diff --git a/R_Scripts/lmer_Q64_1.R b/R_Scripts/lmer_Q64_1.R
new file mode 100644
index 0000000000000000000000000000000000000000..c9120c6c7d6b68e5c22277f50445f64d66f3aeb9
--- /dev/null
+++ b/R_Scripts/lmer_Q64_1.R
@@ -0,0 +1,60 @@
+####Model 6 ######
+
+load("./Data/ECHOES_raw_data_int_survey.rdata")
+database <- ECHOES_raw_data_int_survey
+########################
+#      PMI             #
+########################
+database$Q53_1.z<-z_st(database$Q53_1)
+database$Q54_1.z<-z_st(database$Q54_1)
+database$Q61_1.z<-z_st(database$Q61_1)
+database$Q62.z  <-z_st(database$Q62)
+database$Q31_1.z<-z_st(database$Q31_1)
+database$Q49_1.z<-z_st(database$Q49_1)
+database$Q33_1.z<-z_st(database$Q33_1)
+database$Q34.z<-z_st(database$Q34)
+database$PMI<-(database$Q53_1.z+database$Q54_1.z+database$Q61_1.z+database$Q62.z+database$Q31_1.z+database$Q49_1.z+database$Q33_1.z+database$Q34.z)/8
+
+########################
+#      CMI             #
+########################
+database$Q36_1.z<-z_st(database$Q36_1)
+database$Q37_1.z<-z_st(database$Q37_1)
+database$Q38_1.z<-z_st(database$Q38_1)
+database$Q39_1.z<-z_st(database$Q39_1)
+database$Q48_1.z<-z_st(database$Q48_1)
+database$CMI<-((database$Q36_1.z+database$Q37_1.z+database$Q38_1.z+database$Q39_1.z)/4+ database$Q48_1.z)/2
+database$ID<- as.numeric(database$Q35_1)
+database$Acceptance<- as.numeric(database$Q64_1)
+
+fit <- lmer(Acceptance ~ 1 + CMI + ID + PMI + CMI:ID+( 1 + CMI + PMI | country_sample ), data = database, REML = TRUE)
+summary(fit)
+mean(database$ID)+sd(database$ID)
+mean(database$ID)-sd(database$ID)
+min<-min(database$CMI)
+max<-max(database$CMI)
+
+plot_model(fit, type = "pred", terms = c("CMI", "ID [2.352634,4.390395]"),ci.lvl=0.95 , title = "") + 
+  scale_color_discrete(labels = c("-1 SD",  "+1 SD"))
+
+ggsave("Graphs/Q64.png")
+ggsave("Graphs/Figure_6.tiff")
+
+
+hist_PMI<-ggplot(data = database, aes(x = PMI)) +
+  geom_histogram(binwidth = 0.3, fill = "#00BFC4", color = "#00BFC4", alpha = 0.2) +
+  xlab("PMI") +
+  ylab("Frequency")
+
+
+hist_CMI<-ggplot(data = database, aes(x = CMI)) +
+  geom_histogram(binwidth = 0.3, fill = "#F8766D", color = "#F8766D", alpha = 0.2) +
+    xlab("CMI") +
+  ylab("Frequency")
+hist_PMI
+
+ggarrange( hist_PMI, hist_CMI, 
+          labels = c("A", "B"),
+          ncol = 2, nrow = 1)
+ggsave("Graphs/Histogram.png")
+ggsave("Graphs/Histogram.tiff")