 0.99 0.99 0.99 0.98 77 28
## FS.B3_BFFAM 14 0.99 0.99 0.98 0.98 76 30
## FS.B4_BFFAM 14 1.00 1.00 1.00 0.99 76 31cor(FS$BenScale_BFFAM, use= "complete.obs")## FS.B1_BFFAM FS.B2_BFFAM FS.B3_BFFAM FS.B4_BFFAM
## FS.B1_BFFAM 1.0000000 0.9727136 0.9797318 0.9868653
## FS.B2_BFFAM 0.9727136 1.0000000 0.9648551 0.9925309
## FS.B3_BFFAM 0.9797318 0.9648551 1.0000000 0.9694491
## FS.B4_BFFAM 0.9868653 0.9925309 0.9694491 1.0000000### Support
FS$S1_BFFAM <- as.numeric(FS$Sup_BF_Fam_40)
FS$S2_BFFAM <- as.numeric(FS$Sup_BF_Fam_42)
FS$S3_BFFAM <- as.numeric(FS$Sup_BF_Fam_43)
FS$S4_BFFAM <- as.numeric(FS$Sup_BF_Fam_45)
hist(FS$S1_BFFAM)
hist(FS$S2_BFFAM)
hist(FS$S3_BFFAM)
hist(FS$S4_BFFAM)
FS$SupScore_BFFAM <- rowMeans(FS [, c( "S1_BFFAM" , "S2_BFFAM", "S3_BFFAM", "S4_BFFAM")], na.rm=TRUE)
describe(FS$SupScore_BFFAM)## FS$SupScore_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 13 0.998 81.2 20.98 40.81 55.27
## .25 .50 .75 .90 .95
## 78.31 85.12 95.12 99.62 100.00
##
## lowest : 30.25 46.50 75.75 78.00 79.25, highest: 88.75 90.25 96.75 98.75 100.00
##
## Value 30.25 46.50 75.75 78.00 79.25 82.25 84.50 85.75 88.75
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071
##
## Value 90.25 96.75 98.75 100.00
## Frequency 1 1 1 2
## Proportion 0.071 0.071 0.071 0.143sd(FS$SupScore_BFFAM, na.rm = TRUE)## [1] 20.13803FS$SupScale_BFFAM <- data.frame(FS$S1_BFFAM, FS$S2_BFFAM, FS$S3_BFFAM, FS$S4_BFFAM)
describe(FS$SupScale_BFFAM)## FS$SupScale_BFFAM
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.S1_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 10 0.976 82.21 22.03 39.75 51.90
## .25 .50 .75 .90 .95
## 81.25 86.50 99.00 100.00 100.00
##
## lowest : 30 45 68 80 85, highest: 86 87 88 96 100
##
## Value 30 45 68 80 85 86 87 88 96 100
## Frequency 1 1 1 1 1 2 1 1 1 4
## Proportion 0.071 0.071 0.071 0.071 0.071 0.143 0.071 0.071 0.071 0.286
## --------------------------------------------------------------------------------
## FS.S2_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 11 0.989 83.21 19.86 46.55 62.00
## .25 .50 .75 .90 .95
## 78.75 90.00 94.25 100.00 100.00
##
## lowest : 29 56 76 77 84, highest: 90 91 92 95 100
##
## Value 29 56 76 77 84 85 90 91 92 95 100
## Frequency 1 1 1 1 1 1 2 1 1 1 3
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.143 0.071 0.071 0.071 0.214
## --------------------------------------------------------------------------------
## FS.S3_BFFAM
## n missing distinct Info Mean Gmd .05 .10
## 14 90 11 0.978 79.5 23.11 38.75 51.20
## .25 .50 .75 .90 .95
## 73.75 84.50 97.25 100.00 100.00
##
## lowest : 29 44 68 73 76, highest: 84 85 86 89 100
##
## Value 29 44 68 73 76 79 84 85 86 89 100
## Frequency 1 1 1 1 1 1 1 1 1 1 4
## Proportion 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.071 0.286
## --------------------------------------------------------------------------------
## FS.S4_BFFAM
## n missing distinct Info Mean Gmd
## 14 90 8 0.974 79.86 21.12
##
## lowest : 33 41 77 81 87, highest: 81 87 90 93 100
##
## Value 33 41 77 81 87 90 93 100
## Frequency 1 1 3 3 1 1 1 3
## Proportion 0.071 0.071 0.214 0.214 0.071 0.071 0.071 0.214
## --------------------------------------------------------------------------------psych::alpha(FS$SupScale_BFFAM)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$SupScale_BFFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.99 0.99 0.98 0.95 70 0.0023 81 20 0.95
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.98 0.99 0.99
## Duhachek 0.98 0.99 0.99
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.S1_BFFAM 0.98 0.98 0.97 0.94 49 0.0035 2.3e-04 0.94
## FS.S2_BFFAM 0.99 0.99 0.98 0.96 78 0.0022 2.6e-05 0.96
## FS.S3_BFFAM 0.98 0.98 0.97 0.94 47 0.0036 4.2e-04 0.94
## FS.S4_BFFAM 0.98 0.98 0.97 0.94 46 0.0037 6.5e-04 0.93
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.S1_BFFAM 14 0.98 0.98 0.98 0.97 82 21
## FS.S2_BFFAM 14 0.97 0.97 0.95 0.94 83 20
## FS.S3_BFFAM 14 0.98 0.98 0.98 0.97 80 21
## FS.S4_BFFAM 14 0.98 0.98 0.98 0.97 80 20cor(FS$SupScale_BFFAM, use= "complete.obs")## FS.S1_BFFAM FS.S2_BFFAM FS.S3_BFFAM FS.S4_BFFAM
## FS.S1_BFFAM 1.0000000 0.9208410 0.9685487 0.9617508
## FS.S2_BFFAM 0.9208410 1.0000000 0.9286463 0.9383029
## FS.S3_BFFAM 0.9685487 0.9286463 1.0000000 0.9584763
## FS.S4_BFFAM 0.9617508 0.9383029 0.9584763 1.0000000Enhanced Weathering
Quiz Questions & Attention Check
### Minutes spent reading instructions before proceeding to next page in the survey
describe(FS$EWFam_InstTime_Page.Submit)## FS$EWFam_InstTime_Page.Submit
## n missing distinct Info Mean Gmd
## 9 95 9 1 5.291 6.187
##
## lowest : 0.977 1.120 1.148 2.140 3.101, highest: 3.101 3.627 4.002 14.591 16.917
##
## Value 0.977 1.120 1.148 2.140 3.101 3.627 4.002 14.591 16.917
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111INSTminutes_EWFAM <- (mean(FS$EWFam_InstTime_Page.Submit, na.rm = TRUE)/60)
print(INSTminutes_EWFAM)## [1] 0.08819074## Time Spent Reading Technology Description (before proceeding to next page of survey)
### Raw Time Spent (Seconds)
describe(FS$EW_Fam_Time_Page.Submit)## FS$EW_Fam_Time_Page.Submit
## n missing distinct Info Mean Gmd
## 9 95 9 1 14.7 14.97
##
## lowest : 1.263 3.123 6.053 6.195 7.894, highest: 7.894 19.002 21.987 27.802 38.943
##
## Value 1.263 3.123 6.053 6.195 7.894 19.002 21.987 27.802 38.943
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 sd(FS$EW_Fam_Time_Page.Submit, na.rm = TRUE)## [1] 12.93735 range(FS$EW_Fam_Time_Page.Submit, na.rm = TRUE)## [1] 1.263 38.943 ### Convert to Minutes
TECHminutes_EWFAM <- (mean(FS$EW_Fam_Time_Page.Submit, na.rm = TRUE)/60)
print(TECHminutes_EWFAM)## [1] 0.2449296## Attention and Manipulation Checks
### Attention Check 1: What technology did you just read about?
FS$ATN_EW_FAM1 <- as.numeric(as.character(FS$EW_Fam_ATN))
FS$ATN_EW_FAM <- factor(FS$ATN_EW_FAM1, levels = c(1, 2, 3, 4),
labels = c("Biochar", "Biofuel", "Enhanced Weathering", "Wind Energy"))
describe(FS$ATN_EW_FAM)## FS$ATN_EW_FAM
## n missing distinct
## 9 95 3
##
## Value Biochar Enhanced Weathering Wind Energy
## Frequency 1 7 1
## Proportion 0.111 0.778 0.111 #### Time Spent answering attention check #1:
##### (Seconds)
describe(FS$EW_Fam_ATNTime_Page.Submit)## FS$EW_Fam_ATNTime_Page.Submit
## n missing distinct Info Mean Gmd
## 9 95 9 1 6.955 3.583
##
## lowest : 3.246 4.784 5.104 5.689 5.900, highest: 5.900 5.993 8.333 9.101 14.441
##
## Value 3.246 4.784 5.104 5.689 5.900 5.993 8.333 9.101 14.441
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 sd(FS$EW_Fam_ATNTime_Page.Submit, na.rm = TRUE)## [1] 3.318165 range(FS$EW_Fam_ATNTime_Page.Submit, na.rm = TRUE)## [1] 3.246 14.441 ##### (Minutes)
ATN1_EWFAM <- (mean(FS$EW_Fam_ATNTime_Page.Submit, na.rm = TRUE)/60)
print(ATN1_EWFAM)## [1] 0.1159093### Attention Check 2: Describe as much as you can about the technology you read about.
