rm(list = ls())
date()

## [1] "Sun May  9 18:06:17 2021"

sessionInfo()

## R version 4.0.5 (2021-03-31)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.27     R6_2.5.0          jsonlite_1.7.2    magrittr_2.0.1   
##  [5] evaluate_0.14     rlang_0.4.11      stringi_1.5.3     jquerylib_0.1.4  
##  [9] bslib_0.2.4       rmarkdown_2.7     tools_4.0.5       stringr_1.4.0    
## [13] xfun_0.22         yaml_2.2.1        compiler_4.0.5    htmltools_0.5.1.1
## [17] knitr_1.33        sass_0.3.1

Библиотеки

library(stringr)
library(Hmisc)

## Loading required package: lattice

## Loading required package: survival

## Loading required package: Formula

## Loading required package: ggplot2

## 
## Attaching package: 'Hmisc'

## The following objects are masked from 'package:base':
## 
##     format.pval, units

library(corrplot)

## corrplot 0.84 loaded

library(psych)

## 
## Attaching package: 'psych'

## The following object is masked from 'package:Hmisc':
## 
##     describe

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

library(reshape2)
#library(xtable)
library(knitr)
#library(dplyr)

Шкалы методик

Опросник ДУМЭОЛП

диагностика уровня морально-этической ответственности личности И.Г. Тимощука

D1 - Рефлексия на морально-этические ситуации (моральная рефлексия или рефлексия актуализирующаяся в ситуациях связанных с морально-этическими коллизиями и конфликтами).

D2 - Интуиция в морально-этической сфере (нравственная интуиция).

D3 - Экзистенциальный аспект ответственности.

D4 - Альтруистические эмоции.

D5 - Морально-этические ценности.

не используется - Шкала лжи (социальной желательности)

D0 - уровень сформированности морально-этической ответственности

Опросник моральных оснований

M1 - Забота

M2 - Справедливость

M3 - Лояльность

M4 - Уважение

M5 - Чистота

ЭкО 30 - Опросник экологических установок (Кряж И. В.)

K1 - Отрицание экологических проблем

K2 - Экологическая интернальность

K3 - Биоцентризм

K4 - «Деньги» (шкала финансово-экономических приоритетов)

K0 - Общий показатель озабоченности глобальными экологическими проблемами

Импорт таблицы

Data <- read.csv2("../Moral.csv")
Data$Name <- paste0(str_sub(Data$Name, 1, 3), str_sub(Data$Name, -1, -1))
Data$Gender <- as.factor(Data$Gender)

NamesData <- names(Data)

Описательная статистика

summary(Data) %>% 
  kable()

Name	D1	D2	D3	D4	D5	D6	D0	M1	M2	M3	M4	M5	K1	K2	K3	K4	K0	Gender
Length:51	Min. :0.000	Min. :1.000	Min. :0.000	Min. :1.000	Min. :0.000	Min. :0	Min. : 7.00	Min. :12.00	Min. :12.00	Min. : 1.00	Min. : 0.00	Min. : 3.00	Min. :-14.000	Min. :-9.00	Min. :-8.000	Min. :-12.000	Min. :-21.00	f:29
Class :character	1st Qu.:2.000	1st Qu.:2.000	1st Qu.:1.000	1st Qu.:3.000	1st Qu.:2.000	1st Qu.:1	1st Qu.:11.00	1st Qu.:16.00	1st Qu.:17.00	1st Qu.:12.00	1st Qu.: 9.00	1st Qu.:11.00	1st Qu.: -8.000	1st Qu.:-2.00	1st Qu.: 2.000	1st Qu.: -5.500	1st Qu.: 5.50	m:22
Mode :character	Median :2.000	Median :3.000	Median :2.000	Median :4.000	Median :3.000	Median :1	Median :14.00	Median :19.00	Median :19.00	Median :15.00	Median :12.00	Median :15.00	Median : -5.000	Median : 4.00	Median : 6.000	Median : -2.000	Median : 14.00	NA
NA	Mean :2.373	Mean :2.961	Mean :2.176	Mean :3.647	Mean :2.451	Mean :1	Mean :13.61	Mean :19.12	Mean :19.41	Mean :14.37	Mean :12.08	Mean :14.78	Mean : -4.333	Mean : 3.02	Mean : 5.627	Mean : -2.196	Mean : 15.18	NA
NA	3rd Qu.:3.000	3rd Qu.:4.000	3rd Qu.:3.000	3rd Qu.:4.000	3rd Qu.:3.000	3rd Qu.:1	3rd Qu.:16.00	3rd Qu.:21.50	3rd Qu.:22.00	3rd Qu.:17.00	3rd Qu.:16.00	3rd Qu.:17.00	3rd Qu.: -1.500	3rd Qu.: 7.00	3rd Qu.:11.000	3rd Qu.: 2.000	3rd Qu.: 26.50	NA
NA	Max. :5.000	Max. :5.000	Max. :5.000	Max. :5.000	Max. :5.000	Max. :2	Max. :20.00	Max. :31.00	Max. :28.00	Max. :24.00	Max. :21.00	Max. :25.00	Max. : 8.000	Max. :15.00	Max. :14.000	Max. : 10.000	Max. : 47.00	NA

describe(Data, skew = FALSE) %>% 
  kable()

