Мораль-Идентификация с природой
Petr Ya
1/3/2022
rm(list = ls())
date()## [1] "Sun Apr 3 21:17:39 2022"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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_0.6.29 R6_2.5.1 jsonlite_1.7.3 magrittr_2.0.1
## [5] evaluate_0.14 rlang_0.4.12 stringi_1.7.6 jquerylib_0.1.4
## [9] bslib_0.3.1 rmarkdown_2.11 tools_4.1.2 stringr_1.4.0
## [13] xfun_0.29 yaml_2.2.1 fastmap_1.1.0 compiler_4.1.2
## [17] htmltools_0.5.2 knitr_1.37 sass_0.4.0Библиотеки
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(reshape2)Импорт
Data <- read.delim2("Data.CSV")
Data$Gender <- as.factor(Data$Gender)Описательная статистика
Распределения идентичности
hist(Data$Identity)
Модель без учета пола.
# fit <- lm(Identity ~ Care + Fairness + Loyalty + Authority + Sanctity , data = Data)
#
# summary(fit)Gender
mData <- filter(Data, Gender == "male")
fData <- filter(Data, Gender == "female")# ggplot(Data, aes(x = Identity)) +
# geom_histogram(bins = 10) +
# theme_classic()Распеределение в зависимости от пола.
ggplot(Data, aes(x = Identity)) +
geom_histogram(bins = 7) +
facet_grid(rows = vars(Gender)) +
theme_classic()
# Общий график
LabsEn <- c("Care", "Fairness", "Loyalty", "Authority", "Purity")
LabsRu <- c("Забота", "Справедливость", "Лояльность", "Уважение ", "Чистота")DataRu <- Data
names(DataRu)[3:7] <- LabsRu
names(DataRu)[1] <- "Пол"
DataRu[1] <- factor(DataRu$Пол, labels = c("ж", "м"))
meltDataRu <- melt(DataRu
, id.vars = c("Пол", "Age", "Lie", "Identity")
, measure.vars = 3:7)
# head(meltData
ggplot(meltDataRu, aes(x = Identity, y = value, col = Пол)) +
geom_point() +
geom_smooth(method = 'lm') +
facet_grid(cols = vars(variable)) +
labs(x = "Идентификация с природой"
, y = "Моральные основания")## `geom_smooth()` using formula 'y ~ x'
ggplot(meltDataRu, aes(y = Identity, x = value, col = Пол)) +
geom_point() +
geom_smooth(method = 'lm') +
facet_wrap(vars(variable)) +
labs(y = "Идентификация с природой"
, x = "Моральные основания")## `geom_smooth()` using formula 'y ~ x'
# ggplot(Data, aes(x = Identity, y = Care, color = Gender)) +
# geom_point() +
# geom_smooth(method = 'lm')Регрессионные модели
Моральные основания
lm(Identity ~ Care + Fairness + Loyalty + Authority + Sanctity , data = Data) %>% summary()##
## Call:
## lm(formula = Identity ~ Care + Fairness + Loyalty + Authority +
## Sanctity, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.978 -8.858 -0.083 8.199 26.950
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.24788 10.38300 4.551 5.11e-05 ***
## Care 1.44751 0.60998 2.373 0.0227 *
## Fairness -0.15810 0.52840 -0.299 0.7664
## Loyalty 0.24777 0.70907 0.349 0.7286
## Authority -0.02815 0.54685 -0.051 0.9592
## Sanctity -0.20808 0.48326 -0.431 0.6691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13 on 39 degrees of freedom
## Multiple R-squared: 0.1878, Adjusted R-squared: 0.08371
## F-statistic: 1.804 on 5 and 39 DF, p-value: 0.1348Моральные основания + пол
lm(Identity ~ Gender + Care + Fairness + Loyalty + Authority + Sanctity , data = Data) %>%
summary()##
## Call:
## lm(formula = Identity ~ Gender + Care + Fairness + Loyalty +
## Authority + Sanctity, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.321 -8.320 0.277 7.726 26.536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.498504 12.987970 3.811 0.000492 ***
## Gendermale -1.381472 4.686611 -0.295 0.769774
## Care 1.387478 0.649970 2.135 0.039297 *
## Fairness -0.177666 0.538795 -0.330 0.743403
## Loyalty 0.191736 0.742279 0.258 0.797564
## Authority -0.005149 0.558843 -0.009 0.992698
## Sanctity -0.164609 0.510779 -0.322 0.749014
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.15 on 38 degrees of freedom