describe(FS$EW_Fam_ATN2)## FS$EW_Fam_ATN2
## n missing distinct
## 8 96 8
##
## lowest : cant remember I'm not sure about this one It can help alleviate a big part of climate change. It takes carbon samples from rocks to see how the rock is weathering Powdered minerals are added to fields, etc. where due to run-off they end up in waterways, thereby producing bicarbonate
## highest: It takes carbon samples from rocks to see how the rock is weathering Powdered minerals are added to fields, etc. where due to run-off they end up in waterways, thereby producing bicarbonate That weathering happens naturally without any interference The process of releasing elements into the atmosphere to provoke rain. This is about using weathering for fuel with minerals found in the ocean #### Time spent answering attention check #2
##### (Seconds)
describe(FS$EW_Fam_ATN2TIME_Page.Submit)## FS$EW_Fam_ATN2TIME_Page.Submit
## n missing distinct Info Mean Gmd
## 9 95 9 1 36.89 33.21
##
## lowest : 5.642 10.244 16.000 26.986 28.102, highest: 28.102 36.100 52.079 57.591 99.247
##
## Value 5.642 10.244 16.000 26.986 28.102 36.100 52.079 57.591 99.247
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 sd(FS$EW_Fam_ATN2TIME_Page.Submit, na.rm = TRUE)## [1] 29.25409 range(FS$EW_Fam_ATN2TIME_Page.Submit, na.rm = TRUE)## [1] 5.642 99.247 ##### (Minutes)
ATN2_EWFAM <- (mean(FS$EW_Fam_ATN2TIME_Page.Submit, na.rm = TRUE)/60)
print(ATN2_EWFAM)## [1] 0.6147981## Quiz Questions
FS$EW_Fam_Quiz1## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA 2 3 2 3 2 2 2 4 2 NA NA NA NA NA NA NA NA
## [101] NA NA NA NAFS$EW_Fam_Quiz2## [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
## [51] 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
## [76] NA NA NA NA NA NA NA NA 4 3 4 4 4 3 2 2 3 NA NA NA NA NA NA NA NA
## [101] NA NA NA NAFS$EW_Fam_Quiz3## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA 1 1 1 1 1 1 1 2 2 NA NA NA NA NA NA NA NA
## [101] NA NA NA NAFS$EW_Fam_Quiz4## [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
## [51] 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
## [76] NA NA NA NA NA NA NA NA 2 2 1 1 2 1 2 1 2 NA NA NA NA NA NA NA NA
## [101] NA NA NA NAFS$EW_Fam_QuizTime_Page.Submit## [1] NA NA NA NA NA NA NA NA NA
## [10] NA NA NA NA NA NA NA NA NA
## [19] NA NA NA NA NA NA NA NA NA
## [28] NA NA NA NA NA NA NA NA NA
## [37] NA NA NA NA NA NA NA NA NA
## [46] NA NA NA NA NA NA NA NA NA
## [55] NA NA NA NA NA NA NA NA NA
## [64] NA NA NA NA NA NA NA NA NA
## [73] NA NA NA NA NA NA NA NA NA
## [82] NA NA 119.259 91.221 262.403 78.000 53.427 11.549 69.499
## [91] 20.672 59.928 NA NA NA NA NA NA NA
## [100] NA NA NA NA NATechnology Ratings
### Naturalness
FS$N1_EWFAM <- as.numeric(FS$Nat_EW_Fam_30)
FS$N2R_EWFAM <- as.numeric(100 - FS$Nat_EW_Fam_31)
FS$N3R_EWFAM <- as.numeric(100 - FS$Nat_EW_Fam_35)
FS$N4R_EWFAM <- as.numeric(100- FS$Nat_EW_Fam_36)
hist(FS$N1_EWFAM)
hist(FS$N2R_EWFAM)
hist(FS$N3R_EWFAM)
hist(FS$N4R_EWFAM)
FS$NatScore_EWFAM <- rowMeans(FS [, c( "N1_EWFAM" , "N2R_EWFAM", "N3R_EWFAM", "N4R_EWFAM")], na.rm=TRUE)
describe(FS$NatScore_EWFAM)## FS$NatScore_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 50.5 20.54
##
## lowest : 25.00 27.25 44.50 49.00 51.00, highest: 51.00 54.00 56.00 67.50 80.25
##
## Value 25.00 27.25 44.50 49.00 51.00 54.00 56.00 67.50 80.25
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111sd(FS$NatScore_EWFAM, na.rm = TRUE)## [1] 17.48526FS$NatScale_EWFAM <- data.frame(FS$N1_EWFAM, FS$N2R_EWFAM, FS$N3R_EWFAM, FS$N4R_EWFAM)
describe(FS$NatScale_EWFAM)## FS$NatScale_EWFAM
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.N1_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 70.44 28.28
##
## lowest : 30 37 61 64 80, highest: 64 80 86 88 100
##
## Value 30 37 61 64 80 86 88 100
## Frequency 1 1 1 1 1 1 2 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.222 0.111
## --------------------------------------------------------------------------------
## FS.N2R_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 45.44 31.94
##
## lowest : 17 18 19 33 36, highest: 36 48 76 80 82
##
## Value 17 18 19 33 36 48 76 80 82
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.N3R_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 26.56 16.11
##
## lowest : 0 17 20 29 34, highest: 29 34 35 39 45
##
## Value 0 17 20 29 34 35 39 45
## Frequency 1 1 2 1 1 1 1 1
## Proportion 0.111 0.111 0.222 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.N4R_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 59.56 36.44
##
## lowest : 14 20 42 60 61, highest: 60 61 62 77 100
##
## Value 14 20 42 60 61 62 77 100
## Frequency 1 1 1 1 1 1 1 2
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.222
## --------------------------------------------------------------------------------psych::alpha(FS$NatScale_EWFAM)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$NatScale_EWFAM): 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 ( FS.N3R_EWFAM ) 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 = FS$NatScale_EWFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.66 0.6 0.84 0.28 1.5 0.048 50 17 0.34
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.54 0.66 0.76
## Duhachek 0.57 0.66 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.N1_EWFAM 0.37 0.34 0.47 0.14 0.51 0.092 0.13 0.349
## FS.N2R_EWFAM 0.59 0.44 0.80 0.21 0.77 0.051 0.40 -0.042
## FS.N3R_EWFAM 0.77 0.78 0.81 0.54 3.50 0.041 0.11 0.349
## FS.N4R_EWFAM 0.47 0.45 0.43 0.22 0.83 0.079 0.05 0.340
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.N1_EWFAM 9 0.88 0.82 0.86 0.7416 70 24
## FS.N2R_EWFAM 9 0.73 0.76 0.58 0.4470 45 27
## FS.N3R_EWFAM 9 0.20 0.39 0.25 0.0061 27 14
## FS.N4R_EWFAM 9 0.84 0.74 0.78 0.6019 60 31cor(FS$NatScale_EWFAM, use= "complete.obs")## FS.N1_EWFAM FS.N2R_EWFAM FS.N3R_EWFAM FS.N4R_EWFAM
## FS.N1_EWFAM 1.00000000 0.3403211 -0.04208525 0.9248821
## FS.N2R_EWFAM 0.34032106 1.0000000 0.35309423 0.3494836
## FS.N3R_EWFAM -0.04208525 0.3530942 1.00000000 -0.2676695
## FS.N4R_EWFAM 0.92488209 0.3494836 -0.26766949 1.0000000### Familiarity
FS$Fam_EWFAM <- as.numeric(FS$Fam_EW_Fam_34)
hist(FS$Fam_EWFAM)
describe(FS$Fam_EWFAM)## FS$Fam_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 30.78 26.61
##
## lowest : 1 6 17 19 26, highest: 26 39 52 57 60
##
## Value 1 6 17 19 26 39 52 57 60
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111sd(FS$Fam_EWFAM, na.rm = TRUE)## [1] 22.11209### Understanding
FS$Und_EWFAM <- as.numeric(FS$Fam_EW_Fam_33)
hist(FS$Und_EWFAM)
describe(FS$Und_EWFAM)## FS$Und_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 54.11 26.06
##
## lowest : 18 26 45 53 55, highest: 55 63 64 81 82
##
## Value 18 26 45 53 55 63 64 81 82
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111sd(FS$Und_EWFAM, na.rm = TRUE)## [1] 21.95703### Fluency
FS$Fluency_EWFAM <- as.numeric(FS$Fluency_EW_Fam_34)
hist(FS$Fluency_EWFAM)
describe(FS$Fluency_EWFAM)## FS$Fluency_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 73.11 21.61
##
## lowest : 35 53 58 79 83, highest: 83 85 87 88 90
##
## Value 35 53 58 79 83 85 87 88 90
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111sd(FS$Fluency_EWFAM, na.rm = TRUE)## [1] 19.55406### Risk
FS$R1_EWFAM <- as.numeric(FS$Risk_EW_Fam_32)
FS$R2_EWFAM <- as.numeric(FS$Risk_EW_Fam_33)
FS$R3_EWFAM <- as.numeric(FS$Risk_EW_Fam_34)
hist(FS$R1_EWFAM)
hist(FS$R2_EWFAM)
hist(FS$R3_EWFAM)
FS$RiskScore_EWFAM <- rowMeans(FS [, c( "R1_EWFAM" , "R2_EWFAM", "R3_EWFAM")], na.rm=TRUE)
describe(FS$RiskScore_EWFAM)## FS$RiskScore_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 43.37 20.24
##
## lowest : 21.33333 28.00000 28.33333 31.33333 42.00000
## highest: 42.00000 53.66667 54.66667 65.33333 65.66667
##
## Value 21.33333 28.00000 28.33333 31.33333 42.00000 53.66667 54.66667
## Frequency 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111
##
## Value 65.33333 65.66667
## Frequency 1 1
## Proportion 0.111 0.111sd(FS$RiskScore_EWFAM, na.rm = TRUE)## [1] 16.98483FS$RiskScale_EWFAM <- data.frame(FS$R1_EWFAM, FS$R2_EWFAM, FS$R3_EWFAM)
describe(FS$RiskScale_EWFAM)## FS$RiskScale_EWFAM
##
## 3 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.R1_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 34.22 27.83
##
## lowest : 0 16 20 22 25, highest: 25 41 52 62 70
##
## Value 0 16 20 22 25 41 52 62 70
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.R2_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 27.78 25.11
##
## lowest : 0 12 14 15 19, highest: 19 34 36 55 65
##
## Value 0 12 14 15 19 34 36 55 65
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.R3_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 68.11 26.44
##
## lowest : 30 50 54 70 82, highest: 70 82 87 92 94
##
## Value 30 50 54 70 82 87 92 94
## Frequency 1 1 2 1 1 1 1 1
## Proportion 0.111 0.111 0.222 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------psych::alpha(FS$RiskScale_EWFAM)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$RiskScale_EWFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.63 0.63 0.73 0.36 1.7 0.066 43 17 0.19
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.49 0.63 0.74