	vars	n	mean	sd	min	max	range	se
Name*	1	51	26.000000	14.866069	1	51	50	2.0816660
D1	2	51	2.372549	1.094729	0	5	5	0.1532927
D2	3	51	2.960784	1.182553	1	5	4	0.1655905
D3	4	51	2.176471	1.071557	0	5	5	0.1500481
D4	5	51	3.647059	1.073751	1	5	4	0.1503552
D5	6	51	2.450980	1.082843	0	5	5	0.1516284
D6	7	51	1.000000	0.663325	0	2	2	0.0928841
D0	8	51	13.607843	3.219183	7	20	13	0.4507757
M1	9	51	19.117647	4.510641	12	31	19	0.6316161
M2	10	51	19.411765	3.965736	12	28	16	0.5553141
M3	11	51	14.372549	4.467486	1	24	23	0.6255732
M4	12	51	12.078431	4.840839	0	21	21	0.6778531
M5	13	51	14.784314	5.041086	3	25	22	0.7058932
K1	14	51	-4.333333	5.256108	-14	8	22	0.7360023
K2	15	51	3.019608	5.633792	-9	15	24	0.7888886
K3	16	51	5.627451	5.942931	-8	14	22	0.8321768
K4	17	51	-2.196078	5.063673	-12	10	22	0.7090560
K0	18	51	15.176471	16.582769	-21	47	68	2.3220521
Gender*	19	51	1.431372	0.500196	1	2	1	0.0700415

Распределение по полу f - женский m - мужской

table(Data$Gender) %>% 
  kable()

Var1	Freq
f	29
m	22

describe(Data ~ Gender, skew = FALSE, ranges = FALSE)

## 
##  Descriptive statistics by group 
## group: f
##         vars  n  mean    sd   se
## Name*      1 29 15.00  8.51 1.58
## D1         2 29  2.52  1.06 0.20
## D2         3 29  3.00  1.10 0.20
## D3         4 29  2.17  1.14 0.21
## D4         5 29  3.79  1.01 0.19
## D5         6 29  2.55  0.95 0.18
## D6         7 29  1.10  0.67 0.13
## D0         8 29 14.03  3.34 0.62
## M1         9 29 20.10  3.99 0.74
## M2        10 29 20.00  3.30 0.61
## M3        11 29 15.03  4.03 0.75
## M4        12 29 12.07  5.38 1.00
## M5        13 29 15.97  4.70 0.87
## K1        14 29 -4.86  4.88 0.91
## K2        15 29  4.62  4.87 0.90
## K3        16 29  7.24  5.13 0.95
## K4        17 29 -3.31  5.03 0.93
## K0        18 29 20.03 14.10 2.62
## Gender*   19 29  1.00  0.00 0.00
## ------------------------------------------------------------ 
## group: m
##         vars  n  mean    sd   se
## Name*      1 22 11.50  6.49 1.38
## D1         2 22  2.18  1.14 0.24
## D2         3 22  2.91  1.31 0.28
## D3         4 22  2.18  1.01 0.21
## D4         5 22  3.45  1.14 0.24
## D5         6 22  2.32  1.25 0.27
## D6         7 22  0.86  0.64 0.14
## D0         8 22 13.05  3.03 0.65
## M1         9 22 17.82  4.91 1.05
## M2        10 22 18.64  4.68 1.00
## M3        11 22 13.50  4.94 1.05
## M4        12 22 12.09  4.15 0.88
## M5        13 22 13.23  5.15 1.10
## K1        14 22 -3.64  5.76 1.23
## K2        15 22  0.91  5.98 1.27
## K3        16 22  3.50  6.38 1.36
## K4        17 22 -0.73  4.83 1.03
## K0        18 22  8.77 17.72 3.78
## Gender*   19 22  2.00  0.00 0.00

Распределение по шкалам

ДУМЭОЛП

ggplot(Data, aes(Gender, D0)) +
        geom_boxplot()

Data %>% 
        melt(id.vars = c("Name", "Gender"), measure.vars = c("D1", "D2", "D3", "D4", "D5")) %>% 
        ggplot( aes(Gender, value, col = variable)) +
        geom_boxplot()

### Моральные основания

Data %>% 
        melt(id.vars = c("Name", "Gender"), measure.vars = c("M1", "M2", "M3", "M4", "M5")) %>% 
        ggplot( aes(Gender, value, col = variable)) +
        geom_boxplot()

ЭкО

ggplot(Data, aes(Gender, K0)) +
        geom_boxplot()

Data %>% 
        melt(id.vars = c("Name", "Gender"), measure.vars = c("K1", "K2", "K3", "K4")) %>% 
        ggplot( aes(Gender, value, col = variable)) +
        geom_boxplot()

# Значимость различий между М и Ж

for (i in 2:18) {
        w <- wilcox.test(Data[Data$Gender == "f", i]
            , Data[Data$Gender == "m", i]
            , exact=FALSE)
        print(names(Data[i]))
        print(w)
}

## [1] "D1"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 374, p-value = 0.2805
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "D2"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 335, p-value = 0.7607
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "D3"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 317.5, p-value = 0.9842
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "D4"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 373.5, p-value = 0.2813
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "D5"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 358.5, p-value = 0.4378
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "D6"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 379, p-value = 0.2048
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "D0"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 377.5, p-value = 0.2678
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "M1"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 427, p-value = 0.0401
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "M2"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 395, p-value = 0.1494
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "M3"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 387, p-value = 0.1971
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "M4"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 326, p-value = 0.9012
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "M5"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 402.5, p-value = 0.113
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "K1"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 271, p-value = 0.3647
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "K2"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 440, p-value = 0.02161
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "K3"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 431, p-value = 0.03347
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "K4"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 236, p-value = 0.1159
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "K0"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Data[Data$Gender == "f", i] and Data[Data$Gender == "m", i]
## W = 435, p-value = 0.02796
## alternative hypothesis: true location shift is not equal to 0

Список значимых различий между М и Ж (* <.05) и средние значения

for (i in 2:18) {
        w <- wilcox.test(Data[Data$Gender == "f", i]
            , Data[Data$Gender == "m", i]
            , exact=FALSE)
        
        if (w$p.value <= 0.05) {
          print(paste("Шкала: ", c(NamesData[i])
                  , round(w$p.value, 4)
                  , t(aggregate(Data[i]
                                , by = list(Data$Gender)
                                , FUN = mean))
                  , " "))
        }
}