## Multiple R-squared: 0.1897, Adjusted R-squared: 0.06174
## F-statistic: 1.483 on 6 and 38 DF, p-value: 0.2103Моральные основания во взаимодействии с полом
lm(Identity ~ Gender : (Care + Fairness + Loyalty + Authority + Sanctity) , data = Data) %>%
summary()##
## Call:
## lm(formula = Identity ~ Gender:(Care + Fairness + Loyalty + Authority +
## Sanctity), data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.824 -8.935 -0.863 7.001 25.083
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.8818 11.8277 4.133 0.000221 ***
## Genderfemale:Care 2.5580 1.1575 2.210 0.033937 *
## Gendermale:Care 0.9859 0.7453 1.323 0.194745
## Genderfemale:Fairness -0.9803 0.8773 -1.117 0.271633
## Gendermale:Fairness 0.2886 0.7803 0.370 0.713794
## Genderfemale:Loyalty -0.4198 1.0606 -0.396 0.694689
## Gendermale:Loyalty 0.5201 1.0535 0.494 0.624748
## Genderfemale:Authority 1.4633 1.2818 1.142 0.261585
## Gendermale:Authority -0.4190 0.6883 -0.609 0.546770
## Genderfemale:Sanctity -1.0531 0.9796 -1.075 0.289919
## Gendermale:Sanctity -0.2820 0.7107 -0.397 0.693958
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.31 on 34 degrees of freedom
## Multiple R-squared: 0.2573, Adjusted R-squared: 0.03882
## F-statistic: 1.178 on 10 and 34 DF, p-value: 0.339Забота
lm(Identity ~ Care, data = Data) %>%
summary()##
## Call:
## lm(formula = Identity ~ Care, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.4048 -8.8446 -0.8446 7.9072 27.3470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.6037 8.8863 5.244 4.53e-06 ***
## Care 1.3120 0.4269 3.073 0.00367 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.44 on 43 degrees of freedom
## Multiple R-squared: 0.1801, Adjusted R-squared: 0.161
## F-statistic: 9.444 on 1 and 43 DF, p-value: 0.00367Забота + пол
lm(Identity ~ Care + Gender, data = Data) %>%
summary()##
## Call:
## lm(formula = Identity ~ Care + Gender, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.031 -8.124 -0.898 7.778 27.067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.3944 11.1205 4.442 6.37e-05 ***
## Care 1.2255 0.4768 2.570 0.0138 *
## Gendermale -1.7804 4.1916 -0.425 0.6732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.56 on 42 degrees of freedom
## Multiple R-squared: 0.1836, Adjusted R-squared: 0.1447
## F-statistic: 4.722 on 2 and 42 DF, p-value: 0.01413Забота во взаимодействии с полом
lm(Identity ~ Gender : Care , data = Data) %>%
summary()##
## Call:
## lm(formula = Identity ~ Gender:Care, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.307 -7.977 -0.791 7.768 26.564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.2504 9.5479 5.054 8.95e-06 ***
## Genderfemale:Care 1.2822 0.4348 2.949 0.00519 **
## Gendermale:Care 1.1863 0.4984 2.380 0.02192 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.54 on 42 degrees of freedom
## Multiple R-squared: 0.1849, Adjusted R-squared: 0.1461
## F-statistic: 4.765 on 2 and 42 DF, p-value: 0.01364Забота и пол + их взаимодействие
lm(Identity ~ Gender * Care , data = Data) %>%
summary()##
## Call:
## lm(formula = Identity ~ Gender * Care, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.953 -7.960 -0.867 7.409 24.656
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.0046 21.3200 1.923 0.0614 .
