Load Packages
library(psych)
library(readr)
library(tidyverse)
## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
## Registered S3 method overwritten by 'rvest':
## method from
## read_xml.response xml2
## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.1.1 ✔ purrr 0.3.2
## ✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
## ✔ tidyr 0.8.3 ✔ stringr 1.4.0
## ✔ ggplot2 3.1.1 ✔ forcats 0.4.0
## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ ggplot2::%+%() masks psych::%+%()
## ✖ ggplot2::alpha() masks psych::alpha()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(lme4)
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following object is masked from 'package:tidyr':
##
## expand
library(Matrix)
library(contrast)
## Loading required package: rms
## Loading required package: Hmisc
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:dplyr':
##
## src, summarize
## The following object is masked from 'package:psych':
##
## describe
## The following objects are masked from 'package:base':
##
## format.pval, units
## Loading required package: SparseM
##
## Attaching package: 'SparseM'
## The following object is masked from 'package:base':
##
## backsolve
library(car)
## Loading required package: carData
## Registered S3 methods overwritten by 'car':
## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4
##
## Attaching package: 'car'
## The following objects are masked from 'package:rms':
##
## Predict, vif
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
## The following object is masked from 'package:psych':
##
## logit
library(lmerTest)
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
library(languageR)
library(corrplot)
## corrplot 0.84 loaded
library(effects)
## Use the command
## lattice::trellis.par.set(effectsTheme())
## to customize lattice options for effects plots.
## See ?effectsTheme for details.
library(sjPlot)
library(sjstats)
##
## Attaching package: 'sjstats'
## The following object is masked from 'package:Hmisc':
##
## deff
## The following objects are masked from 'package:psych':
##
## pca, phi
library(lattice)
library(simr)
##
## Attaching package: 'simr'
## The following object is masked from 'package:stringr':
##
## fixed
library(longpower)
## Loading required package: nlme
##
## Attaching package: 'nlme'
## The following objects are masked from 'package:simr':
##
## coef<-, getData
## The following object is masked from 'package:lme4':
##
## lmList
## The following object is masked from 'package:dplyr':
##
## collapse
library(pwr)
Set WD
setwd("~/Desktop/Desktop - Yrian’s MacBook Pro/Graduate School/Projects/Motivated Sampling Project/Political Study 2/Data")
Load in the data
personality.master.data.Dem <- read.csv("Pol2_2019.personality.master.data.Democrat.csv")
personality.master.data.Rep <- read.csv("Pol2_2019.personality.master.data.Republican.csv")
personality.master.data.Dem <- personality.master.data.Dem[,-c(1)]
personality.master.data.Rep <- personality.master.data.Rep[,-c(1)]
Adding a variable for dem.est and rep.est + status to in and out group estimates.
personality.master.data.Dem$In.Est <- personality.master.data.Dem$Dem.Est
personality.master.data.Dem$Out.Est <- personality.master.data.Dem$Rep.Est
personality.master.data.Rep$In.Est <- personality.master.data.Rep$Rep.Est
personality.master.data.Rep$Out.Est <- personality.master.data.Rep$Dem.Est
personality.master.data.Dem$In.status <- personality.master.data.Dem$demStatus
personality.master.data.Dem$Out.status <- personality.master.data.Dem$repStatus
personality.master.data.Rep$In.status <- personality.master.data.Rep$repStatus
personality.master.data.Rep$Out.status <- personality.master.data.Rep$demStatus
Combine into one data frame and make first sample and participan factors.
master.personality.Both <- rbind(personality.master.data.Dem, personality.master.data.Rep)
master.personality.Both <- as.data.frame(master.personality.Both)
master.personality.Both$firstSample <- factor(master.personality.Both$firstSample)
master.personality.Both$Participant <- factor(master.personality.Both$Participant)
Attention Checks
#Count of attention checks. 0 is none, 1 is one and 2 is 2.
table(master.personality.Both$Att)
##
## 0 1 2 3
## 442 450 22 9
hist(master.personality.Both$Att)
##Remove all participants who missed > 3
#master.personality.Both <- master.personality.Both[-c(914, 825, 794, 685, 572, 518, 410, 217, 115),]
master.personality.Both <- master.personality.Both[-c(863, 833, 794, 685, 572, 518, 410, 217, 115),]
##the follow participants will be deleted for attention check failures: 2435637, 3274001, 6757740, 4849095, 8186716, 8145059, 6780356, 4839071, 5101340
#master.personality.Both <- master.personality.Both[-c(753, 196, 185),]
#master.personality.Both <- master.personality.Both[-c(373, 34),]
rownames(master.personality.Both) <- 1:nrow(master.personality.Both)
#same_cond <- master.personality.Both[(master.personality.Both$Condition==2),]
#row 35 and 375 have 50 trials
#table(master.personality.Both$n_trials)
#view(master.personality.Both$n_trials==50)
Subsetting collapsed data frame to make a correlation matrix with only the pertinent variables
mydata <- master.personality.Both[, c(3, 4, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 38,41, 42, 43, 44, 45, 46, 47)]
mydata$Val <- as.numeric(mydata$Val)
mydata$Val[mydata$Val == "pos"] <- 1
mydata$Val[mydata$Val == "neg"] <- 0
Demographics
##Age
mean(master.personality.Both$age)
## [1] 40.6849
hist(master.personality.Both$age)
##Gender 0 = male; 1 = female
table(master.personality.Both$gender)
##
## 0 1
## 418 495
##Political Affiliation
table(master.personality.Both$Pol.Affil)
##
## 1 2 3 5 6 7
## 199 174 106 146 176 113
##Education
hist(master.personality.Both$education)
##personality.master.data.dem$income[personality.master.data.dem$income == "Less than $10,000" ] <- 0
#"$10,000 to $19,999" <- 1
#"$20,000 to $29,999" <- 2
#"$30,000 to $39,999" <- 3
#"$40,000 to $49,999" <- 4
#"$50,000 to $59,999" <- 5
#"$60,000 to $69,999" <- 6
#"$70,000 to $79,999" <- 7
#"$80,000 to $89,999" <- 7
#"$90,000 to $99,999" <- 7
#"$100,000 to $149,999"- 7
#"$150,000 or more" <- 7
hist(master.personality.Both$income)
Correlation Matrix
m <- cor(mydata, use="pairwise.complete.obs")
#compute p.values for m to put in graph later.