## [1] "Шкала:  M1 0.0401 f  "        "Шкала:  M1 0.0401 20.10345  "
## [3] "Шкала:  M1 0.0401 m  "        "Шкала:  M1 0.0401 17.81818  "
## [1] "Шкала:  K2 0.0216 f  "         "Шкала:  K2 0.0216 4.6206897  "
## [3] "Шкала:  K2 0.0216 m  "         "Шкала:  K2 0.0216 0.9090909  "
## [1] "Шкала:  K3 0.0335 f  "        "Шкала:  K3 0.0335 7.241379  "
## [3] "Шкала:  K3 0.0335 m  "        "Шкала:  K3 0.0335 3.500000  "
## [1] "Шкала:  K0 0.028 f  "         "Шкала:  K0 0.028 20.034483  "
## [3] "Шкала:  K0 0.028 m  "         "Шкала:  K0 0.028  8.772727  "

Корреляционная матрица 1

corPlot(Data[2:18])

corr.test(Data[2:18], method = "spearman")

## Call:corr.test(x = Data[2:18], method = "spearman")
## Correlation matrix 
##       D1    D2    D3    D4    D5    D6    D0    M1    M2    M3    M4    M5
## D1  1.00  0.23  0.55  0.13  0.02 -0.08  0.59  0.01  0.19 -0.01  0.01 -0.10
## D2  0.23  1.00  0.19  0.20  0.03  0.08  0.62  0.04  0.16  0.00  0.00 -0.14
## D3  0.55  0.19  1.00 -0.10  0.36 -0.13  0.65 -0.21 -0.01  0.00  0.04 -0.13
## D4  0.13  0.20 -0.10  1.00  0.01  0.15  0.45  0.26  0.23 -0.23 -0.19 -0.09
## D5  0.02  0.03  0.36  0.01  1.00 -0.05  0.51  0.19  0.13  0.27  0.19  0.15
## D6 -0.08  0.08 -0.13  0.15 -0.05  1.00 -0.02  0.07  0.08  0.16 -0.05  0.30
## D0  0.59  0.62  0.65  0.45  0.51 -0.02  1.00  0.15  0.28  0.04  0.00 -0.08
## M1  0.01  0.04 -0.21  0.26  0.19  0.07  0.15  1.00  0.44  0.29  0.04  0.39
## M2  0.19  0.16 -0.01  0.23  0.13  0.08  0.28  0.44  1.00  0.20  0.08  0.30
## M3 -0.01  0.00  0.00 -0.23  0.27  0.16  0.04  0.29  0.20  1.00  0.72  0.65
## M4  0.01  0.00  0.04 -0.19  0.19 -0.05  0.00  0.04  0.08  0.72  1.00  0.49
## M5 -0.10 -0.14 -0.13 -0.09  0.15  0.30 -0.08  0.39  0.30  0.65  0.49  1.00
## K1 -0.22 -0.04  0.05 -0.13  0.21  0.11 -0.03  0.08  0.05  0.17  0.13  0.14
## K2  0.09  0.02 -0.27  0.22  0.07  0.27  0.07  0.46  0.51  0.08 -0.06  0.24
## K3  0.32  0.09 -0.13  0.31 -0.11  0.11  0.18  0.31  0.30 -0.19 -0.36  0.07
## K4 -0.18 -0.06 -0.08 -0.20 -0.17  0.13 -0.23 -0.02  0.03  0.14  0.24  0.20
## K0  0.27  0.05 -0.13  0.28 -0.02  0.08  0.16  0.25  0.25 -0.12 -0.27  0.05
##       K1    K2    K3    K4    K0
## D1 -0.22  0.09  0.32 -0.18  0.27
## D2 -0.04  0.02  0.09 -0.06  0.05
## D3  0.05 -0.27 -0.13 -0.08 -0.13
## D4 -0.13  0.22  0.31 -0.20  0.28
## D5  0.21  0.07 -0.11 -0.17 -0.02
## D6  0.11  0.27  0.11  0.13  0.08
## D0 -0.03  0.07  0.18 -0.23  0.16
## M1  0.08  0.46  0.31 -0.02  0.25
## M2  0.05  0.51  0.30  0.03  0.25
## M3  0.17  0.08 -0.19  0.14 -0.12
## M4  0.13 -0.06 -0.36  0.24 -0.27
## M5  0.14  0.24  0.07  0.20  0.05
## K1  1.00 -0.12 -0.42  0.45 -0.61
## K2 -0.12  1.00  0.58 -0.24  0.66
## K3 -0.42  0.58  1.00 -0.57  0.89
## K4  0.45 -0.24 -0.57  1.00 -0.74
## K0 -0.61  0.66  0.89 -0.74  1.00
## Sample Size 
## [1] 51
## Probability values (Entries above the diagonal are adjusted for multiple tests.) 
##      D1   D2   D3   D4   D5   D6   D0   M1   M2   M3   M4   M5   K1   K2   K3
## D1 0.00 1.00 0.00 1.00 1.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## D2 0.10 0.00 1.00 1.00 1.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## D3 0.00 0.18 0.00 1.00 1.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## D4 0.36 0.15 0.51 0.00 1.00 1.00 0.12 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## D5 0.91 0.83 0.01 0.95 0.00 1.00 0.02 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## D6 0.58 0.57 0.37 0.28 0.74 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## D0 0.00 0.00 0.00 0.00 0.00 0.90 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## M1 0.93 0.79 0.14 0.07 0.18 0.62 0.30 0.00 0.13 1.00 1.00 0.59 1.00 0.07 1.00
## M2 0.19 0.27 0.95 0.11 0.35 0.59 0.05 0.00 0.00 1.00 1.00 1.00 1.00 0.01 1.00
## M3 0.96 0.99 1.00 0.10 0.05 0.27 0.79 0.04 0.15 0.00 0.00 0.00 1.00 1.00 1.00
## M4 0.94 0.97 0.80 0.19 0.19 0.74 0.98 0.76 0.57 0.00 0.00 0.04 1.00 1.00 1.00
## M5 0.49 0.32 0.35 0.51 0.28 0.03 0.59 0.01 0.03 0.00 0.00 0.00 1.00 1.00 1.00
## K1 0.12 0.77 0.71 0.35 0.14 0.43 0.85 0.60 0.74 0.24 0.37 0.34 0.00 1.00 0.27
## K2 0.55 0.90 0.05 0.13 0.65 0.06 0.63 0.00 0.00 0.56 0.70 0.10 0.38 0.00 0.00
## K3 0.02 0.51 0.38 0.03 0.45 0.43 0.21 0.03 0.03 0.19 0.01 0.62 0.00 0.00 0.00
## K4 0.21 0.66 0.59 0.17 0.24 0.35 0.10 0.91 0.83 0.34 0.09 0.17 0.00 0.08 0.00
## K0 0.06 0.73 0.37 0.05 0.91 0.59 0.25 0.08 0.07 0.40 0.06 0.70 0.00 0.00 0.00
##      K4 K0
## D1 1.00  1
## D2 1.00  1
## D3 1.00  1
## D4 1.00  1
## D5 1.00  1
## D6 1.00  1
## D0 1.00  1
## M1 1.00  1
## M2 1.00  1
## M3 1.00  1
## M4 1.00  1
## M5 1.00  1
## K1 0.12  0
## K2 1.00  0
## K3 0.00  0
## K4 0.00  0
## K0 0.00  0
## 
##  To see confidence intervals of the correlations, print with the short=FALSE option

