# Set the working directory to the location on your computer.
x <- getwd()
setwd(x)

library(knitr)
library(reshape2)
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, union

library(lattice)
library(magrittr)
library(lme4)

## Warning: package 'lme4' was built under R version 3.6.2

## Loading required package: Matrix

library(lmerTest)

## Warning: package 'lmerTest' was built under R version 3.6.2

## 
## Attaching package: 'lmerTest'

## The following object is masked from 'package:lme4':
## 
##     lmer

## The following object is masked from 'package:stats':
## 
##     step

library(optimx)

## Warning: package 'optimx' was built under R version 3.6.3

library(knitr)
opts_chunk$set(fig.path='figure/',fig.ext=NULL)

data <- read.table("bfijudgement.w.snsfreq.csv" ,header = T, sep = ",")
colnames(data) [1]<- "JID"

#IG - Instagram
#TW - Twitter
#sr - selfreport 
#Con/Cond - condition

IG.Cond <- read.table("IGCon.csv", header = T, sep = ",")
TW.Cond <- read.table("TWCon.csv", header = T, sep = ",")

#sometimes the first column in the data set will have a weird name. This is for changing that.
names(IG.Cond)[names(IG.Cond) == "ï..ConType"] <- "ConType"
names(TW.Cond)[names(TW.Cond) == "ï..ConType"] <- "ConType"


# Each target has a realistic accuracy criterion composed of a self-rating of the BFI2-S
# read in target self-reports
sr.targets = read.table("targets1.csv", fill= TRUE, header = T, sep = ",")

colnames(sr.targets) [1]<- 'TID'

# In order to properly organize the data, there must be a seperate dataframe for each targets' self-ratings.
# Create dataframes for those targets that have validity ratings based on the BFI2S.
sr.1   <- sr.targets %>% filter(TID == "1")

sr.2   <- sr.targets %>% filter(TID == "2")

sr.3   <- sr.targets %>% filter(TID == "3")

sr.4   <- sr.targets %>% filter(TID == "4")

sr.5   <- sr.targets %>% filter(TID == "5")
sr.6   <- sr.targets %>% filter(TID == "6")

sr.7   <- sr.targets %>% filter(TID == "7")

sr.8   <- sr.targets %>% filter(TID == "8")

sr.9   <- sr.targets %>% filter(TID == "9")

sr.10   <- sr.targets %>% filter(TID == "10")
sr.11  <- sr.targets %>% filter(TID == "11")

sr.12   <- sr.targets %>% filter(TID == "12")

sr.13   <- sr.targets %>% filter(TID == "13")

sr.14   <- sr.targets %>% filter(TID == "14")

sr.15   <- sr.targets %>% filter(TID == "15")
sr.16   <- sr.targets %>% filter(TID == "16")

sr.17   <- sr.targets %>% filter(TID == "17")

sr.18   <- sr.targets %>% filter(TID == "18")

sr.19   <- sr.targets %>% filter(TID == "19")

sr.20   <- sr.targets %>% filter(TID == "20")
sr.21   <- sr.targets %>% filter(TID == "21")

sr.22   <- sr.targets %>% filter(TID == "22")

sr.23   <- sr.targets %>% filter(TID == "23")

sr.24   <- sr.targets %>% filter(TID == "24")

sr.25   <- sr.targets %>% filter(TID == "25")
sr.26   <- sr.targets %>% filter(TID == "26")

sr.27   <- sr.targets %>% filter(TID == "27")

sr.28   <- sr.targets %>% filter(TID == "28")

sr.29   <- sr.targets %>% filter(TID == "29")

sr.30   <- sr.targets %>% filter(TID == "30")
sr.31   <- sr.targets %>% filter(TID == "31")

sr.32   <- sr.targets %>% filter(TID == "32")

sr.33   <- sr.targets %>% filter(TID == "33")

sr.34   <- sr.targets %>% filter(TID == "34")

sr.35   <- sr.targets %>% filter(TID == "35")
sr.36   <- sr.targets %>% filter(TID == "36")

sr.37   <- sr.targets %>% filter(TID == "37")

sr.38   <- sr.targets %>% filter(TID == "38")

sr.39   <- sr.targets %>% filter(TID == "39")

sr.40   <- sr.targets %>% filter(TID == "40")
sr.41   <- sr.targets %>% filter(TID == "41")

sr.42   <- sr.targets %>% filter(TID == "42")

sr.43   <- sr.targets %>% filter(TID == "43")

sr.44   <- sr.targets %>% filter(TID == "44")

sr.45   <- sr.targets %>% filter(TID == "45")
sr.46   <- sr.targets %>% filter(TID == "46")

sr.47   <- sr.targets %>% filter(TID == "47")

sr.48   <- sr.targets %>% filter(TID == "48")

sr.49   <- sr.targets %>% filter(TID == "49")

sr.50   <- sr.targets %>% filter(TID == "50")
sr.51   <- sr.targets %>% filter(TID == "51")

sr.52   <- sr.targets %>% filter(TID == "52")

sr.53   <- sr.targets %>% filter(TID == "53")

sr.54   <- sr.targets %>% filter(TID == "54")

sr.55   <- sr.targets %>% filter(TID == "55")
sr.56   <- sr.targets %>% filter(TID == "56")

sr.57   <- sr.targets %>% filter(TID == "57")

sr.58   <- sr.targets %>% filter(TID == "58")

sr.59   <- sr.targets %>% filter(TID == "59")

sr.60   <- sr.targets %>% filter(TID == "60")
sr.61   <- sr.targets %>% filter(TID == "61")

sr.62   <- sr.targets %>% filter(TID == "62")

sr.63   <- sr.targets %>% filter(TID == "63")

sr.64   <- sr.targets %>% filter(TID == "64")

sr.65   <- sr.targets %>% filter(TID == "65")
sr.66   <- sr.targets %>% filter(TID == "66")

sr.67   <- sr.targets %>% filter(TID == "67")

sr.68   <- sr.targets %>% filter(TID == "68")

sr.69   <- sr.targets %>% filter(TID == "69")

sr.70   <- sr.targets %>% filter(TID == "70")
sr.71   <- sr.targets %>% filter(TID == "71")

sr.72   <- sr.targets %>% filter(TID == "72")

sr.73   <- sr.targets %>% filter(TID == "73")

sr.74   <- sr.targets %>% filter(TID == "74")

sr.75   <- sr.targets %>% filter(TID == "75")
sr.76   <- sr.targets %>% filter(TID == "76")

sr.77   <- sr.targets %>% filter(TID == "77")

sr.78   <- sr.targets %>% filter(TID == "78")

sr.79   <- sr.targets %>% filter(TID == "79")

sr.80   <- sr.targets %>% filter(TID == "80")
sr.81   <- sr.targets %>% filter(TID == "81")

sr.82   <- sr.targets %>% filter(TID == "82")

sr.83   <- sr.targets %>% filter(TID == "83")

sr.84   <- sr.targets %>% filter(TID == "84")

sr.85   <- sr.targets %>% filter(TID == "85")
sr.86   <- sr.targets %>% filter(TID == "86")

sr.87   <- sr.targets %>% filter(TID == "87")

sr.88   <- sr.targets %>% filter(TID == "88")

sr.89   <- sr.targets %>% filter(TID == "89")

sr.90   <- sr.targets %>% filter(TID == "90")
sr.91   <- sr.targets %>% filter(TID == "91")

sr.92   <- sr.targets %>% filter(TID == "92")

sr.93   <- sr.targets %>% filter(TID == "93")

sr.94   <- sr.targets %>% filter(TID == "94")

sr.95   <- sr.targets %>% filter(TID == "95")
sr.96   <- sr.targets %>% filter(TID == "96")

sr.97   <- sr.targets %>% filter(TID == "97")

sr.98   <- sr.targets %>% filter(TID == "98")

sr.99   <- sr.targets %>% filter(TID == "99")

sr.100   <- sr.targets %>% filter(TID == "100")
sr.101   <- sr.targets %>% filter(TID == "101")

sr.102   <- sr.targets %>% filter(TID == "102")

sr.103   <- sr.targets %>% filter(TID == "103")

sr.104   <- sr.targets %>% filter(TID == "104")

sr.105   <- sr.targets %>% filter(TID == "105")
sr.106   <- sr.targets %>% filter(TID == "106")

sr.107   <- sr.targets %>% filter(TID == "107")

sr.108   <- sr.targets %>% filter(TID == "108")

sr.109   <- sr.targets %>% filter(TID == "109")

sr.110   <- sr.targets %>% filter(TID == "110")
sr.111   <- sr.targets %>% filter(TID == "111")
sr.112   <- sr.targets %>% filter(TID == "112")

sr.113   <- sr.targets %>% filter(TID == "113")

sr.114   <- sr.targets %>% filter(TID == "114")

sr.115   <- sr.targets %>% filter(TID == "115")

sr.116   <- sr.targets %>% filter(TID == "116")
sr.117   <- sr.targets %>% filter(TID == "117")

sr.118   <- sr.targets %>% filter(TID == "118")

sr.119   <- sr.targets %>% filter(TID == "119")

sr.120   <- sr.targets %>% filter(TID == "120")

sr.121   <- sr.targets %>% filter(TID == "121")
sr.122   <- sr.targets %>% filter(TID == "122")

sr.123   <- sr.targets %>% filter(TID == "123")

sr.124   <- sr.targets %>% filter(TID == "124")

sr.125   <- sr.targets %>% filter(TID == "125")

sr.126   <- sr.targets %>% filter(TID == "126")
sr.127   <- sr.targets %>% filter(TID == "127")

sr.128   <- sr.targets %>% filter(TID == "128")

sr.129   <- sr.targets %>% filter(TID == "129")

sr.130   <- sr.targets %>% filter(TID == "130")
sr.131   <- sr.targets %>% filter(TID == "131")
sr.132   <- sr.targets %>% filter(TID == "132")

sr.133   <- sr.targets %>% filter(TID == "133")

sr.134   <- sr.targets %>% filter(TID == "134")

sr.135   <- sr.targets %>% filter(TID == "135")

sr.136   <- sr.targets %>% filter(TID == "136")
sr.137   <- sr.targets %>% filter(TID == "137")

sr.138   <- sr.targets %>% filter(TID == "138")

sr.139   <- sr.targets %>% filter(TID == "139")

sr.140   <- sr.targets %>% filter(TID == "140")
sr.141   <- sr.targets %>% filter(TID == "141")
sr.142   <- sr.targets %>% filter(TID == "142")

sr.143   <- sr.targets %>% filter(TID == "143")

sr.144   <- sr.targets %>% filter(TID == "144")

sr.145   <- sr.targets %>% filter(TID == "145")

sr.146   <- sr.targets %>% filter(TID == "146")
sr.147   <- sr.targets %>% filter(TID == "147")

sr.148   <- sr.targets %>% filter(TID == "148")

sr.149   <- sr.targets %>% filter(TID == "149")

sr.150   <- sr.targets %>% filter(TID == "150")




# Import the normative profile for each of the BFI2-S.
normbfi = read.table("NormativeBFI2.csv", fill=TRUE, header = T, sep = ",")

longcrit.1.bfi2 <- melt(sr.1, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.2.bfi2 <- melt(sr.2, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.3.bfi2 <- melt(sr.3, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.4.bfi2 <- melt(sr.4, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.5.bfi2 <- melt(sr.5, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.6.bfi2 <- melt(sr.6, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.7.bfi2 <- melt(sr.7, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.8.bfi2 <- melt(sr.8, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.9.bfi2 <- melt(sr.9, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.10.bfi2 <- melt(sr.10, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.11.bfi2 <- melt(sr.11, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.12.bfi2 <- melt(sr.12, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.13.bfi2 <- melt(sr.13, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.14.bfi2 <- melt(sr.14, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.15.bfi2 <- melt(sr.15, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.16.bfi2 <- melt(sr.16, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.17.bfi2 <- melt(sr.17, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.18.bfi2 <- melt(sr.18, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.19.bfi2 <- melt(sr.19, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.20.bfi2 <- melt(sr.20, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.21.bfi2 <- melt(sr.21, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.22.bfi2 <- melt(sr.22, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.23.bfi2 <- melt(sr.23, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.24.bfi2 <- melt(sr.24, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.25.bfi2 <- melt(sr.25, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.26.bfi2 <- melt(sr.26, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.27.bfi2 <- melt(sr.27, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.28.bfi2 <- melt(sr.28, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.29.bfi2 <- melt(sr.29, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.30.bfi2 <- melt(sr.30, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.31.bfi2 <- melt(sr.31, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.32.bfi2 <- melt(sr.32, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.33.bfi2 <- melt(sr.33, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.34.bfi2 <- melt(sr.34, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.35.bfi2 <- melt(sr.35, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.36.bfi2 <- melt(sr.36, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.37.bfi2 <- melt(sr.37, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.38.bfi2 <- melt(sr.38, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.39.bfi2 <- melt(sr.39, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.40.bfi2 <- melt(sr.40, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.41.bfi2 <- melt(sr.41, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.42.bfi2 <- melt(sr.42, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.43.bfi2 <- melt(sr.43, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.44.bfi2 <- melt(sr.44, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.45.bfi2 <- melt(sr.45, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.46.bfi2 <- melt(sr.46, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.47.bfi2 <- melt(sr.47, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.48.bfi2 <- melt(sr.48, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.49.bfi2 <- melt(sr.49, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.50.bfi2 <- melt(sr.50, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.51.bfi2 <- melt(sr.51, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.52.bfi2 <- melt(sr.52, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.53.bfi2 <- melt(sr.53, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.54.bfi2 <- melt(sr.54, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.55.bfi2 <- melt(sr.55, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.56.bfi2 <- melt(sr.56, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.57.bfi2 <- melt(sr.57, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.58.bfi2 <- melt(sr.58, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.59.bfi2 <- melt(sr.59, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.60.bfi2 <- melt(sr.60, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.61.bfi2 <- melt(sr.61, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.62.bfi2 <- melt(sr.62, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.63.bfi2 <- melt(sr.63, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.64.bfi2 <- melt(sr.64, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.65.bfi2 <- melt(sr.65, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.66.bfi2 <- melt(sr.66, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.67.bfi2 <- melt(sr.67, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.68.bfi2 <- melt(sr.68, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.69.bfi2 <- melt(sr.69, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.70.bfi2 <- melt(sr.70, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.71.bfi2 <- melt(sr.71, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.72.bfi2 <- melt(sr.72, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.73.bfi2 <- melt(sr.73, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.74.bfi2 <- melt(sr.74, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.75.bfi2 <- melt(sr.75, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.76.bfi2 <- melt(sr.76, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.77.bfi2 <- melt(sr.77, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.78.bfi2 <- melt(sr.78, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.79.bfi2 <- melt(sr.79, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.80.bfi2 <- melt(sr.80, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.81.bfi2 <- melt(sr.81, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.82.bfi2 <- melt(sr.82, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.83.bfi2 <- melt(sr.83, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.84.bfi2 <- melt(sr.84, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.85.bfi2 <- melt(sr.85, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.86.bfi2 <- melt(sr.86, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.87.bfi2 <- melt(sr.87, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.88.bfi2 <- melt(sr.88, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.89.bfi2 <- melt(sr.89, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.90.bfi2 <- melt(sr.90, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.91.bfi2 <- melt(sr.91, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.92.bfi2 <- melt(sr.92, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.93.bfi2 <- melt(sr.93, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.94.bfi2 <- melt(sr.94, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.95.bfi2 <- melt(sr.95, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.96.bfi2 <- melt(sr.96, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.97.bfi2 <- melt(sr.97, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.98.bfi2 <- melt(sr.98, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.99.bfi2 <- melt(sr.99, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.100.bfi2 <- melt(sr.100, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.101.bfi2 <- melt(sr.101, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.102.bfi2 <- melt(sr.102, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.103.bfi2 <- melt(sr.103, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.104.bfi2 <- melt(sr.104, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.105.bfi2 <- melt(sr.105, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.106.bfi2 <- melt(sr.106, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.107.bfi2 <- melt(sr.107, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.108.bfi2 <- melt(sr.108, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.109.bfi2 <- melt(sr.109, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.110.bfi2 <- melt(sr.110, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.111.bfi2 <- melt(sr.111, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.112.bfi2 <- melt(sr.112, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.113.bfi2 <- melt(sr.113, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.114.bfi2 <- melt(sr.114, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.115.bfi2 <- melt(sr.115, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.116.bfi2 <- melt(sr.116, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.117.bfi2 <- melt(sr.117, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.118.bfi2 <- melt(sr.118, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.119.bfi2 <- melt(sr.119, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.120.bfi2 <- melt(sr.120, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.121.bfi2 <- melt(sr.121, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.122.bfi2 <- melt(sr.122, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.123.bfi2 <- melt(sr.123, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.124.bfi2 <- melt(sr.124, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.125.bfi2 <- melt(sr.125, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.126.bfi2 <- melt(sr.126, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.127.bfi2 <- melt(sr.127, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.128.bfi2 <- melt(sr.128, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.129.bfi2 <- melt(sr.129, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.130.bfi2 <- melt(sr.130, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.131.bfi2 <- melt(sr.131, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.132.bfi2 <- melt(sr.132, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.133.bfi2 <- melt(sr.133, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.134.bfi2 <- melt(sr.134, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.135.bfi2 <- melt(sr.135, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.136.bfi2 <- melt(sr.136, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.137.bfi2 <- melt(sr.137, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.138.bfi2 <- melt(sr.138, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.139.bfi2 <- melt(sr.139, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.140.bfi2 <- melt(sr.140, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.141.bfi2 <- melt(sr.141, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.142.bfi2 <- melt(sr.142, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.143.bfi2 <- melt(sr.143, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.144.bfi2 <- melt(sr.144, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.145.bfi2 <- melt(sr.145, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.146.bfi2 <- melt(sr.146, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.147.bfi2 <- melt(sr.147, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.148.bfi2 <- melt(sr.148, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.149.bfi2 <- melt(sr.149, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")
longcrit.150.bfi2 <- melt(sr.150, id.vars = c(1), measure.vars = c(2:31), variable.name = "Sr.item", value.name = "Sr")

longnorm.bfi2 <- melt(normbfi, measure.vars = c(1:30), variable.name = "Norm.item", value.name = "Norm")