pval <- psych::corr.test(m, adjust="none")$p
#make a correlation matrix that has p.values and titles.
corrplot(cor(m),insig="p-value")
#corrplot(cor(m), type="upper", p.mat=pval, insig="p-value",
# tl.pos="n", sig.level=0)
#corrplot(cor(m), type="lower", add=T, tl.pos="d", cl.pos="n")
Creating long format data set for point-estimates
#####Ceating a long format data set to look at Point-Estimates (DV) from the master data (both rep and dem)
Evaluation.in.out <- data.frame(rep(master.personality.Both$Participant,2), rep(master.personality.Both$Condition,2),
c(master.personality.Both$In.Est, master.personality.Both$Out.Est), factor(rep(c(1,2), each=914), labels = c("In", "Out")),
rep(master.personality.Both$Val, 2), rep(master.personality.Both$group, 2), rep(master.personality.Both$In.status, 2),
rep(master.personality.Both$Out.status, 2), rep(master.personality.Both$SE_Importance, 2), rep(master.personality.Both$SE_Mem,2),
rep(master.personality.Both$SE_Private, 2), rep(master.personality.Both$SE_Public, 2), rep(master.personality.Both$SDO, 2))
##Ranaming the variables
names(Evaluation.in.out) <- c("Participant", "Condition", "P.Estimates", "Evaluated.Group", "Valence", "Group", "In.Status", "Out.status",
"SE.Importance", "SE.Mem", "SE.Private", "SE.Public", "SDO")
##Making sampled group into character so that it can be effects coded
Evaluation.in.out$Evaluated.GroupString <- as.character(Evaluation.in.out$Evaluated.Group)
Evaluation.in.out$GroupString <- as.character(Evaluation.in.out$Group)
Evaluation.in.out$ValenceString <- as.character(Evaluation.in.out $Valence)
Plotting Sampling behavior
SEFunctionForggplot <- function(vector) {
y <- mean(vector, na.rm = TRUE)
ymin <- y - sd(vector, na.rm = TRUE) / sqrt(length(vector))
ymax <- y + sd(vector, na.rm = TRUE) / sqrt(length(vector))
return(data.frame(y = y, ymin = ymin, ymax = ymax))
}
####For political sampling behavior
# tiff("example.tiff", more arguments)
ggplot(sampled.in.out, aes(as.factor(Condition), n_trials,
color = paste(as.factor(Valence), as.factor(Samp_Group)))) +
stat_summary(fun.y = mean, geom = "point", position = position_dodge(.4),
size = 3) +
stat_summary(fun.data = SEFunctionForggplot, geom = "errorbar",
position = position_dodge(.4), width = .3, size = 1) +
scale_x_discrete(labels = c("Worse", "Same", "Better"),
name = "Condition") +
scale_y_continuous(name = "Trials (N)") +
scale_color_manual(name = "Valence, \nSampling Group",
labels = c("\nNegative, \nIn-group\n",
"\nNegative, \nOut-Group\n",
"\nPositive, \nIn-group\n",
"\nPositive, \nOut-Group\n"),
values = c("darkgoldenrod1", "darkorange3",
"steelblue1", "steelblue4")) +
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank())
# dev.off()
Plotting Point-estimates
ggplot(Evaluation.in.out, aes(as.factor(Condition), P.Estimates,
color = paste(as.factor(Valence), as.factor(Evaluated.Group)))) +
stat_summary(fun.y = mean, geom = "point", position = position_dodge(.4),
size = 3) +
stat_summary(fun.data = SEFunctionForggplot, geom = "errorbar",
position = position_dodge(.4), width = .3, size = 1) +
scale_x_discrete(labels = c("worse", "Same", "Better"),
name = "Condition") +
scale_y_continuous(name = "Point.Estimate") +
scale_color_manual(name = "Valence, \nSampling Group",
labels = c("\nNegative, \nIn-group\n",
"\nNegative, \nOut-Group\n",
"\nPositive, \nIn-group\n",
"\nPositive, \nOut-Group\n"),
values = c("steelblue4", "darkorange3",
"steelblue1", "darkgoldenrod1")) +
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank())
Effects and Dummy coding categorical predictors for the sampling models
#Because it gets more complex to calculate the throw away group when you effects code, I am essentially making all iterations
#Change conditions to 1 2 3 for clarity
sampled.in.out$Condition[sampled.in.out$Condition == 1] <- "Worse"
sampled.in.out$Condition[sampled.in.out$Condition == 2] <- "Same"
sampled.in.out$Condition[sampled.in.out$Condition == 3] <- "Better"
#Effects + dummy coding for. call it _a.
sampled.in.out$Condition_a[sampled.in.out$Condition == "Worse"] <- -1
sampled.in.out$Condition_a[sampled.in.out$Condition == "Same"] <- 1
sampled.in.out$Condition_a[sampled.in.out$Condition == "Better"] <- 0
sampled.in.out$Condition_a_eff <- factor(sampled.in.out$Condition_a)
sampled.in.out$Condition_a_dum <- factor(sampled.in.out$Condition,
levels = c("Worse", "Better", "Same"))
#Effects + dummy coding, let's call it _c.