Графики ##ДУМЭОЛП х ЭкО

CorMatrix <- cor(Data[c(2:6, 8, 14:18)], method = "spearman")
#round(CorMatrix, 2)

plot(Data[c(2:6, 8, 14:18)], pch = ".", cex = 0.5)

#pairs(x, ...) есть спецфункция для матриц диаграмм рассеивания

Моральные основания х ЭкО

CorMatrix <- cor(Data[9:18], method = "spearman")
round(CorMatrix, 2)

##       M1   M2    M3    M4   M5    K1    K2    K3    K4    K0
## M1  1.00 0.44  0.29  0.04 0.39  0.08  0.46  0.31 -0.02  0.25
## M2  0.44 1.00  0.20  0.08 0.30  0.05  0.51  0.30  0.03  0.25
## M3  0.29 0.20  1.00  0.72 0.65  0.17  0.08 -0.19  0.14 -0.12
## M4  0.04 0.08  0.72  1.00 0.49  0.13 -0.06 -0.36  0.24 -0.27
## M5  0.39 0.30  0.65  0.49 1.00  0.14  0.24  0.07  0.20  0.05
## K1  0.08 0.05  0.17  0.13 0.14  1.00 -0.12 -0.42  0.45 -0.61
## K2  0.46 0.51  0.08 -0.06 0.24 -0.12  1.00  0.58 -0.24  0.66
## K3  0.31 0.30 -0.19 -0.36 0.07 -0.42  0.58  1.00 -0.57  0.89
## K4 -0.02 0.03  0.14  0.24 0.20  0.45 -0.24 -0.57  1.00 -0.74
## K0  0.25 0.25 -0.12 -0.27 0.05 -0.61  0.66  0.89 -0.74  1.00

plot(Data[c(9:18)], pch = ".", cex = 0.5)

Матрицы

Графическая матрица

ДУМЭОЛП х ЭкО

M1 <- Data[c(2:6, 8, 14:18)] %>% 
    as.matrix() %>% 
    rcorr(type = c("spearman"))

M1$r %>%
        corrplot.mixed(p.mat = M1$P
                       , sig.level = 0.05
                       , insig = "label_sig"#"pch" #
                       , pch.cex = 1
                       , lower.col = "black"
                       , number.cex = 0.7)

ДУМЭОЛП х ЭкО

Data[c(2:6, 8, 14:18)] %>% 
as.matrix() %>% 
rcorr(type = c("spearman"))

##       D1    D2    D3    D4    D5    D0    K1    K2    K3    K4    K0
## D1  1.00  0.23  0.55  0.13  0.02  0.59 -0.22  0.09  0.32 -0.18  0.27
## D2  0.23  1.00  0.19  0.20  0.03  0.62 -0.04  0.02  0.09 -0.06  0.05
## D3  0.55  0.19  1.00 -0.10  0.36  0.65  0.05 -0.27 -0.13 -0.08 -0.13
## D4  0.13  0.20 -0.10  1.00  0.01  0.45 -0.13  0.22  0.31 -0.20  0.28
## D5  0.02  0.03  0.36  0.01  1.00  0.51  0.21  0.07 -0.11 -0.17 -0.02
## D0  0.59  0.62  0.65  0.45  0.51  1.00 -0.03  0.07  0.18 -0.23  0.16
## K1 -0.22 -0.04  0.05 -0.13  0.21 -0.03  1.00 -0.12 -0.42  0.45 -0.61
## K2  0.09  0.02 -0.27  0.22  0.07  0.07 -0.12  1.00  0.58 -0.24  0.66
## K3  0.32  0.09 -0.13  0.31 -0.11  0.18 -0.42  0.58  1.00 -0.57  0.89
## K4 -0.18 -0.06 -0.08 -0.20 -0.17 -0.23  0.45 -0.24 -0.57  1.00 -0.74
## K0  0.27  0.05 -0.13  0.28 -0.02  0.16 -0.61  0.66  0.89 -0.74  1.00
## 
## n= 51 
## 
## 
## P
##    D1     D2     D3     D4     D5     D0     K1     K2     K3     K4     K0    
## D1        0.1000 0.0000 0.3596 0.9064 0.0000 0.1217 0.5513 0.0203 0.2104 0.0575
## D2 0.1000        0.1820 0.1529 0.8341 0.0000 0.7738 0.9017 0.5134 0.6565 0.7297
## D3 0.0000 0.1820        0.5066 0.0089 0.0000 0.7088 0.0526 0.3804 0.5911 0.3727
## D4 0.3596 0.1529 0.5066        0.9531 0.0010 0.3453 0.1297 0.0259 0.1685 0.0492
## D5 0.9064 0.8341 0.0089 0.9531        0.0001 0.1422 0.6504 0.4494 0.2364 0.9091
## D0 0.0000 0.0000 0.0000 0.0010 0.0001        0.8488 0.6252 0.2062 0.1003 0.2549
## K1 0.1217 0.7738 0.7088 0.3453 0.1422 0.8488        0.3827 0.0023 0.0010 0.0000
## K2 0.5513 0.9017 0.0526 0.1297 0.6504 0.6252 0.3827        0.0000 0.0838 0.0000
## K3 0.0203 0.5134 0.3804 0.0259 0.4494 0.2062 0.0023 0.0000        0.0000 0.0000
## K4 0.2104 0.6565 0.5911 0.1685 0.2364 0.1003 0.0010 0.0838 0.0000        0.0000
## K0 0.0575 0.7297 0.3727 0.0492 0.9091 0.2549 0.0000 0.0000 0.0000 0.0000