## Duhachek 0.50 0.63 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.R1_EWFAM 0.071 0.071 0.037 0.037 0.076 0.182 NA 0.037
## FS.R2_EWFAM 0.319 0.320 0.190 0.190 0.470 0.133 NA 0.190
## FS.R3_EWFAM 0.924 0.926 0.861 0.861 12.428 0.015 NA 0.861
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.R1_EWFAM 9 0.90 0.90 0.93 0.72 34 23
## FS.R2_EWFAM 9 0.83 0.83 0.85 0.59 28 21
## FS.R3_EWFAM 9 0.54 0.54 0.17 0.12 68 22cor(FS$RiskScale_EWFAM, use= "complete.obs")## FS.R1_EWFAM FS.R2_EWFAM FS.R3_EWFAM
## FS.R1_EWFAM 1.0000000 0.86137745 0.19014250
## FS.R2_EWFAM 0.8613774 1.00000000 0.03665473
## FS.R3_EWFAM 0.1901425 0.03665473 1.00000000### Benefit
FS$B1_EWFAM <- as.numeric(FS$Ben_EW_Fam_40)
FS$B2_EWFAM <- as.numeric(FS$Ben_EW_Fam_42)
FS$B3_EWFAM <- as.numeric(FS$Ben_EW_Fam_43)
FS$B4_EWFAM <- as.numeric(FS$Ben_EW_Fam_51)
hist(FS$B1_EWFAM)
hist(FS$B2_EWFAM)
hist(FS$B3_EWFAM)
hist(FS$B4_EWFAM)
FS$BenScore_EWFAM <- rowMeans(FS [, c( "B1_EWFAM" , "B2_EWFAM", "B3_EWFAM", "B4_EWFAM")], na.rm=TRUE)
describe(FS$BenScore_EWFAM)## FS$BenScore_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 77.08 17.08
##
## lowest : 58.25 60.00 63.50 75.25 75.50, highest: 75.50 84.25 88.50 92.50 96.00
##
## Value 58.25 60.00 63.50 75.25 75.50 84.25 88.50 92.50 96.00
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111sd(FS$BenScore_EWFAM, na.rm = TRUE)## [1] 14.20442FS$BenScale_EWFAM <- data.frame(FS$B1_EWFAM, FS$B2_EWFAM, FS$B3_EWFAM, FS$B4_EWFAM)
describe(FS$BenScale_EWFAM)## FS$BenScale_EWFAM
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.B1_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 71.56 17.94
##
## lowest : 52 60 69 70 80, highest: 70 80 82 84 95
##
## Value 52 60 69 70 80 82 84 95
## Frequency 2 1 1 1 1 1 1 1
## Proportion 0.222 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.B2_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 76.78 21.17
##
## lowest : 52 53 60 77 79, highest: 79 87 91 92 100
##
## Value 52 53 60 77 79 87 91 92 100
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.B3_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 81 14.67
##
## lowest : 60 64 78 81 83, highest: 83 84 88 91 100
##
## Value 60 64 78 81 83 84 88 91 100
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.B4_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 79 18
##
## lowest : 52 68 70 74 75, highest: 75 87 92 93 100
##
## Value 52 68 70 74 75 87 92 93 100
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------psych::alpha(FS$BenScale_EWFAM)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$BenScale_EWFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.97 0.83 19 0.0074 77 14 0.85
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.95 0.96
## Duhachek 0.93 0.95 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.B1_EWFAM 0.91 0.92 0.95 0.79 11.1 0.0132 0.02454 0.79
## FS.B2_EWFAM 0.90 0.90 0.90 0.76 9.4 0.0162 0.01938 0.74
## FS.B3_EWFAM 0.97 0.98 0.97 0.93 40.3 0.0041 0.00048 0.94
## FS.B4_EWFAM 0.93 0.93 0.93 0.83 14.3 0.0108 0.01119 0.79
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.B1_EWFAM 9 0.97 0.96 0.95 0.94 72 15
## FS.B2_EWFAM 9 0.99 0.99 0.99 0.98 77 18
## FS.B3_EWFAM 9 0.83 0.85 0.79 0.74 81 13
## FS.B4_EWFAM 9 0.94 0.93 0.93 0.89 79 15cor(FS$BenScale_EWFAM, use= "complete.obs")## FS.B1_EWFAM FS.B2_EWFAM FS.B3_EWFAM FS.B4_EWFAM
## FS.B1_EWFAM 1.0000000 0.9452105 0.7427242 0.9053680
## FS.B2_EWFAM 0.9452105 1.0000000 0.7908963 0.9415400
## FS.B3_EWFAM 0.7427242 0.7908963 1.0000000 0.6283347
## FS.B4_EWFAM 0.9053680 0.9415400 0.6283347 1.0000000### Support
FS$S1_EWFAM <- as.numeric(FS$Sup_EW_Fam_40)
FS$S2_EWFAM <- as.numeric(FS$Sup_EW_Fam_42)
FS$S3_EWFAM <- as.numeric(FS$Sup_EW_Fam_43)
FS$S4_EWFAM <- as.numeric(FS$Sup_EW_Fam_45)
hist(FS$S1_EWFAM)
hist(FS$S2_EWFAM)
hist(FS$S3_EWFAM)
hist(FS$S4_EWFAM)
FS$SupScore_EWFAM <- rowMeans(FS [, c( "S1_EWFAM" , "S2_EWFAM", "S3_EWFAM", "S4_EWFAM")], na.rm=TRUE)
describe(FS$SupScore_EWFAM)## FS$SupScore_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 69.25 29.19
##
## lowest : 25.75 46.50 52.00 53.75 83.75, highest: 83.75 86.00 86.75 88.75 100.00
##
## Value 25.75 46.50 52.00 53.75 83.75 86.00 86.75 88.75 100.00
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111sd(FS$SupScore_EWFAM, na.rm = TRUE)## [1] 25.17563FS$SupScale_EWFAM <- data.frame(FS$S1_EWFAM, FS$S2_EWFAM, FS$S3_EWFAM, FS$S4_EWFAM)
describe(FS$SupScale_EWFAM)## FS$SupScale_EWFAM
##
## 4 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.S1_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 74.22 24.33
##
## lowest : 50 52 53 55 86, highest: 55 86 87 98 100
##
## Value 50 52 53 55 86 87 98 100
## Frequency 1 1 1 1 1 2 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.222 0.111 0.111
## --------------------------------------------------------------------------------
## FS.S2_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 74.33 23.11
##
## lowest : 51 52 65 82 84, highest: 82 84 88 95 100
##
## Value 51 52 65 82 84 88 95 100
## Frequency 1 2 1 1 1 1 1 1
## Proportion 0.111 0.222 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FS.S3_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 8 0.992 62.67 37.56
##
## lowest : 0 24 53 54 80, highest: 54 80 83 85 100
##
## Value 0 24 53 54 80 83 85 100
## Frequency 1 1 1 1 1 1 2 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.222 0.111
## --------------------------------------------------------------------------------
## FS.S4_EWFAM
## n missing distinct Info Mean Gmd
## 9 95 9 1 65.78 35.39
##
## lowest : 0 44 53 54 79, highest: 79 85 87 90 100
##
## Value 0 44 53 54 79 85 87 90 100
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## --------------------------------------------------------------------------------psych::alpha(FS$SupScale_EWFAM)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$SupScale_EWFAM)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.97 0.98 0.88 30 0.0063 69 25 0.87
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.95 0.97
## Duhachek 0.94 0.95 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.S1_EWFAM 0.94 0.96 0.95 0.88 22 0.0076 0.0072 0.84
## FS.S2_EWFAM 0.95 0.97 0.96 0.90 28 0.0063 0.0042 0.87
## FS.S3_EWFAM 0.93 0.96 0.94 0.88 21 0.0106 0.0023 0.86
## FS.S4_EWFAM 0.92 0.95 0.94 0.87 21 0.0113 0.0028 0.87
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.S1_EWFAM 9 0.94 0.96 0.94 0.91 74 21
## FS.S2_EWFAM 9 0.92 0.94 0.92 0.88 74 20
## FS.S3_EWFAM 9 0.97 0.96 0.96 0.95 63 33
## FS.S4_EWFAM 9 0.98 0.96 0.96 0.95 66 31cor(FS$SupScale_EWFAM, use= "complete.obs")## FS.S1_EWFAM FS.S2_EWFAM FS.S3_EWFAM FS.S4_EWFAM
## FS.S1_EWFAM 1.0000000 0.9304713 0.8685514 0.8626115
## FS.S2_EWFAM 0.9304713 1.0000000 0.8256677 0.8370002
## FS.S3_EWFAM 0.8685514 0.8256677 1.0000000 0.9781287
## FS.S4_EWFAM 0.8626115 0.8370002 0.9781287 1.0000000Behavior (Petition)
# Petition: "Would you sign a petition to your congressional representative to ask them to support investment in the development of the technology you read about today?" (1 = Yes, I would, 2 = No, I would not)
FS$Petition1 <- as.numeric(as.character(FS$Petition))
FS$Pet <- factor(FS$Petition1, levels = c(40, 42),
labels = c("Yes I would", "No I would not"))
describe(FS$Pet)## FS$Pet
## n missing distinct
## 104 0 2
##
## Value Yes I would No I would not
## Frequency 74 30
## Proportion 0.712 0.288table(FS$Pet)##
## Yes I would No I would not
## 74 30# Behavior (Petition, click on link)
FS$Click1 <- as.numeric(as.character(FS$clicked))
FS$Click <- factor(FS$Click1, levels = c(0, 1),
labels = c("Did not click to sign", "Clicked to sign"))
describe(FS$Click)## FS$Click
## n missing distinct
## 104 0 2
##
## Value Did not click to sign Clicked to sign
## Frequency 98 6
## Proportion 0.942 0.058table(FS$Click)##
## Did not click to sign Clicked to sign
## 98 6Individual 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.
FS$ATNS1 <- as.numeric(FS$ATNS_36)
FS$ATNS2 <- as.numeric(FS$ATNS_37)
FS$ATNS3 <- as.numeric(FS$ATNS_38)
# Reverse Code Item 2
FS$ATNS2R <- (100- FS$ATNS2)
describe(FS$ATNS2R)## FS$ATNS2R
## n missing distinct Info Mean Gmd .05 .10
## 104 0 64 0.999 48.95 32.35 8.45 14.30
## .25 .50 .75 .90 .95
## 26.50 44.50 70.25 91.00 96.70
##
## lowest : 0 8 11 12 13, highest: 94 95 97 98 100describe(FS$ATNS1)## FS$ATNS1
## n missing distinct Info Mean Gmd .05 .10
## 104 0 57 0.999 55.98 31.55 0.00 13.60
## .25 .50 .75 .90 .95
## 36.75 60.00 76.25 89.70 97.80
##
## lowest : 0 3 9 12 13, highest: 89 90 91 99 100describe(FS$ATNS2R)## FS$ATNS2R
## n missing distinct Info Mean Gmd .05 .10
## 104 0 64 0.999 48.95 32.35 8.45 14.30
## .25 .50 .75 .90 .95
## 26.50 44.50 70.25 91.00 96.70
##
## lowest : 0 8 11 12 13, highest: 94 95 97 98 100describe(FS$ATNS3)## FS$ATNS3
## n missing distinct Info Mean Gmd .05 .10
## 104 0 60 0.999 51.66 34.46 0.00 8.60
## .25 .50 .75 .90 .95
## 29.75 51.50 75.75 90.00 100.00
##
## lowest : 0 4 7 8 10, highest: 90 92 93 96 100range(FS$ATNS1, na.rm=TRUE)## [1] 0 100range(FS$ATNS2R, na.rm=TRUE)## [1] 0 100range(FS$ATNS3, na.rm=TRUE)## [1] 0 100hist(FS$ATNS1, main = 'ATNS #1: People who push for technological fixes to environmental problems are underestimating the risks.')
hist(FS$ATNS2R, main = 'ATNS #2: People who say we shouldn’t tamper with nature are just being naïve.')
hist(FS$ATNS3, main = 'ATNS #3: Human beings have no right to meddle with the natural environment.')
