Kindness Fictional Project
Chloe San Miguel
March 2023
library(knitr)
library(gtools)## Warning: package 'gtools' was built under R version 4.1.3library(multicon)## Loading required package: psych##
## Attaching package: 'psych'## The following object is masked from 'package:gtools':
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## logit## Loading required package: abind## Loading required package: foreachlibrary(psych)
library(dplyr)##
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(Hmisc)## Warning: package 'Hmisc' was built under R version 4.1.3## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.1.3##
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## %+%, alpha##
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## src, summarize## The following object is masked from 'package:psych':
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## describe## The following objects are masked from 'package:base':
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## format.pval, unitslibrary(usdm)## Warning: package 'usdm' was built under R version 4.1.3## Loading required package: sp## Warning: package 'sp' was built under R version 4.1.3## Loading required package: raster## Warning: package 'raster' was built under R version 4.1.3##
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## selectlibrary(car)## Warning: package 'car' was built under R version 4.1.3## Loading required package: carData## Warning: package 'carData' was built under R version 4.1.3##
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## recode## The following object is masked from 'package:psych':
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## logit## The following object is masked from 'package:gtools':
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## logit# Set the working directory.
x <- getwd()
setwd(x)
#read in CSV file
mydata = read.csv("kindness data ac removed.csv", header=T, sep=",")names(mydata)## [1] "VIA1" "VIA2" "VIA3" "VIA4"
## [5] "VIA5" "VIA6" "VIA7" "R_VIA8"
## [9] "R_VIA9" "R_VIA10" "PSA_1" "PSA_2"
## [13] "PSA_3" "PSA_4" "PSA_5" "PSA_6"
## [17] "PSA_7" "PSA_8" "PSA_9" "PSA_10"
## [21] "PSA_11" "PSA_12" "PSA_13" "PSA_14"
## [25] "PSA_15" "PSA_16" "R_BFI2.S_1_E" "BFI2.S_2_A"
## [29] "R_BFI2.S_3_C" "BFI2.S_4_N" "BFI2.S_5_O" "BFI2.S_6_E"
## [33] "R_BFI2.S_7_A" "R_BFI2.S_8_C" "BFI2.S_9_N" "R_BFI2.S_10_O"
## [37] "BFI2.S_11_E" "BFI2.S_12_A" "BFI2.S_13_C" "R_BFI2.S_14_N"
## [41] "BFI2.S_15_O" "BFI2.S_16_E" "R_BFI2.S_17_A" "BFI2.S_18_C"
## [45] "R_BFI2.S_19_N" "R_BFI2.S_20_O" "R_BFI2.S_21_E" "BFI2.S_22_A"
## [49] "BFI2.S_23_C" "R_BFI2.S_24_N" "BFI2.S_25_O" "R_BFI2.S_26_E"
## [53] "R_BFI2.S_27_A" "R_BFI2.S_28_C" "BFI2.S_29_N" "R_BFI2.S_30_O"
## [57] "Age" "Education" "Marital" "ACCheck"#create dataframe with only BFI items
BFIitems <- mydata %>% dplyr::select(R_BFI2.S_1_E, BFI2.S_2_A, R_BFI2.S_3_C, BFI2.S_4_N, BFI2.S_5_O, BFI2.S_6_E, R_BFI2.S_7_A, R_BFI2.S_8_C, BFI2.S_9_N, R_BFI2.S_10_O, BFI2.S_11_E, BFI2.S_12_A, BFI2.S_13_C, R_BFI2.S_14_N, BFI2.S_15_O, BFI2.S_16_E, R_BFI2.S_17_A, BFI2.S_18_C, R_BFI2.S_19_N, R_BFI2.S_20_O, R_BFI2.S_21_E, BFI2.S_22_A, BFI2.S_23_C, R_BFI2.S_24_N, BFI2.S_25_O, R_BFI2.S_26_E, R_BFI2.S_27_A, R_BFI2.S_28_C, BFI2.S_29_N, R_BFI2.S_30_O)
describe(BFIitems)## BFIitems
##
## 30 Variables 148 Observations
## --------------------------------------------------------------------------------
## R_BFI2.S_1_E
## n missing distinct Info Mean Gmd
## 148 0 5 0.954 3.236 1.66
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 27 25 22 34 40
## Proportion 0.182 0.169 0.149 0.230 0.270
## --------------------------------------------------------------------------------
## BFI2.S_2_A
## n missing distinct Info Mean Gmd
## 148 0 5 0.958 3.034 1.544
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 23 36 31 29 29