## Gendermale 9.1111 23.9072 0.381 0.7051
## Care 1.5979 0.9376 1.704 0.0959 .
## Gendermale:Care -0.5057 1.0926 -0.463 0.6459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.67 on 41 degrees of freedom
## Multiple R-squared: 0.1878, Adjusted R-squared: 0.1284
## F-statistic: 3.161 on 3 and 41 DF, p-value: 0.03462Подбор модели
без взаимодействия факторов.
lm(Identity ~ Gender + Care + Fairness + Loyalty + Authority + Sanctity, data = Data) %>%
step() %>%
summary()## Start: AIC=238.27
## Identity ~ Gender + Care + Fairness + Loyalty + Authority + Sanctity
##
## Df Sum of Sq RSS AIC
## - Authority 1 0.01 6571.4 236.27
## - Loyalty 1 11.54 6582.9 236.35
## - Gender 1 15.03 6586.4 236.38
## - Sanctity 1 17.96 6589.3 236.40
## - Fairness 1 18.80 6590.2 236.40
## <none> 6571.4 238.27
## - Care 1 788.02 7359.4 241.37
##
## Step: AIC=236.27
## Identity ~ Gender + Care + Fairness + Loyalty + Sanctity
##
## Df Sum of Sq RSS AIC
## - Loyalty 1 13.41 6584.8 234.36
## - Gender 1 15.46 6586.9 234.38
## - Sanctity 1 18.40 6589.8 234.40
## - Fairness 1 18.80 6590.2 234.40
## <none> 6571.4 236.27
## - Care 1 789.40 7360.8 239.38
##
## Step: AIC=234.36
## Identity ~ Gender + Care + Fairness + Sanctity
##
## Df Sum of Sq RSS AIC
## - Sanctity 1 7.55 6592.4 232.41
## - Fairness 1 19.18 6604.0 232.50
## - Gender 1 23.60 6608.4 232.53
## <none> 6584.8 234.36
## - Care 1 829.83 7414.6 237.71
##
## Step: AIC=232.42
## Identity ~ Gender + Care + Fairness
##
## Df Sum of Sq RSS AIC
## - Fairness 1 28.51 6620.9 230.61
## - Gender 1 32.09 6624.4 230.63
## <none> 6592.4 232.41
## - Care 1 867.87 7460.2 235.98
##
## Step: AIC=230.61
## Identity ~ Gender + Care
##
## Df Sum of Sq RSS AIC
## - Gender 1 28.44 6649.3 228.80
## <none> 6620.9 230.61
## - Care 1 1041.28 7662.2 235.18
##
## Step: AIC=228.8
## Identity ~ Care
##
## Df Sum of Sq RSS AIC
## <none> 6649.3 228.80
## - Care 1 1460.3 8109.6 235.74##
## Call:
## lm(formula = Identity ~ Care, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.4048 -8.8446 -0.8446 7.9072 27.3470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.6037 8.8863 5.244 4.53e-06 ***
## Care 1.3120 0.4269 3.073 0.00367 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.44 on 43 degrees of freedom
## Multiple R-squared: 0.1801, Adjusted R-squared: 0.161
## F-statistic: 9.444 on 1 and 43 DF, p-value: 0.00367с взаимодействием факторов (пол:основания)
lm(Identity ~ Gender : (Care + Fairness + Loyalty + Authority + Sanctity) , data = Data) %>%
step() %>%
summary()## Start: AIC=242.35
## Identity ~ Gender:(Care + Fairness + Loyalty + Authority + Sanctity)
##
## Df Sum of Sq RSS AIC
## - Gender:Loyalty 2 73.26 6096.5 238.90
## - Gender:Sanctity 2 232.33 6255.6 240.06
## - Gender:Fairness 2 247.40 6270.7 240.16
## - Gender:Authority 2 294.56 6317.9 240.50
## <none> 6023.3 242.35
## - Gender:Care 2 1059.06 7082.4 245.64
##
## Step: AIC=238.9
## Identity ~ Gender:Care + Gender:Fairness + Gender:Authority +
## Gender:Sanctity
##
## Df Sum of Sq RSS AIC
## - Gender:Authority 2 239.33 6335.9 236.63
## - Gender:Sanctity 2 255.55 6352.1 236.74
## - Gender:Fairness 2 261.80 6358.4 236.79
## <none> 