# BFI2S
longratings.1.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2:31), variable.name = "Rating.item", value.name = "Rating")
longratings.1.bfi2 <- longratings.1.bfi2[order(longratings.1.bfi2$JID, longratings.1.bfi2$Rating.item),]

longratings.2.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(32:61), variable.name = "Rating.item", value.name = "Rating")
longratings.2.bfi2 <- longratings.2.bfi2[order(longratings.2.bfi2$JID, longratings.2.bfi2$Rating.item),]

longratings.3.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(62:91), variable.name = "Rating.item", value.name = "Rating")
longratings.3.bfi2 <- longratings.3.bfi2[order(longratings.3.bfi2$JID, longratings.3.bfi2$Rating.item),]

longratings.4.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(92:121), variable.name = "Rating.item", value.name = "Rating")
longratings.4.bfi2 <- longratings.4.bfi2[order(longratings.4.bfi2$JID, longratings.4.bfi2$Rating.item),]

longratings.5.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(122:151), variable.name = "Rating.item", value.name = "Rating")
longratings.5.bfi2 <- longratings.5.bfi2[order(longratings.5.bfi2$JID, longratings.5.bfi2$Rating.item),]

longratings.6.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(152:181), variable.name = "Rating.item", value.name = "Rating")
longratings.6.bfi2 <- longratings.6.bfi2[order(longratings.6.bfi2$JID, longratings.6.bfi2$Rating.item),]

longratings.7.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(182:211), variable.name = "Rating.item", value.name = "Rating")
longratings.7.bfi2 <- longratings.7.bfi2[order(longratings.7.bfi2$JID, longratings.7.bfi2$Rating.item),]

longratings.8.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(212:241), variable.name = "Rating.item", value.name = "Rating")
longratings.8.bfi2 <- longratings.8.bfi2[order(longratings.8.bfi2$JID, longratings.8.bfi2$Rating.item),]

longratings.9.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(242:271), variable.name = "Rating.item", value.name = "Rating")
longratings.9.bfi2 <- longratings.9.bfi2[order(longratings.9.bfi2$JID, longratings.9.bfi2$Rating.item),]

longratings.10.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(272:301), variable.name = "Rating.item", value.name = "Rating")
longratings.10.bfi2 <- longratings.10.bfi2[order(longratings.10.bfi2$JID, longratings.10.bfi2$Rating.item),]

longratings.11.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(302:331), variable.name = "Rating.item", value.name = "Rating")
longratings.11.bfi2 <- longratings.11.bfi2[order(longratings.11.bfi2$JID, longratings.11.bfi2$Rating.item),]

longratings.12.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(332:361), variable.name = "Rating.item", value.name = "Rating")
longratings.12.bfi2 <- longratings.12.bfi2[order(longratings.12.bfi2$JID, longratings.12.bfi2$Rating.item),]

longratings.13.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(362:391), variable.name = "Rating.item", value.name = "Rating")
longratings.13.bfi2 <- longratings.13.bfi2[order(longratings.13.bfi2$JID, longratings.13.bfi2$Rating.item),]

longratings.14.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(392:421), variable.name = "Rating.item", value.name = "Rating")
longratings.14.bfi2 <- longratings.14.bfi2[order(longratings.14.bfi2$JID, longratings.14.bfi2$Rating.item),]

longratings.15.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(422:451), variable.name = "Rating.item", value.name = "Rating")
longratings.15.bfi2 <- longratings.15.bfi2[order(longratings.15.bfi2$JID, longratings.15.bfi2$Rating.item),]

longratings.16.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(452:481), variable.name = "Rating.item", value.name = "Rating")
longratings.16.bfi2 <- longratings.16.bfi2[order(longratings.16.bfi2$JID, longratings.16.bfi2$Rating.item),]

longratings.17.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(482:511), variable.name = "Rating.item", value.name = "Rating")
longratings.17.bfi2 <- longratings.17.bfi2[order(longratings.17.bfi2$JID, longratings.17.bfi2$Rating.item),]

longratings.18.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(512:541), variable.name = "Rating.item", value.name = "Rating")
longratings.18.bfi2 <- longratings.18.bfi2[order(longratings.18.bfi2$JID, longratings.18.bfi2$Rating.item),]

longratings.19.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(542:571), variable.name = "Rating.item", value.name = "Rating")
longratings.19.bfi2 <- longratings.19.bfi2[order(longratings.19.bfi2$JID, longratings.19.bfi2$Rating.item),]

longratings.20.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(572:601), variable.name = "Rating.item", value.name = "Rating")
longratings.20.bfi2 <- longratings.20.bfi2[order(longratings.20.bfi2$JID, longratings.20.bfi2$Rating.item),]

longratings.21.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(602:631), variable.name = "Rating.item", value.name = "Rating")
longratings.21.bfi2 <- longratings.21.bfi2[order(longratings.21.bfi2$JID, longratings.21.bfi2$Rating.item),]

longratings.22.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(632:661), variable.name = "Rating.item", value.name = "Rating")
longratings.22.bfi2 <- longratings.22.bfi2[order(longratings.22.bfi2$JID, longratings.22.bfi2$Rating.item),]

longratings.23.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(662:691), variable.name = "Rating.item", value.name = "Rating")
longratings.23.bfi2 <- longratings.23.bfi2[order(longratings.23.bfi2$JID, longratings.23.bfi2$Rating.item),]

longratings.24.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(692:721), variable.name = "Rating.item", value.name = "Rating")
longratings.24.bfi2 <- longratings.24.bfi2[order(longratings.24.bfi2$JID, longratings.24.bfi2$Rating.item),]

longratings.25.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(722:751), variable.name = "Rating.item", value.name = "Rating")
longratings.25.bfi2 <- longratings.25.bfi2[order(longratings.25.bfi2$JID, longratings.25.bfi2$Rating.item),]

longratings.26.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(752:781), variable.name = "Rating.item", value.name = "Rating")
longratings.26.bfi2 <- longratings.26.bfi2[order(longratings.26.bfi2$JID, longratings.26.bfi2$Rating.item),]

longratings.27.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(782:811), variable.name = "Rating.item", value.name = "Rating")
longratings.27.bfi2 <- longratings.27.bfi2[order(longratings.27.bfi2$JID, longratings.27.bfi2$Rating.item),]

longratings.28.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(812:841), variable.name = "Rating.item", value.name = "Rating")
longratings.28.bfi2 <- longratings.28.bfi2[order(longratings.28.bfi2$JID, longratings.28.bfi2$Rating.item),]

longratings.29.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(842:871), variable.name = "Rating.item", value.name = "Rating")
longratings.29.bfi2 <- longratings.29.bfi2[order(longratings.29.bfi2$JID, longratings.29.bfi2$Rating.item),]

longratings.30.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(872:901), variable.name = "Rating.item", value.name = "Rating")
longratings.30.bfi2 <- longratings.30.bfi2[order(longratings.30.bfi2$JID, longratings.30.bfi2$Rating.item),]

longratings.31.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(902:931), variable.name = "Rating.item", value.name = "Rating")
longratings.31.bfi2 <- longratings.31.bfi2[order(longratings.31.bfi2$JID, longratings.31.bfi2$Rating.item),]

longratings.32.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(932:961), variable.name = "Rating.item", value.name = "Rating")
longratings.32.bfi2 <- longratings.32.bfi2[order(longratings.32.bfi2$JID, longratings.32.bfi2$Rating.item),]

longratings.33.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(962:991), variable.name = "Rating.item", value.name = "Rating")
longratings.33.bfi2 <- longratings.33.bfi2[order(longratings.33.bfi2$JID, longratings.33.bfi2$Rating.item),]

longratings.34.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(992:1021), variable.name = "Rating.item", value.name = "Rating")
longratings.34.bfi2 <- longratings.34.bfi2[order(longratings.34.bfi2$JID, longratings.34.bfi2$Rating.item),]

longratings.35.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1022:1051), variable.name = "Rating.item", value.name = "Rating")
longratings.35.bfi2 <- longratings.35.bfi2[order(longratings.35.bfi2$JID, longratings.35.bfi2$Rating.item),]

longratings.36.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1052:1081), variable.name = "Rating.item", value.name = "Rating")
longratings.36.bfi2 <- longratings.36.bfi2[order(longratings.36.bfi2$JID, longratings.36.bfi2$Rating.item),]

longratings.37.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1082:1111), variable.name = "Rating.item", value.name = "Rating")
longratings.37.bfi2 <- longratings.37.bfi2[order(longratings.37.bfi2$JID, longratings.37.bfi2$Rating.item),]

longratings.38.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1112:1141), variable.name = "Rating.item", value.name = "Rating")
longratings.38.bfi2 <- longratings.38.bfi2[order(longratings.38.bfi2$JID, longratings.38.bfi2$Rating.item),]

longratings.39.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1142:1171), variable.name = "Rating.item", value.name = "Rating")
longratings.39.bfi2 <- longratings.39.bfi2[order(longratings.39.bfi2$JID, longratings.39.bfi2$Rating.item),]

longratings.40.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1172:1201), variable.name = "Rating.item", value.name = "Rating")
longratings.40.bfi2 <- longratings.40.bfi2[order(longratings.40.bfi2$JID, longratings.40.bfi2$Rating.item),]

longratings.41.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1202:1231), variable.name = "Rating.item", value.name = "Rating")
longratings.41.bfi2 <- longratings.1.bfi2[order(longratings.41.bfi2$JID, longratings.41.bfi2$Rating.item),]

longratings.42.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1232:1261), variable.name = "Rating.item", value.name = "Rating")
longratings.42.bfi2 <- longratings.42.bfi2[order(longratings.42.bfi2$JID, longratings.42.bfi2$Rating.item),]

longratings.43.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1262:1291), variable.name = "Rating.item", value.name = "Rating")
longratings.43.bfi2 <- longratings.43.bfi2[order(longratings.43.bfi2$JID, longratings.43.bfi2$Rating.item),]

longratings.44.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1292:1321), variable.name = "Rating.item", value.name = "Rating")
longratings.44.bfi2 <- longratings.44.bfi2[order(longratings.44.bfi2$JID, longratings.44.bfi2$Rating.item),]

longratings.45.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1322:1351), variable.name = "Rating.item", value.name = "Rating")
longratings.45.bfi2 <- longratings.45.bfi2[order(longratings.45.bfi2$JID, longratings.45.bfi2$Rating.item),]

longratings.46.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1352:1381), variable.name = "Rating.item", value.name = "Rating")
longratings.46.bfi2 <- longratings.46.bfi2[order(longratings.46.bfi2$JID, longratings.46.bfi2$Rating.item),]

longratings.47.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1382:1411), variable.name = "Rating.item", value.name = "Rating")
longratings.47.bfi2 <- longratings.47.bfi2[order(longratings.47.bfi2$JID, longratings.47.bfi2$Rating.item),]

longratings.48.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1412:1441), variable.name = "Rating.item", value.name = "Rating")
longratings.48.bfi2 <- longratings.48.bfi2[order(longratings.48.bfi2$JID, longratings.48.bfi2$Rating.item),]

longratings.49.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1442:1471), variable.name = "Rating.item", value.name = "Rating")
longratings.49.bfi2 <- longratings.49.bfi2[order(longratings.49.bfi2$JID, longratings.49.bfi2$Rating.item),]

longratings.50.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1472:1501), variable.name = "Rating.item", value.name = "Rating")
longratings.50.bfi2 <- longratings.50.bfi2[order(longratings.50.bfi2$JID, longratings.50.bfi2$Rating.item),]

longratings.51.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1502:1531), variable.name = "Rating.item", value.name = "Rating")
longratings.51.bfi2 <- longratings.51.bfi2[order(longratings.51.bfi2$JID, longratings.51.bfi2$Rating.item),]

longratings.52.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1532:1561), variable.name = "Rating.item", value.name = "Rating")
longratings.52.bfi2 <- longratings.52.bfi2[order(longratings.52.bfi2$JID, longratings.52.bfi2$Rating.item),]

longratings.53.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1562:1591), variable.name = "Rating.item", value.name = "Rating")
longratings.53.bfi2 <- longratings.53.bfi2[order(longratings.53.bfi2$JID, longratings.53.bfi2$Rating.item),]

longratings.54.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1592:1621), variable.name = "Rating.item", value.name = "Rating")
longratings.54.bfi2 <- longratings.54.bfi2[order(longratings.54.bfi2$JID, longratings.54.bfi2$Rating.item),]

longratings.55.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1622:1651), variable.name = "Rating.item", value.name = "Rating")
longratings.55.bfi2 <- longratings.55.bfi2[order(longratings.55.bfi2$JID, longratings.55.bfi2$Rating.item),]

longratings.56.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1652:1681), variable.name = "Rating.item", value.name = "Rating")
longratings.56.bfi2 <- longratings.56.bfi2[order(longratings.56.bfi2$JID, longratings.56.bfi2$Rating.item),]

longratings.57.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1682:1711), variable.name = "Rating.item", value.name = "Rating")
longratings.57.bfi2 <- longratings.57.bfi2[order(longratings.57.bfi2$JID, longratings.57.bfi2$Rating.item),]

longratings.58.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1712:1741), variable.name = "Rating.item", value.name = "Rating")
longratings.58.bfi2 <- longratings.58.bfi2[order(longratings.58.bfi2$JID, longratings.58.bfi2$Rating.item),]

longratings.59.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1742:1771), variable.name = "Rating.item", value.name = "Rating")
longratings.59.bfi2 <- longratings.59.bfi2[order(longratings.59.bfi2$JID, longratings.59.bfi2$Rating.item),]

longratings.60.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1772:1801), variable.name = "Rating.item", value.name = "Rating")
longratings.60.bfi2 <- longratings.60.bfi2[order(longratings.60.bfi2$JID, longratings.60.bfi2$Rating.item),]

longratings.61.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1802:1831), variable.name = "Rating.item", value.name = "Rating")
longratings.61.bfi2 <- longratings.61.bfi2[order(longratings.61.bfi2$JID, longratings.61.bfi2$Rating.item),]