FS$ATNS_Scale <- data.frame(FS$ATNS1, FS$ATNS2R, FS$ATNS3)
psych::alpha(FS$ATNS_Scale)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(FS$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 ( FS.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 = FS$ATNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.094 0.078 0.23 0.027 0.085 0.15 52 17 -0.08
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.26 0.09 0.36
## Duhachek -0.21 0.09 0.39
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.ATNS1 -0.17 -0.17 -0.08 -0.08 -0.15 0.230 NA -0.08
## FS.ATNS2R 0.62 0.62 0.45 0.45 1.63 0.074 NA 0.45
## FS.ATNS3 -0.81 -0.81 -0.29 -0.29 -0.45 0.354 NA -0.29
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.ATNS1 104 0.65 0.65 0.50 0.14 56 28
## FS.ATNS2R 104 0.35 0.36 -0.33 -0.21 49 28
## FS.ATNS3 104 0.78 0.77 0.68 0.31 52 30describe(FS$ATNS_Scale)## FS$ATNS_Scale
##
## 3 Variables 104 Observations
## --------------------------------------------------------------------------------
## FS.ATNS1
## n missing distinct Info Mean Gmd .05 .10
## 104 0 57 0.999 55.98 31.55 0.00 13.60
## .25 .50 .75 .90 .95
## 36.75 60.00 76.25 89.70 97.80
##
## lowest : 0 3 9 12 13, highest: 89 90 91 99 100
## --------------------------------------------------------------------------------
## FS.ATNS2R
## n missing distinct Info Mean Gmd .05 .10
## 104 0 64 0.999 48.95 32.35 8.45 14.30
## .25 .50 .75 .90 .95
## 26.50 44.50 70.25 91.00 96.70
##
## lowest : 0 8 11 12 13, highest: 94 95 97 98 100
## --------------------------------------------------------------------------------
## FS.ATNS3
## n missing distinct Info Mean Gmd .05 .10
## 104 0 60 0.999 51.66 34.46 0.00 8.60
## .25 .50 .75 .90 .95
## 29.75 51.50 75.75 90.00 100.00
##
## lowest : 0 4 7 8 10, highest: 90 92 93 96 100
## --------------------------------------------------------------------------------FS$ATNS_Score <- rowMeans(FS [, c("ATNS1", "ATNS2R", "ATNS3")], na.rm=TRUE)
describe(FS$ATNS_Score)## FS$ATNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 104 0 74 1 52.2 18.58 27.33 32.77
## .25 .50 .75 .90 .95
## 41.67 53.17 61.33 70.87 79.07
##
## lowest : 0.00000 23.00000 24.33333 26.00000 27.33333
## highest: 84.66667 88.33333 90.66667 98.00000 100.00000hist(FS$ATNS_Score)
cor(FS$ATNS_Scale, use= "complete.obs")## FS.ATNS1 FS.ATNS2R FS.ATNS3
## FS.ATNS1 1.0000000 -0.28713434 0.44937384
## FS.ATNS2R -0.2871343 1.00000000 -0.07974749
## FS.ATNS3 0.4493738 -0.07974749 1.00000000Climate Change Belief
# Climate Change Belief: How much do you agree or disagree with the following statements?
## Item #1: Climate change is happening.
## Item #3: Human activity is largely responsible for recent climate change.
FS$CCB1 <- FS$ClimateChangeBelief_48
FS$CCB2 <- FS$ClimateChangeBelief_49
#Climate Change Belief Descriptives
describe(FS$CCB1)## FS$CCB1
## n missing distinct Info Mean Gmd .05 .10
## 104 0 45 0.976 74.84 30.65 2.15 23.60
## .25 .50 .75 .90 .95
## 68.75 83.50 100.00 100.00 100.00
##
## lowest : 0 2 3 7 11, highest: 95 96 98 99 100range(FS$CCB1, na.rm=TRUE)## [1] 0 100describe(FS$CCB2)## FS$CCB2
## n missing distinct Info Mean Gmd .05 .10
## 104 0 56 0.991 72.72 28.24 11.2 30.8
## .25 .50 .75 .90 .95
## 62.0 79.5 95.0 100.0 100.0
##
## lowest : 0 3 7 9 10, highest: 96 97 98 99 100range(FS$CCB2, na.rm=TRUE)## [1] 0 100#Climate Change Belief Histograms
hist(FS$CCB1, main = 'Climate Change Belief #1: Climate change is happening."')
hist(FS$CCB2, main = 'Climate Change Belief #2:Human activity is largely responsible for recent climate change."')
FS$CCB_Score <- rowMeans(FS[, c('CCB1', 'CCB2')], na.rm=T)
describe(FS$CCB_Score)## FS$CCB_Score
## n missing distinct Info Mean Gmd .05 .10
## 104 0 71 0.993 73.78 28.28 25.08 30.40
## .25 .50 .75 .90 .95
## 63.38 80.00 96.00 100.00 100.00
##
## lowest : 0.0 3.0 5.5 9.0 25.0, highest: 98.0 98.5 99.0 99.5 100.0FS$CCB_Scale <- data.frame(FS$CCB1, FS$CCB2)
psych::alpha(FS$CCB_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = FS$CCB_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.77 0.77 6.6 0.026 74 26 0.77
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.80 0.87 0.91
## Duhachek 0.81 0.87 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## FS.CCB1 0.87 0.77 0.59 0.77 3.3 NA 0 0.77
## FS.CCB2 0.68 0.77 0.59 0.77 3.3 NA 0 0.77
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## FS.CCB1 104 0.95 0.94 0.82 0.77 75 30
## FS.CCB2 104 0.93 0.94 0.82 0.77 73 26cor(FS$CCB_Scale, use= "complete.obs")## FS.CCB1 FS.CCB2
## FS.CCB1 1.0000000 0.7684542
## FS.CCB2 0.7684542 1.0000000Demographics
## Gender ("What is your gender identity?" [ 1 = woman, 2 = man, 3 = prefer to self-describe, 4 = non-binary])
FS$Gen <- as.numeric(as.character(FS$Gender))
FS$Gender <- factor(FS$Gen, levels = c(1, 2, 3, 4),
labels = c("Woman", "Man", "Prefer to self-describe", "Non-binary"))
table(FS$Gender)##
## Woman Man Prefer to self-describe
## 65 39 0
## Non-binary
## 0## Age ("How old are you?")
range(FS$Age, na.rm = T)## [1] 19 80describe(FS$Age, na.rm = T)## FS$Age
## n missing distinct Info Mean Gmd .05 .10
## 104 0 52 0.999 49.41 19.55 25.00 28.30
## .25 .50 .75 .90 .95
## 35.00 47.50 65.00 71.00 74.85
##
## lowest : 19 20 21 22 25, highest: 74 75 76 77 80sd(FS$Age, na.rm = T)## [1] 16.89063# 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.).
FS$EdNum <- as.numeric(as.character(FS$Education))
hist(FS$EdNum, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
FS$EDU <- factor(FS$EdNum, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
labels = c("Elementary/Grammar School", "Middle School", "High School or Equivalent", "Vocational/Technical School (2 years)", "Some College", "College or University (4 years)", "Master's Degree (MS, MA, MBA, etc.)", "Doctoral Degree (PhD)", "Professional Degree (MD, JD, etc.)", "Other"))
table(FS$EDU)##
## Elementary/Grammar School Middle School
## 0 0
## High School or Equivalent Vocational/Technical School (2 years)
## 36 5
## Some College College or University (4 years)
## 26 25
## Master's Degree (MS, MA, MBA, etc.) Doctoral Degree (PhD)
## 10 0
## Professional Degree (MD, JD, etc.) Other
## 2 0# Region: "Which of the following best describes the area you live in?" (1 = Urban, 2 = Suburban, 3 = Rural)
FS$REG <- as.numeric(as.character(FS$Region))
FS$Region <- factor(FS$REG, levels = c(1, 2, 3),
labels = c("Urban", "Suburban", "Rural"))
table(FS$Region)##
## Urban Suburban Rural
## 35 46 23# Primary Language Spoken
# Ethnicity: "Which racial or ethnic group best describes you?" (1 = Asian, Asian-American, 2 = Black, Black American, 3 = Hispanic/Latino-American, 4 = Native American, 5 = Native Pacific Islander, 6 = White/Caucasian-American, 7 = Other)
FS$E <- as.numeric(as.character(FS$Ethnicity))
FS$Eth <- factor(FS$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7),
labels = c("Asian", "Black", "Hispanic", "Nat Amer", "Nat Pac", "White", "Other"))
table(FS$Eth)##
## Asian Black Hispanic Nat Amer Nat Pac White Other
## 3 18 4 2 0 77 0print(FS$Dem_Ethnicity_7_TEXT)## NULL# Subjective Social Status
FS$SSS <- FS$SSS_US
describe(FS$SSS)## FS$SSS
## n missing distinct Info Mean Gmd .05 .10
## 104 0 10 0.978 5.548 2.419 2.0 3.0
## .25 .50 .75 .90 .95
## 4.0 5.0 7.0 8.7 9.0
##
## lowest : 1 2 3 4 5, highest: 6 7 8 9 10
##
## Value 1 2 3 4 5 6 7 8 9 10
## Frequency 5 2 9 16 23 16 11 11 9 2
## Proportion 0.048 0.019 0.087 0.154 0.221 0.154 0.106 0.106 0.087 0.019range(FS$SSS)## [1] 1 10hist(FS$SSS)
sd(FS$SSS, na.rm = TRUE)## [1] 2.140138Political 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)
FS$Party <- FS$Party
FS$DemStrength <- FS$DemStrength
FS$RepStrength <- FS$RepStrength
FS$PartyClose <- FS$CloserTo
describe(FS$Party)## FS$Party
## n missing distinct Info Mean Gmd
## 104 0 5 0.876 2.154 0.966
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 25 47 27 1 4
## Proportion 0.240 0.452 0.260 0.010 0.038describe(FS$DemStrength)## FS$DemStrength
## n missing distinct Info Mean Gmd
## 47 57 2 0.601 1.277 0.4089
##
## Value 1 2
## Frequency 34 13
## Proportion 0.723 0.277describe(FS$RepStrength)## FS$RepStrength
## n missing distinct Info Mean Gmd
## 25 79 2 0.548 1.24 0.38
##
## Value 1 2
## Frequency 19 6
## Proportion 0.76 0.24describe(FS$PartyClose)## FS$PartyClose
## n missing distinct Info Mean Gmd
## 32 72 3 0.851 2.125 0.9798
##
## Value 1 2 3
## Frequency 11 6 15
## Proportion 0.344 0.188 0.469FS$PartyFull <- NA
FS$PartyFull[FS$DemStrength == 1] <- -3
FS$PartyFull[FS$DemStrength == 2] <- -2
FS$PartyFull[FS$PartyClose == 1] <- -1
FS$PartyFull[FS$PartyClose == 3] <- 0
FS$PartyFull[FS$PartyClose == 2] <- 1
FS$PartyFull[FS$RepStrength == 2] <- 2
FS$PartyFull[FS$RepStrength == 1] <- 3
describe(FS$PartyFull)## FS$PartyFull
## n missing distinct Info Mean Gmd
## 104 0 7 0.953 -0.6154 2.535
##
## lowest : -3 -2 -1 0 1, highest: -1 0 1 2 3
##
## Value -3 -2 -1 0 1 2 3
## Frequency 34 13 11 15 6 6 19
## Proportion 0.327 0.125 0.106 0.144 0.058 0.058 0.183hist(FS$PartyFull , main = 'Party Identification')
Long Form