Моральные основания х ЭкО

M1 <- Data[c(9:18)] %>% 
    as.matrix() %>% 
    rcorr(type = c("spearman"))

M1$r %>%
        corrplot.mixed(p.mat = M1$P
                       , sig.level = 0.05
                       , insig = "label_sig"
                       , pch.cex = 1
                       , lower.col = "black"
                       , number.cex = .7)

Моральные основания х ЭкО

Data[c(9:18)] %>% 
as.matrix() %>% 
rcorr(type = c("spearman"))

##       M1   M2    M3    M4   M5    K1    K2    K3    K4    K0
## M1  1.00 0.44  0.29  0.04 0.39  0.08  0.46  0.31 -0.02  0.25
## M2  0.44 1.00  0.20  0.08 0.30  0.05  0.51  0.30  0.03  0.25
## M3  0.29 0.20  1.00  0.72 0.65  0.17  0.08 -0.19  0.14 -0.12
## M4  0.04 0.08  0.72  1.00 0.49  0.13 -0.06 -0.36  0.24 -0.27
## M5  0.39 0.30  0.65  0.49 1.00  0.14  0.24  0.07  0.20  0.05
## K1  0.08 0.05  0.17  0.13 0.14  1.00 -0.12 -0.42  0.45 -0.61
## K2  0.46 0.51  0.08 -0.06 0.24 -0.12  1.00  0.58 -0.24  0.66
## K3  0.31 0.30 -0.19 -0.36 0.07 -0.42  0.58  1.00 -0.57  0.89
## K4 -0.02 0.03  0.14  0.24 0.20  0.45 -0.24 -0.57  1.00 -0.74
## K0  0.25 0.25 -0.12 -0.27 0.05 -0.61  0.66  0.89 -0.74  1.00
## 
## n= 51 
## 
## 
## P
##    M1     M2     M3     M4     M5     K1     K2     K3     K4     K0    
## M1        0.0011 0.0404 0.7588 0.0051 0.5989 0.0006 0.0283 0.9083 0.0813
## M2 0.0011        0.1499 0.5656 0.0344 0.7405 0.0001 0.0330 0.8251 0.0732
## M3 0.0404 0.1499        0.0000 0.0000 0.2409 0.5641 0.1916 0.3356 0.3989
## M4 0.7588 0.5656 0.0000        0.0003 0.3734 0.6991 0.0095 0.0906 0.0567
## M5 0.0051 0.0344 0.0000 0.0003        0.3379 0.0963 0.6245 0.1655 0.7022
## K1 0.5989 0.7405 0.2409 0.3734 0.3379        0.3827 0.0023 0.0010 0.0000
## K2 0.0006 0.0001 0.5641 0.6991 0.0963 0.3827        0.0000 0.0838 0.0000
## K3 0.0283 0.0330 0.1916 0.0095 0.6245 0.0023 0.0000        0.0000 0.0000
## K4 0.9083 0.8251 0.3356 0.0906 0.1655 0.0010 0.0838 0.0000        0.0000
## K0 0.0813 0.0732 0.3989 0.0567 0.7022 0.0000 0.0000 0.0000 0.0000

Матрицы /вариант

Data[c(2:6, 8, 14:18)] %>% 
pairs.panels(method = "spearman",
             hist.col = "cornflowerblue",
             density = T, ellipses = F,
             pch=".",
             stars=TRUE,
             gap = 0)

Data[c(9:18)] %>% 
pairs.panels(method = "spearman",
             hist.col = "cornflowerblue",
             density = T, ellipses = F,
             pch=".",
             stars=TRUE,
             gap = 0)

Data[c(2:6,9:13)] %>% 
pairs.panels(method = "spearman",
             hist.col = "cornflowerblue",
             density = T, ellipses = F,
             pch=".",
             stars=TRUE,
             gap = 0)

Регрессионный анализ

###Влияние пола на Кряж

lm(K0 ~ Gender, Data) %>% 
  summary()

## 
## Call:
## lm(formula = K0 ~ Gender, data = Data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.773 -10.034  -2.773  10.096  33.227 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   20.034      2.926   6.848 1.14e-08 ***
## Genderm      -11.262      4.454  -2.528   0.0147 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.76 on 49 degrees of freedom
## Multiple R-squared:  0.1154, Adjusted R-squared:  0.09734 
## F-statistic: 6.392 on 1 and 49 DF,  p-value: 0.01474