longratings.62.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1832:1861), variable.name = "Rating.item", value.name = "Rating")
longratings.62.bfi2 <- longratings.62.bfi2[order(longratings.62.bfi2$JID, longratings.62.bfi2$Rating.item),]

longratings.63.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1862:1891), variable.name = "Rating.item", value.name = "Rating")
longratings.63.bfi2 <- longratings.63.bfi2[order(longratings.63.bfi2$JID, longratings.63.bfi2$Rating.item),]

longratings.64.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1892:1921), variable.name = "Rating.item", value.name = "Rating")
longratings.64.bfi2 <- longratings.64.bfi2[order(longratings.64.bfi2$JID, longratings.64.bfi2$Rating.item),]

longratings.65.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1922:1951), variable.name = "Rating.item", value.name = "Rating")
longratings.65.bfi2 <- longratings.65.bfi2[order(longratings.65.bfi2$JID, longratings.65.bfi2$Rating.item),]

longratings.66.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1952:1981), variable.name = "Rating.item", value.name = "Rating")
longratings.66.bfi2 <- longratings.66.bfi2[order(longratings.66.bfi2$JID, longratings.66.bfi2$Rating.item),]

longratings.67.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(1982:2011), variable.name = "Rating.item", value.name = "Rating")
longratings.67.bfi2 <- longratings.67.bfi2[order(longratings.67.bfi2$JID, longratings.67.bfi2$Rating.item),]

longratings.68.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2012:2041), variable.name = "Rating.item", value.name = "Rating")
longratings.68.bfi2 <- longratings.68.bfi2[order(longratings.68.bfi2$JID, longratings.68.bfi2$Rating.item),]

longratings.69.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2042:2071), variable.name = "Rating.item", value.name = "Rating")
longratings.69.bfi2 <- longratings.69.bfi2[order(longratings.69.bfi2$JID, longratings.69.bfi2$Rating.item),]

longratings.70.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2072:2101), variable.name = "Rating.item", value.name = "Rating")
longratings.70.bfi2 <- longratings.70.bfi2[order(longratings.70.bfi2$JID, longratings.70.bfi2$Rating.item),]

longratings.71.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2102:2131), variable.name = "Rating.item", value.name = "Rating")
longratings.71.bfi2 <- longratings.71.bfi2[order(longratings.71.bfi2$JID, longratings.71.bfi2$Rating.item),]

longratings.72.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2132:2161), variable.name = "Rating.item", value.name = "Rating")
longratings.72.bfi2 <- longratings.72.bfi2[order(longratings.72.bfi2$JID, longratings.72.bfi2$Rating.item),]

longratings.73.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2162:2191), variable.name = "Rating.item", value.name = "Rating")
longratings.73.bfi2 <- longratings.73.bfi2[order(longratings.73.bfi2$JID, longratings.73.bfi2$Rating.item),]

longratings.74.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2192:2221), variable.name = "Rating.item", value.name = "Rating")
longratings.74.bfi2 <- longratings.74.bfi2[order(longratings.74.bfi2$JID, longratings.74.bfi2$Rating.item),]

longratings.75.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2222:2251), variable.name = "Rating.item", value.name = "Rating")
longratings.75.bfi2 <- longratings.75.bfi2[order(longratings.75.bfi2$JID, longratings.75.bfi2$Rating.item),]

longratings.76.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2252:2281), variable.name = "Rating.item", value.name = "Rating")
longratings.76.bfi2 <- longratings.76.bfi2[order(longratings.76.bfi2$JID, longratings.76.bfi2$Rating.item),]

longratings.77.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2282:2311), variable.name = "Rating.item", value.name = "Rating")
longratings.77.bfi2 <- longratings.77.bfi2[order(longratings.77.bfi2$JID, longratings.77.bfi2$Rating.item),]

longratings.78.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2312:2341), variable.name = "Rating.item", value.name = "Rating")
longratings.78.bfi2 <- longratings.78.bfi2[order(longratings.78.bfi2$JID, longratings.78.bfi2$Rating.item),]

longratings.79.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2342:2371), variable.name = "Rating.item", value.name = "Rating")
longratings.79.bfi2 <- longratings.79.bfi2[order(longratings.79.bfi2$JID, longratings.79.bfi2$Rating.item),]

longratings.80.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2372:2401), variable.name = "Rating.item", value.name = "Rating")
longratings.80.bfi2 <- longratings.80.bfi2[order(longratings.80.bfi2$JID, longratings.80.bfi2$Rating.item),]

longratings.81.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2402:2431), variable.name = "Rating.item", value.name = "Rating")
longratings.81.bfi2 <- longratings.81.bfi2[order(longratings.81.bfi2$JID, longratings.81.bfi2$Rating.item),]

longratings.82.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2432:2461), variable.name = "Rating.item", value.name = "Rating")
longratings.82.bfi2 <- longratings.82.bfi2[order(longratings.82.bfi2$JID, longratings.82.bfi2$Rating.item),]

longratings.83.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2462:2491), variable.name = "Rating.item", value.name = "Rating")
longratings.83.bfi2 <- longratings.83.bfi2[order(longratings.83.bfi2$JID, longratings.83.bfi2$Rating.item),]

longratings.84.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2492:2521), variable.name = "Rating.item", value.name = "Rating")
longratings.84.bfi2 <- longratings.84.bfi2[order(longratings.84.bfi2$JID, longratings.84.bfi2$Rating.item),]

longratings.85.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2522:2551), variable.name = "Rating.item", value.name = "Rating")
longratings.85.bfi2 <- longratings.85.bfi2[order(longratings.85.bfi2$JID, longratings.85.bfi2$Rating.item),]

longratings.86.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2552:2581), variable.name = "Rating.item", value.name = "Rating")
longratings.86.bfi2 <- longratings.86.bfi2[order(longratings.86.bfi2$JID, longratings.86.bfi2$Rating.item),]

longratings.87.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2582:2611), variable.name = "Rating.item", value.name = "Rating")
longratings.87.bfi2 <- longratings.87.bfi2[order(longratings.87.bfi2$JID, longratings.87.bfi2$Rating.item),]

longratings.88.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2612:2641), variable.name = "Rating.item", value.name = "Rating")
longratings.88.bfi2 <- longratings.88.bfi2[order(longratings.88.bfi2$JID, longratings.88.bfi2$Rating.item),]

longratings.89.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2462:2671), variable.name = "Rating.item", value.name = "Rating")
longratings.89.bfi2 <- longratings.89.bfi2[order(longratings.89.bfi2$JID, longratings.89.bfi2$Rating.item),]

longratings.90.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2672:2701), variable.name = "Rating.item", value.name = "Rating")
longratings.90.bfi2 <- longratings.90.bfi2[order(longratings.90.bfi2$JID, longratings.90.bfi2$Rating.item),]

longratings.91.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2702:2731), variable.name = "Rating.item", value.name = "Rating")
longratings.91.bfi2 <- longratings.91.bfi2[order(longratings.91.bfi2$JID, longratings.91.bfi2$Rating.item),]

longratings.92.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2732:2761), variable.name = "Rating.item", value.name = "Rating")
longratings.92.bfi2 <- longratings.92.bfi2[order(longratings.92.bfi2$JID, longratings.92.bfi2$Rating.item),]

longratings.93.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2762:2791), variable.name = "Rating.item", value.name = "Rating")
longratings.93.bfi2 <- longratings.93.bfi2[order(longratings.93.bfi2$JID, longratings.93.bfi2$Rating.item),]

longratings.94.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2792:2821), variable.name = "Rating.item", value.name = "Rating")
longratings.94.bfi2 <- longratings.94.bfi2[order(longratings.94.bfi2$JID, longratings.94.bfi2$Rating.item),]

longratings.95.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2822:2851), variable.name = "Rating.item", value.name = "Rating")
longratings.95.bfi2 <- longratings.95.bfi2[order(longratings.95.bfi2$JID, longratings.95.bfi2$Rating.item),]

longratings.96.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2852:2881), variable.name = "Rating.item", value.name = "Rating")
longratings.96.bfi2 <- longratings.96.bfi2[order(longratings.96.bfi2$JID, longratings.96.bfi2$Rating.item),]

longratings.97.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2882:2911), variable.name = "Rating.item", value.name = "Rating")
longratings.97.bfi2 <- longratings.97.bfi2[order(longratings.97.bfi2$JID, longratings.97.bfi2$Rating.item),]

longratings.98.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2912:2941), variable.name = "Rating.item", value.name = "Rating")
longratings.98.bfi2 <- longratings.98.bfi2[order(longratings.98.bfi2$JID, longratings.98.bfi2$Rating.item),]

longratings.99.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2942:2971), variable.name = "Rating.item", value.name = "Rating")
longratings.99.bfi2 <- longratings.99.bfi2[order(longratings.99.bfi2$JID, longratings.99.bfi2$Rating.item),]

longratings.100.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(2972:3001), variable.name = "Rating.item", value.name = "Rating")
longratings.100.bfi2 <- longratings.100.bfi2[order(longratings.100.bfi2$JID, longratings.100.bfi2$Rating.item),]

longratings.101.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3002:3031), variable.name = "Rating.item", value.name = "Rating")
longratings.101.bfi2 <- longratings.101.bfi2[order(longratings.101.bfi2$JID, longratings.101.bfi2$Rating.item),]

longratings.102.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3032:3061), variable.name = "Rating.item", value.name = "Rating")
longratings.102.bfi2 <- longratings.102.bfi2[order(longratings.102.bfi2$JID, longratings.102.bfi2$Rating.item),]

longratings.103.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3062:3091), variable.name = "Rating.item", value.name = "Rating")
longratings.103.bfi2 <- longratings.103.bfi2[order(longratings.103.bfi2$JID, longratings.103.bfi2$Rating.item),]

longratings.104.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3092:3121), variable.name = "Rating.item", value.name = "Rating")
longratings.104.bfi2 <- longratings.104.bfi2[order(longratings.104.bfi2$JID, longratings.104.bfi2$Rating.item),]

longratings.105.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3122:3151), variable.name = "Rating.item", value.name = "Rating")
longratings.105.bfi2 <- longratings.105.bfi2[order(longratings.105.bfi2$JID, longratings.105.bfi2$Rating.item),]

longratings.106.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3152:3181), variable.name = "Rating.item", value.name = "Rating")
longratings.106.bfi2 <- longratings.106.bfi2[order(longratings.106.bfi2$JID, longratings.106.bfi2$Rating.item),]

longratings.107.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3182:3211), variable.name = "Rating.item", value.name = "Rating")
longratings.107.bfi2 <- longratings.107.bfi2[order(longratings.107.bfi2$JID, longratings.107.bfi2$Rating.item),]

longratings.108.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3212:3241), variable.name = "Rating.item", value.name = "Rating")
longratings.108.bfi2 <- longratings.108.bfi2[order(longratings.108.bfi2$JID, longratings.108.bfi2$Rating.item),]

longratings.109.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3242:3271), variable.name = "Rating.item", value.name = "Rating")
longratings.109.bfi2 <- longratings.109.bfi2[order(longratings.109.bfi2$JID, longratings.109.bfi2$Rating.item),]

longratings.110.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3272:3301), variable.name = "Rating.item", value.name = "Rating")
longratings.110.bfi2 <- longratings.110.bfi2[order(longratings.110.bfi2$JID, longratings.110.bfi2$Rating.item),]

longratings.111.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3302:3331), variable.name = "Rating.item", value.name = "Rating")
longratings.111.bfi2 <- longratings.111.bfi2[order(longratings.111.bfi2$JID, longratings.111.bfi2$Rating.item),]

longratings.112.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3332:3361), variable.name = "Rating.item", value.name = "Rating")
longratings.112.bfi2 <- longratings.112.bfi2[order(longratings.112.bfi2$JID, longratings.112.bfi2$Rating.item),]

longratings.113.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3362:3391), variable.name = "Rating.item", value.name = "Rating")
longratings.113.bfi2 <- longratings.113.bfi2[order(longratings.113.bfi2$JID, longratings.113.bfi2$Rating.item),]

longratings.114.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3392:3421), variable.name = "Rating.item", value.name = "Rating")
longratings.114.bfi2 <- longratings.114.bfi2[order(longratings.114.bfi2$JID, longratings.114.bfi2$Rating.item),]

longratings.115.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3422:3451), variable.name = "Rating.item", value.name = "Rating")
longratings.115.bfi2 <- longratings.115.bfi2[order(longratings.115.bfi2$JID, longratings.115.bfi2$Rating.item),]

longratings.116.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3452:3481), variable.name = "Rating.item", value.name = "Rating")
longratings.116.bfi2 <- longratings.116.bfi2[order(longratings.116.bfi2$JID, longratings.116.bfi2$Rating.item),]

longratings.117.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3482:3511), variable.name = "Rating.item", value.name = "Rating")
longratings.117.bfi2 <- longratings.117.bfi2[order(longratings.117.bfi2$JID, longratings.117.bfi2$Rating.item),]

longratings.118.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3512:3541), variable.name = "Rating.item", value.name = "Rating")
longratings.118.bfi2 <- longratings.118.bfi2[order(longratings.118.bfi2$JID, longratings.118.bfi2$Rating.item),]

longratings.119.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3542:3571), variable.name = "Rating.item", value.name = "Rating")
longratings.119.bfi2 <- longratings.119.bfi2[order(longratings.119.bfi2$JID, longratings.119.bfi2$Rating.item),]

longratings.120.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3572:3601), variable.name = "Rating.item", value.name = "Rating")
longratings.120.bfi2 <- longratings.120.bfi2[order(longratings.120.bfi2$JID, longratings.120.bfi2$Rating.item),]

longratings.121.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3602:3631), variable.name = "Rating.item", value.name = "Rating")
longratings.121.bfi2 <- longratings.121.bfi2[order(longratings.121.bfi2$JID, longratings.121.bfi2$Rating.item),]

longratings.122.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3632:3661), variable.name = "Rating.item", value.name = "Rating")
longratings.122.bfi2 <- longratings.112.bfi2[order(longratings.112.bfi2$JID, longratings.122.bfi2$Rating.item),]

longratings.123.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3662:3691), variable.name = "Rating.item", value.name = "Rating")
longratings.123.bfi2 <- longratings.123.bfi2[order(longratings.123.bfi2$JID, longratings.123.bfi2$Rating.item),]

longratings.124.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3692:3721), variable.name = "Rating.item", value.name = "Rating")
longratings.124.bfi2 <- longratings.124.bfi2[order(longratings.124.bfi2$JID, longratings.124.bfi2$Rating.item),]

longratings.125.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3722:3751), variable.name = "Rating.item", value.name = "Rating")
longratings.125.bfi2 <- longratings.125.bfi2[order(longratings.125.bfi2$JID, longratings.125.bfi2$Rating.item),]

longratings.126.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3752:3781), variable.name = "Rating.item", value.name = "Rating")
longratings.126.bfi2 <- longratings.126.bfi2[order(longratings.126.bfi2$JID, longratings.126.bfi2$Rating.item),]

longratings.127.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3782:3811), variable.name = "Rating.item", value.name = "Rating")
longratings.127.bfi2 <- longratings.127.bfi2[order(longratings.127.bfi2$JID, longratings.127.bfi2$Rating.item),]

longratings.128.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3812:3841), variable.name = "Rating.item", value.name = "Rating")
longratings.128.bfi2 <- longratings.128.bfi2[order(longratings.128.bfi2$JID, longratings.128.bfi2$Rating.item),]

longratings.129.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3842:3871), variable.name = "Rating.item", value.name = "Rating")
longratings.129.bfi2 <- longratings.129.bfi2[order(longratings.129.bfi2$JID, longratings.129.bfi2$Rating.item),]

longratings.130.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3872:3901), variable.name = "Rating.item", value.name = "Rating")
longratings.130.bfi2 <- longratings.130.bfi2[order(longratings.130.bfi2$JID, longratings.130.bfi2$Rating.item),]

longratings.131.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3902:3931), variable.name = "Rating.item", value.name = "Rating")
longratings.131.bfi2 <- longratings.131.bfi2[order(longratings.131.bfi2$JID, longratings.131.bfi2$Rating.item),]