## Proportion 0.155 0.243 0.209 0.196 0.196
## --------------------------------------------------------------------------------
## R_BFI2.S_3_C
## n missing distinct Info Mean Gmd
## 148 0 5 0.958 2.98 1.66
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 33 27 31 24 33
## Proportion 0.223 0.182 0.209 0.162 0.223
## --------------------------------------------------------------------------------
## BFI2.S_4_N
## n missing distinct Info Mean Gmd
## 148 0 5 0.956 3.095 1.66
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 33 19 31 31 34
## Proportion 0.223 0.128 0.209 0.209 0.230
## --------------------------------------------------------------------------------
## BFI2.S_5_O
## n missing distinct Info Mean Gmd
## 148 0 5 0.956 3.216 1.564
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 22 26 34 30 36
## Proportion 0.149 0.176 0.230 0.203 0.243
## --------------------------------------------------------------------------------
## BFI2.S_6_E
## n missing distinct Info Mean Gmd
## 148 0 5 0.954 3.203 1.503
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 18 30 38 28 34
## Proportion 0.122 0.203 0.257 0.189 0.230
## --------------------------------------------------------------------------------
## R_BFI2.S_7_A
## n missing distinct Info Mean Gmd
## 148 0 5 0.958 3.081 1.638
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 27 33 23 31 34
## Proportion 0.182 0.223 0.155 0.209 0.230
## --------------------------------------------------------------------------------
## R_BFI2.S_8_C
## n missing distinct Info Mean Gmd
## 148 0 5 0.959 3.128 1.605
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 26 27 31 30 34
## Proportion 0.176 0.182 0.209 0.203 0.230
## --------------------------------------------------------------------------------
## BFI2.S_9_N
## n missing distinct Info Mean Gmd
## 148 0 5 0.958 3.088 1.661
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 29 30 23 31 35
## Proportion 0.196 0.203 0.155 0.209 0.236
## --------------------------------------------------------------------------------
## R_BFI2.S_10_O
## n missing distinct Info Mean Gmd
## 148 0 5 0.957 2.811 1.552
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 32 37 28 29 22
## Proportion 0.216 0.250 0.189 0.196 0.149
## --------------------------------------------------------------------------------
## BFI2.S_11_E
## n missing distinct Info Mean Gmd
## 148 0 5 0.956 2.953 1.652
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 37 21 30 32 28
## Proportion 0.250 0.142 0.203 0.216 0.189
## --------------------------------------------------------------------------------
## BFI2.S_12_A
## n missing distinct Info Mean Gmd
## 148 0 5 0.955 3.182 1.665
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 29 25 21 36 37
## Proportion 0.196 0.169 0.142 0.243 0.250
## --------------------------------------------------------------------------------
## BFI2.S_13_C
## n missing distinct Info Mean Gmd
## 148 0 5 0.959 3.088 1.548
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 24 30 32 33 29
## Proportion 0.162 0.203 0.216 0.223 0.196
## --------------------------------------------------------------------------------
## R_BFI2.S_14_N
## n missing distinct Info Mean Gmd
## 148 0 5 0.956 2.946 1.631
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 29 39 22 27 31
## Proportion 0.196 0.264 0.149 0.182 0.209
## --------------------------------------------------------------------------------
## BFI2.S_15_O
## n missing distinct Info Mean Gmd
## 148 0 5 0.956 3.02 1.535
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 29 23 36 36 24
## Proportion 0.196 0.155 0.243 0.243 0.162
## --------------------------------------------------------------------------------
## BFI2.S_16_E
## n missing distinct Info Mean Gmd
## 148 0 5 0.955 3.108 1.464
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 19 33 35 35 26
## Proportion 0.128 0.223 0.236 0.236 0.176
## --------------------------------------------------------------------------------
## R_BFI2.S_17_A
## n missing distinct Info Mean Gmd
## 148 0 5 0.949 3.311 1.643
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 22 30 21 30 45