sampled.in.out$Condition_c[sampled.in.out$Condition == "Worse"] <- -1
sampled.in.out$Condition_c[sampled.in.out$Condition == "Same"] <- 0
sampled.in.out$Condition_c[sampled.in.out$Condition == "Better"] <- 1
sampled.in.out$Condition_c_eff <- factor(sampled.in.out$Condition_c)
sampled.in.out$Condition_c_dum <- factor(sampled.in.out$Condition,
levels = c("Worse", "Same", "Better"))
#Effects + dummy coding, let's call it _d.
sampled.in.out$Condition_d[sampled.in.out$Condition == "Worse"] <- 1
sampled.in.out$Condition_d[sampled.in.out$Condition == "Same"] <- 0
sampled.in.out$Condition_d[sampled.in.out$Condition == "Better"] <- -1
sampled.in.out$Condition_d_eff <- factor(sampled.in.out$Condition_d)
sampled.in.out$Condition_d_dum <- factor(sampled.in.out$Condition,
levels = c("Same", "Better", "Worse"))
##contrast coding where we collapse better and worse condition and compare to same group
sampled.in.out$Condition_contr[sampled.in.out$Condition == "Worse"] <- 0
sampled.in.out$Condition_contr[sampled.in.out$Condition== "Same"] <- 1
sampled.in.out$Condition_contr[sampled.in.out$Condition == "Better"] <- 0
sampled.in.out$Condition_contr[sampled.in.out$Condition_contr == 0] <- "Other"
sampled.in.out$Condition_contr[sampled.in.out$Condition_contr == 1] <- "Same"
sampled.in.out$Condition_contr_dum <- factor(sampled.in.out$Condition_contr,
levels = c("Same","Other"))
sampled.in.out$Condition_contr_eff <- factor(sampled.in.out$Condition_contr,
levels = c("Same","Other"))
#effects + dummy coding In and Out group with out as thro-away (Samp_Group)
sampled.in.out$Samp_GroupB_eff <- sampled.in.out$Samp_GroupString
sampled.in.out$Samp_GroupB_dum <- sampled.in.out$Samp_GroupString
sampled.in.out$Samp_GroupB_eff <- as.factor(sampled.in.out$Samp_GroupB_eff)
sampled.in.out$Samp_GroupB_dum <- as.factor(sampled.in.out$Samp_GroupB_dum)
sampled.in.out$Samp_GroupB_eff <- factor(sampled.in.out$Samp_GroupB_eff,
levels = c("In", "Out"))
sampled.in.out$Samp_GroupB_dum <- factor(sampled.in.out$Samp_GroupB_dum,
levels = c("In", "Out"))
#effects + dummy coding In and Out group with in as throw-away (Samp_Group1)
sampled.in.out$Samp_GroupB_eff1 <- sampled.in.out$Samp_GroupString
sampled.in.out$Samp_GroupB_dum1 <- sampled.in.out$Samp_GroupString
sampled.in.out$Samp_GroupB_eff1 <- as.factor(sampled.in.out$Samp_GroupB_eff1)
sampled.in.out$Samp_GroupB_dum1 <- as.factor(sampled.in.out$Samp_GroupB_dum1)
sampled.in.out$Samp_GroupB_eff1 <- factor(sampled.in.out$Samp_GroupB_eff1,
levels = c("Out", "In"))
sampled.in.out$Samp_GroupB_dum1 <- factor(sampled.in.out$Samp_GroupB_dum1,
levels = c("Out", "In"))
#effects + dummy coding group so that Rep is throw-away (Group)
sampled.in.out$Group_eff <- sampled.in.out$Group
sampled.in.out$Group_dum <- sampled.in.out$Group
sampled.in.out$Group_eff <- as.factor(sampled.in.out$Group_eff)
sampled.in.out$Group_dum <- as.factor(sampled.in.out$Group_dum)
sampled.in.out$Group_eff <- factor(sampled.in.out$Group_eff,
levels = c("Dem", "Rep"))
sampled.in.out$Group_dum <- factor(sampled.in.out$Group_dum,
levels = c("Dem", "Rep"))
#effects + dummy coding group so that Dem is reference (Group1)
sampled.in.out$Group_eff1 <- sampled.in.out$Group
sampled.in.out$Group_dum1 <- sampled.in.out$Group
sampled.in.out$Group_eff1 <- as.factor(sampled.in.out$Group_eff1)
sampled.in.out$Group_dum1 <- as.factor(sampled.in.out$Group_dum1)
sampled.in.out$Group_eff1 <- factor(sampled.in.out$Group_eff1,
levels = c("Rep", "Dem"))
sampled.in.out$Group_dum1 <- factor(sampled.in.out$Group_dum1,
levels = c("Rep", "Dem"))
#coding valence
sampled.in.out$Valence_eff <- sampled.in.out$ValenceString
sampled.in.out$Valence_dum <- sampled.in.out$ValenceString
sampled.in.out$Valence_eff <- as.factor(sampled.in.out$Valence_eff)
sampled.in.out$Valence_dum <- as.factor(sampled.in.out$Valence_dum)
sampled.in.out$Valence_eff1[sampled.in.out$ValenceString == "pos"] <- "pos"
sampled.in.out$Valence_eff1[sampled.in.out$ValenceString == "neg"] <- "shitty"
sampled.in.out$Valence_dum1 <- sampled.in.out$Valence_eff1
#sorry about the naming convention here. I needed a name with a letter lower in the alphabet.
sampled.in.out$Valence_eff1 <- as.factor(sampled.in.out$Valence_eff1)
sampled.in.out$Valence_dum1 <- as.factor(sampled.in.out$Valence_dum1)
Applying the contr functions for sampling models.