longratings.132.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3932:3961), variable.name = "Rating.item", value.name = "Rating")
longratings.132.bfi2 <- longratings.132.bfi2[order(longratings.132.bfi2$JID, longratings.132.bfi2$Rating.item),]

longratings.133.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3962:3991), variable.name = "Rating.item", value.name = "Rating")
longratings.133.bfi2 <- longratings.133.bfi2[order(longratings.133.bfi2$JID, longratings.133.bfi2$Rating.item),]

longratings.134.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(3992:4021), variable.name = "Rating.item", value.name = "Rating")
longratings.134.bfi2 <- longratings.134.bfi2[order(longratings.134.bfi2$JID, longratings.134.bfi2$Rating.item),]

longratings.135.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4022:4051), variable.name = "Rating.item", value.name = "Rating")
longratings.135.bfi2 <- longratings.135.bfi2[order(longratings.135.bfi2$JID, longratings.135.bfi2$Rating.item),]

longratings.136.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4052:4081), variable.name = "Rating.item", value.name = "Rating")
longratings.136.bfi2 <- longratings.136.bfi2[order(longratings.136.bfi2$JID, longratings.136.bfi2$Rating.item),]

longratings.137.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4082:4111), variable.name = "Rating.item", value.name = "Rating")
longratings.137.bfi2 <- longratings.137.bfi2[order(longratings.137.bfi2$JID, longratings.137.bfi2$Rating.item),]

longratings.138.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4112:4141), variable.name = "Rating.item", value.name = "Rating")
longratings.138.bfi2 <- longratings.138.bfi2[order(longratings.138.bfi2$JID, longratings.138.bfi2$Rating.item),]

longratings.139.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4142:4171), variable.name = "Rating.item", value.name = "Rating")
longratings.139.bfi2 <- longratings.139.bfi2[order(longratings.139.bfi2$JID, longratings.139.bfi2$Rating.item),]

longratings.140.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4172:4201), variable.name = "Rating.item", value.name = "Rating")
longratings.140.bfi2 <- longratings.140.bfi2[order(longratings.140.bfi2$JID, longratings.140.bfi2$Rating.item),]

longratings.141.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4202:4231), variable.name = "Rating.item", value.name = "Rating")
longratings.141.bfi2 <- longratings.141.bfi2[order(longratings.141.bfi2$JID, longratings.141.bfi2$Rating.item),]

longratings.142.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4232:4261), variable.name = "Rating.item", value.name = "Rating")
longratings.142.bfi2 <- longratings.142.bfi2[order(longratings.142.bfi2$JID, longratings.142.bfi2$Rating.item),]

longratings.143.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4262:4291), variable.name = "Rating.item", value.name = "Rating")
longratings.143.bfi2 <- longratings.143.bfi2[order(longratings.143.bfi2$JID, longratings.143.bfi2$Rating.item),]

longratings.144.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4292:4321), variable.name = "Rating.item", value.name = "Rating")
longratings.144.bfi2 <- longratings.144.bfi2[order(longratings.144.bfi2$JID, longratings.144.bfi2$Rating.item),]

longratings.145.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4322:4351), variable.name = "Rating.item", value.name = "Rating")
longratings.145.bfi2 <- longratings.145.bfi2[order(longratings.145.bfi2$JID, longratings.145.bfi2$Rating.item),]

longratings.146.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4352:4381), variable.name = "Rating.item", value.name = "Rating")
longratings.146.bfi2 <- longratings.146.bfi2[order(longratings.146.bfi2$JID, longratings.146.bfi2$Rating.item),]

longratings.147.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4382:4411), variable.name = "Rating.item", value.name = "Rating")
longratings.147.bfi2 <- longratings.147.bfi2[order(longratings.147.bfi2$JID, longratings.147.bfi2$Rating.item),]

longratings.148.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4412:4441), variable.name = "Rating.item", value.name = "Rating")
longratings.148.bfi2 <- longratings.148.bfi2[order(longratings.148.bfi2$JID, longratings.148.bfi2$Rating.item),]

longratings.149.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4442:4471), variable.name = "Rating.item", value.name = "Rating")
longratings.149.bfi2 <- longratings.149.bfi2[order(longratings.149.bfi2$JID, longratings.149.bfi2$Rating.item),]

longratings.150.bfi2 <- melt(data, id.vars = c(1, 4502:4504), measure.vars = c(4472:4501), variable.name = "Rating.item", value.name = "Rating")
longratings.150.bfi2 <- longratings.150.bfi2[order(longratings.150.bfi2$JID, longratings.150.bfi2$Rating.item),]

SAM.1IG.bfi2 <- cbind(longratings.1.bfi2,longnorm.bfi2,longcrit.1.bfi2,IG.Cond)
SAM.2TW.bfi2 <- cbind(longratings.2.bfi2,longnorm.bfi2,longcrit.2.bfi2, TW.Cond)
SAM.3IG.bfi2 <- cbind(longratings.3.bfi2,longnorm.bfi2,longcrit.3.bfi2,IG.Cond)
SAM.4TW.bfi2 <- cbind(longratings.4.bfi2,longnorm.bfi2,longcrit.4.bfi2, TW.Cond)
SAM.5IG.bfi2 <- cbind(longratings.5.bfi2,longnorm.bfi2,longcrit.5.bfi2,IG.Cond)
SAM.6TW.bfi2 <- cbind(longratings.6.bfi2,longnorm.bfi2,longcrit.6.bfi2, TW.Cond)
SAM.7TW.bfi2 <- cbind(longratings.7.bfi2,longnorm.bfi2,longcrit.7.bfi2, TW.Cond)
SAM.8IG.bfi2 <- cbind(longratings.8.bfi2,longnorm.bfi2,longcrit.8.bfi2,IG.Cond)
SAM.9TW.bfi2 <- cbind(longratings.9.bfi2,longnorm.bfi2,longcrit.9.bfi2, TW.Cond)
SAM.10IG.bfi2 <- cbind(longratings.10.bfi2,longnorm.bfi2,longcrit.10.bfi2,IG.Cond)
SAM.11TW.bfi2 <- cbind(longratings.11.bfi2,longnorm.bfi2,longcrit.11.bfi2, TW.Cond)
SAM.12IG.bfi2 <- cbind(longratings.12.bfi2,longnorm.bfi2,longcrit.12.bfi2,IG.Cond)
SAM.13IG.bfi2 <- cbind(longratings.13.bfi2,longnorm.bfi2,longcrit.13.bfi2,IG.Cond)
SAM.14TW.bfi2 <- cbind(longratings.14.bfi2,longnorm.bfi2,longcrit.14.bfi2, TW.Cond)
SAM.15IG.bfi2 <- cbind(longratings.15.bfi2,longnorm.bfi2,longcrit.15.bfi2,IG.Cond)
SAM.16TW.bfi2 <- cbind(longratings.16.bfi2,longnorm.bfi2,longcrit.16.bfi2, TW.Cond)
SAM.17IG.bfi2 <- cbind(longratings.17.bfi2,longnorm.bfi2,longcrit.17.bfi2,IG.Cond)
SAM.18TW.bfi2 <- cbind(longratings.18.bfi2,longnorm.bfi2,longcrit.18.bfi2, TW.Cond)
SAM.19IG.bfi2 <- cbind(longratings.19.bfi2,longnorm.bfi2,longcrit.19.bfi2,IG.Cond)
SAM.20TW.bfi2 <- cbind(longratings.20.bfi2,longnorm.bfi2,longcrit.20.bfi2, TW.Cond)
SAM.21IG.bfi2 <- cbind(longratings.21.bfi2,longnorm.bfi2,longcrit.21.bfi2,IG.Cond)
SAM.22TW.bfi2 <- cbind(longratings.22.bfi2,longnorm.bfi2,longcrit.22.bfi2, TW.Cond)
SAM.23IG.bfi2 <- cbind(longratings.23.bfi2,longnorm.bfi2,longcrit.23.bfi2,IG.Cond)
SAM.24TW.bfi2 <- cbind(longratings.24.bfi2,longnorm.bfi2,longcrit.24.bfi2, TW.Cond)
SAM.25TW.bfi2 <- cbind(longratings.25.bfi2,longnorm.bfi2,longcrit.25.bfi2, TW.Cond)
SAM.26IG.bfi2 <- cbind(longratings.26.bfi2,longnorm.bfi2,longcrit.26.bfi2,IG.Cond)
SAM.27TW.bfi2 <- cbind(longratings.27.bfi2,longnorm.bfi2,longcrit.27.bfi2, TW.Cond)
SAM.28IG.bfi2 <- cbind(longratings.28.bfi2,longnorm.bfi2,longcrit.28.bfi2,IG.Cond)
SAM.29TW.bfi2 <- cbind(longratings.29.bfi2,longnorm.bfi2,longcrit.29.bfi2, TW.Cond)
SAM.30IG.bfi2 <- cbind(longratings.30.bfi2,longnorm.bfi2,longcrit.30.bfi2,IG.Cond)
SAM.31IG.bfi2 <- cbind(longratings.31.bfi2,longnorm.bfi2,longcrit.31.bfi2,IG.Cond)
SAM.32TW.bfi2 <- cbind(longratings.32.bfi2,longnorm.bfi2,longcrit.32.bfi2, TW.Cond)
SAM.33IG.bfi2 <- cbind(longratings.33.bfi2,longnorm.bfi2,longcrit.33.bfi2,IG.Cond)
SAM.34TW.bfi2 <- cbind(longratings.34.bfi2,longnorm.bfi2,longcrit.34.bfi2, TW.Cond)
SAM.35IG.bfi2 <- cbind(longratings.35.bfi2,longnorm.bfi2,longcrit.35.bfi2,IG.Cond)
SAM.36TW.bfi2 <- cbind(longratings.36.bfi2,longnorm.bfi2,longcrit.36.bfi2, TW.Cond)
SAM.37IG.bfi2 <- cbind(longratings.37.bfi2,longnorm.bfi2,longcrit.37.bfi2,IG.Cond)
SAM.38TW.bfi2 <- cbind(longratings.38.bfi2,longnorm.bfi2,longcrit.38.bfi2, TW.Cond)
SAM.39IG.bfi2 <- cbind(longratings.39.bfi2,longnorm.bfi2,longcrit.39.bfi2,IG.Cond)
SAM.40TW.bfi2 <- cbind(longratings.40.bfi2,longnorm.bfi2,longcrit.40.bfi2, TW.Cond)
SAM.41IG.bfi2 <- cbind(longratings.41.bfi2,longnorm.bfi2,longcrit.41.bfi2,IG.Cond)
SAM.42TW.bfi2 <- cbind(longratings.42.bfi2,longnorm.bfi2,longcrit.42.bfi2, TW.Cond)
SAM.43TW.bfi2 <- cbind(longratings.43.bfi2,longnorm.bfi2,longcrit.43.bfi2, TW.Cond)
SAM.44IG.bfi2 <- cbind(longratings.44.bfi2,longnorm.bfi2,longcrit.44.bfi2,IG.Cond)
SAM.45TW.bfi2 <- cbind(longratings.45.bfi2,longnorm.bfi2,longcrit.45.bfi2, TW.Cond)
SAM.46IG.bfi2 <- cbind(longratings.46.bfi2,longnorm.bfi2,longcrit.46.bfi2,IG.Cond)
SAM.47TW.bfi2 <- cbind(longratings.47.bfi2,longnorm.bfi2,longcrit.47.bfi2, TW.Cond)
SAM.48IG.bfi2 <- cbind(longratings.48.bfi2,longnorm.bfi2,longcrit.48.bfi2,IG.Cond)
SAM.49IG.bfi2 <- cbind(longratings.49.bfi2,longnorm.bfi2,longcrit.49.bfi2,IG.Cond)
SAM.50TW.bfi2 <- cbind(longratings.50.bfi2,longnorm.bfi2,longcrit.50.bfi2, TW.Cond)
SAM.51IG.bfi2 <- cbind(longratings.51.bfi2,longnorm.bfi2,longcrit.51.bfi2,IG.Cond)
SAM.52TW.bfi2 <- cbind(longratings.52.bfi2,longnorm.bfi2,longcrit.52.bfi2, TW.Cond)
SAM.53IG.bfi2 <- cbind(longratings.53.bfi2,longnorm.bfi2,longcrit.53.bfi2,IG.Cond)
SAM.54TW.bfi2 <- cbind(longratings.54.bfi2,longnorm.bfi2,longcrit.54.bfi2, TW.Cond)
SAM.55IG.bfi2 <- cbind(longratings.55.bfi2,longnorm.bfi2,longcrit.55.bfi2,IG.Cond)
SAM.56TW.bfi2 <- cbind(longratings.56.bfi2,longnorm.bfi2,longcrit.56.bfi2, TW.Cond)
SAM.57IG.bfi2 <- cbind(longratings.57.bfi2,longnorm.bfi2,longcrit.57.bfi2,IG.Cond)
SAM.58TW.bfi2 <- cbind(longratings.58.bfi2,longnorm.bfi2,longcrit.58.bfi2, TW.Cond)
SAM.59IG.bfi2 <- cbind(longratings.59.bfi2,longnorm.bfi2,longcrit.59.bfi2,IG.Cond)
SAM.60TW.bfi2 <- cbind(longratings.60.bfi2,longnorm.bfi2,longcrit.60.bfi2, TW.Cond)
SAM.61TW.bfi2 <- cbind(longratings.61.bfi2,longnorm.bfi2,longcrit.61.bfi2, TW.Cond)
SAM.62IG.bfi2 <- cbind(longratings.62.bfi2,longnorm.bfi2,longcrit.62.bfi2,IG.Cond)
SAM.63TW.bfi2 <- cbind(longratings.63.bfi2,longnorm.bfi2,longcrit.63.bfi2, TW.Cond)
SAM.64IG.bfi2 <- cbind(longratings.64.bfi2,longnorm.bfi2,longcrit.64.bfi2,IG.Cond)
SAM.65TW.bfi2 <- cbind(longratings.65.bfi2,longnorm.bfi2,longcrit.65.bfi2, TW.Cond)
SAM.66IG.bfi2 <- cbind(longratings.66.bfi2,longnorm.bfi2,longcrit.66.bfi2,IG.Cond)
SAM.67IG.bfi2 <- cbind(longratings.67.bfi2,longnorm.bfi2,longcrit.67.bfi2,IG.Cond)
SAM.68TW.bfi2 <- cbind(longratings.68.bfi2,longnorm.bfi2,longcrit.68.bfi2, TW.Cond)
SAM.69IG.bfi2 <- cbind(longratings.69.bfi2,longnorm.bfi2,longcrit.69.bfi2,IG.Cond)
SAM.70TW.bfi2 <- cbind(longratings.70.bfi2,longnorm.bfi2,longcrit.70.bfi2, TW.Cond)
SAM.71IG.bfi2 <- cbind(longratings.71.bfi2,longnorm.bfi2,longcrit.71.bfi2,IG.Cond)
SAM.72TW.bfi2 <- cbind(longratings.72.bfi2,longnorm.bfi2,longcrit.72.bfi2, TW.Cond)
SAM.73IG.bfi2 <- cbind(longratings.73.bfi2,longnorm.bfi2,longcrit.73.bfi2,IG.Cond)
SAM.74TW.bfi2 <- cbind(longratings.74.bfi2,longnorm.bfi2,longcrit.74.bfi2, TW.Cond)
SAM.75IG.bfi2 <- cbind(longratings.75.bfi2,longnorm.bfi2,longcrit.75.bfi2,IG.Cond)
SAM.76TW.bfi2 <- cbind(longratings.76.bfi2,longnorm.bfi2,longcrit.76.bfi2, TW.Cond)
SAM.77IG.bfi2 <- cbind(longratings.77.bfi2,longnorm.bfi2,longcrit.77.bfi2,IG.Cond)
SAM.78TW.bfi2 <- cbind(longratings.78.bfi2,longnorm.bfi2,longcrit.78.bfi2, TW.Cond)
SAM.79TW.bfi2 <- cbind(longratings.79.bfi2,longnorm.bfi2,longcrit.79.bfi2, TW.Cond)
SAM.80IG.bfi2 <- cbind(longratings.80.bfi2,longnorm.bfi2,longcrit.80.bfi2,IG.Cond)
SAM.81TW.bfi2 <- cbind(longratings.81.bfi2,longnorm.bfi2,longcrit.81.bfi2, TW.Cond)
SAM.82IG.bfi2 <- cbind(longratings.82.bfi2,longnorm.bfi2,longcrit.82.bfi2,IG.Cond)
SAM.83TW.bfi2 <- cbind(longratings.83.bfi2,longnorm.bfi2,longcrit.83.bfi2, TW.Cond)
SAM.84IG.bfi2 <- cbind(longratings.84.bfi2,longnorm.bfi2,longcrit.84.bfi2,IG.Cond)
SAM.85IG.bfi2 <- cbind(longratings.85.bfi2,longnorm.bfi2,longcrit.85.bfi2,IG.Cond)
SAM.86TW.bfi2 <- cbind(longratings.86.bfi2,longnorm.bfi2,longcrit.86.bfi2, TW.Cond)
SAM.87IG.bfi2 <- cbind(longratings.87.bfi2,longnorm.bfi2,longcrit.87.bfi2,IG.Cond)
SAM.88TW.bfi2 <- cbind(longratings.88.bfi2,longnorm.bfi2,longcrit.88.bfi2, TW.Cond)
SAM.89IG.bfi2 <- cbind(longratings.89.bfi2,longnorm.bfi2,longcrit.89.bfi2,IG.Cond)
SAM.90TW.bfi2 <- cbind(longratings.90.bfi2,longnorm.bfi2,longcrit.90.bfi2, TW.Cond)
SAM.91IG.bfi2 <- cbind(longratings.91.bfi2,longnorm.bfi2,longcrit.91.bfi2,IG.Cond)
SAM.92TW.bfi2 <- cbind(longratings.92.bfi2,longnorm.bfi2,longcrit.92.bfi2, TW.Cond)
SAM.93IG.bfi2 <- cbind(longratings.93.bfi2,longnorm.bfi2,longcrit.93.bfi2,IG.Cond)
SAM.94TW.bfi2 <- cbind(longratings.94.bfi2,longnorm.bfi2,longcrit.94.bfi2, TW.Cond)
SAM.95IG.bfi2 <- cbind(longratings.95.bfi2,longnorm.bfi2,longcrit.95.bfi2,IG.Cond)
SAM.96TW.bfi2 <- cbind(longratings.96.bfi2,longnorm.bfi2,longcrit.96.bfi2, TW.Cond)
SAM.97TW.bfi2 <- cbind(longratings.97.bfi2,longnorm.bfi2,longcrit.97.bfi2, TW.Cond)
SAM.98IG.bfi2 <- cbind(longratings.98.bfi2,longnorm.bfi2,longcrit.98.bfi2,IG.Cond)
SAM.99TW.bfi2 <- cbind(longratings.99.bfi2,longnorm.bfi2,longcrit.99.bfi2, TW.Cond)
SAM.100IG.bfi2 <- cbind(longratings.100.bfi2,longnorm.bfi2,longcrit.100.bfi2,IG.Cond)
SAM.101TW.bfi2 <- cbind(longratings.101.bfi2,longnorm.bfi2,longcrit.101.bfi2, TW.Cond)
SAM.102IG.bfi2 <- cbind(longratings.102.bfi2,longnorm.bfi2,longcrit.102.bfi2,IG.Cond)
SAM.103IG.bfi2 <- cbind(longratings.103.bfi2,longnorm.bfi2,longcrit.103.bfi2,IG.Cond)
SAM.104TW.bfi2 <- cbind(longratings.104.bfi2,longnorm.bfi2,longcrit.104.bfi2, TW.Cond)
SAM.105IG.bfi2 <- cbind(longratings.105.bfi2,longnorm.bfi2,longcrit.105.bfi2,IG.Cond)
SAM.106TW.bfi2 <- cbind(longratings.106.bfi2,longnorm.bfi2,longcrit.106.bfi2, TW.Cond)
SAM.107IG.bfi2 <- cbind(longratings.107.bfi2,longnorm.bfi2,longcrit.107.bfi2,IG.Cond)
SAM.108TW.bfi2 <- cbind(longratings.108.bfi2,longnorm.bfi2,longcrit.108.bfi2, TW.Cond)
SAM.109IG.bfi2 <- cbind(longratings.109.bfi2,longnorm.bfi2,longcrit.109.bfi2,IG.Cond)
SAM.110TW.bfi2 <- cbind(longratings.110.bfi2,longnorm.bfi2,longcrit.110.bfi2, TW.Cond)
SAM.111IG.bfi2 <- cbind(longratings.111.bfi2,longnorm.bfi2,longcrit.111.bfi2,IG.Cond)
SAM.112TW.bfi2 <- cbind(longratings.112.bfi2,longnorm.bfi2,longcrit.112.bfi2, TW.Cond)
SAM.113IG.bfi2 <- cbind(longratings.113.bfi2,longnorm.bfi2,longcrit.113.bfi2,IG.Cond)
SAM.114TW.bfi2 <- cbind(longratings.114.bfi2,longnorm.bfi2,longcrit.114.bfi2, TW.Cond)
SAM.115TW.bfi2 <- cbind(longratings.115.bfi2,longnorm.bfi2,longcrit.115.bfi2, TW.Cond)
SAM.116IG.bfi2 <- cbind(longratings.116.bfi2,longnorm.bfi2,longcrit.116.bfi2,IG.Cond)
SAM.117TW.bfi2 <- cbind(longratings.117.bfi2,longnorm.bfi2,longcrit.117.bfi2, TW.Cond)
SAM.118IG.bfi2 <- cbind(longratings.118.bfi2,longnorm.bfi2,longcrit.118.bfi2,IG.Cond)
SAM.119TW.bfi2 <- cbind(longratings.119.bfi2,longnorm.bfi2,longcrit.119.bfi2, TW.Cond)
SAM.120IG.bfi2 <- cbind(longratings.120.bfi2,longnorm.bfi2,longcrit.120.bfi2,IG.Cond)
SAM.121IG.bfi2 <- cbind(longratings.121.bfi2,longnorm.bfi2,longcrit.121.bfi2,IG.Cond)
SAM.122TW.bfi2 <- cbind(longratings.122.bfi2,longnorm.bfi2,longcrit.122.bfi2, TW.Cond)
SAM.123IG.bfi2 <- cbind(longratings.123.bfi2,longnorm.bfi2,longcrit.123.bfi2,IG.Cond)
SAM.124TW.bfi2 <- cbind(longratings.124.bfi2,longnorm.bfi2,longcrit.124.bfi2, TW.Cond)
SAM.125IG.bfi2 <- cbind(longratings.125.bfi2,longnorm.bfi2,longcrit.125.bfi2,IG.Cond)
SAM.126TW.bfi2 <- cbind(longratings.126.bfi2,longnorm.bfi2,longcrit.126.bfi2, TW.Cond)
SAM.127IG.bfi2 <- cbind(longratings.127.bfi2,longnorm.bfi2,longcrit.127.bfi2,IG.Cond)
SAM.128TW.bfi2 <- cbind(longratings.128.bfi2,longnorm.bfi2,longcrit.128.bfi2, TW.Cond)
SAM.129IG.bfi2 <- cbind(longratings.129.bfi2,longnorm.bfi2,longcrit.129.bfi2,IG.Cond)
SAM.130TW.bfi2 <- cbind(longratings.130.bfi2,longnorm.bfi2,longcrit.130.bfi2, TW.Cond)
SAM.131IG.bfi2 <- cbind(longratings.131.bfi2,longnorm.bfi2,longcrit.131.bfi2,IG.Cond)
SAM.132TW.bfi2 <- cbind(longratings.132.bfi2,longnorm.bfi2,longcrit.132.bfi2, TW.Cond)
SAM.133TW.bfi2 <- cbind(longratings.133.bfi2,longnorm.bfi2,longcrit.133.bfi2, TW.Cond)
SAM.134IG.bfi2 <- cbind(longratings.134.bfi2,longnorm.bfi2,longcrit.134.bfi2,IG.Cond)
SAM.135TW.bfi2 <- cbind(longratings.135.bfi2,longnorm.bfi2,longcrit.135.bfi2, TW.Cond)
SAM.136IG.bfi2 <- cbind(longratings.136.bfi2,longnorm.bfi2,longcrit.136.bfi2,IG.Cond)
SAM.137TW.bfi2 <- cbind(longratings.137.bfi2,longnorm.bfi2,longcrit.137.bfi2, TW.Cond)
SAM.138IG.bfi2 <- cbind(longratings.138.bfi2,longnorm.bfi2,longcrit.138.bfi2,IG.Cond)
SAM.139IG.bfi2 <- cbind(longratings.139.bfi2,longnorm.bfi2,longcrit.139.bfi2,IG.Cond)
SAM.140TW.bfi2 <- cbind(longratings.140.bfi2,longnorm.bfi2,longcrit.140.bfi2, TW.Cond)
SAM.141IG.bfi2 <- cbind(longratings.141.bfi2,longnorm.bfi2,longcrit.141.bfi2,IG.Cond)
SAM.142TW.bfi2 <- cbind(longratings.142.bfi2,longnorm.bfi2,longcrit.142.bfi2, TW.Cond)
SAM.143IG.bfi2 <- cbind(longratings.143.bfi2,longnorm.bfi2,longcrit.143.bfi2,IG.Cond)
SAM.144TW.bfi2 <- cbind(longratings.144.bfi2,longnorm.bfi2,longcrit.144.bfi2, TW.Cond)
SAM.145IG.bfi2 <- cbind(longratings.145.bfi2,longnorm.bfi2,longcrit.145.bfi2,IG.Cond)
SAM.146TW.bfi2 <- cbind(longratings.146.bfi2,longnorm.bfi2,longcrit.146.bfi2, TW.Cond)
SAM.147IG.bfi2 <- cbind(longratings.147.bfi2,longnorm.bfi2,longcrit.147.bfi2,IG.Cond)
SAM.148TW.bfi2 <- cbind(longratings.148.bfi2,longnorm.bfi2,longcrit.148.bfi2, TW.Cond)
SAM.149IG.bfi2 <- cbind(longratings.149.bfi2,longnorm.bfi2,longcrit.149.bfi2,IG.Cond)
SAM.150TW.bfi2 <- cbind(longratings.150.bfi2,longnorm.bfi2,longcrit.150.bfi2, TW.Cond)