## Proportion 0.149 0.203 0.142 0.203 0.304
## --------------------------------------------------------------------------------
## BFI2.S_18_C
## n missing distinct Info Mean Gmd
## 148 0 5 0.956 2.919 1.634
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 30 39 23 25 31
## Proportion 0.203 0.264 0.155 0.169 0.209
## --------------------------------------------------------------------------------
## R_BFI2.S_19_N
## n missing distinct Info Mean Gmd
## 148 0 5 0.956 3.209 1.62
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 27 22 28 35 36
## Proportion 0.182 0.149 0.189 0.236 0.243
## --------------------------------------------------------------------------------
## R_BFI2.S_20_O
## n missing distinct Info Mean Gmd
## 148 0 5 0.958 2.885 1.669
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 36 31 25 26 30
## Proportion 0.243 0.209 0.169 0.176 0.203
## --------------------------------------------------------------------------------
## R_BFI2.S_21_E
## n missing distinct Info Mean Gmd
## 148 0 5 0.958 2.953 1.653
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 34 26 33 23 32
## Proportion 0.230 0.176 0.223 0.155 0.216
## --------------------------------------------------------------------------------
## BFI2.S_22_A
## n missing distinct Info Mean Gmd
## 148 0 5 0.957 3 1.557
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 30 24 35 34 25
## Proportion 0.203 0.162 0.236 0.230 0.169
## --------------------------------------------------------------------------------
## BFI2.S_23_C
## n missing distinct Info Mean Gmd
## 148 0 5 0.959 3.074 1.637
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 29 27 30 28 34
## Proportion 0.196 0.182 0.203 0.189 0.230
## --------------------------------------------------------------------------------
## R_BFI2.S_24_N
## n missing distinct Info Mean Gmd
## 148 0 5 0.956 3.101 1.554
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 27 22 37 33 29
## Proportion 0.182 0.149 0.250 0.223 0.196
## --------------------------------------------------------------------------------
## BFI2.S_25_O
## n missing distinct Info Mean Gmd
## 148 0 5 0.956 2.764 1.577
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 35 37 27 26 23
## Proportion 0.236 0.250 0.182 0.176 0.155
## --------------------------------------------------------------------------------
## R_BFI2.S_26_E
## n missing distinct Info Mean Gmd
## 148 0 5 0.956 2.986 1.539
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 30 24 35 36 23
## Proportion 0.203 0.162 0.236 0.243 0.155
## --------------------------------------------------------------------------------
## R_BFI2.S_27_A
## n missing distinct Info Mean Gmd
## 148 0 5 0.959 2.959 1.652
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 35 25 28 31 29
## Proportion 0.236 0.169 0.189 0.209 0.196
## --------------------------------------------------------------------------------
## R_BFI2.S_28_C
## n missing distinct Info Mean Gmd
## 148 0 5 0.959 3.149 1.588
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 25 27 30 33 33
## Proportion 0.169 0.182 0.203 0.223 0.223
## --------------------------------------------------------------------------------
## BFI2.S_29_N
## n missing distinct Info Mean Gmd
## 148 0 5 0.948 2.926 1.524
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 26 41 20 40 21
## Proportion 0.176 0.277 0.135 0.270 0.142
## --------------------------------------------------------------------------------
## R_BFI2.S_30_O
## n missing distinct Info Mean Gmd
## 148 0 5 0.958 3.02 1.527
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 25 30 37 29 27
## Proportion 0.169 0.203 0.250 0.196 0.182
## --------------------------------------------------------------------------------#create key for scoring. Items with negative signs reversed scored.