6096.5 238.90
## - Gender:Care 2 1063.99 7160.5 242.14
##
## Step: AIC=236.63
## Identity ~ Gender:Care + Gender:Fairness + Gender:Sanctity
##
## Df Sum of Sq RSS AIC
## - Gender:Sanctity 2 67.01 6402.9 233.10
## - Gender:Fairness 2 241.48 6577.4 234.31
## <none> 6335.9 236.63
## - Gender:Care 2 1105.26 7441.1 239.87
##
## Step: AIC=233.1
## Identity ~ Gender:Care + Gender:Fairness
##
## Df Sum of Sq RSS AIC
## - Gender:Fairness 2 206.89 6609.8 230.53
## <none> 6402.9 233.10
## - Gender:Care 2 1124.36 7527.3 236.38
##
## Step: AIC=230.53
## Identity ~ Gender:Care
##
## Df Sum of Sq RSS AIC
## <none> 6609.8 230.53
## - Gender:Care 2 1499.9 8109.6 235.74##
## Call:
## lm(formula = Identity ~ Gender:Care, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.307 -7.977 -0.791 7.768 26.564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.2504 9.5479 5.054 8.95e-06 ***
## Genderfemale:Care 1.2822 0.4348 2.949 0.00519 **
## Gendermale:Care 1.1863 0.4984 2.380 0.02192 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.54 on 42 degrees of freedom
## Multiple R-squared: 0.1849, Adjusted R-squared: 0.1461
## F-statistic: 4.765 on 2 and 42 DF, p-value: 0.01364все факторы с их взаимодействием
Взаимодействия второго уровня
lm(Identity ~ (Gender + Care + Fairness + Loyalty + Authority + Sanctity)^2 , data = Data) %>%
step(steps = 10000) %>%
summary()## Start: AIC=241.95
## Identity ~ (Gender + Care + Fairness + Loyalty + Authority +
## Sanctity)^2
##
## Df Sum of Sq RSS AIC
## - Loyalty:Authority 1 9.79 3671.1 240.07
## - Gender:Loyalty 1 12.86 3674.2 240.11
## - Care:Authority 1 13.50 3674.9 240.12
## - Care:Loyalty 1 45.99 3707.4 240.51
## - Gender:Fairness 1 57.86 3719.2 240.66
## - Gender:Authority 1 62.71 3724.1 240.72
## - Care:Sanctity 1 148.90 3810.3 241.75
## - Fairness:Authority 1 162.77 3824.1 241.91
## <none> 3661.4 241.95
## - Gender:Sanctity 1 168.69 3830.1 241.98
## - Care:Fairness 1 267.71 3929.1 243.13
## - Loyalty:Sanctity 1 314.98 3976.3 243.66
## - Fairness:Loyalty 1 479.01 4140.4 245.49
## - Gender:Care 1 605.37 4266.7 246.84
## - Fairness:Sanctity 1 721.68 4383.0 248.05
## - Authority:Sanctity 1 852.33 4513.7 249.37
##
## Step: AIC=240.07
## Identity ~ Gender + Care + Fairness + Loyalty + Authority + Sanctity +
## Gender:Care + Gender:Fairness + Gender:Loyalty + Gender:Authority +
## Gender:Sanctity + Care:Fairness + Care:Loyalty + Care:Authority +
## Care:Sanctity + Fairness:Loyalty + Fairness:Authority + Fairness:Sanctity +
## Loyalty:Sanctity + Authority:Sanctity
##
## Df Sum of Sq RSS AIC
## - Care:Authority 1 8.56 3679.7 238.18
## - Gender:Loyalty 1 34.21 3705.4 238.49
## - Gender:Fairness 1 49.25 3720.4 238.67
## - Gender:Authority 1 56.30 3727.4 238.76
## - Care:Loyalty 1 57.54 3728.7 238.77
## <none> 3671.1 240.07
## - Fairness:Authority 1 194.04 3865.2 240.39
## - Gender:Sanctity 1 194.36 3865.5 240.39
## - Care:Sanctity 1 280.68 3951.8 241.39
## - Care:Fairness 1 306.10 3977.2 241.68
## - Fairness:Loyalty 1 478.23 4149.4 243.58
## - Gender:Care 1 600.57 4271.7 244.89
## - Loyalty:Sanctity 1 635.06 4306.2 245.25
## - Fairness:Sanctity 1 833.79 4504.9 247.28