SAM.bfi<- rbind(SAM.1IG.bfi2,SAM.2TW.bfi2,SAM.3IG.bfi2,SAM.4TW.bfi2,
SAM.5IG.bfi2,
SAM.6TW.bfi2,
SAM.7TW.bfi2,
SAM.8IG.bfi2,
SAM.9TW.bfi2,
SAM.10IG.bfi2,
SAM.11TW.bfi2,
SAM.12IG.bfi2,
SAM.13IG.bfi2,
SAM.14TW.bfi2,
SAM.15IG.bfi2,
SAM.16TW.bfi2,
SAM.17IG.bfi2,
SAM.18TW.bfi2,
SAM.19IG.bfi2,
SAM.20TW.bfi2,
SAM.21IG.bfi2,
SAM.22TW.bfi2,
SAM.23IG.bfi2,
SAM.24TW.bfi2,
SAM.25TW.bfi2,
SAM.26IG.bfi2,
SAM.27TW.bfi2,
SAM.28IG.bfi2,
SAM.29TW.bfi2,
SAM.30IG.bfi2,
SAM.31IG.bfi2,
SAM.32TW.bfi2,
SAM.33IG.bfi2,
SAM.34TW.bfi2,
SAM.35IG.bfi2,
SAM.36TW.bfi2,
SAM.37IG.bfi2,
SAM.38TW.bfi2,
SAM.39IG.bfi2,
SAM.40TW.bfi2,
SAM.41IG.bfi2,
SAM.42TW.bfi2,
SAM.43TW.bfi2,
SAM.44IG.bfi2,
SAM.45TW.bfi2,
SAM.46IG.bfi2,
SAM.47TW.bfi2,
SAM.48IG.bfi2,
SAM.49IG.bfi2,
SAM.50TW.bfi2,
SAM.51IG.bfi2,
SAM.52TW.bfi2,
SAM.53IG.bfi2,
SAM.54TW.bfi2,
SAM.55IG.bfi2,
SAM.56TW.bfi2,
SAM.57IG.bfi2,
SAM.58TW.bfi2,
SAM.59IG.bfi2,
SAM.60TW.bfi2,
SAM.61TW.bfi2,
SAM.62IG.bfi2,
SAM.63TW.bfi2,
SAM.64IG.bfi2,
SAM.65TW.bfi2,
SAM.66IG.bfi2,
SAM.67IG.bfi2,
SAM.68TW.bfi2,
SAM.69IG.bfi2,
SAM.70TW.bfi2,
SAM.71IG.bfi2,
SAM.72TW.bfi2,
SAM.73IG.bfi2,
SAM.74TW.bfi2,
SAM.75IG.bfi2,
SAM.76TW.bfi2,
SAM.77IG.bfi2,
SAM.78TW.bfi2,
SAM.79TW.bfi2,
SAM.80IG.bfi2,
SAM.81TW.bfi2,
SAM.82IG.bfi2,
SAM.83TW.bfi2,
SAM.84IG.bfi2,
SAM.85IG.bfi2,
SAM.86TW.bfi2,
SAM.87IG.bfi2,
SAM.88TW.bfi2,
SAM.89IG.bfi2,
SAM.90TW.bfi2,
SAM.91IG.bfi2,
SAM.92TW.bfi2,
SAM.93IG.bfi2,
SAM.94TW.bfi2,
SAM.95IG.bfi2,
SAM.96TW.bfi2,
SAM.97TW.bfi2,
SAM.98IG.bfi2,
SAM.99TW.bfi2,
SAM.100IG.bfi2,
SAM.101TW.bfi2,
SAM.102IG.bfi2,
SAM.103IG.bfi2,
SAM.104TW.bfi2,
SAM.105IG.bfi2,
SAM.106TW.bfi2,
SAM.107IG.bfi2,
SAM.108TW.bfi2,
SAM.109IG.bfi2,
SAM.110TW.bfi2,
SAM.111IG.bfi2,
SAM.112TW.bfi2,
SAM.113IG.bfi2,
SAM.114TW.bfi2,
SAM.115TW.bfi2,
SAM.116IG.bfi2,
SAM.117TW.bfi2,
SAM.118IG.bfi2,
SAM.119TW.bfi2,
SAM.120IG.bfi2,
SAM.121IG.bfi2,
SAM.122TW.bfi2,
SAM.123IG.bfi2,
SAM.124TW.bfi2,
SAM.125IG.bfi2,
SAM.126TW.bfi2,
SAM.127IG.bfi2,
SAM.128TW.bfi2,
SAM.129IG.bfi2,
SAM.130TW.bfi2,
SAM.131IG.bfi2,
SAM.132TW.bfi2,
SAM.133TW.bfi2,
SAM.134IG.bfi2,
SAM.135TW.bfi2,
SAM.136IG.bfi2,
SAM.137TW.bfi2,
SAM.138IG.bfi2,
SAM.139IG.bfi2,
SAM.140TW.bfi2,
SAM.141IG.bfi2,
SAM.142TW.bfi2,
SAM.143IG.bfi2,
SAM.144TW.bfi2,
SAM.145IG.bfi2, 
SAM.146TW.bfi2, 
SAM.147IG.bfi2,
SAM.148TW.bfi2, 
SAM.149IG.bfi2, 
SAM.150TW.bfi2)