BFIkeys.list<-list("Ext"=c(-1, 6, 11, 16, -21, -26), "Agr"=c(2, -7, 12, -17, 22, -27), "Con"=c(-3, -8, 13, 18, 23, -28), "Neur"=c(4, 9, -14, -19, -24, 29), "Open"=c(5, -10, 15, -20, 25, -30))
#Score measure and enter scores in a data frame
BFIout <- scoreTest(BFIitems, BFIkeys.list, rel=T, maxScore=5, minScore=1, check.keys=T)## Warning in alpha(data.frame(x), check.keys = check.keys): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.
## Warning in alpha(data.frame(x), check.keys = check.keys): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.
## Warning in alpha(data.frame(x), check.keys = check.keys): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.
## Warning in alpha(data.frame(x), check.keys = check.keys): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.describe(BFIout$scores)## BFIout$scores
##
## 5 Variables 148 Observations
## --------------------------------------------------------------------------------
## Ext
## n missing distinct Info Mean Gmd .05 .10
## 148 0 16 0.991 3.015 0.631 2.058 2.333
## .25 .50 .75 .90 .95
## 2.667 3.000 3.333 3.833 4.000
##
## lowest : 1.833333 2.000000 2.166667 2.333333 2.500000
## highest: 3.666667 3.833333 4.000000 4.166667 4.333333
##
## 1.83333333333333 (5, 0.034), 2 (3, 0.020), 2.16666666666667 (6, 0.041),
## 2.33333333333333 (5, 0.034), 2.5 (11, 0.074), 2.66666666666667 (18, 0.122),
## 2.83333333333333 (17, 0.115), 3 (17, 0.115), 3.16666666666667 (21, 0.142),
## 3.33333333333333 (11, 0.074), 3.5 (8, 0.054), 3.66666666666667 (9, 0.061),
## 3.83333333333333 (7, 0.047), 4 (7, 0.047), 4.16666666666667 (2, 0.014),
## 4.33333333333333 (1, 0.007)
## --------------------------------------------------------------------------------
## Agr
## n missing distinct Info Mean Gmd .05 .10
## 148 0 19 0.991 2.977 0.7067 1.833 2.167
## .25 .50 .75 .90 .95
## 2.500 3.000 3.375 3.833 4.000
##
## lowest : 1.500000 1.666667 1.833333 2.000000 2.166667
## highest: 3.833333 4.000000 4.166667 4.333333 4.500000
##
## 1.5 (2, 0.014), 1.66666666666667 (3, 0.020), 1.83333333333333 (4, 0.027), 2 (2,
## 0.014), 2.16666666666667 (7, 0.047), 2.33333333333333 (10, 0.068), 2.5 (12,
## 0.081), 2.66666666666667 (6, 0.041), 2.83333333333333 (24, 0.162), 3 (14,
## 0.095), 3.16666666666667 (16, 0.108), 3.33333333333333 (11, 0.074), 3.5 (15,
## 0.101), 3.66666666666667 (3, 0.020), 3.83333333333333 (10, 0.068), 4 (3,
## 0.020), 4.16666666666667 (3, 0.020), 4.33333333333333 (2, 0.014), 4.5 (1,
## 0.007)
## --------------------------------------------------------------------------------
## Con
## n missing distinct Info Mean Gmd .05 .10
## 148 0 17 0.993 2.971 0.6872 2.000 2.167
## .25 .50 .75 .90 .95
## 2.500 3.000 3.333 3.833 4.000
##
## lowest : 1.666667 1.833333 2.000000 2.166667 2.333333
## highest: 3.666667 3.833333 4.000000 4.166667 4.333333
##
## 1.66666666666667 (4, 0.027), 1.83333333333333 (3, 0.020), 2 (3, 0.020),
## 2.16666666666667 (6, 0.041), 2.33333333333333 (11, 0.074), 2.5 (11, 0.074),
## 2.66666666666667 (19, 0.128), 2.83333333333333 (12, 0.081), 3 (18, 0.122),
## 3.16666666666667 (15, 0.101), 3.33333333333333 (11, 0.074), 3.5 (10, 0.068),
## 3.66666666666667 (6, 0.041), 3.83333333333333 (8, 0.054), 4 (7, 0.047),
## 4.16666666666667 (3, 0.020), 4.33333333333333 (1, 0.007)
## --------------------------------------------------------------------------------
## Neur
## n missing distinct Info Mean Gmd .05 .10
## 148 0 15 0.99 2.975 0.6438 2.058 2.167
## .25 .50 .75 .90 .95
## 2.625 3.000 3.333 3.667 4.000
##
## lowest : 1.833333 2.000000 2.166667 2.333333 2.500000
## highest: 3.500000 3.666667 3.833333 4.000000 4.166667
##
## 1.83333333333333 (2, 0.014), 2 (6, 0.041), 2.16666666666667 (9, 0.061),
## 2.33333333333333 (6, 0.041), 2.5 (14, 0.095), 2.66666666666667 (23, 0.155),
## 2.83333333333333 (11, 0.074), 3 (11, 0.074), 3.16666666666667 (21, 0.142),
## 3.33333333333333 (13, 0.088), 3.5 (7, 0.047), 3.66666666666667 (11, 0.074),
## 3.83333333333333 (3, 0.020), 4 (8, 0.054), 4.16666666666667 (3, 0.020)
## --------------------------------------------------------------------------------
## Open
## n missing distinct Info Mean Gmd .05 .10
## 148 0 16 0.99 3.047 0.6379 2.058 2.333
## .25 .50 .75 .90 .95
## 2.667 3.000 3.500 3.833 3.942
##
## lowest : 1.833333 2.000000 2.166667 2.333333 2.500000
## highest: 3.666667 3.833333 4.000000 4.166667 4.333333
##
## 1.83333333333333 (5, 0.034), 2 (3, 0.020), 2.16666666666667 (4, 0.027),
## 2.33333333333333 (8, 0.054), 2.5 (13, 0.088), 2.66666666666667 (8, 0.054),
## 2.83333333333333 (17, 0.115), 3 (18, 0.122), 3.16666666666667 (23, 0.155),
## 3.33333333333333 (11, 0.074), 3.5 (11, 0.074), 3.66666666666667 (8, 0.054),
## 3.83333333333333 (11, 0.074), 4 (3, 0.020), 4.16666666666667 (4, 0.027),
## 4.33333333333333 (1, 0.007)
## --------------------------------------------------------------------------------BFIout$rel## raw_alpha std.alpha G6(smc) average_r S/N ase mean
## Ext 0.08986836 0.09931858 0.0972824 0.01804675 0.1102705 0.11593469 2.962838
## Agr 0.15318865 0.15592532 0.1553861 0.02986861 0.1847293 0.10772893 2.977477
## Con 0.15545746 0.15708274 0.1592229 0.03012372 0.1863561 0.10726943 2.916667
## Neur 0.27690589 0.27836321 0.2594490 0.06040627 0.3857387 0.09190162 3.027027
## Open 0.22809062 0.22935937 0.2382879 0.04725938 0.2976217 0.09780354 3.024775
## sd NA
## Ext 0.5931140 0.01199299
## Agr 0.6250166 0.04113753
## Con 0.6225481 0.04127326
## Neur 0.6624167 0.06533663
## Open 0.6300502 0.05347805BFI.scores <-data.frame(BFIout$scores)
#create dataframe with only PSA items
PSAitems <- mydata %>% dplyr::select(PSA_1, PSA_2, PSA_3, PSA_4, PSA_5, PSA_6, PSA_7, PSA_8, PSA_9, PSA_10, PSA_11, PSA_12, PSA_13, PSA_13, PSA_14, PSA_15, PSA_16)
PSAscore <- PSAitems %>% rowMeans
PSA.score<-data.frame(PSAscore)
#create dataframe with only VIA items
VIAitems <- mydata %>% dplyr::select(VIA1, VIA2, VIA3, VIA4, VIA5, VIA6, VIA7, R_VIA8, R_VIA9, R_VIA10)
#create key for scoring. Items with negative signs reverse scored.