# Control Condition
FS$Naturalness.BioControl <- FS$NatScore_BioControl
length(FS$Naturalness.BioControl)## [1] 104FS$Naturalness.BFControl <- FS$NatScore_BFControl
length(FS$Naturalness.BFControl)## [1] 104FS$Naturalness.EWControl <- FS$NatScore_EWControl
length(FS$Naturalness.EWControl)## [1] 104FS$Familiarity.BioControl <- FS$Fam_BioControl
length(FS$Familiarity.BioControl)## [1] 104FS$Familiarity.BFControl <- FS$Fam_BFControl
length(FS$Familiarity.BFControl)## [1] 104FS$Familiarity.EWControl <- FS$Fam_EWControl
length(FS$Familiarity.EWControl)## [1] 104FS$Understanding.BioControl <- FS$Und_BioControl
length(FS$Understanding.BioControl)## [1] 104FS$Understanding.BFControl <- FS$Und_BFControl
length(FS$Understanding.BFControl)## [1] 104FS$Understanding.EWControl <- FS$Und_EWControl
length(FS$Understanding.EWControl)## [1] 104FS$Fluency.BioControl <- FS$Fluency_BioControl
length(FS$Fluency.BioControl)## [1] 104FS$Fluency.BFControl <- FS$Fluency_BFControl
length(FS$Fluency.BFControl)## [1] 104FS$Fluency.EWControl <- FS$Fluency_EWControl
length(FS$Fluency.EWControl)## [1] 104FS$Benefit.BioControl <- FS$BenScore_BioControl
length(FS$Benefit.BioControl)## [1] 104FS$Benefit.BFControl <- FS$BenScore_BFControl
length(FS$Benefit.BFControl)## [1] 104FS$Benefit.EWControl <- FS$BenScore_EWControl
length(FS$Benefit.EWControl)## [1] 104FS$Support.BioControl <- FS$SupScore_BioControl
length(FS$Support.BioControl)## [1] 104FS$Support.BFControl <- FS$SupScore_BFControl
length(FS$Support.BFControl)## [1] 104FS$Support.EWControl <- FS$SupScore_EWControl
length(FS$Support.EWControl)## [1] 104FS$Risk.BioControl <- FS$RiskScore_BioControl
length(FS$Risk.BioControl)## [1] 104FS$Risk.BFControl <- FS$RiskScore_BFControl
length(FS$Risk.BFControl)## [1] 104FS$Risk.EWControl <- FS$RiskScore_EWControl
length(FS$Risk.EWControl)## [1] 104# Unfamiliar Condition
FS$Naturalness.BioUnfamiliar <- FS$NatScore_BioUF
length(FS$Naturalness.BioUnfamiliar)## [1] 104FS$Naturalness.BFUnfamiliar <- FS$NatScore_BFUF
length(FS$Naturalness.BFUnfamiliar)## [1] 104FS$Naturalness.EWUnfamiliar <- FS$NatScore_EWUF
length(FS$Naturalness.EWUnfamiliar)## [1] 104FS$Familiarity.BioUnfamiliar <- FS$Fam_BioUF
length(FS$Familiarity.BioUnfamiliar)## [1] 104FS$Familiarity.BFUnfamiliar <- FS$Fam_BFUF
length(FS$Familiarity.BFUnfamiliar)## [1] 104FS$Familiarity.EWUnfamiliar <- FS$Fam_EWUF
length(FS$Familiarity.EWUnfamiliar)## [1] 104FS$Understanding.BioUnfamiliar <- FS$Und_BioUF
length(FS$Understanding.BioUnfamiliar)## [1] 104FS$Understanding.BFUnfamiliar <- FS$Und_BFUF
length(FS$Understanding.BFUnfamiliar)## [1] 104FS$Understanding.EWUnfamiliar <- FS$Und_EWUF
length(FS$Understanding.EWUnfamiliar)## [1] 104FS$Fluency.BioUnfamiliar <- FS$Fluency_BioUF
length(FS$Fluency.BioUnfamiliar)## [1] 104FS$Fluency.BFUnfamiliar <- FS$Fluency_BFUF
length(FS$Fluency.BFUnfamiliar)## [1] 104FS$Fluency.EWUnfamiliar <- FS$Fluency_EWUF
length(FS$Fluency.EWUnfamiliar)## [1] 104FS$Benefit.BioUnfamiliar <- FS$BenScore_BIOUF
length(FS$Benefit.BioUnfamiliar)## [1] 104FS$Benefit.BFUnfamiliar <- FS$BenScore_BFUF
length(FS$Benefit.BFUnfamiliar)## [1] 104FS$Benefit.EWUnfamiliar <- FS$BenScore_EWUF
length(FS$Benefit.EWUnfamiliar)## [1] 104FS$Support.BioUnfamiliar <- FS$SupScore_BIOUF
length(FS$Support.BioUnfamiliar)## [1] 104FS$Support.BFUnfamiliar <- FS$SupScore_BFUF
length(FS$Support.BFUnfamiliar)## [1] 104FS$Support.EWUnfamiliar <- FS$SupScore_EWUF
length(FS$Support.EWUnfamiliar)## [1] 104FS$Risk.BioUnfamiliar <- FS$RiskScore_BIOUF
length(FS$Risk.BioUnfamiliar)## [1] 104FS$Risk.BFUnfamiliar <- FS$RiskScore_BFUF
length(FS$Risk.BFUnfamiliar)## [1] 104FS$Risk.EWUnfamiliar <- FS$RiskScore_EWUF
length(FS$Risk.EWUnfamiliar)## [1] 104# Familiar Condition
FS$Naturalness.BioFamiliar <- FS$NatScore_BioFAM
length(FS$Naturalness.BioFamiliar)## [1] 104FS$Naturalness.BFFamiliar <- FS$NatScore_BFFAM
length(FS$Naturalness.BFFamiliar)## [1] 104FS$Naturalness.EWFamiliar <- FS$NatScore_EWFAM
length(FS$Naturalness.EWFamiliar)## [1] 104FS$Familiarity.BioFamiliar <- FS$Fam_BioFAM
length(FS$Familiarity.BioFamiliar)## [1] 104FS$Familiarity.BFFamiliar <- FS$Fam_BFFAM
length(FS$Familiarity.BFFamiliar)## [1] 104FS$Familiarity.EWFamiliar <- FS$Fam_EWFAM
length(FS$Familiarity.EWFamiliar)## [1] 104FS$Understanding.BioFamiliar <- FS$Und_BioFAM
length(FS$Understanding.BioFamiliar)## [1] 104FS$Understanding.BFFamiliar <- FS$Und_BFFAM
length(FS$Understanding.BFFamiliar)## [1] 104FS$Understanding.EWFamiliar <- FS$Und_EWFAM
length(FS$Understanding.EWFamiliar)## [1] 104FS$Fluency.BioFamiliar <- FS$Fluency_BioFAM
length(FS$Fluency.BioFamiliar)## [1] 104FS$Fluency.BFFamiliar <- FS$Fluency_BFFAM
length(FS$Fluency.BFFamiliar)## [1] 104FS$Fluency.EWFamiliar <- FS$Fluency_EWFAM
length(FS$Fluency.EWFamiliar)## [1] 104FS$Benefit.BioFamiliar <- FS$BenScore_BioFAM
length(FS$Benefit.BioFamiliar)## [1] 104FS$Benefit.BFFamiliar <- FS$BenScore_BFFAM
length(FS$Benefit.BFFamiliar)## [1] 104FS$Benefit.EWFamiliar <- FS$BenScore_EWFAM
length(FS$Benefit.EWFamiliar)## [1] 104FS$Support.BioFamiliar <- FS$SupScore_BioFAM
length(FS$Support.BioFamiliar)## [1] 104FS$Support.BFFamiliar <- FS$SupScore_BFFAM
length(FS$Support.BFFamiliar)## [1] 104FS$Support.EWFamiliar <- FS$SupScore_EWFAM
length(FS$Support.EWFamiliar)## [1] 104FS$Risk.BioFamiliar <- FS$RiskScore_BioFAM
length(FS$Risk.BioFamiliar)## [1] 104FS$Risk.BFFamiliar <- FS$RiskScore_BFFAM
length(FS$Risk.BioFamiliar)## [1] 104FS$Risk.EWFamiliar <- FS$RiskScore_EWFAM
length(FS$Risk.EWFamiliar)## [1] 104#Rename Variables to Switch to Long Format
library(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## steplibrary(lme4)
#Reshape to long form
FSvector <- c("Naturalness.BioControl", "Naturalness.BFControl", "Naturalness.EWControl", "Familiarity.BioControl", "Familiarity.BFControl", "Familiarity.EWControl", "Naturalness.BioFamiliar","Naturalness.BFFamiliar", "Naturalness.EWFamiliar", "Familiarity.BioFamiliar", "Familiarity.BFFamiliar", "Familiarity.EWFamiliar", "Naturalness.BioUnfamiliar", "Naturalness.BFUnfamiliar", "Naturalness.EWUnfamiliar", "Familiarity.BioUnfamiliar", "Familiarity.BFUnfamiliar", "Familiarity.EWUnfamiliar", "Understanding.BioFamiliar",
"Understanding.BFFamiliar", "Understanding.EWFamiliar", "Understanding.BioUnfamiliar", "Understanding.BFUnfamiliar", "Understanding.EWUnfamiliar","Understanding.BioControl", "Understanding.BFControl", "Understanding.EWControl", "Fluency.BioFamiliar", "Fluency.BFFamiliar", "Fluency.EWFamiliar", "Fluency.BioUnfamiliar", "Fluency.BFUnfamiliar", "Fluency.EWUnfamiliar", "Fluency.BioControl", "Fluency.BFControl", "Fluency.EWControl", "Benefit.BioFamiliar", "Benefit.BFFamiliar", "Benefit.EWFamiliar", "Benefit.BioUnfamiliar", "Benefit.BFUnfamiliar", "Benefit.EWUnfamiliar", "Benefit.BioControl", "Benefit.BFControl", "Benefit.EWControl", "Support.BioFamiliar", "Support.BFFamiliar", "Support.EWFamiliar", "Support.BioUnfamiliar", "Support.BFUnfamiliar", "Support.EWUnfamiliar", "Support.BioControl", "Support.BFControl", "Support.EWControl", "Risk.BioControl", "Risk.BFControl", "Risk.EWControl", "Risk.BioUnfamiliar", "Risk.BFUnfamiliar", "Risk.EWUnfamiliar", "Risk.BioFamiliar", "Risk.BFFamiliar", "Risk.EWFamiliar")
A <- reshape(data = FS,
varying = FSvector,
timevar = "Type",
direction = "long")Correlations
# Check variable lengths
length(A$Naturalness)## [1] 936length(A$Familiarity)## [1] 936length(A$Support)## [1] 936length(A$Fluency)## [1] 936length(A$Understanding)## [1] 936length(A$Benefit)## [1] 936A$cor <- data.frame (A$Naturalness, A$Familiarity)
cor.test(A$Naturalness, A$Familiarity, use= "complete.obs")##
## Pearson's product-moment correlation
##
## data: A$Naturalness and A$Familiarity
## t = -2.1835, df = 102, p-value = 0.03129
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.38810913 -0.01952201
## sample estimates:
## cor
## -0.2113158ANOVAS
Center Variables
#Center variables
describe(FS$Benefit)## FS$Benefit
## n missing distinct Info Mean Gmd .05 .10
## 104 0 84 0.999 71.22 25.26 29.95 46.60
## .25 .50 .75 .90 .95
## 59.38 75.12 88.62 99.68 100.00
##
## lowest : 0.00 2.00 4.50 5.00 17.25, highest: 97.75 98.00 99.50 99.75 100.00FS$Benefit.c <- FS$Benefit - 71.22
describe(FS$Risk)## FS$Risk
## n missing distinct Info Mean Gmd .05 .10
## 104 0 73 0.999 48.38 21.1 19.92 28.83
## .25 .50 .75 .90 .95
## 33.92 45.67 61.33 77.90 82.18
##
## lowest : 1.00 15.67 17.33 17.67 19.00, highest: 83.00 83.67 89.33 95.33 96.33FS$Risk.c <- FS$Risk - 48.38Contrast Codes
### Dummy Codes Comparing Conditions (1 = control, 2 = unfamiliar, 3 = familiar)
### Dummy Codes Comparing Conditions
FS$Control <- ifelse(FS$Condition == "Control", 1, 0)
FS$Unfamiliar <- ifelse(FS$Condition == "Unfamiliar", 1, 0)
FS$Familiar <- ifelse(FS$Condition == "Familiar", 1, 0)