Влияние пола на Думеолпа

lm(D0 ~ Gender, Data) %>% 
  summary()

## 
## Call:
## lm(formula = D0 ~ Gender, data = Data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.0345 -2.0455 -0.0345  1.9655  5.9655 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  14.0345     0.5967  23.521   <2e-16 ***
## Genderm      -0.9890     0.9085  -1.089    0.282    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.213 on 49 degrees of freedom
## Multiple R-squared:  0.02362,    Adjusted R-squared:  0.00369 
## F-statistic: 1.185 on 1 and 49 DF,  p-value: 0.2816

Влияние пола на Моральные ценности

lm(M1 ~ Gender, Data) %>% 
  summary()

## 
## Call:
## lm(formula = M1 ~ Gender, data = Data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1034 -2.8182 -0.1034  2.1818 13.1818 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  20.1034     0.8185  24.562   <2e-16 ***
## Genderm      -2.2853     1.2462  -1.834   0.0728 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.408 on 49 degrees of freedom
## Multiple R-squared:  0.06422,    Adjusted R-squared:  0.04512 
## F-statistic: 3.363 on 1 and 49 DF,  p-value: 0.07276

lm(M2 ~ Gender, Data) %>% 
  summary()

## 
## Call:
## lm(formula = M2 ~ Gender, data = Data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.0000 -2.3182 -0.6364  2.0000  9.3636 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  20.0000     0.7328  27.292   <2e-16 ***
## Genderm      -1.3636     1.1157  -1.222    0.227    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.946 on 49 degrees of freedom
## Multiple R-squared:  0.02958,    Adjusted R-squared:  0.009778 
## F-statistic: 1.494 on 1 and 49 DF,  p-value: 0.2275

lm(M3 ~ Gender, Data) %>% 
  summary()

## 
## Call:
## lm(formula = M3 ~ Gender, data = Data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.5000  -2.5345   0.9655   2.7328  10.5000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  15.0345     0.8256  18.211   <2e-16 ***
## Genderm      -1.5345     1.2570  -1.221    0.228    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.446 on 49 degrees of freedom
## Multiple R-squared:  0.02952,    Adjusted R-squared:  0.009712 
## F-statistic:  1.49 on 1 and 49 DF,  p-value: 0.228

lm(M4 ~ Gender, Data) %>% 
  summary()

## 
## Call:
## lm(formula = M4 ~ Gender, data = Data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.0690  -3.0690  -0.0909   3.9091   8.9310 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 12.06897    0.90805  13.291   <2e-16 ***
## Genderm      0.02194    1.38255   0.016    0.987    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.89 on 49 degrees of freedom
## Multiple R-squared:  5.141e-06,  Adjusted R-squared:  -0.0204 
## F-statistic: 0.0002519 on 1 and 49 DF,  p-value: 0.9874

lm(M5 ~ Gender, Data) %>% 
  summary()

## 
## Call:
## lm(formula = M5 ~ Gender, data = Data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.2273  -2.2273  -0.2273   3.2727   9.0345 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   15.966      0.910  17.544   <2e-16 ***
## Genderm       -2.738      1.386  -1.976   0.0538 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.901 on 49 degrees of freedom
## Multiple R-squared:  0.07382,    Adjusted R-squared:  0.05492 
## F-statistic: 3.906 on 1 and 49 DF,  p-value: 0.05377

Полная модель без взаимодействий

lm(K0 ~ . -Name -K1 -K2 -K3 -K4, Data) %>% 
  summary()

## 
## Call:
## lm(formula = K0 ~ . - Name - K1 - K2 - K3 - K4, data = Data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.073  -7.409  -1.342   6.052  33.024 
## 
## Coefficients: (1 not defined because of singularities)
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   7.4161    15.6791   0.473   0.6389  
## D1            6.3033     2.3356   2.699   0.0103 *
## D2            0.4368     1.9543   0.223   0.8244  
## D3           -6.2039     2.6350  -2.354   0.0238 *
## D4            2.8217     2.2619   1.247   0.2199  
## D5            1.0982     2.2876   0.480   0.6339  
## D6           -1.5495     3.4927  -0.444   0.6598  
## D0                NA         NA      NA       NA  
## M1           -0.3275     0.6244  -0.524   0.6030  
## M2            0.3251     0.5849   0.556   0.5815  
## M3            0.6163     0.7193   0.857   0.3969  
## M4           -1.2843     0.5864  -2.190   0.0347 *
## M5            0.2153     0.6341   0.340   0.7360  
## Genderm      -6.9507     4.4482  -1.563   0.1264  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.4 on 38 degrees of freedom
## Multiple R-squared:  0.4269, Adjusted R-squared:  0.246 
## F-statistic: 2.359 on 12 and 38 DF,  p-value: 0.02209

Поиск оптимальной модели

lm(K0 ~ . -Name -K1 -K2 -K3 -K4, Data) %>% 
  step()  %>% 
  summary()