Evaluation.in.out$Condition[Evaluation.in.out$Condition == 1] <- "Worse"
Evaluation.in.out$Condition[Evaluation.in.out$Condition == 2] <- "Same"
Evaluation.in.out$Condition[Evaluation.in.out$Condition == 3] <- "Better"
##Making the contrasts with dummy alternatives
contrasts(sampled.in.out$Condition_a_eff) <-contr.sum(3)
contrasts(sampled.in.out$Condition_a_dum) <-contr.sum(3)
contrasts(sampled.in.out$Condition_a_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(sampled.in.out$Condition_a_eff)) = c("Worse", "Better")
colnames(contrasts(sampled.in.out$Condition_a_dum)) = c("Worse", "Better")
contrasts(sampled.in.out$Condition_c_eff) <-contr.sum(3)
contrasts(sampled.in.out$Condition_c_dum) <-contr.sum(3)
contrasts(sampled.in.out$Condition_c_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(sampled.in.out$Condition_c_eff)) = c("Worse", "Same")
colnames(contrasts(sampled.in.out$Condition_c_dum)) = c("Worse", "Same")
contrasts(sampled.in.out$Condition_d_eff) <-contr.sum(3)
contrasts(sampled.in.out$Condition_d_dum) <-contr.sum(3)
contrasts(sampled.in.out$Condition_d_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(sampled.in.out$Condition_d_eff)) = c("Better", "Same")
colnames(contrasts(sampled.in.out$Condition_d_dum)) = c("Same", "Better")
contrasts(sampled.in.out$Condition_contr_eff) <-contr.sum(2)
contrasts(sampled.in.out$Condition_contr_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Condition_contr_eff)) = c("Same")
colnames(contrasts(sampled.in.out$Condition_contr_dum)) = c("Same")
contrasts(sampled.in.out$Samp_GroupB_eff) <-contr.sum(2)
contrasts(sampled.in.out$Samp_GroupB_dum) <-contr.sum(2)
contrasts(sampled.in.out$Samp_GroupB_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Samp_GroupB_eff)) = c("In.Group")
colnames(contrasts(sampled.in.out$Samp_GroupB_dum)) = c("In.Group")
contrasts(sampled.in.out$Samp_GroupB_eff1) <-contr.sum(2)
contrasts(sampled.in.out$Samp_GroupB_dum1) <-contr.sum(2)
contrasts(sampled.in.out$Samp_GroupB_dum1) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Samp_GroupB_eff1)) = c("Out.Group")
colnames(contrasts(sampled.in.out$Samp_GroupB_dum1)) = c("Out.Group")
contrasts(sampled.in.out$Group_eff) <-contr.sum(2)
contrasts(sampled.in.out$Group_dum) <-contr.sum(2)
contrasts(sampled.in.out$Group_eff) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Group_eff)) = c("Dem")
colnames(contrasts(sampled.in.out$Group_dum)) = c("Dem")
contrasts(sampled.in.out$Group_eff1) <-contr.sum(2)
contrasts(sampled.in.out$Group_dum1) <-contr.sum(2)
contrasts(sampled.in.out$Group_eff1) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Group_eff1)) = c("Rep")
colnames(contrasts(sampled.in.out$Group_dum1)) = c("Rep")
contrasts(sampled.in.out$Valence_eff) <-contr.sum(2)
contrasts(sampled.in.out$Valence_dum) <-contr.sum(2)
contrasts(sampled.in.out$Valence_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Valence_eff)) = c("Neg")
colnames(contrasts(sampled.in.out$Valence_dum)) = c("Neg")
contrasts(sampled.in.out$Valence_eff1) <-contr.sum(2)
contrasts(sampled.in.out$Valence_dum1) <-contr.sum(2)
contrasts(sampled.in.out$Valence_dum1) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Valence_eff1)) = c("Pos")
colnames(contrasts(sampled.in.out$Valence_dum1)) = c("Pos")
Effects and Dummy coding categorical predictors for the point-estimate models.
#same deal as above but for the PE data set
#Change conditions to 1 2 3 for clarity
Evaluation.in.out$Condition[Evaluation.in.out$Condition == 1] <- "Worse"
Evaluation.in.out$Condition[Evaluation.in.out$Condition == 2] <- "Same"
Evaluation.in.out$Condition[Evaluation.in.out$Condition == 3] <- "Better"
Evaluation.in.out$Condition_a[Evaluation.in.out$Condition == "Worse"] <- -1
Evaluation.in.out$Condition_a[Evaluation.in.out$Condition == "Same"] <- 1
Evaluation.in.out$Condition_a[Evaluation.in.out$Condition == "Better"] <- 0
Evaluation.in.out$Condition_a_eff <- factor(Evaluation.in.out$Condition_a)
Evaluation.in.out$Condition_a_dum <- factor(Evaluation.in.out$Condition,
levels = c("Worse", "Better", "Same"))
Evaluation.in.out$Condition_c[Evaluation.in.out$Condition == "Worse"] <- -1
Evaluation.in.out$Condition_c[Evaluation.in.out$Condition == "Same"] <- 0
Evaluation.in.out$Condition_c[Evaluation.in.out$Condition == "Better"] <- 1
Evaluation.in.out$Condition_c_eff <- factor(Evaluation.in.out$Condition_c)
Evaluation.in.out$Condition_c_dum <- factor(Evaluation.in.out$Condition,
levels = c("Worse", "Same", "Better"))
Evaluation.in.out$Condition_d[Evaluation.in.out$Condition == "Worse"] <- 1
Evaluation.in.out$Condition_d[Evaluation.in.out$Condition == "Same"] <- 0
Evaluation.in.out$Condition_d[Evaluation.in.out$Condition == "Better"] <- -1
Evaluation.in.out$Condition_d_eff <- factor(Evaluation.in.out$Condition_d)
Evaluation.in.out$Condition_d_dum <- factor(Evaluation.in.out$Condition,
levels = c("Same", "Better", "Worse"))
Evaluation.in.out$Evaluated.Group_eff <- Evaluation.in.out$Evaluated.GroupString
Evaluation.in.out$Evaluated.Group_dum <- Evaluation.in.out$Evaluated.GroupString
Evaluation.in.out$Evaluated.Group_eff <- as.factor(Evaluation.in.out$Evaluated.Group_eff)