### Dummy Codes Comparing Technology Types (1 = biochar, 2 = biofuel, 3 = enhanced weathering)
FS$Biochar <- ifelse(FS$Technology == "Biochar", 1, 0)
FS$Biofuel <- ifelse(FS$Technology == "Biofuel", 1, 0)
FS$EnhancedWeathering <- ifelse(FS$Technology == "Enhanced Weathering", 1, 0)
### Full Set of 8 Codes for 9 total groups (split by technology and condition; k-1)
A$C1 <- (-2/9)*(A$Type == 'BioControl') + (-2/9)*(A$Type == 'BFControl') + (-2/9)*(A$Type == 'EWControl') + (1/9)*(A$Type == 'BioUnfamiliar') + (1/9)*(A$Type == 'BFUnfamiliar') + (1/9)*(A$Type == 'EWUnfamiliar') + (1/9)*(A$Type == 'BioFamiliar') + (1/9)*(A$Type == 'BFFamiliar') + (1/9)*(A$Type == 'EWFamiliar')
A$C2 <- (-1/9)*(A$Type == 'BioControl') + (-1/9)*(A$Type == 'BFControl') + (-1/9)*(A$Type == 'EWControl') + (-1/9)*(A$Type == 'BioUnfamiliar') + (-1/9)*(A$Type == 'BFUnfamiliar') + (-1/9)*(A$Type == 'EWUnfamiliar') + (2/9)*(A$Type == 'BioFamiliar') + (2/9)*(A$Type == 'BFFamiliar') + (2/9)*(A$Type == 'EWFamiliar')
A$C3 <- (0)*(A$Type == 'BioControl') + (0)*(A$Type == 'BFControl') + (0)*(A$Type == 'EWControl') + (0)*(A$Type == 'BioUnfamiliar') + (0)*(A$Type == 'BFUnfamiliar') + (-1/9)*(A$Type == 'EWUnfamiliar') + (1/9)*(A$Type == 'BioFamiliar') + (0)*(A$Type == 'BFFamiliar') + (0)*(A$Type == 'EWFamiliar')
A$C4 <- (-1/3)*(A$Type == 'BioControl') + (2/3)*(A$Type == 'BFControl') + (-1/3)*(A$Type == 'EWControl') + (-1/3)*(A$Type == 'BioUnfamiliar') + (2/3)*(A$Type == 'BFUnfamiliar') + (-1/3)*(A$Type == 'EWUnfamiliar') + (-1/3)*(A$Type == 'BioFamiliar') + (2/3)*(A$Type == 'BFFamiliar') + (-1/3)*(A$Type == 'EWFamiliar')
A$C5 <- (-1/9)*(A$Type == 'BioControl') + (1/9)*(A$Type == 'BFControl') + (0)*(A$Type == 'EWControl') + (0)*(A$Type == 'BioUnfamiliar') + (0)*(A$Type == 'BFUnfamiliar') + (0)*(A$Type == 'EWUnfamiliar') + (0)*(A$Type == 'BioFamiliar') + (0)*(A$Type == 'BFFamiliar') + (0)*(A$Type == 'EWFamiliar')
A$C6 <- (0)*(A$Type == 'BioControl') + (-1/9)*(A$Type == 'BFControl') + (1/9)*(A$Type == 'EWControl') + (0)*(A$Type == 'BioUnfamiliar') + (0)*(A$Type == 'BFUnfamiliar') + (0)*(A$Type == 'EWUnfamiliar') + (0)*(A$Type == 'BioFamiliar') + (0)*(A$Type == 'BFFamiliar') + (0)*(A$Type == 'EWFamiliar')
A$C7 <- (0)*(A$Type == 'BioControl') + (0)*(A$Type == 'BFControl') + (-1/9)*(A$Type == 'EWControl') + (1/9)*(A$Type == 'BioUnfamiliar') + (0)*(A$Type == 'BFUnfamiliar') + (0)*(A$Type == 'EWUnfamiliar') + (0)*(A$Type == 'BioFamiliar') + (0)*(A$Type == 'BFFamiliar') + (0)*(A$Type == 'EWFamiliar')
A$C8 <- (0)*(A$Type == 'BioControl') + (0)*(A$Type == 'BFControl') + (0)*(A$Type == 'EWControl') + (-1/9)*(A$Type == 'BioUnfamiliar') + (1/9)*(A$Type == 'BFUnfamiliar') + (0)*(A$Type == 'EWUnfamiliar') + (0)*(A$Type == 'BioFamiliar') + (0)*(A$Type == 'BFFamiliar') + (0)*(A$Type == 'EWFamiliar')
# Testing correlations between contrast codes
# Create a matrix of the contrast codes
contrast_matrix <- cbind(A$C1, A$C2, A$C3, A$C4, A$C5, A$C6, A$C7, A$C8)
# Calculate the correlation matrix
cor_matrix <- cor(contrast_matrix)
# Print the correlation matrix
print(cor_matrix)## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.000000e+00 5.000000e-01 4.503609e-19 6.126910e-16 2.379191e-17
## [2,] 5.000000e-01 1.000000e+00 5.000000e-01 -3.668940e-16 -7.469566e-18
## [3,] 4.503609e-19 5.000000e-01 1.000000e+00 2.401925e-18 1.734379e-34
## [4,] 6.126910e-16 -3.668940e-16 2.401925e-18 1.000000e+00 5.000000e-01
## [5,] 2.379191e-17 -7.469566e-18 1.734379e-34 5.000000e-01 1.000000e+00
## [6,] 2.517573e-17 -6.777654e-18 1.640628e-34 -5.000000e-01 -5.000000e-01
## [7,] 5.000000e-01 -1.013837e-17 -2.985006e-32 -1.611168e-18 1.420389e-34
## [8,] 3.898186e-19 1.942606e-17 3.750008e-35 5.000000e-01 4.347851e-34
## [,6] [,7] [,8]
## [1,] 2.517573e-17 5.000000e-01 3.898186e-19
## [2,] -6.777654e-18 -1.013837e-17 1.942606e-17
## [3,] 1.640628e-34 -2.985006e-32 3.750008e-35
## [4,] -5.000000e-01 -1.611168e-18 5.000000e-01
## [5,] -5.000000e-01 1.420389e-34 4.347851e-34
## [6,] 1.000000e+00 -5.000000e-01 1.449069e-34
## [7,] -5.000000e-01 1.000000e+00 -5.000000e-01
## [8,] 1.449069e-34 -5.000000e-01 1.000000e+00Models
Familiarity
## Condition: (intercept = control, D1 = unfamiliar, D2 = familiar)
## Technology Type: (intercept = biochar, T1 = biofuel, T2 = enhanced weathering)
# Is there any differences between all three conditions on self reported familiarity?
modZ.1 <- lm(Familiarity ~ Condition, data = FS)
summary(modZ.1)##
## Call:
## lm(formula = Familiarity ~ Condition, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.844 -22.389 -5.139 24.569 60.861
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.8438 5.2809 8.870 2.74e-14 ***
## ConditionFamiliar -7.7049 7.2579 -1.062 0.291
## ConditionUnfamiliar -0.9826 7.2579 -0.135 0.893
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.87 on 101 degrees of freedom
## Multiple R-squared: 0.01348, Adjusted R-squared: -0.006058
## F-statistic: 0.6899 on 2 and 101 DF, p-value: 0.504tab_model(modZ.1,
show.stat = T, show.se = T)| Familiarity | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.84 | 5.28 | 36.37 – 57.32 | 8.87 | <0.001 |
| Condition [Familiar] | -7.70 | 7.26 | -22.10 – 6.69 | -1.06 | 0.291 |
| Condition [Unfamiliar] | -0.98 | 7.26 | -15.38 – 13.42 | -0.14 | 0.893 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.013 / -0.006 | ||||
# Are there differences between conditions (0 = control [intercept], D1 = unfamiliar, D2 = familiar) on self-reported familiarity (0 = least familiar, 100 = most familiar)?
modA.1 <- lm(Familiarity ~ Unfamiliar + Familiar, data = FS)
modC.1 <- lm(Familiarity ~ 1, data = FS)
summary(modA.1)##
## Call:
## lm(formula = Familiarity ~ Unfamiliar + Familiar, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.844 -22.389 -5.139 24.569 60.861
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.8438 5.2809 8.870 2.74e-14 ***
## Unfamiliar -0.9826 7.2579 -0.135 0.893
## Familiar -7.7049 7.2579 -1.062 0.291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.87 on 101 degrees of freedom
## Multiple R-squared: 0.01348, Adjusted R-squared: -0.006058
## F-statistic: 0.6899 on 2 and 101 DF, p-value: 0.504tab_model(modA.1,
show.stat = T, show.se = T)| Familiarity | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.84 | 5.28 | 36.37 – 57.32 | 8.87 | <0.001 |
| Unfamiliar | -0.98 | 7.26 | -15.38 – 13.42 | -0.14 | 0.893 |
| Familiar | -7.70 | 7.26 | -22.10 – 6.69 | -1.06 | 0.291 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.013 / -0.006 | ||||
anova(modA.1, modC.1)# Are there differences between technology types (0 = biochar [intercept], T1 = biofuel, T2 = enhanced weathering) on self-reported familiarity (0 = least familiar, 100 = most familiar)?
modA.2 <- lm(Familiarity ~ Biochar + Biofuel, data = FS)
summary(modA.2)##
## Call:
## lm(formula = Familiarity ~ Biochar + Biofuel, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.10 -21.50 -2.00 22.70 62.63
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.367 5.362 6.969 3.34e-10 ***
## Biochar 3.133 7.260 0.432 0.6670
## Biofuel 14.739 7.173 2.055 0.0425 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.37 on 101 degrees of freedom
## Multiple R-squared: 0.04657, Adjusted R-squared: 0.02769
## F-statistic: 2.467 on 2 and 101 DF, p-value: 0.08998tab_model(modA.2,
show.stat = T, show.se = T)| Familiarity | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 37.37 | 5.36 | 26.73 – 48.00 | 6.97 | <0.001 |
| Biochar | 3.13 | 7.26 | -11.27 – 17.54 | 0.43 | 0.667 |
| Biofuel | 14.74 | 7.17 | 0.51 – 28.97 | 2.05 | 0.042 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.047 / 0.028 | ||||
Unnaturalness
# Is there any differences between all three conditions on perceived unnaturalness of the technology?
modZ.2 <- lm(Unnaturalness ~ Condition, data = FS)
summary(modZ.2)##
## Call:
## lm(formula = Unnaturalness ~ Condition, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.306 -9.859 -0.656 10.257 43.444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55.500 2.789 19.900 <2e-16 ***
## ConditionFamiliar -3.444 3.833 -0.899 0.371
## ConditionUnfamiliar -4.938 3.833 -1.288 0.201
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.78 on 101 degrees of freedom
## Multiple R-squared: 0.01679, Adjusted R-squared: -0.002681
## F-statistic: 0.8623 on 2 and 101 DF, p-value: 0.4253tab_model(modZ.2,
show.stat = T, show.se = T)| Unnaturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 55.50 | 2.79 | 49.97 – 61.03 | 19.90 | <0.001 |
| Condition [Familiar] | -3.44 | 3.83 | -11.05 – 4.16 | -0.90 | 0.371 |
| Condition [Unfamiliar] | -4.94 | 3.83 | -12.54 – 2.67 | -1.29 | 0.201 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.017 / -0.003 | ||||
# Are there differences between conditions (0 = control [intercept], D1 = unfamiliar, D2 = familiar) on naturalness perceptions (0 = more natural, 100 = less natural)?