## Start:  AIC=283.05
## K0 ~ (Name + D1 + D2 + D3 + D4 + D5 + D6 + D0 + M1 + M2 + M3 + 
##     M4 + M5 + K1 + K2 + K3 + K4 + Gender) - Name - K1 - K2 - 
##     K3 - K4
## 
## 
## Step:  AIC=283.05
## K0 ~ D1 + D2 + D3 + D4 + D5 + D6 + M1 + M2 + M3 + M4 + M5 + Gender
## 
##          Df Sum of Sq    RSS    AIC
## - D2      1     10.36 7889.8 281.12
## - M5      1     23.91 7903.4 281.20
## - D6      1     40.81 7920.3 281.31
## - D5      1     47.79 7927.2 281.36
## - M1      1     57.03 7936.5 281.42
## - M2      1     64.08 7943.5 281.46
## - M3      1    152.23 8031.7 282.02
## <none>                7879.4 283.05
## - D4      1    322.69 8202.1 283.10
## - Gender  1    506.29 8385.7 284.23
## - M4      1    994.60 8874.1 287.11
## - D3      1   1149.47 9028.9 287.99
## - D1      1   1510.31 9389.8 289.99
## 
## Step:  AIC=281.12
## K0 ~ D1 + D3 + D4 + D5 + D6 + M1 + M2 + M3 + M4 + M5 + Gender
## 
##          Df Sum of Sq    RSS    AIC
## - M5      1     17.63 7907.4 279.23
## - D6      1     35.28 7925.1 279.34
## - D5      1     45.24 7935.0 279.41
## - M1      1     49.64 7939.4 279.44
## - M2      1     66.06 7955.9 279.54
## - M3      1    152.61 8042.4 280.09
## <none>                7889.8 281.12
## - D4      1    351.88 8241.7 281.34
## - Gender  1    506.09 8395.9 282.29
## - M4      1    991.28 8881.1 285.15
## - D3      1   1142.52 9032.3 286.01
## - D1      1   1555.73 9445.5 288.30
## 
## Step:  AIC=279.23
## K0 ~ D1 + D3 + D4 + D5 + D6 + M1 + M2 + M3 + M4 + Gender
## 
##          Df Sum of Sq    RSS    AIC
## - D6      1     22.63 7930.1 277.38
## - M1      1     37.83 7945.3 277.47
## - D5      1     54.99 7962.4 277.58
## - M2      1     75.29 7982.7 277.71
## - M3      1    190.40 8097.8 278.44
## <none>                7907.4 279.23
## - D4      1    334.45 8241.9 279.34
## - Gender  1    555.47 8462.9 280.69
## - M4      1    989.50 8896.9 283.24
## - D3      1   1206.28 9113.7 284.47
## - D1      1   1542.43 9449.9 286.32
## 
## Step:  AIC=277.38
## K0 ~ D1 + D3 + D4 + D5 + M1 + M2 + M3 + M4 + Gender
## 
##          Df Sum of Sq    RSS    AIC
## - M1      1     35.77 7965.8 275.61
## - D5      1     64.10 7994.2 275.79
## - M2      1     73.69 8003.8 275.85
## - M3      1    173.77 8103.8 276.48
## - D4      1    316.01 8246.1 277.37
## <none>                7930.1 277.38
## - Gender  1    534.57 8464.6 278.70
## - M4      1    968.60 8898.7 281.25
## - D3      1   1206.61 9136.7 282.60
## - D1      1   1589.65 9519.7 284.69
## 
## Step:  AIC=275.61
## K0 ~ D1 + D3 + D4 + D5 + M2 + M3 + M4 + Gender
## 
##          Df Sum of Sq    RSS    AIC
## - M2      1     51.63 8017.5 273.94
## - D5      1     56.10 8021.9 273.96
## - M3      1    138.34 8104.2 274.48
## - D4      1    282.17 8248.0 275.38
## <none>                7965.8 275.61
## - Gender  1    513.21 8479.0 276.79
## - M4      1    932.84 8898.7 279.25
## - D3      1   1179.33 9145.2 280.65
## - D1      1   1575.51 9541.3 282.81
## 
## Step:  AIC=273.94
## K0 ~ D1 + D3 + D4 + D5 + M3 + M4 + Gender
## 
##          Df Sum of Sq    RSS    AIC
## - D5      1     71.02 8088.5 272.38
## - M3      1    168.74 8186.2 273.00
## <none>                8017.5 273.94
## - D4      1    336.95 8354.4 274.04
## - Gender  1    534.86 8552.3 275.23
## - M4      1    945.62 8963.1 277.62
## - D3      1   1293.47 9310.9 279.56
## - D1      1   1777.30 9794.8 282.15
## 
## Step:  AIC=272.38
## K0 ~ D1 + D3 + D4 + M3 + M4 + Gender
## 
##          Df Sum of Sq    RSS    AIC
## - M3      1    244.72 8333.2 271.90
## <none>                8088.5 272.38
## - D4      1    416.01 8504.5 272.94
## - Gender  1    567.67 8656.1 273.84
## - M4      1    944.90 9033.4 276.02
## - D3      1   1286.58 9375.1 277.91
## - D1      1   1707.64 9796.1 280.15
## 
## Step:  AIC=271.91
## K0 ~ D1 + D3 + D4 + M4 + Gender
## 
##          Df Sum of Sq     RSS    AIC
## <none>                 8333.2 271.90
## - D4      1    359.73  8692.9 272.06
## - M4      1    713.84  9047.0 274.10
## - Gender  1    800.65  9133.9 274.58
## - D3      1   1196.16  9529.4 276.75
## - D1      1   1680.51 10013.7 279.27

## 
## Call:
## lm(formula = K0 ~ D1 + D3 + D4 + M4 + Gender, data = Data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.807  -7.571  -1.646   7.899  33.042 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  15.4944    10.9008   1.421  0.16209   
## D1            6.2526     2.0756   3.012  0.00424 **
## D3           -5.3055     2.0875  -2.542  0.01455 * 
## D4            2.6393     1.8937   1.394  0.17024   
## M4           -0.8024     0.4087  -1.963  0.05580 . 
## Genderm      -8.2034     3.9452  -2.079  0.04332 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.61 on 45 degrees of freedom
## Multiple R-squared:  0.3939, Adjusted R-squared:  0.3266 
## F-statistic:  5.85 on 5 and 45 DF,  p-value: 0.0003032

lm(K0 ~ Gender : (D1 + D2 + D3+ D4 + D5 + M1 + M2 + M3 + M4 + M5), Data) %>% 
  #step() %>% 
  summary()