## - Authority:Sanctity 1 1042.71 4713.9 249.32
##
## Step: AIC=238.18
## Identity ~ Gender + Care + Fairness + Loyalty + Authority + Sanctity +
## Gender:Care + Gender:Fairness + Gender:Loyalty + Gender:Authority +
## Gender:Sanctity + Care:Fairness + Care:Loyalty + Care:Sanctity +
## Fairness:Loyalty + Fairness:Authority + Fairness:Sanctity +
## Loyalty:Sanctity + Authority:Sanctity
##
## Df Sum of Sq RSS AIC
## - Gender:Loyalty 1 29.50 3709.2 236.54
## - Gender:Fairness 1 41.37 3721.1 236.68
## - Care:Loyalty 1 49.77 3729.5 236.78
## - Gender:Authority 1 92.38 3772.1 237.29
## <none> 3679.7 238.18
## - Fairness:Authority 1 192.45 3872.2 238.47
## - Gender:Sanctity 1 219.31 3899.0 238.78
## - Care:Sanctity 1 275.83 3955.5 239.43
## - Care:Fairness 1 359.76 4039.5 240.37
## - Fairness:Loyalty 1 523.68 4203.4 242.16
## - Gender:Care 1 640.62 4320.3 243.40
## - Loyalty:Sanctity 1 652.27 4332.0 243.52
## - Fairness:Sanctity 1 866.46 4546.2 245.69
## - Authority:Sanctity 1 1141.15 4820.9 248.33
##
## Step: AIC=236.54
## Identity ~ Gender + Care + Fairness + Loyalty + Authority + Sanctity +
## Gender:Care + Gender:Fairness + Gender:Authority + Gender:Sanctity +
## Care:Fairness + Care:Loyalty + Care:Sanctity + Fairness:Loyalty +
## Fairness:Authority + Fairness:Sanctity + Loyalty:Sanctity +
## Authority:Sanctity
##
## Df Sum of Sq RSS AIC
## - Care:Loyalty 1 34.30 3743.5 234.95
## - Gender:Fairness 1 40.46 3749.7 235.02
## <none> 3709.2 236.54
## - Gender:Authority 1 185.83 3895.0 236.74
## - Gender:Sanctity 1 193.38 3902.6 236.82
## - Fairness:Authority 1 199.48 3908.7 236.89
## - Care:Sanctity 1 253.76 3963.0 237.51
## - Care:Fairness 1 369.01 4078.2 238.80
## - Fairness:Loyalty 1 495.75 4205.0 240.18
## - Loyalty:Sanctity 1 626.40 4335.6 241.56
## - Gender:Care 1 655.14 4364.4 241.85
## - Fairness:Sanctity 1 845.96 4555.2 243.78
## - Authority:Sanctity 1 1115.08 4824.3 246.36
##
## Step: AIC=234.95
## Identity ~ Gender + Care + Fairness + Loyalty + Authority + Sanctity +
## Gender:Care + Gender:Fairness + Gender:Authority + Gender:Sanctity +
## Care:Fairness + Care:Sanctity + Fairness:Loyalty + Fairness:Authority +
## Fairness:Sanctity + Loyalty:Sanctity + Authority:Sanctity
##
## Df Sum of Sq RSS AIC
## - Gender:Fairness 1 38.42 3781.9 233.41
## - Gender:Authority 1 165.99 3909.5 234.90
## <none> 3743.5 234.95
## - Fairness:Authority 1 220.39 3963.9 235.52
## - Gender:Sanctity 1 232.82 3976.3 235.66
## - Care:Sanctity 1 247.11 3990.6 235.83
## - Care:Fairness 1 372.20 4115.7 237.22
## - Gender:Care 1 652.72 4396.2 240.18
## - Loyalty:Sanctity 1 679.34 4422.9 240.46
## - Fairness:Loyalty 1 794.89 4538.4 241.62
## - Fairness:Sanctity 1 1095.26 4838.8 244.50
## - Authority:Sanctity 1 1196.52 4940.0 245.43
##
## Step: AIC=233.41
## Identity ~ Gender + Care + Fairness + Loyalty + Authority + Sanctity +
## Gender:Care + Gender:Authority + Gender:Sanctity + Care:Fairness +
## Care:Sanctity + Fairness:Loyalty + Fairness:Authority + Fairness:Sanctity +
## Loyalty:Sanctity + Authority:Sanctity
##
## Df Sum of Sq RSS AIC
## - Gender:Authority 1 146.36 3928.3 233.12
## <none> 3781.9 233.41
## - Fairness:Authority 