VIAkeys.list<-list("VIA.Kindness" = c(1, 2, 3, 4, 5, 6, 7, -8, -9, -10))
#score measure and enter scores in a data frame
VIAout <- scoreTest(VIAitems, VIAkeys.list, rel = T, maxScore = 5, minScore = 1, check.keys = T)## Warning in alpha(data.frame(x), check.keys = check.keys): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.describe(VIAout$scores)## VIAout$scores
##
## 1 Variables 148 Observations
## --------------------------------------------------------------------------------
## VIA.Kindness
## n missing distinct Info Mean Gmd .05 .10
## 148 0 22 0.994 2.954 0.4967 2.20 2.37
## .25 .50 .75 .90 .95
## 2.70 3.00 3.30 3.50 3.60
##
## lowest : 1.7 2.0 2.1 2.2 2.3, highest: 3.6 3.7 3.8 4.0 4.1
## --------------------------------------------------------------------------------VIAout$rel## raw_alpha std.alpha G6(smc) average_r S/N ase
## VIA.Kindness 0.1762038 0.1759178 0.209134 0.02090094 0.2134712 0.100619
## mean sd NA
## VIA.Kindness 2.948649 0.4881572 0.02123895VIA.score<-data.frame(VIAout$scores)
#combine datasets
mydatascored <- data.frame(BFI.scores, VIA.score, PSA.score)names(mydatascored)## [1] "Ext" "Agr" "Con" "Neur" "Open"
## [6] "VIA.Kindness" "PSAscore"#check histograms for normality
hist(mydatascored$Ext)
hist(mydatascored$Agr)
hist(mydatascored$Open)
hist(mydatascored$Con)
hist(mydatascored$Neur)
hist(mydatascored$VIA.Kindness)
hist(mydatascored$PSAscore)
#check normality using psych package - might need to remove Hmisc
describe(mydatascored)## mydatascored
##
## 7 Variables 148 Observations
## --------------------------------------------------------------------------------
## Ext
## n missing distinct Info Mean Gmd .05 .10
## 148 0 16 0.991 3.015 0.631 2.058 2.333
## .25 .50 .75 .90 .95
## 2.667 3.000 3.333 3.833 4.000
##
## lowest : 1.833333 2.000000 2.166667 2.333333 2.500000
## highest: 3.666667 3.833333 4.000000 4.166667 4.333333
##
## 1.83333333333333 (5, 0.034), 2 (3, 0.020), 2.16666666666667 (6, 0.041),
## 2.33333333333333 (5, 0.034), 2.5 (11, 0.074), 2.66666666666667 (18, 0.122),
## 2.83333333333333 (17, 0.115), 3 (17, 0.115), 3.16666666666667 (21, 0.142),
## 3.33333333333333 (11, 0.074), 3.5 (8, 0.054), 3.66666666666667 (9, 0.061),
## 3.83333333333333 (7, 0.047), 4 (7, 0.047), 4.16666666666667 (2, 0.014),
## 4.33333333333333 (1, 0.007)
## --------------------------------------------------------------------------------
## Agr
## n missing distinct Info Mean Gmd .05 .10
## 148 0 19 0.991 2.977 0.7067 1.833 2.167
## .25 .50 .75 .90 .95
## 2.500 3.000 3.375 3.833 4.000
##
## lowest : 1.500000 1.666667 1.833333 2.000000 2.166667
## highest: 3.833333 4.000000 4.166667 4.333333 4.500000
##
## 1.5 (2, 0.014), 1.66666666666667 (3, 0.020), 1.83333333333333 (4, 0.027), 2 (2,
## 0.014), 2.16666666666667 (7, 0.047), 2.33333333333333 (10, 0.068), 2.5 (12,
## 0.081), 2.66666666666667 (6, 0.041), 2.83333333333333 (24, 0.162), 3 (14,
## 0.095), 3.16666666666667 (16, 0.108), 3.33333333333333 (11, 0.074), 3.5 (15,
## 0.101), 3.66666666666667 (3, 0.020), 3.83333333333333 (10, 0.068), 4 (3,
## 0.020), 4.16666666666667 (3, 0.020), 4.33333333333333 (2, 0.014), 4.5 (1,
## 0.007)
## --------------------------------------------------------------------------------
## Con
## n missing distinct Info Mean Gmd .05 .10
## 148 0 17 0.993 2.971 0.6872 2.000 2.167
## .25 .50 .75 .90 .95
## 2.500 3.000 3.333 3.833 4.000
##
## lowest : 1.666667 1.833333 2.000000 2.166667 2.333333
## highest: 3.666667 3.833333 4.000000 4.166667 4.333333
##
## 1.66666666666667 (4, 0.027), 1.83333333333333 (3, 0.020), 2 (3, 0.020),
## 2.16666666666667 (6, 0.041), 2.33333333333333 (11, 0.074), 2.5 (11, 0.074),
## 2.66666666666667 (19, 0.128), 2.83333333333333 (12, 0.081), 3 (18, 0.122),
## 3.16666666666667 (15, 0.101), 3.33333333333333 (11, 0.074), 3.5 (10, 0.068),
## 3.66666666666667 (6, 0.041), 3.83333333333333 (8, 0.054), 4 (7, 0.047),
## 4.16666666666667 (3, 0.020), 4.33333333333333 (1, 0.007)
## --------------------------------------------------------------------------------
## Neur
## n missing distinct Info Mean Gmd .05 .10
## 148 0 15 0.99 2.975 0.6438 2.058 2.167
## .25 .50 .75 .90 .95
## 2.625 3.000 3.333 3.667 4.000
##
## lowest : 1.833333 2.000000 2.166667 2.333333 2.500000
## highest: 3.500000 3.666667 3.833333 4.000000 4.166667
##
## 1.83333333333333 (2, 0.014), 2 (6, 0.041), 2.16666666666667 (9, 0.061),
## 2.33333333333333 (6, 0.041), 2.5 (14, 0.095), 2.66666666666667 (23, 0.155),
## 2.83333333333333 (11, 0.074), 3 (11, 0.074), 3.16666666666667 (21, 0.142),
## 3.33333333333333 (13, 0.088), 3.5 (7, 0.047), 3.66666666666667 (11, 0.074),
## 3.83333333333333 (3, 0.020), 4 (8, 0.054), 4.16666666666667 (3, 0.020)
## --------------------------------------------------------------------------------
## Open
## n missing distinct Info Mean Gmd .05 .10
## 148 0 16 0.99 3.047 0.6379 2.058 2.333
## .25 .50 .75 .90 .95
## 2.667 3.000 3.500 3.833 3.942
##
## lowest : 1.833333 2.000000 2.166667 2.333333 2.500000
## highest: 3.666667 3.833333 4.000000 4.166667 4.333333
##
## 1.83333333333333 (5, 0.034), 2 (3, 0.020), 2.16666666666667 (4, 0.027),
## 2.33333333333333 (8, 0.054), 2.5 (13, 0.088), 2.66666666666667 (8, 0.054),
## 2.83333333333333 (17, 0.115), 3 (18, 0.122), 3.16666666666667 (23, 0.155),
## 3.33333333333333 (11, 0.074), 3.5 (11, 0.074), 3.66666666666667 (8, 0.054),
## 3.83333333333333 (11, 0.074), 4 (3, 0.020), 4.16666666666667 (4, 0.027),
## 4.33333333333333 (1, 0.007)
## --------------------------------------------------------------------------------
## VIA.Kindness
## n missing distinct Info Mean Gmd .05 .10
## 148 0 22 0.994 2.954 0.4967 2.20 2.37
## .25 .50 .75 .90 .95
## 2.70 3.00 3.30 3.50 3.60
##
## lowest : 1.7 2.0 2.1 2.2 2.3, highest: 3.6 3.7 3.8 4.0 4.1
## --------------------------------------------------------------------------------
## PSAscore
## n missing distinct Info Mean Gmd .05 .10
## 148 0 27 0.996 2.995 0.3955 2.438 2.562
## .25 .50 .75 .90 .95
## 2.797 2.969 3.250 3.456 3.541
##
## lowest : 2.1250 2.3125 2.3750 2.4375 2.5000, highest: 3.6250 3.6875 3.7500 3.8125 3.8750
## --------------------------------------------------------------------------------library(Hmisc)
#correlation table, examine independence of observations