modA.3 <- lm(Unnaturalness ~ Unfamiliar + Familiar, data = FS)
modC.3 <- lm(Unnaturalness ~ 1, data = FS)
summary(modA.3)##
## Call:
## lm(formula = Unnaturalness ~ Unfamiliar + Familiar, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.306 -9.859 -0.656 10.257 43.444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55.500 2.789 19.900 <2e-16 ***
## Unfamiliar -4.937 3.833 -1.288 0.201
## Familiar -3.444 3.833 -0.899 0.371
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.78 on 101 degrees of freedom
## Multiple R-squared: 0.01679, Adjusted R-squared: -0.002681
## F-statistic: 0.8623 on 2 and 101 DF, p-value: 0.4253tab_model(modA.3,
show.stat = T, show.se = T)| Unnaturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 55.50 | 2.79 | 49.97 – 61.03 | 19.90 | <0.001 |
| Unfamiliar | -4.94 | 3.83 | -12.54 – 2.67 | -1.29 | 0.201 |
| Familiar | -3.44 | 3.83 | -11.05 – 4.16 | -0.90 | 0.371 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.017 / -0.003 | ||||
anova(modA.3, modC.3)# Are there differences between technology types (0 = biochar [intercept], T1 = biofuel, T2 = enhanced weathering) on perceptions of unnaturalness (0 = least natural, 100 = most natural)?
modA.4 <- lm(Unnaturalness ~ Biochar + Biofuel, data = FS)
summary(modA.4)##
## Call:
## lm(formula = Unnaturalness ~ Biochar + Biofuel, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37.800 -9.868 0.439 10.415 41.319
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.550 2.793 20.607 < 2e-16 ***
## Biochar -3.369 3.781 -0.891 0.37501
## Biofuel -10.359 3.736 -2.773 0.00662 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.3 on 101 degrees of freedom
## Multiple R-squared: 0.07575, Adjusted R-squared: 0.05745
## F-statistic: 4.139 on 2 and 101 DF, p-value: 0.01872tab_model(modA.4,
show.stat = T, show.se = T)| Unnaturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.55 | 2.79 | 52.01 – 63.09 | 20.61 | <0.001 |
| Biochar | -3.37 | 3.78 | -10.87 – 4.13 | -0.89 | 0.375 |
| Biofuel | -10.36 | 3.74 | -17.77 – -2.95 | -2.77 | 0.007 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.076 / 0.057 | ||||
Support
# Is there any differences between all three conditions on perceived unnaturalness of the technology?
modZ.7 <- lm(Support ~ Condition, data = FS)
summary(modZ.7)##
## Call:
## lm(formula = Support ~ Condition, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -66.516 -13.569 2.046 18.796 33.570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 66.42969 4.22360 15.728 <2e-16 ***
## ConditionFamiliar 0.08642 5.80478 0.015 0.988
## ConditionUnfamiliar 8.04948 5.80478 1.387 0.169
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.89 on 101 degrees of freedom
## Multiple R-squared: 0.02549, Adjusted R-squared: 0.006192
## F-statistic: 1.321 on 2 and 101 DF, p-value: 0.2715tab_model(modZ.7,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 66.43 | 4.22 | 58.05 – 74.81 | 15.73 | <0.001 |
| Condition [Familiar] | 0.09 | 5.80 | -11.43 – 11.60 | 0.01 | 0.988 |
| Condition [Unfamiliar] | 8.05 | 5.80 | -3.47 – 19.56 | 1.39 | 0.169 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.025 / 0.006 | ||||
# Are there differences between conditions (0 = control [intercept], D1 = unfamiliar, D2 = familiar) on naturalness perceptions (0 = more natural, 100 = less natural)?
modAZ.3 <- lm(Support ~ Unfamiliar + Familiar, data = FS)
modCZ.3 <- lm(Support ~ 1, data = FS)
summary(modAZ.3)##
## Call:
## lm(formula = Support ~ Unfamiliar + Familiar, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -66.516 -13.569 2.046 18.796 33.570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 66.42969 4.22360 15.728 <2e-16 ***
## Unfamiliar 8.04948 5.80478 1.387 0.169
## Familiar 0.08642 5.80478 0.015 0.988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.89 on 101 degrees of freedom
## Multiple R-squared: 0.02549, Adjusted R-squared: 0.006192
## F-statistic: 1.321 on 2 and 101 DF, p-value: 0.2715tab_model(modAZ.3,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 66.43 | 4.22 | 58.05 – 74.81 | 15.73 | <0.001 |
| Unfamiliar | 8.05 | 5.80 | -3.47 – 19.56 | 1.39 | 0.169 |
| Familiar | 0.09 | 5.80 | -11.43 – 11.60 | 0.01 | 0.988 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.025 / 0.006 | ||||
anova(modAZ.3, modCZ.3)# Are there differences between technology types (0 = biochar [intercept], T1 = biofuel, T2 = enhanced weathering) on perceptions of unnaturalness (0 = least natural, 100 = most natural)?
modAZ.4 <- lm(Support ~ Biochar + Biofuel, data = FS)
summary(modAZ.4)##
## Call:
## lm(formula = Support ~ Biochar + Biofuel, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.88 -13.67 -0.27 20.28 35.29
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 64.711 4.314 15.000 <2e-16 ***
## Biochar 1.164 5.841 0.199 0.8425
## Biofuel 11.309 5.771 1.960 0.0528 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.63 on 101 degrees of freedom
## Multiple R-squared: 0.04681, Adjusted R-squared: 0.02794
## F-statistic: 2.48 on 2 and 101 DF, p-value: 0.08881tab_model(modAZ.4,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 64.71 | 4.31 | 56.15 – 73.27 | 15.00 | <0.001 |
| Biochar | 1.16 | 5.84 | -10.42 – 12.75 | 0.20 | 0.842 |
| Biofuel | 11.31 | 5.77 | -0.14 – 22.76 | 1.96 | 0.053 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.047 / 0.028 | ||||
Risk
# Is there any differences between all three conditions on perceived risk of the technology, over and above benefit?
modZ.3 <- lm(Risk ~ Condition + Benefit.c, data = FS)
summary(modZ.3)##
## Call:
## lm(formula = Risk ~ Condition + Benefit.c, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39.899 -12.989 -3.870 9.046 43.606
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.86534 3.26923 16.782 <2e-16 ***
## ConditionFamiliar -10.13815 4.49405 -2.256 0.0263 *
## ConditionUnfamiliar -8.60775 4.50247 -1.912 0.0588 .
## Benefit.c 0.05530 0.07923 0.698 0.4868
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.49 on 100 degrees of freedom
## Multiple R-squared: 0.0591, Adjusted R-squared: 0.03087
## F-statistic: 2.094 on 3 and 100 DF, p-value: 0.1058tab_model(modZ.3,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.87 | 3.27 | 48.38 – 61.35 | 16.78 | <0.001 |
| Condition [Familiar] | -10.14 | 4.49 | -19.05 – -1.22 | -2.26 | 0.026 |
| Condition [Unfamiliar] | -8.61 | 4.50 | -17.54 – 0.33 | -1.91 | 0.059 |
| Benefit c | 0.06 | 0.08 | -0.10 – 0.21 | 0.70 | 0.487 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.059 / 0.031 | ||||
# Are there differences between conditions (0 = control [intercept], D1 = unfamiliar, D2 = familiar) on risk perceptions (0 = less risky, 100 = more risky), over and above benefits (0 = less beneficial, 100 = more beneficial)?
modA.5 <- lm(Risk ~ Unfamiliar + Familiar+ Benefit.c, data = FS)
modC.5 <- lm(Risk ~ Benefit.c, data = FS)
summary(modA.5)##
## Call:
## lm(formula = Risk ~ Unfamiliar + Familiar + Benefit.c, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39.899 -12.989 -3.870 9.046 43.606
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.86534 3.26923 16.782 <2e-16 ***
## Unfamiliar -8.60775 4.50247 -1.912 0.0588 .
## Familiar -10.13815 4.49405 -2.256 0.0263 *
## Benefit.c 0.05530 0.07923 0.698 0.4868
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.49 on 100 degrees of freedom
## Multiple R-squared: 0.0591, Adjusted R-squared: 0.03087
## F-statistic: 2.094 on 3 and 100 DF, p-value: 0.1058tab_model(modA.5,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.87 | 3.27 | 48.38 – 61.35 | 16.78 | <0.001 |
| Unfamiliar | -8.61 | 4.50 | -17.54 – 0.33 | -1.91 | 0.059 |
| Familiar | -10.14 | 4.49 | -19.05 – -1.22 | -2.26 | 0.026 |
| Benefit c | 0.06 | 0.08 | -0.10 – 0.21 | 0.70 | 0.487 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.059 / 0.031 | ||||
anova(modA.5, modC.5)# Are there differences between technology types (0 = biochar [intercept], T1 = biofuel, T2 = enhanced weathering) on perceptions of risk (0 = least risky, 100 = most risky), over and above perceived benefits?
modA.6 <- lm(Risk ~ Biochar + Biofuel + Benefit.c, data = FS)
summary(modA.6)##
## Call:
## lm(formula = Risk ~ Biochar + Biofuel + Benefit.c, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.665 -13.681 -1.665 13.237 49.373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.75011 3.47087 14.046 <2e-16 ***
## Biochar -1.87953 4.69535 -0.400 0.690
## Biofuel 0.75779 4.67750 0.162 0.872
## Benefit.c 0.04631 0.08225 0.563 0.575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.99 on 100 degrees of freedom
## Multiple R-squared: 0.007994, Adjusted R-squared: -0.02177
## F-statistic: 0.2686 on 3 and 100 DF, p-value: 0.8479tab_model(modA.6,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 48.75 | 3.47 | 41.86 – 55.64 | 14.05 | <0.001 |
| Biochar | -1.88 | 4.70 | -11.19 – 7.44 | -0.40 | 0.690 |
| Biofuel | 0.76 | 4.68 | -8.52 – 10.04 | 0.16 | 0.872 |
| Benefit c | 0.05 | 0.08 | -0.12 – 0.21 | 0.56 | 0.575 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.008 / -0.022 | ||||
Benefit
# Is there any differences between all three conditions on perceived risk of the technology, over and above benefit?
modZ.4 <- lm(Benefit~ Condition + Risk.c, data = FS)
summary(modZ.4)##
## Call:
## lm(formula = Benefit ~ Condition + Risk.c, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -70.810 -12.482 3.283 15.983 33.486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 69.88042 4.19450 16.660 <2e-16 ***
## ConditionFamiliar -0.67376 5.80046 -0.116 0.908
## ConditionUnfamiliar 4.54789 5.75397 0.790 0.431
## Risk.c 0.08768 0.12561 0.698 0.487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.28 on 100 degrees of freedom
## Multiple R-squared: 0.01478, Adjusted R-squared: -0.01477
## F-statistic: 0.5002 on 3 and 100 DF, p-value: 0.683tab_model(modZ.4,
show.stat = T, show.se = T)| Benefit | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 69.88 | 4.19 | 61.56 – 78.20 | 16.66 | <0.001 |
| Condition [Familiar] | -0.67 | 5.80 | -12.18 – 10.83 | -0.12 | 0.908 |
| Condition [Unfamiliar] | 4.55 | 5.75 | -6.87 – 15.96 | 0.79 | 0.431 |
| Risk c | 0.09 | 0.13 | -0.16 – 0.34 | 0.70 | 0.487 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.015 / -0.015 | ||||
# Are there differences between conditions (0 = control [intercept], D1 = unfamiliar, D2 = familiar) on benefit perceptions (0 = less benficial, 100 = more beneficial), over and above risks (0 = less risky, 100 = more risk)?