1 206.23 3988.2 233.80
## - Care:Sanctity 1 297.47 4079.4 234.82
## - Gender:Sanctity 1 326.21 4108.1 235.13
## - Gender:Care 1 628.91 4410.8 238.33
## - Loyalty:Sanctity 1 687.41 4469.3 238.93
## - Fairness:Loyalty 1 959.91 4741.8 241.59
## - Care:Fairness 1 1016.51 4798.4 242.12
## - Fairness:Sanctity 1 1060.65 4842.6 242.53
## - Authority:Sanctity 1 1250.04 5032.0 244.26
##
## Step: AIC=233.12
## Identity ~ Gender + Care + Fairness + Loyalty + Authority + Sanctity +
## Gender:Care + Gender:Sanctity + Care:Fairness + Care:Sanctity +
## Fairness:Loyalty + Fairness:Authority + Fairness:Sanctity +
## Loyalty:Sanctity + Authority:Sanctity
##
## Df Sum of Sq RSS AIC
## - Fairness:Authority 1 155.44 4083.7 232.87
## <none> 3928.3 233.12
## - Gender:Sanctity 1 196.50 4124.8 233.31
## - Care:Sanctity 1 316.97 4245.3 234.61
## - Gender:Care 1 624.12 4552.4 237.75
## - Loyalty:Sanctity 1 662.37 4590.7 238.13
## - Fairness:Loyalty 1 938.64 4866.9 240.76
## - Care:Fairness 1 990.31 4918.6 241.24
## - Fairness:Sanctity 1 1040.35 4968.6 241.69
## - Authority:Sanctity 1 1186.58 5114.9 243.00
##
## Step: AIC=232.86
## Identity ~ Gender + Care + Fairness + Loyalty + Authority + Sanctity +
## Gender:Care + Gender:Sanctity + Care:Fairness + Care:Sanctity +
## Fairness:Loyalty + Fairness:Sanctity + Loyalty:Sanctity +
## Authority:Sanctity
##
## Df Sum of Sq RSS AIC
## <none> 4083.7 232.87
## - Gender:Sanctity 1 232.95 4316.7 233.36
## - Care:Sanctity 1 270.95 4354.7 233.76
## - Loyalty:Sanctity 1 534.03 4617.8 236.40
## - Gender:Care 1 653.37 4737.1 237.54
## - Fairness:Loyalty 1 811.19 4894.9 239.02
## - Fairness:Sanctity 1 950.35 5034.1 240.28
## - Care:Fairness 1 995.53 5079.3 240.68
## - Authority:Sanctity 1 1351.33 5435.1 243.73##
## Call:
## lm(formula = Identity ~ Gender + Care + Fairness + Loyalty +
## Authority + Sanctity + Gender:Care + Gender:Sanctity + Care:Fairness +
## Care:Sanctity + Fairness:Loyalty + Fairness:Sanctity + Loyalty:Sanctity +
## Authority:Sanctity, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.5742 -6.7531 -0.9228 5.9870 22.1438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -60.1540 55.0719 -1.092 0.283404
## Gendermale 55.3643 30.5707 1.811 0.080162 .
## Care 8.4652 3.7869 2.235 0.032984 *
## Fairness 5.3647 2.7712 1.936 0.062356 .
## Loyalty 13.4362 4.8837 2.751 0.009970 **
## Authority -6.0851 2.0057 -3.034 0.004949 **
## Sanctity -12.7805 3.3882 -3.772 0.000711 ***
## Gendermale:Care -3.4512 1.5753 -2.191 0.036368 *
## Gendermale:Sanctity 1.2503 0.9558 1.308 0.200750
## Care:Fairness -0.3337 0.1234 -2.704 0.011168 *
## Care:Sanctity 0.1736 0.1230 1.411 0.168577
## Fairness:Loyalty -0.4325 0.1772 -2.441 0.020756 *
## Fairness:Sanctity 0.4233 0.1602 2.642 0.012959 *
## Loyalty:Sanctity -0.2885 0.1456 -1.981 0.056861 .
## Authority:Sanctity 0.3518 0.1117 3.151 0.003676 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.67 on 30 degrees of freedom
## Multiple R-squared: 0.4964, Adjusted R-squared: 0.2614
## F-statistic: 2.113 on 14 and 30 DF, p-value: 0.04202- это взаимодействи.
- это простое и взаимодействие This is the same as first + second + first:second.