mydatamatrix<-as.matrix(mydatascored)
rcorr(mydatamatrix)## Ext Agr Con Neur Open VIA.Kindness PSAscore
## Ext 1.00 -0.12 -0.04 0.05 0.14 0.03 -0.19
## Agr -0.12 1.00 -0.03 0.07 0.02 0.15 -0.05
## Con -0.04 -0.03 1.00 0.01 0.07 -0.08 0.02
## Neur 0.05 0.07 0.01 1.00 0.02 0.04 0.12
## Open 0.14 0.02 0.07 0.02 1.00 0.05 -0.09
## VIA.Kindness 0.03 0.15 -0.08 0.04 0.05 1.00 -0.11
## PSAscore -0.19 -0.05 0.02 0.12 -0.09 -0.11 1.00
##
## n= 148
##
##
## P
## Ext Agr Con Neur Open VIA.Kindness PSAscore
## Ext 0.1333 0.6556 0.5324 0.0823 0.7291 0.0230
## Agr 0.1333 0.7281 0.4182 0.8372 0.0742 0.5808
## Con 0.6556 0.7281 0.9459 0.4181 0.3193 0.8115
## Neur 0.5324 0.4182 0.9459 0.8055 0.6435 0.1602
## Open 0.0823 0.8372 0.4181 0.8055 0.5788 0.2703
## VIA.Kindness 0.7291 0.0742 0.3193 0.6435 0.5788 0.1680
## PSAscore 0.0230 0.5808 0.8115 0.1602 0.2703 0.1680#multicollinearity
vif.1<-usdm::vif(mydatascored)
vif.1## Variables VIF
## 1 Ext 1.084345
## 2 Agr 1.050305
## 3 Con 1.014812
## 4 Neur 1.028788
## 5 Open 1.034037
## 6 VIA.Kindness 1.044339
## 7 PSAscore 1.076651#create first model
VIA.Kindness.Model<-lm(VIA.Kindness ~ Agr + Open, data = mydatascored)
summary(VIA.Kindness.Model)##
## Call:
## lm(formula = VIA.Kindness ~ Agr + Open, data = mydatascored)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.19175 -0.24326 -0.00049 0.31604 1.13956
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.54354 0.26076 9.754 <2e-16 ***
## Agr 0.10304 0.05775 1.784 0.0765 .
## Open 0.03404 0.06421 0.530 0.5968
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4375 on 145 degrees of freedom
## Multiple R-squared: 0.02356, Adjusted R-squared: 0.01009
## F-statistic: 1.749 on 2 and 145 DF, p-value: 0.1776#checking assumptions
#homoscedasticity
plot(VIA.Kindness.Model, which = 1)
#normally distributed residuals
plot(VIA.Kindness.Model, which = 2)
#checking lineraity
plot(VIA.Kindness~Agr, data=mydatascored)
plot(VIA.Kindness~Open, data=mydatascored)
#All assumptions met for first model
#create second model
PSA.Model<-lm(PSAscore ~ Agr + Open, data = mydatascored)
summary(PSA.Model)##
## Call:
## lm(formula = PSAscore ~ Agr + Open, data = mydatascored)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85814 -0.23247 -0.01943 0.25162 0.84098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.23898 0.20821 15.556 <2e-16 ***
## Agr -0.02467 0.04611 -0.535 0.593
## Open -0.05612 0.05127 -1.095 0.276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3494 on 145 degrees of freedom
## Multiple R-squared: 0.01027, Adjusted R-squared: -0.00338
## F-statistic: 0.7524 on 2 and 145 DF, p-value: 0.4731#checking assumptions
#homoscedasticity
plot(PSA.Model, which = 1)
#plot looks a little odd, check homoscedasticity with Breusch-Pagen test
ncvTest(PSA.Model)## Non-constant Variance Score Test
## Variance formula: ~ fitted.values
## Chisquare = 0.3191687, Df = 1, p = 0.57211#normally distributed residuals
plot(PSA.Model, which = 2)
#checking lineraity
plot(PSAscore~Agr, data=mydatascored)
plot(PSAscore~Open, data=mydatascored)
#All assumptions met for second model```