SAM.bfi.IG <- rbind(SAM.1IG.bfi2, SAM.3IG.bfi2,
SAM.5IG.bfi2,
SAM.8IG.bfi2,
SAM.10IG.bfi2,
SAM.12IG.bfi2,
SAM.13IG.bfi2,
SAM.15IG.bfi2,
SAM.17IG.bfi2,
SAM.19IG.bfi2,
SAM.21IG.bfi2,
SAM.23IG.bfi2,
SAM.26IG.bfi2,
SAM.28IG.bfi2,
SAM.30IG.bfi2,
SAM.31IG.bfi2,
SAM.33IG.bfi2,
SAM.35IG.bfi2,
SAM.37IG.bfi2,
SAM.39IG.bfi2,
SAM.41IG.bfi2,
SAM.44IG.bfi2,
SAM.46IG.bfi2,
SAM.48IG.bfi2,
SAM.49IG.bfi2,
SAM.51IG.bfi2,
SAM.53IG.bfi2,
SAM.55IG.bfi2,
SAM.57IG.bfi2,
SAM.59IG.bfi2,
SAM.62IG.bfi2,
SAM.64IG.bfi2,
SAM.66IG.bfi2,
SAM.67IG.bfi2,
SAM.69IG.bfi2,
SAM.71IG.bfi2,
SAM.73IG.bfi2,
SAM.75IG.bfi2,
SAM.77IG.bfi2,
SAM.80IG.bfi2,
SAM.82IG.bfi2,
SAM.84IG.bfi2,
SAM.85IG.bfi2,
SAM.87IG.bfi2,
SAM.89IG.bfi2,
SAM.91IG.bfi2,
SAM.93IG.bfi2,
SAM.95IG.bfi2,
SAM.98IG.bfi2,
SAM.100IG.bfi2,
SAM.102IG.bfi2,
SAM.103IG.bfi2,
SAM.105IG.bfi2,
SAM.107IG.bfi2,
SAM.109IG.bfi2,
SAM.111IG.bfi2,
SAM.113IG.bfi2,
SAM.116IG.bfi2,
SAM.118IG.bfi2,
SAM.120IG.bfi2,
SAM.121IG.bfi2,
SAM.123IG.bfi2,
SAM.125IG.bfi2,
SAM.127IG.bfi2,
SAM.129IG.bfi2,
SAM.131IG.bfi2,
SAM.134IG.bfi2,
SAM.136IG.bfi2,
SAM.138IG.bfi2,
SAM.139IG.bfi2,
SAM.141IG.bfi2,
SAM.143IG.bfi2,
SAM.145IG.bfi2, 
SAM.147IG.bfi2,
SAM.149IG.bfi2)

SAM.bfi.TW <- rbind(SAM.2TW.bfi2, SAM.4TW.bfi2,
SAM.6TW.bfi2,
SAM.7TW.bfi2,
SAM.9TW.bfi2,
SAM.11TW.bfi2,
SAM.14TW.bfi2,
SAM.16TW.bfi2,
SAM.18TW.bfi2,
SAM.20TW.bfi2,
SAM.22TW.bfi2,
SAM.24TW.bfi2,
SAM.25TW.bfi2,
SAM.27TW.bfi2,
SAM.29TW.bfi2,
SAM.32TW.bfi2,
SAM.34TW.bfi2,
SAM.36TW.bfi2,
SAM.38TW.bfi2,
SAM.40TW.bfi2,
SAM.42TW.bfi2,
SAM.43TW.bfi2,
SAM.45TW.bfi2,
SAM.47TW.bfi2,
SAM.50TW.bfi2,
SAM.52TW.bfi2,
SAM.54TW.bfi2,
SAM.56TW.bfi2,
SAM.58TW.bfi2,
SAM.60TW.bfi2,
SAM.61TW.bfi2,
SAM.63TW.bfi2,
SAM.65TW.bfi2,
SAM.68TW.bfi2,
SAM.70TW.bfi2,
SAM.72TW.bfi2,
SAM.74TW.bfi2,
SAM.76TW.bfi2,
SAM.78TW.bfi2,
SAM.79TW.bfi2,
SAM.81TW.bfi2,
SAM.83TW.bfi2,
SAM.86TW.bfi2,
SAM.88TW.bfi2,
SAM.90TW.bfi2,
SAM.92TW.bfi2,
SAM.94TW.bfi2,
SAM.96TW.bfi2,
SAM.97TW.bfi2,
SAM.99TW.bfi2,
SAM.101TW.bfi2,
SAM.104TW.bfi2,
SAM.106TW.bfi2,
SAM.108TW.bfi2,
SAM.110TW.bfi2,
SAM.112TW.bfi2,
SAM.114TW.bfi2,
SAM.115TW.bfi2,
SAM.117TW.bfi2,
SAM.119TW.bfi2,
SAM.122TW.bfi2,
SAM.124TW.bfi2,
SAM.126TW.bfi2,
SAM.128TW.bfi2,
SAM.130TW.bfi2,
SAM.132TW.bfi2,
SAM.133TW.bfi2,
SAM.135TW.bfi2,
SAM.137TW.bfi2,
SAM.140TW.bfi2,
SAM.142TW.bfi2,
SAM.144TW.bfi2,
SAM.146TW.bfi2, 
SAM.148TW.bfi2, 
SAM.150TW.bfi2)

# Subtract the Normative profile from the predictors and then mean center predictors for each personality measure

#Full model
SAM.bfi$Norm.MC <- SAM.bfi$Norm - mean(SAM.bfi$Norm, na.rm=TRUE)

SAM.bfi$SRminusNorm <- SAM.bfi$Sr - SAM.bfi$Norm
SAM.bfi$SR.MC <- SAM.bfi$SRminusNorm - (mean(SAM.bfi$SRminusNorm, na.rm=TRUE))

#Instagram model
SAM.bfi.IG$Norm.MC <- SAM.bfi.IG$Norm - mean(SAM.bfi.IG$Norm, na.rm=TRUE)

SAM.bfi.IG$SRminusNorm <- SAM.bfi.IG$Sr - SAM.bfi.IG$Norm
SAM.bfi.IG$SR.MC <- SAM.bfi.IG$SRminusNorm - (mean(SAM.bfi.IG$SRminusNorm, na.rm=TRUE))

#Twitter model
SAM.bfi.TW$Norm.MC <- SAM.bfi.TW$Norm - mean(SAM.bfi.TW$Norm, na.rm=TRUE)

SAM.bfi.TW$SRminusNorm <- SAM.bfi.TW$Sr - SAM.bfi.TW$Norm
SAM.bfi.TW$SR.MC <- SAM.bfi.TW$SRminusNorm - (mean(SAM.bfi.TW$SRminusNorm, na.rm=TRUE))

#full model
summary(bfi2.full <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.bfi, control = lmerControl(optimizer ='bobyqa')))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.bfi
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 146505.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5010 -0.7378  0.0294  0.7374  3.6340 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr       
##  JID      (Intercept) 0.0985048 0.31385             
##           SR.MC       0.0096161 0.09806  -0.42      
##           Norm.MC     0.1205096 0.34714  -0.33  0.77
##  TID      (Intercept) 0.0008005 0.02829             
##           SR.MC       0.0162651 0.12753  0.66       
##           Norm.MC     0.0661125 0.25712  0.75  0.63 
##  Residual             1.1032336 1.05035             
## Number of obs: 49186, groups:  JID, 277; TID, 150
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.21857    0.01968 282.85806 163.521   <2e-16 ***
## SR.MC         0.12430    0.01273 212.84883   9.767   <2e-16 ***
## Norm.MC       0.39519    0.03067 321.90241  12.885   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.125       
## Norm.MC -0.154  0.613

#instagram
summary(bfi2.IG <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.bfi.IG, control = lmerControl(optimizer ='bobyqa')))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.bfi.IG
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 76405.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2867 -0.7007  0.0301  0.7250  3.2314 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr       
##  JID      (Intercept) 0.0994883 0.31542             
##           SR.MC       0.0143931 0.11997  -0.39      
##           Norm.MC     0.1557260 0.39462  -0.38  0.85
##  TID      (Intercept) 0.0006115 0.02473             
##           SR.MC       0.0134800 0.11610  0.29       
##           Norm.MC     0.0312600 0.17681  0.46  0.39 
##  Residual             1.0846753 1.04148             
## Number of obs: 25678, groups:  JID, 272; TID, 75
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.22921    0.02054 263.11360  157.20   <2e-16 ***
## SR.MC         0.16557    0.01642  97.82407   10.09   <2e-16 ***
## Norm.MC       0.52357    0.03333 182.44454   15.71   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.132       
## Norm.MC -0.216  0.494

#twitter
summary(bfi2.TW <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.bfi.TW, control = lmerControl(optimizer ='bobyqa')))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.bfi.TW
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 70355.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1753 -0.7546  0.0183  0.7459  3.3081 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.096303 0.31033             
##           SR.MC       0.012250 0.11068  -0.32      
##           Norm.MC     0.137508 0.37082  -0.19  0.71
##  TID      (Intercept) 0.001404 0.03747             
##           SR.MC       0.013593 0.11659  0.59       
##           Norm.MC     0.048579 0.22041  0.78  0.63 
##  Residual             1.098907 1.04829             
## Number of obs: 23508, groups:  JID, 271; TID, 75
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.21080    0.02060 263.61053 155.839  < 2e-16 ***
## SR.MC         0.08557    0.01623  95.18750   5.273 8.39e-07 ***
## Norm.MC       0.26845    0.03573 142.84646   7.513 5.77e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.017       
## Norm.MC  0.006  0.579

#comparing Instagram and Twitter

SAM.bfi$ConTW <-0
SAM.bfi$ConTW [SAM.bfi$ConType==-1]<-1


summary(bfi2.comparison <- lmer(Rating ~ 1 + SR.MC*ConTW + Norm.MC*ConTW  +(1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.bfi, control = lmerControl(optimizer ='bobyqa')))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## Rating ~ 1 + SR.MC * ConTW + Norm.MC * ConTW + (1 + SR.MC + Norm.MC |  
##     JID) + (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.bfi
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 146482.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4745 -0.7380  0.0281  0.7385  3.6159 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr       
##  JID      (Intercept) 0.0985418 0.31391             
##           SR.MC       0.0096572 0.09827  -0.42      
##           Norm.MC     0.1212652 0.34823  -0.33  0.77
##  TID      (Intercept) 0.0007333 0.02708             
##           SR.MC       0.0145087 0.12045  0.60       
##           Norm.MC     0.0467786 0.21628  0.70  0.56 
##  Residual             1.1032267 1.05035             
## Number of obs: 49186, groups:  JID, 277; TID, 150
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     3.22793    0.02037 312.21779 158.483  < 2e-16 ***
## SR.MC           0.16571    0.01629 182.66087  10.171  < 2e-16 ***
## ConTW          -0.01886    0.01077 124.70538  -1.751 0.082365 .  
## Norm.MC         0.52827    0.03444 277.34740  15.338  < 2e-16 ***
## SR.MC:ConTW    -0.08261    0.02145 140.11820  -3.852 0.000178 ***
## ConTW:Norm.MC  -0.26532    0.03855 131.39862  -6.883 2.17e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) SR.MC  ConTW  Nrm.MC SR.MC:
## SR.MC       -0.067                            
## ConTW       -0.259 -0.141                     
## Norm.MC     -0.108  0.541 -0.143              
## SR.MC:ConTW -0.056 -0.658  0.219 -0.281       
## CnTW:Nrm.MC -0.068 -0.331  0.258 -0.560  0.502

#RQ5
#Judge familiarity with SNS, Twitter, and Instagram

summary(BFI.sns <- lmer(Rating ~ 1 +SR.MC*SNSFreq + Norm.MC*SNSFreq + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.bfi, control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb'))))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC * SNSFreq + Norm.MC * SNSFreq + (1 + SR.MC +  
##     Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.bfi
## Control: 
## lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))
## 
## REML criterion at convergence: 138528.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4779 -0.7357  0.0340  0.7342  3.6120 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr       
##  JID      (Intercept) 0.1051195 0.32422             
##           SR.MC       0.0095531 0.09774  -0.47      
##           Norm.MC     0.1182230 0.34384  -0.37  0.75
##  TID      (Intercept) 0.0008571 0.02928             
##           SR.MC       0.0161454 0.12706  0.66       
##           Norm.MC     0.0636822 0.25235  0.79  0.64 
##  Residual             1.1169650 1.05687             
## Number of obs: 46313, groups:  JID, 253; TID, 150
## 
## Fixed effects:
##                   Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       3.239366   0.129039 251.031578  25.104  < 2e-16 ***
## SR.MC             0.019419   0.047714 257.637530   0.407 0.684349    
## SNSFreq          -0.001761   0.023017 250.766649  -0.077 0.939070    
## Norm.MC          -0.095244   0.146276 262.922019  -0.651 0.515534    
## SR.MC:SNSFreq     0.018810   0.008314 235.697767   2.262 0.024579 *  
## SNSFreq:Norm.MC   0.088688   0.025850 253.576948   3.431 0.000702 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) SR.MC  SNSFrq Nrm.MC SR.MC:
## SR.MC       -0.351                            
## SNSFreq     -0.986  0.349                     
## Norm.MC     -0.316  0.624  0.313              
## SR.MC:SNSFr  0.357 -0.963 -0.362 -0.611       
## SNSFrq:N.MC  0.316 -0.603 -0.321 -0.977  0.626

summary(BFI.sns.TW <- lmer(Rating ~ 1 +SR.MC*TwitFreq + Norm.MC*TwitFreq + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.bfi.TW, control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb'))))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## Rating ~ 1 + SR.MC * TwitFreq + Norm.MC * TwitFreq + (1 + SR.MC +  
##     Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.bfi.TW
## Control: 
## lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))
## 
## REML criterion at convergence: 66533.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1704 -0.7505  0.0220  0.7392  3.2895 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.097352 0.31201             
##           SR.MC       0.012260 0.11073  -0.32      
##           Norm.MC     0.140014 0.37418  -0.23  0.73
##  TID      (Intercept) 0.001489 0.03859             
##           SR.MC       0.013852 0.11769  0.57       
##           Norm.MC     0.043616 0.20884  0.77  0.66 
##  Residual             1.112831 1.05491             
## Number of obs: 22135, groups:  JID, 252; TID, 75
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)        3.095801   0.043237 252.998492  71.600  < 2e-16 ***
## SR.MC              0.116062   0.023562 229.914220   4.926 1.61e-06 ***
## TwitFreq           0.034011   0.010561 247.188281   3.221 0.001451 ** 
## Norm.MC            0.198134   0.059416 280.804837   3.335 0.000969 ***
## SR.MC:TwitFreq    -0.008739   0.004701 221.721240  -1.859 0.064351 .  
## TwitFreq:Norm.MC   0.019381   0.013328 241.460487   1.454 0.147218    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) SR.MC  TwtFrq Nrm.MC SR.MC:
## SR.MC       -0.144                            
## TwitFreq    -0.868  0.156                     
## Norm.MC     -0.140  0.566  0.151              
## SR.MC:TwtFr  0.192 -0.710 -0.221 -0.442       
## TwtFrq:N.MC  0.164 -0.394 -0.190 -0.799  0.553

summary(BFI.sns.IG <- lmer(Rating ~ 1 +SR.MC*IGFreq + Norm.MC*IGFreq + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.bfi.IG, control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb'))))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC * IGFreq + Norm.MC * IGFreq + (1 + SR.MC +  
##     Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.bfi.IG
## Control: 
## lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))
## 
## REML criterion at convergence: 75842.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2851 -0.6989  0.0304  0.7259  3.2465 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr       
##  JID      (Intercept) 0.0964805 0.3106              
##           SR.MC       0.0143459 0.1198   -0.40      
##           Norm.MC     0.1575721 0.3970   -0.42  0.86
##  TID      (Intercept) 0.0007451 0.0273              
##           SR.MC       0.0134669 0.1160   0.28       
##           Norm.MC     0.0330664 0.1818   0.52  0.41 
##  Residual             1.0832605 1.0408              
## Number of obs: 25498, groups:  JID, 267; TID, 75
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)      3.076956   0.055113 266.434898  55.830  < 2e-16 ***
## SR.MC            0.196449   0.028981 291.752474   6.779 6.72e-11 ***
## IGFreq           0.035053   0.011443 264.741010   3.063  0.00242 ** 
## Norm.MC          0.468142   0.076229 290.969446   6.141 2.68e-09 ***
## SR.MC:IGFreq    -0.006463   0.005342 246.501562  -1.210  0.22749    
## IGFreq:Norm.MC   0.013338   0.015264 259.160247   0.874  0.38303    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) SR.MC  IGFreq Nrm.MC SR.MC:
## SR.MC       -0.253                            
## IGFreq      -0.928  0.242                     
## Norm.MC     -0.329  0.621  0.314              
## SR.MC:IGFrq  0.272 -0.824 -0.293 -0.599       
## IGFrq:Nr.MC  0.325 -0.551 -0.350 -0.895  0.669