modA.7 <- lm(Benefit ~ Unfamiliar + Familiar + Risk.c, data = FS)
modC.7 <- lm(Benefit ~ Risk.c, data = FS)
summary(modA.7)##
## Call:
## lm(formula = Benefit ~ Unfamiliar + Familiar + Risk.c, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -70.810 -12.482 3.283 15.983 33.486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 69.88042 4.19450 16.660 <2e-16 ***
## Unfamiliar 4.54789 5.75397 0.790 0.431
## Familiar -0.67376 5.80046 -0.116 0.908
## Risk.c 0.08768 0.12561 0.698 0.487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.28 on 100 degrees of freedom
## Multiple R-squared: 0.01478, Adjusted R-squared: -0.01477
## F-statistic: 0.5002 on 3 and 100 DF, p-value: 0.683tab_model(modA.7,
show.stat = T, show.se = T)| Benefit | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 69.88 | 4.19 | 61.56 – 78.20 | 16.66 | <0.001 |
| Unfamiliar | 4.55 | 5.75 | -6.87 – 15.96 | 0.79 | 0.431 |
| Familiar | -0.67 | 5.80 | -12.18 – 10.83 | -0.12 | 0.908 |
| Risk c | 0.09 | 0.13 | -0.16 – 0.34 | 0.70 | 0.487 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.015 / -0.015 | ||||
anova(modA.7,modC.7)# Are there differences between technology types (0 = biochar [intercept], T1 = biofuel, T2 = enhanced weathering) on perceptions of benefits (0 = least beneficial, 100 = most beneficial ), over and above perceived risks?
modA.8 <- lm(Benefit ~ Biochar + Biofuel + Risk.c, data = FS)
summary(modA.8)##
## Call:
## lm(formula = Benefit ~ Biochar + Biofuel + Risk.c, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -71.09 -12.02 2.28 17.73 33.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 69.03991 4.20790 16.407 <2e-16 ***
## Biochar -1.50822 5.70227 -0.264 0.792
## Biofuel 7.39923 5.63037 1.314 0.192
## Risk.c 0.06824 0.12120 0.563 0.575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.05 on 100 degrees of freedom
## Multiple R-squared: 0.03457, Adjusted R-squared: 0.005607
## F-statistic: 1.194 on 3 and 100 DF, p-value: 0.3162tab_model(modA.8,
show.stat = T, show.se = T)| Benefit | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 69.04 | 4.21 | 60.69 – 77.39 | 16.41 | <0.001 |
| Biochar | -1.51 | 5.70 | -12.82 – 9.80 | -0.26 | 0.792 |
| Biofuel | 7.40 | 5.63 | -3.77 – 18.57 | 1.31 | 0.192 |
| Risk c | 0.07 | 0.12 | -0.17 – 0.31 | 0.56 | 0.575 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.035 / 0.006 | ||||
Understanding
# Is there any differences between all three conditions on understanding of the technology?
modZ.5 <- lm(Understanding~ Condition, data = FS)
summary(modZ.5)##
## Call:
## lm(formula = Understanding ~ Condition, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -59.156 -25.156 5.844 23.583 41.583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.1562 5.1665 11.450 <2e-16 ***
## ConditionFamiliar -0.7396 7.1006 -0.104 0.917
## ConditionUnfamiliar 2.3866 7.1482 0.334 0.739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.23 on 100 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.002192, Adjusted R-squared: -0.01776
## F-statistic: 0.1098 on 2 and 100 DF, p-value: 0.8961tab_model(modZ.5,
show.stat = T, show.se = T)| Understanding | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.16 | 5.17 | 48.91 – 69.41 | 11.45 | <0.001 |
| Condition [Familiar] | -0.74 | 7.10 | -14.83 – 13.35 | -0.10 | 0.917 |
| Condition [Unfamiliar] | 2.39 | 7.15 | -11.80 – 16.57 | 0.33 | 0.739 |
| Observations | 103 | ||||
| R2 / R2 adjusted | 0.002 / -0.018 | ||||
# Are there differences between conditions (0 = control [intercept], D1 = unfamiliar, D2 = familiar) on understanding of technology (0 = less understood, 100 = more understood)?
modA.9 <- lm(Understanding ~ Unfamiliar + Familiar, data = FS)
modC.9 <- lm(Understanding ~ 1, data = FS)
summary(modA.9)##
## Call:
## lm(formula = Understanding ~ Unfamiliar + Familiar, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -59.156 -25.156 5.844 23.583 41.583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.1563 5.1665 11.450 <2e-16 ***
## Unfamiliar 2.3866 7.1482 0.334 0.739
## Familiar -0.7396 7.1006 -0.104 0.917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.23 on 100 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.002192, Adjusted R-squared: -0.01776
## F-statistic: 0.1098 on 2 and 100 DF, p-value: 0.8961tab_model(modA.9,
show.stat = T, show.se = T)| Understanding | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.16 | 5.17 | 48.91 – 69.41 | 11.45 | <0.001 |
| Unfamiliar | 2.39 | 7.15 | -11.80 – 16.57 | 0.33 | 0.739 |
| Familiar | -0.74 | 7.10 | -14.83 – 13.35 | -0.10 | 0.917 |
| Observations | 103 | ||||
| R2 / R2 adjusted | 0.002 / -0.018 | ||||
anova(modA.9, modC.9)# Are there differences between technology types (0 = biochar [intercept], T1 = biofuel, T2 = enhanced weathering) on understanding of technology (0 = least understood, 100 = most understood)?
modA.10 <- lm(Understanding ~ Biochar + Biofuel, data = FS)
summary(modA.10)##
## Call:
## lm(formula = Understanding ~ Biochar + Biofuel, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -62.757 -19.757 2.243 21.743 48.167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.300 5.070 10.710 <2e-16 ***
## Biochar -2.467 6.865 -0.359 0.720
## Biofuel 17.457 6.823 2.559 0.012 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 27.77 on 100 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.09908, Adjusted R-squared: 0.08106
## F-statistic: 5.499 on 2 and 100 DF, p-value: 0.005425tab_model(modA.10,
show.stat = T, show.se = T)| Understanding | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.30 | 5.07 | 44.24 – 64.36 | 10.71 | <0.001 |
| Biochar | -2.47 | 6.87 | -16.09 – 11.15 | -0.36 | 0.720 |
| Biofuel | 17.46 | 6.82 | 3.92 – 30.99 | 2.56 | 0.012 |
| Observations | 103 | ||||
| R2 / R2 adjusted | 0.099 / 0.081 | ||||
Fluency
# Is there any differences between all three conditions on fluency of the technology?
modZ.6 <- lm(Fluency ~ Condition, data = FS)
summary(modZ.6)##
## Call:
## lm(formula = Fluency ~ Condition, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.333 -10.813 7.681 22.424 28.344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71.656 4.714 15.200 <2e-16 ***
## ConditionFamiliar 5.677 6.479 0.876 0.383
## ConditionUnfamiliar 3.649 6.479 0.563 0.575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.67 on 101 degrees of freedom
## Multiple R-squared: 0.007678, Adjusted R-squared: -0.01197
## F-statistic: 0.3907 on 2 and 101 DF, p-value: 0.6776tab_model(modZ.6,
show.stat = T, show.se = T)| Fluency | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 71.66 | 4.71 | 62.30 – 81.01 | 15.20 | <0.001 |
| Condition [Familiar] | 5.68 | 6.48 | -7.18 – 18.53 | 0.88 | 0.383 |
| Condition [Unfamiliar] | 3.65 | 6.48 | -9.20 – 16.50 | 0.56 | 0.575 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.008 / -0.012 | ||||
# Are there differences between conditions (0 = control [intercept], D1 = unfamiliar, D2 = familiar) on perceived fluency of technology (0 = less fluent, 100 = more fluent)?
modA.11 <- lm(Fluency ~ Unfamiliar + Familiar, data = FS)
modC.11 <- lm(Fluency ~ 1, data = FS)
summary(modA.11)##
## Call:
## lm(formula = Fluency ~ Unfamiliar + Familiar, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.333 -10.813 7.681 22.424 28.344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71.656 4.714 15.200 <2e-16 ***
## Unfamiliar 3.649 6.479 0.563 0.575
## Familiar 5.677 6.479 0.876 0.383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.67 on 101 degrees of freedom
## Multiple R-squared: 0.007678, Adjusted R-squared: -0.01197
## F-statistic: 0.3907 on 2 and 101 DF, p-value: 0.6776tab_model(modA.11,
show.stat = T, show.se = T)| Fluency | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 71.66 | 4.71 | 62.30 – 81.01 | 15.20 | <0.001 |
| Unfamiliar | 3.65 | 6.48 | -9.20 – 16.50 | 0.56 | 0.575 |
| Familiar | 5.68 | 6.48 | -7.18 – 18.53 | 0.88 | 0.383 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.008 / -0.012 | ||||
anova(modA.11,modC.11)# Are there differences between technology types (0 = biochar [intercept], T1 = biofuel, T2 = enhanced weathering) on perceived fluency of technology (0 = less fluent, 100 = more fluent)?
modA.11 <- lm(Fluency ~ Biochar + Biofuel, data = FS)
modC.11 <- lm(Fluency ~ 1, data = FS)
summary(modA.11)##
## Call:
## lm(formula = Fluency ~ Biochar + Biofuel, data = FS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -71.583 -10.918 7.079 20.079 29.933
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 70.067 4.829 14.509 <2e-16 ***
## Biochar 3.517 6.539 0.538 0.592
## Biofuel 9.854 6.460 1.525 0.130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.45 on 101 degrees of freedom
## Multiple R-squared: 0.02378, Adjusted R-squared: 0.004447
## F-statistic: 1.23 on 2 and 101 DF, p-value: 0.2966tab_model(modA.11,
show.stat = T, show.se = T)| Fluency | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 70.07 | 4.83 | 60.49 – 79.65 | 14.51 | <0.001 |
| Biochar | 3.52 | 6.54 | -9.45 – 16.49 | 0.54 | 0.592 |
| Biofuel | 9.85 | 6.46 | -2.96 – 22.67 | 1.53 | 0.130 |
| Observations | 104 | ||||
| R2 / R2 adjusted | 0.024 / 0.004 | ||||
anova(modA.11,modC.11)