## 
## Call:
## lm(formula = K0 ~ Gender:(D1 + D2 + D3 + D4 + D5 + M1 + M2 + 
##     M3 + M4 + M5), data = Data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.3180  -8.2884  -0.9959   6.6143  27.9784 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -4.9907    19.5548  -0.255  0.80030   
## Genderf:D1   -0.3202     4.1352  -0.077  0.93880   
## Genderm:D1    4.1706     3.5984   1.159  0.25559   
## Genderf:D2    2.8149     2.6696   1.054  0.30011   
## Genderm:D2    4.7230     3.4507   1.369  0.18126   
## Genderf:D3   -3.0999     3.5989  -0.861  0.39589   
## Genderm:D3  -10.1017     4.5385  -2.226  0.03369 * 
## Genderf:D4    4.4345     3.0973   1.432  0.16255   
## Genderm:D4    6.4033     4.0583   1.578  0.12509   
## Genderf:D5    1.2108     3.3657   0.360  0.72155   
## Genderm:D5    2.0224     4.4442   0.455  0.65234   
## Genderf:M1    1.6406     0.9914   1.655  0.10837   
## Genderm:M1   -3.4656     1.2361  -2.804  0.00877 **
## Genderf:M2   -0.1994     0.9180  -0.217  0.82951   
## Genderm:M2    0.3876     0.7222   0.537  0.59547   
## Genderf:M3    0.4840     1.0989   0.440  0.66277   
## Genderm:M3    2.4345     1.1147   2.184  0.03691 * 
## Genderf:M4   -0.9222     0.9519  -0.969  0.34040   
## Genderm:M4   -0.2760     1.0654  -0.259  0.79738   
## Genderf:M5   -1.3288     1.1486  -1.157  0.25646   
## Genderm:M5    0.8716     1.0527   0.828  0.41423   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.56 on 30 degrees of freedom
## Multiple R-squared:  0.5987, Adjusted R-squared:  0.3312 
## F-statistic: 2.238 on 20 and 30 DF,  p-value: 0.02234

lm(K0 ~ (Gender + D0 +  M1 + M2 + M3 + M4 + M5), Data) %>% 
  #step() %>% 
  summary()

## 
## Call:
## lm(formula = K0 ~ (Gender + D0 + M1 + M2 + M3 + M4 + M5), data = Data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.306  -8.877   0.153   9.386  28.383 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  5.96717   15.99906   0.373   0.7110  
## Genderm     -8.94388    4.69322  -1.906   0.0634 .
## D0           0.53610    0.72674   0.738   0.4647  
## M1           0.22525    0.60346   0.373   0.7108  
## M2           0.64053    0.61745   1.037   0.3054  
## M3           0.12645    0.73145   0.173   0.8636  
## M4          -1.13916    0.60403  -1.886   0.0661 .
## M5           0.06589    0.61329   0.107   0.9149  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.41 on 43 degrees of freedom
## Multiple R-squared:  0.2569, Adjusted R-squared:  0.1359 
## F-statistic: 2.124 on 7 and 43 DF,  p-value: 0.06126

Регрессия по алгоритмам psych

Gender + D1 + D2 + D3+ D4 + D5 + M1 + M2 + M3 + M4 + M5

setCor(K0 ~ D0 + M1 + M2 + M3 + M4 + M5, data = Data, std = FALSE)

## Call: setCor(y = K0 ~ D0 + M1 + M2 + M3 + M4 + M5, data = Data, std = FALSE)
## 
## Multiple Regression from raw data 
## 
##  DV =  K0 
##             slope    se     t     p lower.ci upper.ci   VIF
## (Intercept) -5.76 15.20 -0.38 0.710   -36.40    24.88 46.81
## D0           0.75  0.74  1.02 0.310    -0.74     2.24 21.60
## M1           0.31  0.62  0.50 0.620    -0.94     1.56 29.96
## M2           0.67  0.64  1.06 0.290    -0.61     1.96 32.07
## M3           0.18  0.75  0.24 0.810    -1.33     1.70 25.93
## M4          -1.29  0.62 -2.10 0.041    -2.54    -0.05 12.98
## M5           0.32  0.62  0.52 0.610    -0.92     1.56 18.75
## 
## Residual Standard Error =  15.87  with  44  degrees of freedom
## 
##  Multiple Regression
##       R   R2  Ruw R2uw Shrunken R2 SE of R2 overall F df1 df2     p
## K0 0.44 0.19 0.08 0.01        0.08     0.08      1.77   6  44 0.128

DataNum <- Data
DataNum$Gender <- as.integer(DataNum$Gender)
DataNum$Gender

##  [1] 2 1 1 2 1 1 2 2 1 2 2 2 1 1 1 2 2 2 2 2 2 1 1 1 2 1 1 1 1 2 2 1 1 1 2 1 2 1
## [39] 2 1 2 1 2 1 1 1 1 1 1 2 1

Нужно вводить корреляционную матрицу с корреляциями дихотомических величин.

CorMatrix <- mixedCor(Data, d = "Gender", c = NamesData[2:18])

#setCor(y = K0 ~ Gender, data = CorMatrix, n.obs = 51)
# setCor(y = 1, x = 2:18, data = CorMatrix, n.obs = 51)

DataImport

Petr Ya

5/6/2021

Шкалы методик

Опросник ДУМЭОЛП

Опросник моральных оснований

ЭкО 30 - Опросник экологических установок (Кряж И. В.)

Описательная статистика

Распределение по шкалам

ДУМЭОЛП

ЭкО

Корреляционная матрица 1

Моральные основания х ЭкО

Графическая матрица

ДУМЭОЛП х ЭкО

Моральные основания х ЭкО

Матрицы /вариант

Регрессионный анализ

Влияние пола на Моральные ценности

Полная модель без взаимодействий

Поиск оптимальной модели

Регрессия по алгоритмам psych