#Openness across SNS
bfi.Open1 <- subset (SAM.bfi, Norm.item == "BFI25", select=c(JID: SR.MC))
bfi.Open2 <- subset (SAM.bfi, Norm.item == "BFI210", select=c(JID: SR.MC))
bfi.Open3 <- subset (SAM.bfi, Norm.item == "BFI215", select=c(JID: SR.MC))
bfi.Open4 <- subset (SAM.bfi, Norm.item == "BFI220", select=c(JID: SR.MC))
bfi.Open5 <- subset (SAM.bfi, Norm.item == "BFI225", select=c(JID: SR.MC))
bfi.Open6 <- subset (SAM.bfi, Norm.item == "BFI230", select=c(JID: SR.MC))

SAM.Open <- rbind( bfi.Open1, bfi.Open2, bfi.Open3, bfi.Open4, bfi.Open5, bfi.Open6)


summary(bfi.Open <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.Open, control = lmerControl(optimizer ='bobyqa')))

## boundary (singular) fit: see ?isSingular

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.Open
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 28008.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2507 -0.6513  0.0042  0.6697  4.0525 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr       
##  JID      (Intercept) 0.1411935 0.37576             
##           SR.MC       0.0144448 0.12019  -0.19      
##           Norm.MC     0.2420353 0.49197  -0.19 -0.02
##  TID      (Intercept) 0.0007772 0.02788             
##           SR.MC       0.0078635 0.08868  0.47       
##           Norm.MC     0.1101500 0.33189  0.93  0.11 
##  Residual             0.8814993 0.93888             
## Number of obs: 9780, groups:  JID, 277; TID, 149
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.15679    0.02491 283.69990 126.729  < 2e-16 ***
## SR.MC         0.04263    0.01571 110.28462   2.713  0.00774 ** 
## Norm.MC       0.39156    0.04234 319.37319   9.247  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.038       
## Norm.MC -0.059  0.017
## convergence code: 0
## boundary (singular) fit: see ?isSingular

#conscientiousness across SNS
bfi.Cons1 <- subset (SAM.bfi, Norm.item == "BFI23", select=c(JID:SR.MC))
bfi.Cons2 <- subset (SAM.bfi, Norm.item == "BFI28", select=c(JID:SR.MC))
bfi.Cons3 <- subset (SAM.bfi, Norm.item == "BFI213", select=c(JID:SR.MC))
bfi.Cons4 <- subset (SAM.bfi, Norm.item == "BFI218", select=c(JID:SR.MC))
bfi.Cons5 <- subset (SAM.bfi, Norm.item == "BFI223", select=c(JID:SR.MC))
bfi.Cons6 <- subset (SAM.bfi, Norm.item == "BFI228", select=c(JID:SR.MC))

SAM.Cons <- rbind(bfi.Cons1, bfi.Cons2, bfi.Cons3, bfi.Cons4, bfi.Cons5, bfi.Cons6)

summary(bfi.Cons <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.Cons, control = lmerControl(optimizer ='bobyqa')))

## boundary (singular) fit: see ?isSingular

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.Cons
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 23481.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0947 -0.6815  0.0200  0.7168  3.2853 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.121565 0.34866             
##           SR.MC       0.007243 0.08511  -0.26      
##           Norm.MC     0.300463 0.54815  -0.59 -0.10
##  TID      (Intercept) 0.006890 0.08301             
##           SR.MC       0.023742 0.15408  -0.15      
##           Norm.MC     0.216680 0.46549  -1.00  0.12
##  Residual             0.901129 0.94928             
## Number of obs: 8150, groups:  JID, 277; TID, 149
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.13182    0.02498 304.77447 125.368  < 2e-16 ***
## SR.MC         0.02499    0.01796 136.67944   1.392    0.166    
## Norm.MC       0.36476    0.05435 265.91165   6.711 1.16e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.096       
## Norm.MC -0.527  0.041
## convergence code: 0
## boundary (singular) fit: see ?isSingular

#Agreeableness across SNS
bfi.Agr1 <- subset (SAM.bfi, Norm.item == "BFI22", select=c(JID:SR.MC))
bfi.Agr2 <- subset (SAM.bfi, Norm.item == "BFI27", select=c(JID:SR.MC))
bfi.Agr3 <- subset (SAM.bfi, Norm.item == "BFI212", select=c(JID:SR.MC))
bfi.Agr4 <- subset (SAM.bfi, Norm.item == "BFI217", select=c(JID:SR.MC))
bfi.Agr5 <- subset (SAM.bfi, Norm.item == "BFI222", select=c(JID:SR.MC))
bfi.Agr6 <- subset (SAM.bfi, Norm.item == "BFI227", select=c(JID:SR.MC))

SAM.Agr<- rbind(bfi.Agr1, bfi.Agr2, bfi.Agr3, bfi.Agr4, bfi.Agr5, bfi.Agr6)
summary(bfi.Agr <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.Agr, control = lmerControl(optimizer ='bobyqa')))

## boundary (singular) fit: see ?isSingular

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.Agr
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 28639.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3176 -0.6341  0.0718  0.6870  2.6562 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.103048 0.32101             
##           SR.MC       0.054789 0.23407  -0.40      
##           Norm.MC     0.090176 0.30029  -0.45  1.00
##  TID      (Intercept) 0.005488 0.07408             
##           SR.MC       0.063753 0.25249   0.00      
##           Norm.MC     0.087572 0.29593  -0.14  0.96
##  Residual             0.960605 0.98010             
## Number of obs: 9801, groups:  JID, 277; TID, 150
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.25016    0.02300 282.11275 141.306   <2e-16 ***
## SR.MC         0.24338    0.02662 223.22640   9.141   <2e-16 ***
## Norm.MC       0.33213    0.03340 207.80032   9.944   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.203       
## Norm.MC -0.255  0.912
## convergence code: 0
## boundary (singular) fit: see ?isSingular

#WExtraversion across SNS
bfi.Ext1 <- subset (SAM.bfi, Norm.item == "BFI21", select=c(JID:SR.MC))
bfi.Ext2 <- subset (SAM.bfi, Norm.item == "BFI26", select=c(JID:SR.MC))
bfi.Ext3 <- subset (SAM.bfi, Norm.item == "BFI211", select=c(JID:SR.MC))
bfi.Ext4 <- subset (SAM.bfi, Norm.item == "BFI216", select=c(JID:SR.MC))
bfi.Ext5 <- subset (SAM.bfi, Norm.item == "BFI221", select=c(JID:SR.MC))
bfi.Ext6 <- subset (SAM.bfi, Norm.item == "BFI226", select=c(JID:SR.MC))

SAM.Ext <- rbind(bfi.Ext1, bfi.Ext2, bfi.Ext3, bfi.Ext4, bfi.Ext5, bfi.Ext6)
summary(bfi.Ext <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.Ext, control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb'))))

## boundary (singular) fit: see ?isSingular

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.Ext
## Control: 
## lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))
## 
## REML criterion at convergence: 29940.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1936 -0.6629  0.0898  0.7004  3.4509 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.098008 0.31306             
##           SR.MC       0.012146 0.11021  -0.37      
##           Norm.MC     0.701828 0.83775  -0.17  0.13
##  TID      (Intercept) 0.002753 0.05247             
##           SR.MC       0.039169 0.19791   0.36      
##           Norm.MC     0.552719 0.74345   0.90 -0.07
##  Residual             1.002720 1.00136             
## Number of obs: 10046, groups:  JID, 277; TID, 150
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.30115    0.02239 297.96916 147.408   <2e-16 ***
## SR.MC         0.02144    0.02141 140.19968   1.001    0.318    
## Norm.MC       1.05016    0.08511 262.99052  12.339   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.048       
## Norm.MC  0.077 -0.027
## convergence code: 0
## boundary (singular) fit: see ?isSingular

#Neuroticism across SNS
bfi.Neur1 <- subset (SAM.bfi, Norm.item == "BFI24", select=c(JID:SR.MC))
bfi.Neur2 <- subset (SAM.bfi, Norm.item == "BFI29", select=c(JID:SR.MC))
bfi.Neur3 <- subset (SAM.bfi, Norm.item == "BFI214", select=c(JID:SR.MC))
bfi.Neur4 <- subset (SAM.bfi, Norm.item == "BFI219", select=c(JID:SR.MC))
bfi.Neur5 <- subset (SAM.bfi, Norm.item == "BFI224", select=c(JID:SR.MC))
bfi.Neur6 <- subset (SAM.bfi, Norm.item == "BFI229", select=c(JID:SR.MC))

SAM.Neur <- rbind (bfi.Neur1, bfi.Neur2, bfi.Neur3, bfi.Neur4, bfi.Neur5, bfi.Neur6)
summary(bfi.Neur <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.Neur, control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb'))))

## boundary (singular) fit: see ?isSingular

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.Neur
## Control: 
## lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))
## 
## REML criterion at convergence: 29225.8
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.11699 -0.69011  0.04925  0.72817  2.79569 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.083082 0.28824             
##           SR.MC       0.012628 0.11237  -0.22      
##           Norm.MC     0.440724 0.66387  -0.10 -0.16
##  TID      (Intercept) 0.002309 0.04805             
##           SR.MC       0.046479 0.21559  0.25       
##           Norm.MC     0.237171 0.48700  1.00  0.27 
##  Residual             1.041722 1.02065             
## Number of obs: 9779, groups:  JID, 277; TID, 149
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.25692    0.02103 285.47968 154.877  < 2e-16 ***
## SR.MC         0.06228    0.02180 159.13167   2.857  0.00485 ** 
## Norm.MC       0.63500    0.06787 235.89120   9.356  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.029       
## Norm.MC  0.108  0.092
## convergence code: 0
## boundary (singular) fit: see ?isSingular

#Openness IG
bfi.IG.Open1 <- subset (SAM.bfi.IG, Norm.item == "BFI25", select=c(JID: SR.MC))
bfi.IG.Open2 <- subset (SAM.bfi.IG, Norm.item == "BFI210", select=c(JID: SR.MC))
bfi.IG.Open3 <- subset (SAM.bfi.IG, Norm.item == "BFI215", select=c(JID: SR.MC))
bfi.IG.Open4 <- subset (SAM.bfi.IG, Norm.item == "BFI220", select=c(JID: SR.MC))
bfi.IG.Open5 <- subset (SAM.bfi.IG, Norm.item == "BFI225", select=c(JID: SR.MC))
bfi.IG.Open6 <- subset (SAM.bfi.IG, Norm.item == "BFI230", select=c(JID: SR.MC))

SAM.IG.Open <- rbind( bfi.IG.Open1, bfi.IG.Open2, bfi.IG.Open3, bfi.IG.Open4, bfi.IG.Open5, bfi.IG.Open6)


summary(bfi..IG.Open <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.IG.Open, control = lmerControl(optimizer ='bobyqa')))

## boundary (singular) fit: see ?isSingular

## Warning: Model failed to converge with 1 negative eigenvalue: -4.7e+01

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.IG.Open
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 14555.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2564 -0.6196  0.0001  0.6403  3.4357 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr       
##  JID      (Intercept) 0.1294783 0.35983             
##           SR.MC       0.0161381 0.12704  -0.13      
##           Norm.MC     0.2927129 0.54103  -0.22 -0.18
##  TID      (Intercept) 0.0001986 0.01409             
##           SR.MC       0.0090862 0.09532  1.00       
##           Norm.MC     0.0688728 0.26244  0.49  0.49 
##  Residual             0.8452638 0.91938             
## Number of obs: 5076, groups:  JID, 272; TID, 74
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.17003    0.02584 277.49118 122.661   <2e-16 ***
## SR.MC         0.04949    0.02092  54.91687   2.366   0.0216 *  
## Norm.MC       0.53470    0.04841 165.82825  11.046   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC    0.015       
## Norm.MC -0.097  0.116
## convergence code: 0
## boundary (singular) fit: see ?isSingular

#conscientiousness  IG
bfi.IG.Cons1 <- subset (SAM.bfi.IG, Norm.item == "BFI23", select=c(JID:SR.MC))
bfi.IG.Cons2 <- subset (SAM.bfi.IG, Norm.item == "BFI28", select=c(JID:SR.MC))
bfi.IG.Cons3 <- subset (SAM.bfi.IG, Norm.item == "BFI213", select=c(JID:SR.MC))
bfi.IG.Cons4 <- subset (SAM.bfi.IG, Norm.item == "BFI218", select=c(JID:SR.MC))
bfi.IG.Cons5 <- subset (SAM.bfi.IG, Norm.item == "BFI223", select=c(JID:SR.MC))
bfi.IG.Cons6 <- subset (SAM.bfi.IG, Norm.item == "BFI228", select=c(JID:SR.MC))

SAM.IG.Cons <- rbind(bfi.IG.Cons1, bfi.IG.Cons2, bfi.IG.Cons3, bfi.IG.Cons4, bfi.IG.Cons5, bfi.IG.Cons6)

summary(bfi.IG.Cons <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.IG.Cons, control = lmerControl(optimizer ='Nelder_Mead')))

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl =
## control$checkConv, : unable to evaluate scaled gradient

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl =
## control$checkConv, : Model failed to converge: degenerate Hessian with 2
## negative eigenvalues

## Warning: Model failed to converge with 2 negative eigenvalues: -1.1e+02
## -1.5e+02

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.IG.Cons
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
## REML criterion at convergence: 12335.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0055 -0.6608  0.0065  0.6944  3.2070 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr       
##  JID      (Intercept) 0.1359171 0.36867             
##           SR.MC       0.0013809 0.03716  -0.92      
##           Norm.MC     0.3733308 0.61101  -0.74  0.55
##  TID      (Intercept) 0.0008479 0.02912             
##           SR.MC       0.0238417 0.15441   1.00      
##           Norm.MC     0.0949100 0.30807  -0.28 -0.30
##  Residual             0.9194715 0.95889             
## Number of obs: 4230, groups:  JID, 272; TID, 74
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.10226    0.02779 267.28269 111.634  < 2e-16 ***
## SR.MC         0.06889    0.02403  55.26030   2.867  0.00586 ** 
## Norm.MC       0.56671    0.05867 148.16426   9.659  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC    0.036       
## Norm.MC -0.449 -0.103
## convergence code: 0
## unable to evaluate scaled gradient
## Model failed to converge: degenerate  Hessian with 2 negative eigenvalues

#Agreeableness IG
bfi.IG.Agr1 <- subset (SAM.bfi.IG, Norm.item == "BFI22", select=c(JID:SR.MC))
bfi.IG.Agr2 <- subset (SAM.bfi.IG, Norm.item == "BFI27", select=c(JID:SR.MC))
bfi.IG.Agr3 <- subset (SAM.bfi.IG, Norm.item == "BFI212", select=c(JID:SR.MC))
bfi.IG.Agr4 <- subset (SAM.bfi.IG, Norm.item == "BFI217", select=c(JID:SR.MC))
bfi.IG.Agr5 <- subset (SAM.bfi.IG, Norm.item == "BFI222", select=c(JID:SR.MC))
bfi.IG.Agr6 <- subset (SAM.bfi.IG, Norm.item == "BFI227", select=c(JID:SR.MC))

SAM.IG.Agr<- rbind(bfi.IG.Agr1, bfi.IG.Agr2, bfi.IG.Agr3, bfi.IG.Agr4, bfi.IG.Agr5, bfi.IG.Agr6)
summary(bfi.IG.Agr <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.IG.Agr, control = lmerControl(optimizer ='bobyqa')))