Evaluation.in.out$Evaluated.Group_dum <- as.factor(Evaluation.in.out$Evaluated.Group_dum)
Evaluation.in.out$Evaluated.Group_eff <- factor(Evaluation.in.out$Evaluated.Group_eff,
levels = c("In", "Out"))
Evaluation.in.out$Evaluated.Group_dum <- factor(Evaluation.in.out$Evaluated.Group_dum,
levels = c("In", "Out"))
Evaluation.in.out$Evaluated.Group_eff1 <- Evaluation.in.out$Evaluated.GroupString
Evaluation.in.out$Evaluated.Group_dum1 <- Evaluation.in.out$Evaluated.GroupString
Evaluation.in.out$Evaluated.Group_eff1 <- as.factor(Evaluation.in.out$Evaluated.Group_eff)
Evaluation.in.out$Evaluated.Group_dum1 <- as.factor(Evaluation.in.out$Evaluated.Group_dum)
Evaluation.in.out$Evaluated.Group_eff1 <- factor(Evaluation.in.out$Evaluated.Group_eff1,
levels = c("Out", "In"))
Evaluation.in.out$Evaluated.Group_dum1 <- factor(Evaluation.in.out$Evaluated.Group_dum1,
levels = c("Out", "In"))
Evaluation.in.out$Group_eff <- Evaluation.in.out$Group
Evaluation.in.out$Group_dum <- Evaluation.in.out$Group
Evaluation.in.out$Group_eff <- as.factor(Evaluation.in.out$Group_eff)
Evaluation.in.out$Group_dum <- as.factor(Evaluation.in.out$Group_dum)
Evaluation.in.out$Group_eff <- factor(Evaluation.in.out$Group_eff,
levels = c("Rep", "Dem"))
Evaluation.in.out$Group_dum <- factor(Evaluation.in.out$Group_dum,
levels = c("Rep", "Dem"))
Evaluation.in.out$Group_eff1 <- Evaluation.in.out$Group
Evaluation.in.out$Group_dum1 <- Evaluation.in.out$Group
Evaluation.in.out$Group_eff1 <- as.factor(Evaluation.in.out$Group_eff)
Evaluation.in.out$Group_dum1 <- as.factor(Evaluation.in.out$Group_dum)
Evaluation.in.out$Group_eff1 <- factor(Evaluation.in.out$Group_eff1,
levels = c("Dem", "Rep"))
Evaluation.in.out$Group_dum1 <- factor(Evaluation.in.out$Group_dum1,
levels = c("Dem", "Rep"))
Evaluation.in.out$Valence_eff <- Evaluation.in.out$ValenceString
Evaluation.in.out$Valence_dum <- Evaluation.in.out$ValenceString
Evaluation.in.out$Valence_eff <- as.factor(Evaluation.in.out$Valence_eff)
Evaluation.in.out$Valence_dum <- as.factor(Evaluation.in.out$Valence_dum)
Evaluation.in.out$Valence_eff1[Evaluation.in.out$ValenceString == "pos"] <- "pos"
Evaluation.in.out$Valence_eff1[Evaluation.in.out$ValenceString == "neg"] <- "shitty"
Evaluation.in.out$Valence_dum1 <- Evaluation.in.out$Valence_eff1
Evaluation.in.out$Valence_eff1 <- as.factor(Evaluation.in.out$Valence_eff1)
Evaluation.in.out$Valence_dum1 <- as.factor(Evaluation.in.out$Valence_dum1)
Applying the contr functions for sampling models.
##Making the contrasts with dummy alternatives
contrasts(Evaluation.in.out$Condition_a_eff) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_a_dum) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_a_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(Evaluation.in.out$Condition_a_eff)) = c("Worse", "Better")
colnames(contrasts(Evaluation.in.out$Condition_a_dum)) = c("Worse", "Better")
contrasts(Evaluation.in.out$Condition_c_eff) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_c_dum) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_c_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(Evaluation.in.out$Condition_c_eff)) = c("Worse", "Same")
colnames(contrasts(Evaluation.in.out$Condition_c_dum)) = c("Worse", "Same")
contrasts(Evaluation.in.out$Condition_d_eff) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_d_dum) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_d_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(Evaluation.in.out$Condition_d_eff)) = c("Better", "Same")
colnames(contrasts(Evaluation.in.out$Condition_d_dum)) = c("Same", "Better")
contrasts(Evaluation.in.out$Evaluated.Group_eff) <-contr.sum(2)
contrasts(Evaluation.in.out$Evaluated.Group_dum) <-contr.sum(2)
contrasts(Evaluation.in.out$Evaluated.Group_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Evaluated.Group_eff)) = c("In.Group")
colnames(contrasts(Evaluation.in.out$Evaluated.Group_dum)) = c("In.Group")
contrasts(Evaluation.in.out$Evaluated.Group_eff1) <-contr.sum(2)
contrasts(Evaluation.in.out$Evaluated.Group_dum1) <-contr.sum(2)
contrasts(Evaluation.in.out$Evaluated.Group_dum1) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Evaluated.Group_eff1)) = c("Out.Group")
colnames(contrasts(Evaluation.in.out$Evaluated.Group_dum1)) = c("Out.Group")
contrasts(Evaluation.in.out$Group_eff) <-contr.sum(2)
contrasts(Evaluation.in.out$Group_dum) <-contr.sum(2)
contrasts(Evaluation.in.out$Group_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Group_eff)) = c("Rep")
colnames(contrasts(Evaluation.in.out$Group_dum)) = c("Rep")
contrasts(Evaluation.in.out$Group_eff1) <-contr.sum(2)
contrasts(Evaluation.in.out$Group_dum1) <-contr.sum(2)
contrasts(Evaluation.in.out$Group_dum1) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Group_eff1)) = c("Dem")
colnames(contrasts(Evaluation.in.out$Group_dum1)) = c("Dem")
contrasts(Evaluation.in.out$Valence_eff) <-contr.sum(2)
contrasts(Evaluation.in.out$Valence_dum) <-contr.sum(2)
contrasts(Evaluation.in.out$Valence_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Valence_eff)) = c("Neg")
