#istall.packages("pastecs")
#nstall.packages("lm.beta")
#nstall.packages("QuantPsyc")
#install.packages("interactions")
#install.packages("jtools")`
#install.packages("multilevel")
#install.packages("boot")
library(pastecs)
## Warning: package 'pastecs' was built under R version 4.5.2
library(lm.beta)
## Warning: package 'lm.beta' was built under R version 4.5.2
library(QuantPsyc)
## Warning: package 'QuantPsyc' was built under R version 4.5.2
## Loading required package: boot
## Warning: package 'boot' was built under R version 4.5.2
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:pastecs':
##
## first, last
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## Loading required package: purrr
## Loading required package: MASS
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## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
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## select
##
## Attaching package: 'QuantPsyc'
## The following object is masked from 'package:lm.beta':
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## lm.beta
## The following object is masked from 'package:base':
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## norm
library(interactions)
## Warning: package 'interactions' was built under R version 4.5.2
library(jtools)
## Warning: package 'jtools' was built under R version 4.5.2
library(multilevel)
## Warning: package 'multilevel' was built under R version 4.5.2
## Loading required package: nlme
##
## Attaching package: 'nlme'
## The following object is masked from 'package:dplyr':
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## collapse
library("boot")
#Import Data
Data <- read.csv("C:\\Users\\lesle\\OneDrive\\Документы\\R\\Human Resources Data .csv", header = TRUE, stringsAsFactors = FALSE)
names(Data)
## [1] "satisfaction_level" "last_evaluation" "number_project"
## [4] "average_monthly_hours" "time_spend_company" "Work_accident"
## [7] "left" "promotion_last_5years" "department"
## [10] "salary"
#Remove scientific notation
options(scipen = 999)
#Moderation with Covariates
#Variables:
#IV: average monthly hours
#Covariate: last evaluation
#DV: satisfaction level
#Moderator: salary
#Center IVs and Moderator
Data$salary.center <- meanCenter(Data$salary)
Data$last.eval.center <- meanCenter(Data$last_evaluation)
Data$avg.center <- meanCenter(Data$average_monthly_hours)
#assumptions test
#DataFrame with Moderation Variables
DataFrame <- Data[, c("satisfaction_level", "last_evaluation", "average_monthly_hours")]
#Descriptive Statistics (mean and SD) and Correlation
stat.desc(DataFrame)
## satisfaction_level last_evaluation average_monthly_hours
## nbr.val 14999.000000000 14999.000000000 14999.0000000
## nbr.null 0.000000000 0.000000000 0.0000000
## nbr.na 0.000000000 0.000000000 0.0000000
## min 0.090000000 0.360000000 96.0000000
## max 1.000000000 1.000000000 310.0000000
## range 0.910000000 0.640000000 214.0000000
## sum 9191.890000000 10740.810000000 3015554.0000000
## median 0.640000000 0.720000000 200.0000000
## mean 0.612833522 0.716101740 201.0503367
## SE.mean 0.002030128 0.001397637 0.4077973
## CI.mean.0.95 0.003979300 0.002739538 0.7993325
## var 0.061817201 0.029298864 2494.3131748
## std.dev 0.248630651 0.171169111 49.9430994
## coef.var 0.405706676 0.239029039 0.2484109
cor(DataFrame, use = "pairwise.complete.obs")
## satisfaction_level last_evaluation average_monthly_hours
## satisfaction_level 1.00000000 0.1050212 -0.02004811
## last_evaluation 0.10502121 1.0000000 0.33974180
## average_monthly_hours -0.02004811 0.3397418 1.00000000
#Assumptions Testing
Assumptions_Mod <- lm(satisfaction_level ~ last.eval.center + avg.center + salary.center, data = Data)
par(mfrow=c(2,2))
plot(Assumptions_Mod)
#Moderation
Mod2 <- lm(satisfaction_level ~ last.eval.center + avg.center * salary.center, data = Data)
summary(Mod2)
##
## Call:
## lm(formula = satisfaction_level ~ last.eval.center + avg.center *
## salary.center, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.58298 -0.15955 0.02843 0.19662 0.46346
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.61284414 0.00201251 304.517 < 0.0000000000000002
## last.eval.center 0.18619064 0.01251955 14.872 < 0.0000000000000002
## avg.center -0.00031235 0.00004285 -7.289 0.000000000000329
## salary.center 0.02018091 0.00315898 6.388 0.000000000172534
## avg.center:salary.center 0.00014885 0.00006416 2.320 0.0204
##
## (Intercept) ***
## last.eval.center ***
## avg.center ***
## salary.center ***
## avg.center:salary.center *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2465 on 14994 degrees of freedom
## Multiple R-squared: 0.01755, Adjusted R-squared: 0.01729
## F-statistic: 66.95 on 4 and 14994 DF, p-value: < 0.00000000000000022
interact_plot(Mod2, pred = avg.center, modx = salary.center)
## Warning: -0.637182950469582 is outside the observed range of salary.center
ModSlopes <- sim_slopes(Mod2, pred = avg.center, modx = salary.center)
## Warning: -0.637182950469582 is outside the observed range of salary.center
ModSlopes
## JOHNSON-NEYMAN INTERVAL
##
## When salary.center is OUTSIDE the interval [1.04, 13.69], the slope of
## avg.center is p < .05.
##
## Note: The range of observed values of salary.center is [-0.59, 1.41]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of avg.center when salary.center = -0.63718295046958195460007 (- 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.00 0.00 -6.94 0.00
##
## Slope of avg.center when salary.center = -0.00000000000000003193214 (Mean):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.00 0.00 -7.29 0.00
##
## Slope of avg.center when salary.center = 0.63718295046958195460007 (+ 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.00 0.00 -3.64 0.00