## boundary (singular) fit: see ?isSingular

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.IG.Agr
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 14841.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1639 -0.5842  0.0630  0.6654  2.8278 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.09574  0.3094              
##           SR.MC       0.07697  0.2774   -0.41      
##           Norm.MC     0.11228  0.3351   -0.43  1.00
##  TID      (Intercept) 0.01384  0.1177              
##           SR.MC       0.05336  0.2310   -0.33      
##           Norm.MC     0.06402  0.2530   -0.51  0.90
##  Residual             0.91113  0.9545              
## Number of obs: 5098, groups:  JID, 272; TID, 75
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.23248    0.02762 138.86205  117.01   <2e-16 ***
## SR.MC         0.35542    0.03418  80.16376   10.40   <2e-16 ***
## Norm.MC       0.47369    0.04064  77.21501   11.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.305       
## Norm.MC -0.368  0.862
## convergence code: 0
## boundary (singular) fit: see ?isSingular

#Extraversion IG
bfi.IG.Ext1 <- subset (SAM.bfi.IG, Norm.item == "BFI21", select=c(JID:SR.MC))
bfi.IG.Ext2 <- subset (SAM.bfi.IG, Norm.item == "BFI26", select=c(JID:SR.MC))
bfi.IG.Ext3 <- subset (SAM.bfi.IG, Norm.item == "BFI211", select=c(JID:SR.MC))
bfi.IG.Ext4 <- subset (SAM.bfi.IG, Norm.item == "BFI216", select=c(JID:SR.MC))
bfi.IG.Ext5 <- subset (SAM.bfi.IG, Norm.item == "BFI221", select=c(JID:SR.MC))
bfi.IG.Ext6 <- subset (SAM.bfi.IG, Norm.item == "BFI226", select=c(JID:SR.MC))

SAM.IG.Ext <- rbind(bfi.IG.Ext1, bfi.IG.Ext2, bfi.IG.Ext3, bfi.IG.Ext4, bfi.IG.Ext5, bfi.IG.Ext6)
summary(bfi.IG.Ext <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.IG.Ext, control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb'))))

## boundary (singular) fit: see ?isSingular

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.IG.Ext
## Control: 
## lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))
## 
## REML criterion at convergence: 15950.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.91381 -0.66546  0.08594  0.70859  2.75724 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr       
##  JID      (Intercept) 0.0891086 0.29851             
##           SR.MC       0.0101651 0.10082  -0.39      
##           Norm.MC     0.8674568 0.93137  -0.18  0.23
##  TID      (Intercept) 0.0008615 0.02935             
##           SR.MC       0.0276354 0.16624  -0.21      
##           Norm.MC     0.5047538 0.71046   0.98 -0.03
##  Residual             0.9801133 0.99001             
## Number of obs: 5352, groups:  JID, 272; TID, 75
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.34290    0.02404 277.20971 139.034   <2e-16 ***
## SR.MC         0.05922    0.02678  71.56336   2.212   0.0302 *  
## Norm.MC       1.18155    0.10861 117.47248  10.879   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.136       
## Norm.MC  0.084 -0.008
## convergence code: 0
## boundary (singular) fit: see ?isSingular

#Neuroticism IG
bfi.IG.Neur1 <- subset (SAM.bfi.IG, Norm.item == "BFI24", select=c(JID:SR.MC))
bfi.IG.Neur2 <- subset (SAM.bfi.IG, Norm.item == "BFI29", select=c(JID:SR.MC))
bfi.IG.Neur3 <- subset (SAM.bfi.IG, Norm.item == "BFI214", select=c(JID:SR.MC))
bfi.IG.Neur4 <- subset (SAM.bfi.IG, Norm.item == "BFI219", select=c(JID:SR.MC))
bfi.IG.Neur5 <- subset (SAM.bfi.IG, Norm.item == "BFI224", select=c(JID:SR.MC))
bfi.IG.Neur6 <- subset (SAM.bfi.IG, Norm.item == "BFI229", select=c(JID:SR.MC))

SAM.IG.Neur <- rbind (bfi.IG.Neur1, bfi.IG.Neur2, bfi.IG.Neur3, bfi.IG.Neur4, bfi.IG.Neur5, bfi.IG.Neur6)
summary(bfi.IG.Neur <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.IG.Neur, control = lmerControl(optimizer ='Nelder_Mead')))

## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper =
## rep.int(Inf, : failure to converge in 10000 evaluations

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl =
## control$checkConv, : unable to evaluate scaled gradient

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl =
## control$checkConv, : Model failed to converge: degenerate Hessian with 1
## negative eigenvalues

## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+01

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.IG.Neur
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
## REML criterion at convergence: 15270.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1640 -0.6371  0.0568  0.7021  2.7476 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr       
##  JID      (Intercept) 0.0858705 0.29304             
##           SR.MC       0.0182622 0.13514  -0.14      
##           Norm.MC     0.8291717 0.91059  -0.17 -0.26
##  TID      (Intercept) 0.0004053 0.02013             
##           SR.MC       0.1360773 0.36889  -0.59      
##           Norm.MC     1.8187436 1.34861   0.22  0.66
##  Residual             0.9912974 0.99564             
## Number of obs: 5076, groups:  JID, 272; TID, 74
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.25855    0.02347 244.52450 138.837  < 2e-16 ***
## SR.MC         0.05317    0.04660  18.01142   1.141    0.269    
## Norm.MC       0.75403    0.17468 598.65064   4.317 1.85e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.086       
## Norm.MC  0.013  0.516
## convergence code: 0
## unable to evaluate scaled gradient
## Model failed to converge: degenerate  Hessian with 1 negative eigenvalues
## failure to converge in 10000 evaluations

#Openness Twitter
bfi.TW.Open1 <- subset (SAM.bfi.TW, Norm.item == "BFI25", select=c(JID: SR.MC))
bfi.TW.Open2 <- subset (SAM.bfi.TW, Norm.item == "BFI210", select=c(JID: SR.MC))
bfi.TW.Open3 <- subset (SAM.bfi.TW, Norm.item == "BFI215", select=c(JID: SR.MC))
bfi.TW.Open4 <- subset (SAM.bfi.TW, Norm.item == "BFI220", select=c(JID: SR.MC))
bfi.TW.Open5 <- subset (SAM.bfi.TW, Norm.item == "BFI225", select=c(JID: SR.MC))
bfi.TW.Open6 <- subset (SAM.bfi.TW, Norm.item == "BFI230", select=c(JID: SR.MC))

SAM.TW.Open <- rbind( bfi.TW.Open1, bfi.TW.Open2, bfi.TW.Open3, bfi.TW.Open4, bfi.TW.Open5, bfi.TW.Open6)


summary(bfi.TW.Open <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.TW.Open, control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb'))))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.TW.Open
## Control: 
## lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))
## 
## REML criterion at convergence: 13668.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8590 -0.6540 -0.0118  0.6689  3.7666 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.137519 0.37084             
##           SR.MC       0.023548 0.15345  -0.14      
##           Norm.MC     0.300259 0.54796  -0.08  0.01
##  TID      (Intercept) 0.000791 0.02812             
##           SR.MC       0.007695 0.08772   0.63      
##           Norm.MC     0.086991 0.29494   0.76 -0.04
##  Residual             0.860977 0.92789             
## Number of obs: 4704, groups:  JID, 271; TID, 75
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.14655    0.02686 271.07782 117.127  < 2e-16 ***
## SR.MC         0.03748    0.02205  47.05651   1.700   0.0958 .  
## Norm.MC       0.24210    0.05106 151.33360   4.741 4.86e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC    0.017       
## Norm.MC  0.027 -0.026

#conscientiousness Twitter
bfi.TW.Cons1 <- subset (SAM.bfi.TW, Norm.item == "BFI23", select=c(JID:SR.MC))
bfi.TW.Cons2 <- subset (SAM.bfi.TW, Norm.item == "BFI28", select=c(JID:SR.MC))
bfi.TW.Cons3 <- subset (SAM.bfi.TW, Norm.item == "BFI213", select=c(JID:SR.MC))
bfi.TW.Cons4 <- subset (SAM.bfi.TW, Norm.item == "BFI218", select=c(JID:SR.MC))
bfi.TW.Cons5 <- subset (SAM.bfi.TW, Norm.item == "BFI223", select=c(JID:SR.MC))
bfi.TW.Cons6 <- subset (SAM.bfi.TW, Norm.item == "BFI228", select=c(JID:SR.MC))

SAM.TW.Cons <- rbind(bfi.TW.Cons1, bfi.TW.Cons2, bfi.TW.Cons3, bfi.TW.Cons4, bfi.TW.Cons5, bfi.TW.Cons6)

summary(bfi.TW.Cons <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.TW.Cons, control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb'))))

## boundary (singular) fit: see ?isSingular

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.TW.Cons
## Control: 
## lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))
## 
## REML criterion at convergence: 11285.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.12118 -0.66176  0.01637  0.71914  2.68729 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.102574 0.32027             
##           SR.MC       0.017055 0.13060   0.07      
##           Norm.MC     0.368180 0.60678  -0.57 -0.27
##  TID      (Intercept) 0.009608 0.09802             
##           SR.MC       0.022335 0.14945  -0.30      
##           Norm.MC     0.215351 0.46406  -0.97  0.06
##  Residual             0.852984 0.92357             
## Number of obs: 3920, groups:  JID, 271; TID, 75
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.16174    0.02780 196.36022 113.737   <2e-16 ***
## SR.MC        -0.00655    0.02523  79.16540  -0.260    0.796    
## Norm.MC       0.17000    0.07080 109.93290   2.401    0.018 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC   -0.087       
## Norm.MC -0.557 -0.023
## convergence code: 0
## boundary (singular) fit: see ?isSingular

#Agreeableness Twitter
bfi.TW.Agr1 <- subset (SAM.bfi.TW, Norm.item == "BFI22", select=c(JID:SR.MC))
bfi.TW.Agr2 <- subset (SAM.bfi.TW, Norm.item == "BFI27", select=c(JID:SR.MC))
bfi.TW.Agr3 <- subset (SAM.bfi.TW, Norm.item == "BFI212", select=c(JID:SR.MC))
bfi.TW.Agr4 <- subset (SAM.bfi.TW, Norm.item == "BFI217", select=c(JID:SR.MC))
bfi.TW.Agr5 <- subset (SAM.bfi.TW, Norm.item == "BFI222", select=c(JID:SR.MC))
bfi.TW.Agr6 <- subset (SAM.bfi.TW, Norm.item == "BFI227", select=c(JID:SR.MC))

SAM.TW.Agr<- rbind(bfi.TW.Agr1, bfi.TW.Agr2, bfi.TW.Agr3, bfi.TW.Agr4, bfi.TW.Agr5, bfi.TW.Agr6)
summary(bfi.TW.Agr <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.TW.Agr, control = lmerControl(optimizer ='bobyqa')))

## boundary (singular) fit: see ?isSingular

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.TW.Agr
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 13874.2
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.89403 -0.66155  0.06191  0.68255  2.76096 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.088173 0.29694             
##           SR.MC       0.069883 0.26435  -0.31      
##           Norm.MC     0.117285 0.34247  -0.38  1.00
##  TID      (Intercept) 0.004181 0.06466             
##           SR.MC       0.039991 0.19998  0.97       
##           Norm.MC     0.053326 0.23092  0.92  0.98 
##  Residual             0.962107 0.98087             
## Number of obs: 4703, groups:  JID, 271; TID, 75
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.26115    0.02472 206.29309 131.913  < 2e-16 ***
## SR.MC         0.14438    0.03071 107.27067   4.702 7.71e-06 ***
## Norm.MC       0.20180    0.03929  95.03118   5.136 1.49e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC
## SR.MC   0.072       
## Norm.MC 0.012  0.895
## convergence code: 0
## boundary (singular) fit: see ?isSingular

#Extraversion on Twitter
bfi.TW.Ext1 <- subset (SAM.bfi.TW, Norm.item == "BFI21", select=c(JID:SR.MC))
bfi.TW.Ext2 <- subset (SAM.bfi.TW, Norm.item == "BFI26", select=c(JID:SR.MC))
bfi.TW.Ext3 <- subset (SAM.bfi.TW, Norm.item == "BFI211", select=c(JID:SR.MC))
bfi.TW.Ext4 <- subset (SAM.bfi.TW, Norm.item == "BFI216", select=c(JID:SR.MC))
bfi.TW.Ext5 <- subset (SAM.bfi.TW, Norm.item == "BFI221", select=c(JID:SR.MC))
bfi.TW.Ext6 <- subset (SAM.bfi.TW, Norm.item == "BFI226", select=c(JID:SR.MC))

SAM.TW.Ext <- rbind(bfi.TW.Ext1, bfi.TW.Ext2, bfi.TW.Ext3, bfi.TW.Ext4, bfi.TW.Ext5, bfi.TW.Ext6)
summary(bfi.TW.Ext <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.TW.Ext, control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb'))))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.TW.Ext
## Control: 
## lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))
## 
## REML criterion at convergence: 14145
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9147 -0.6606  0.1092  0.6805  3.1788 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.085581 0.29254             
##           SR.MC       0.030027 0.17328  -0.28      
##           Norm.MC     0.815356 0.90297  -0.09 -0.08
##  TID      (Intercept) 0.005925 0.07697             
##           SR.MC       0.053895 0.23215   0.37      
##           Norm.MC     0.548996 0.74094   0.59 -0.22
##  Residual             0.972861 0.98634             
## Number of obs: 4694, groups:  JID, 271; TID, 75
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.25773    0.02545 142.24240 127.995  < 2e-16 ***
## SR.MC        -0.02138    0.03369  66.61186  -0.635    0.528    
## Norm.MC       0.88648    0.11115 104.39246   7.976 2.04e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC 
## SR.MC    0.062       
## Norm.MC  0.188 -0.160

#Neuroticism on Twitter
bfi.TW.Neur1 <- subset (SAM.bfi.TW, Norm.item == "BFI24", select=c(JID:SR.MC))
bfi.TW.Neur2 <- subset (SAM.bfi.TW, Norm.item == "BFI29", select=c(JID:SR.MC))
bfi.TW.Neur3 <- subset (SAM.bfi.TW, Norm.item == "BFI214", select=c(JID:SR.MC))
bfi.TW.Neur4 <- subset (SAM.bfi.TW, Norm.item == "BFI219", select=c(JID:SR.MC))
bfi.TW.Neur5 <- subset (SAM.bfi.TW, Norm.item == "BFI224", select=c(JID:SR.MC))
bfi.TW.Neur6 <- subset (SAM.bfi.TW, Norm.item == "BFI229", select=c(JID:SR.MC))

SAM.TW.Neur <- rbind (bfi.TW.Neur1, bfi.TW.Neur2, bfi.TW.Neur3, bfi.TW.Neur4, bfi.TW.Neur5, bfi.TW.Neur6)
summary(bfi.TW.Neur <- lmer(Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) + (1 + SR.MC + Norm.MC | TID), data = SAM.TW.Neur, control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb'))))

## boundary (singular) fit: see ?isSingular

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Rating ~ 1 + SR.MC + Norm.MC + (1 + SR.MC + Norm.MC | JID) +  
##     (1 + SR.MC + Norm.MC | TID)
##    Data: SAM.TW.Neur
## Control: 
## lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))
## 
## REML criterion at convergence: 14216
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.76267 -0.71180  0.04948  0.71779  2.53470 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  JID      (Intercept) 0.082410 0.28707             
##           SR.MC       0.020084 0.14172  -0.18      
##           Norm.MC     0.529431 0.72762   0.09 -0.25
##  TID      (Intercept) 0.003451 0.05875             
##           SR.MC       0.044168 0.21016  0.55       
##           Norm.MC     0.225394 0.47476  0.96  0.31 
##  Residual             1.044769 1.02214             
## Number of obs: 4703, groups:  JID, 271; TID, 75
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   3.25721    0.02464 222.19906 132.169  < 2e-16 ***
## SR.MC         0.06294    0.02999  78.67716   2.099    0.039 *  
## Norm.MC       0.48579    0.08801 111.07009   5.519 2.25e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) SR.MC
## SR.MC   0.080       
## Norm.MC 0.255  0.135
## convergence code: 0
## boundary (singular) fit: see ?isSingular