colnames(contrasts(Evaluation.in.out$Valence_dum)) = c("Neg")
contrasts(Evaluation.in.out$Valence_eff1) <-contr.sum(2)
contrasts(Evaluation.in.out$Valence_dum1) <-contr.sum(2)
contrasts(Evaluation.in.out$Valence_dum1) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Valence_eff1)) = c("Pos")
colnames(contrasts(Evaluation.in.out$Valence_dum1)) = c("Pos")
Creating better, same and worse condition subsets for evaluations
better.only <- Evaluation.in.out[(Evaluation.in.out$Condition=="Better"),]
same.only <- Evaluation.in.out[(Evaluation.in.out$Condition=="Same"),]
worse.only <- Evaluation.in.out[(Evaluation.in.out$Condition=="Worse"),]
Generalized mixed models for Sampling Behavior
Histogram for DV
###Histogram for dv
hist(sampled.in.out$n_trials)
Model 1[sampling]: More in group sampling than outgroup – collapsing across condition and first sample
#here we are dummy coding group with out group as the reference group and effects coding both condition and valence.
collapsed.sampling.1 <- glmer(n_trials~Samp_GroupB_dum*Valence_eff*Condition_c_eff+ (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.1)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: n_trials ~ Samp_GroupB_dum * Valence_eff * Condition_c_eff +
## (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 8689.7 8761.3 -4331.8 8663.7 1815
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8005 -0.3255 -0.0033 0.2619 4.4771
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3545 0.5954
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 1.336794
## Samp_GroupB_dumIn.Group 0.102715
## Valence_effNeg -0.021929
## Condition_c_effWorse -0.007413
## Condition_c_effSame 0.007342
## Samp_GroupB_dumIn.Group:Valence_effNeg 0.010821
## Samp_GroupB_dumIn.Group:Condition_c_effWorse -0.001745
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.032182
## Valence_effNeg:Condition_c_effWorse -0.082427
## Valence_effNeg:Condition_c_effSame 0.060626
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse 0.026078
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame -0.045570
## Std. Error
## (Intercept) 0.025940
## Samp_GroupB_dumIn.Group 0.021139
## Valence_effNeg 0.025525
## Condition_c_effWorse 0.036364
## Condition_c_effSame 0.036289
## Samp_GroupB_dumIn.Group:Valence_effNeg 0.021139
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.030109
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.029969
## Valence_effNeg:Condition_c_effWorse 0.036363
## Valence_effNeg:Condition_c_effSame 0.036288
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse 0.030109
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame 0.029969
## z value
## (Intercept) 51.534
## Samp_GroupB_dumIn.Group 4.859
## Valence_effNeg -0.859
## Condition_c_effWorse -0.204
## Condition_c_effSame 0.202
## Samp_GroupB_dumIn.Group:Valence_effNeg 0.512
## Samp_GroupB_dumIn.Group:Condition_c_effWorse -0.058
## Samp_GroupB_dumIn.Group:Condition_c_effSame 1.074
## Valence_effNeg:Condition_c_effWorse -2.267
## Valence_effNeg:Condition_c_effSame 1.671
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse 0.866
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame -1.521
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Samp_GroupB_dumIn.Group 1.18e-06 ***
## Valence_effNeg 0.3903
## Condition_c_effWorse 0.8385
## Condition_c_effSame 0.8397
## Samp_GroupB_dumIn.Group:Valence_effNeg 0.6087
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.9538
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.2829
## Valence_effNeg:Condition_c_effWorse 0.0234 *
## Valence_effNeg:Condition_c_effSame 0.0948 .
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse 0.3864
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame 0.1284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Vlnc_N Cnd__W Cnd__S Sm_GB_I.G:V_N
## Smp_GrB_I.G -0.429
## Valenc_ffNg 0.000 -0.007
## Cndtn_c_ffW 0.022 -0.009 0.066
## Cndtn_c_ffS 0.013 -0.008 -0.037 -0.519
## Sm_GB_I.G:V_N -0.007 0.011 -0.436 -0.046 0.030
## S_GB_I.G:C__W -0.009 0.020 -0.046 -0.436 0.227 0.098
## S_GB_I.G:C__S -0.008 0.007 0.030 0.228 -0.438 -0.053
## Vlnc_N:C__W 0.064 -0.046 0.021 0.046 -0.020 -0.009
## Vlnc_N:C__S -0.036 0.030 0.015 -0.020 -0.026 -0.008
## S_GB_I.G:V_N:C__W -0.046 0.098 -0.009 -0.039 0.015 0.020
## S_GB_I.G:V_N:C__S 0.030 -0.053 -0.008 0.015 0.014 0.007
## S_GB_I.G:C__W S_GB_I.G:C__S V_N:C__W V_N:C__S
## Smp_GrB_I.G
## Valenc_ffNg
## Cndtn_c_ffW
## Cndtn_c_ffS
## Sm_GB_I.G:V_N
## S_GB_I.G:C__W
## S_GB_I.G:C__S -0.514
## Vlnc_N:C__W -0.039 0.015
## Vlnc_N:C__S 0.015 0.014 -0.519
## S_GB_I.G:V_N:C__W 0.079 -0.037 -0.436 0.227
## S_GB_I.G:V_N:C__S -0.037 -0.027 0.228 -0.438
## S_GB_I.G:V_N:C__W
## Smp_GrB_I.G
## Valenc_ffNg
## Cndtn_c_ffW
## Cndtn_c_ffS
## Sm_GB_I.G:V_N
## S_GB_I.G:C__W
## S_GB_I.G:C__S
## Vlnc_N:C__W
## Vlnc_N:C__S
## S_GB_I.G:V_N:C__W
## S_GB_I.G:V_N:C__S -0.514
Model 2[sampling]: More in group sampling with positive first sample – collapsing across condition
#here we are dummy coding valence and group with negative first sample and out group as the reference group and effects coding condition.
