options(repos = c(CRAN = "https://cloud.r-project.org"))
install.packages("pastecs")
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install.packages("lm.beta")
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install.packages("QuantPsyc")
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install.packages("interactions")
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install.packages("jtools")
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install.packages("multilevel")
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install.packages("boot")
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library(pastecs)
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library(lm.beta)
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library(QuantPsyc)
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##
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library(interactions)
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library(jtools)
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library(multilevel)
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library(boot)
library(readxl)
#Import Data
Data <-read_xlsx(file.choose())
attach(Data)
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$salary.center <- meanCenter(salary)
Data$last.eval.center <- meanCenter(last_evaluation)
Data$avg.center <- meanCenter(average_monthly_hours)
attach(Data)
## The following objects are masked from Data (pos = 3):
##
## average_monthly_hours, department, last_evaluation, left,
## number_project, promotion_last_5years, salary, satisfaction_level,
## time_spend_company, Work_accident
#assumptions test
#DataFrame with Moderation Variables
DataFrame <- Data[, c("satisfaction_level", "last_evaluation", "average_monthly_hours", "salary")]
#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
## salary
## nbr.val 14999.00000000
## nbr.null 0.00000000
## nbr.na 0.00000000
## min 1.00000000
## max 3.00000000
## range 2.00000000
## sum 23919.00000000
## median 2.00000000
## mean 1.59470631
## SE.mean 0.00520275
## CI.mean.0.95 0.01019803
## var 0.40600211
## std.dev 0.63718295
## coef.var 0.39956131
cor(DataFrame)
## satisfaction_level last_evaluation average_monthly_hours
## satisfaction_level 1.00000000 0.10502121 -0.020048113
## last_evaluation 0.10502121 1.00000000 0.339741800
## average_monthly_hours -0.02004811 0.33974180 1.000000000
## salary 0.05002248 -0.01300152 -0.002241879
## salary
## satisfaction_level 0.050022481
## last_evaluation -0.013001516
## average_monthly_hours -0.002241879
## salary 1.000000000
#Assumptions Testing
Assumptions_Mod <- lm(satisfaction_level ~ last_evaluation + average_monthly_hours + salary)
par(mfrow=c(2,2))
plot(Assumptions_Mod)
#Moderation Analysis
Mod <- lm(satisfaction_level ~ avg.center + salary.center + avg.center:salary.center + last.eval.center ,
data = Data)
summary(Mod)
##
## Call:
## lm(formula = satisfaction_level ~ avg.center + salary.center +
## avg.center:salary.center + last.eval.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
## avg.center -0.00031235 0.00004285 -7.289 0.000000000000329
## salary.center 0.02018091 0.00315898 6.388 0.000000000172534
## last.eval.center 0.18619064 0.01251955 14.872 < 0.0000000000000002
## avg.center:salary.center 0.00014885 0.00006416 2.320 0.0204
##
## (Intercept) ***
## avg.center ***
## salary.center ***
## last.eval.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
confint(Mod)
## 2.5 % 97.5 %
## (Intercept) 0.60889937416 0.6167889074
## avg.center -0.00039634813 -0.0002283504
## salary.center 0.01398893538 0.0263728936
## last.eval.center 0.16165079550 0.2107304885
## avg.center:salary.center 0.00002308887 0.0002746072
#Moderation Simple Slopes
Mod2 <- lm(satisfaction_level ~ avg.center + salary.center + avg.center:salary.center, data = Data)
ModSlopes <- sim.slopes(Mod2, salary.center)
ModSlopes
## INT Slope SE LCL UCL
## at zHigh 0.6252951 -0.00003371294 0.00005892912 -0.0001492212 0.00008179534
## at zMean 0.6128406 -0.00009679998 0.00004062352 -0.0001764270 -0.00001717293
## at zLow 0.6003861 -0.00015988703 0.00005665883 -0.0002709453 -0.00004882881
#Moderation Simple Slopes Plot
interact_plot(Mod2, pred = avg.center, modx = salary.center)
## Warning: -0.637182950469582 is outside the observed range of salary.center