collapsed.sampling.2 <- glmer(n_trials~Samp_GroupB_dum*Valence_dum*Condition_c_eff + (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.2)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: n_trials ~ Samp_GroupB_dum * Valence_dum * Condition_c_eff +
## (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 8689.7 8761.3 -4331.8 8663.7 1815
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8005 -0.3255 -0.0033 0.2619 4.4771
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3545 0.5954
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 1.35872
## Samp_GroupB_dumIn.Group 0.09189
## Valence_dumNeg -0.04386
## Condition_c_effWorse 0.07502
## Condition_c_effSame -0.05329
## Samp_GroupB_dumIn.Group:Valence_dumNeg 0.02164
## Samp_GroupB_dumIn.Group:Condition_c_effWorse -0.02782
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.07775
## Valence_dumNeg:Condition_c_effWorse -0.16486
## Valence_dumNeg:Condition_c_effSame 0.12125
## Samp_GroupB_dumIn.Group:Valence_dumNeg:Condition_c_effWorse 0.05216
## Samp_GroupB_dumIn.Group:Valence_dumNeg:Condition_c_effSame -0.09114
## Std. Error
## (Intercept) 0.03640
## Samp_GroupB_dumIn.Group 0.02973
## Valence_dumNeg 0.05105
## Condition_c_effWorse 0.05023
## Condition_c_effSame 0.05199
## Samp_GroupB_dumIn.Group:Valence_dumNeg 0.04228
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.04086
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.04295
## Valence_dumNeg:Condition_c_effWorse 0.07272
## Valence_dumNeg:Condition_c_effSame 0.07257
## Samp_GroupB_dumIn.Group:Valence_dumNeg:Condition_c_effWorse 0.06022
## Samp_GroupB_dumIn.Group:Valence_dumNeg:Condition_c_effSame 0.05994
## z value
## (Intercept) 37.330
## Samp_GroupB_dumIn.Group 3.091
## Valence_dumNeg -0.859
## Condition_c_effWorse 1.493
## Condition_c_effSame -1.025
## Samp_GroupB_dumIn.Group:Valence_dumNeg 0.512
## Samp_GroupB_dumIn.Group:Condition_c_effWorse -0.681
## Samp_GroupB_dumIn.Group:Condition_c_effSame 1.810
## Valence_dumNeg:Condition_c_effWorse -2.267
## Valence_dumNeg:Condition_c_effSame 1.671
## Samp_GroupB_dumIn.Group:Valence_dumNeg:Condition_c_effWorse 0.866
## Samp_GroupB_dumIn.Group:Valence_dumNeg:Condition_c_effSame -1.521
## Pr(>|z|)
## (Intercept) <2e-16 ***
## Samp_GroupB_dumIn.Group 0.0020 **
## Valence_dumNeg 0.3903
## Condition_c_effWorse 0.1354
## Condition_c_effSame 0.3054
## Samp_GroupB_dumIn.Group:Valence_dumNeg 0.6087
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.4959
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.0702 .
## Valence_dumNeg:Condition_c_effWorse 0.0234 *
## Valence_dumNeg:Condition_c_effSame 0.0948 .
## Samp_GroupB_dumIn.Group:Valence_dumNeg:Condition_c_effWorse 0.3864
## Samp_GroupB_dumIn.Group:Valence_dumNeg:Condition_c_effSame 0.1284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Vlnc_N Cnd__W Cnd__S Sm_GB_I.G:V_N
## Smp_GrB_I.G -0.428
## Valenc_dmNg -0.701 0.305
## Cndtn_c_ffW -0.045 0.039 0.033
## Cndtn_c_ffS 0.050 -0.037 -0.036 -0.504
## Sm_GB_I.G:V_N 0.301 -0.703 -0.436 -0.027 0.026
## S_GB_I.G:C__W 0.039 -0.081 -0.028 -0.423 0.218 0.057
## S_GB_I.G:C__S -0.037 0.060 0.026 0.215 -0.441 -0.042
## Vlnc_N:C__W 0.031 -0.027 0.021 -0.691 0.348 -0.009
## Vlnc_N:C__S -0.036 0.027 0.015 0.361 -0.716 -0.008
## S_GB_I.G:V_N:C__W -0.026 0.055 -0.009 0.287 -0.148 0.020
## S_GB_I.G:V_N:C__S 0.027 -0.043 -0.008 -0.154 0.316 0.007
## S_GB_I.G:C__W S_GB_I.G:C__S V_N:C__W V_N:C__S
## Smp_GrB_I.G
## Valenc_dmNg
## Cndtn_c_ffW
## Cndtn_c_ffS
## Sm_GB_I.G:V_N
## S_GB_I.G:C__W
## S_GB_I.G:C__S -0.491
## Vlnc_N:C__W 0.292 -0.148
## Vlnc_N:C__S -0.156 0.316 -0.519
## S_GB_I.G:V_N:C__W -0.679 0.333 -0.436 0.227
## S_GB_I.G:V_N:C__S 0.352 -0.717 0.228 -0.438
## S_GB_I.G:V_N:C__W
## Smp_GrB_I.G
## Valenc_dmNg
## Cndtn_c_ffW
## Cndtn_c_ffS
## Sm_GB_I.G:V_N
## S_GB_I.G:C__W
## S_GB_I.G:C__S
## Vlnc_N:C__W
## Vlnc_N:C__S
## S_GB_I.G:V_N:C__W
## S_GB_I.G:V_N:C__S -0.514
Model 3[sampling]: More in group sampling with negative first sample – collapsing across condition
#here we are dummy coding valence and group with negative first sample and out group as the reference group and effects coding condition.
collapsed.sampling.3 <- glmer(n_trials~Samp_GroupB_dum*Valence_dum1*Condition_c_eff+ (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.3)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: n_trials ~ Samp_GroupB_dum * Valence_dum1 * Condition_c_eff +
## (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 8689.7 8761.3 -4331.8 8663.7 1815
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8005 -0.3255 -0.0033 0.2619 4.4771
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3545 0.5954
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 1.31487
## Samp_GroupB_dumIn.Group 0.11354
## Valence_dum1Pos 0.04386
## Condition_c_effWorse -0.08984
## Condition_c_effSame 0.06797
## Samp_GroupB_dumIn.Group:Valence_dum1Pos -0.02164
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.02433
## Samp_GroupB_dumIn.Group:Condition_c_effSame -0.01339
## Valence_dum1Pos:Condition_c_effWorse 0.16486
## Valence_dum1Pos:Condition_c_effSame -0.12125
## Samp_GroupB_dumIn.Group:Valence_dum1Pos:Condition_c_effWorse -0.05216
## Samp_GroupB_dumIn.Group:Valence_dum1Pos:Condition_c_effSame 0.09114
## Std. Error
## (Intercept) 0.03639
## Samp_GroupB_dumIn.Group 0.03006
## Valence_dum1Pos 0.05105
## Condition_c_effWorse 0.05259
## Condition_c_effSame 0.05064
## Samp_GroupB_dumIn.Group:Valence_dum1Pos 0.04228
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.04423
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.04181
## Valence_dum1Pos:Condition_c_effWorse 0.07273
## Valence_dum1Pos:Condition_c_effSame 0.07258
## Samp_GroupB_dumIn.Group:Valence_dum1Pos:Condition_c_effWorse 0.06022
## Samp_GroupB_dumIn.Group:Valence_dum1Pos:Condition_c_effSame 0.05994
## z value
## (Intercept) 36.136
## Samp_GroupB_dumIn.Group 3.777
## Valence_dum1Pos 0.859
## Condition_c_effWorse -1.708
## Condition_c_effSame 1.342
## Samp_GroupB_dumIn.Group:Valence_dum1Pos -0.512
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.550
## Samp_GroupB_dumIn.Group:Condition_c_effSame -0.320
## Valence_dum1Pos:Condition_c_effWorse 2.267
## Valence_dum1Pos:Condition_c_effSame -1.671
## Samp_GroupB_dumIn.Group:Valence_dum1Pos:Condition_c_effWorse -0.866
## Samp_GroupB_dumIn.Group:Valence_dum1Pos:Condition_c_effSame 1.521
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Samp_GroupB_dumIn.Group 0.000158 ***
## Valence_dum1Pos 0.390274
## Condition_c_effWorse 0.087573 .
## Condition_c_effSame 0.179507
## Samp_GroupB_dumIn.Group:Valence_dum1Pos 0.608723
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.582229
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.748825
## Valence_dum1Pos:Condition_c_effWorse 0.023400 *
## Valence_dum1Pos:Condition_c_effSame 0.094774 .
## Samp_GroupB_dumIn.Group:Valence_dum1Pos:Condition_c_effWorse 0.386399
## Samp_GroupB_dumIn.Group:Valence_dum1Pos:Condition_c_effSame 0.128364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Vln_1P Cnd__W Cnd__S Sm_GB_I.G:V_1P
## Smp_GrB_I.G -0.437
## Valnc_dm1Ps -0.701 0.311
## Cndtn_c_ffW 0.084 -0.054 -0.060
## Cndtn_c_ffS -0.023 0.022 0.016 -0.534
## Sm_GB_I.G:V_1P 0.311 -0.711 -0.436 0.038 -0.016
## S_GB_I.G:C__W -0.053 0.113 0.038 -0.447 0.236 -0.080
## S_GB_I.G:C__S 0.023 -0.047 -0.016 0.240 -0.435 0.033
## Vln_1P:C__W -0.060 0.039 0.021 -0.723 0.386 -0.009
## Vln_1P:C__S 0.015 -0.016 0.015 0.373 -0.698 -0.008
## S_GB_I.G:V_1P:C__W 0.039 -0.083 -0.009 0.329 -0.173 0.020
## S_GB_I.G:V_1P:C__S -0.016 0.033 -0.008 -0.168 0.304 0.007
## S_GB_I.G:C__W S_GB_I.G:C__S V_1P:C__W V_1P:C__S
## Smp_GrB_I.G
## Valnc_dm1Ps
## Cndtn_c_ffW
## Cndtn_c_ffS
## Sm_GB_I.G:V_1P
## S_GB_I.G:C__W
## S_GB_I.G:C__S -0.538
## Vln_1P:C__W 0.323 -0.174
## Vln_1P:C__S -0.165 0.304 -0.519
## S_GB_I.G:V_1P:C__W -0.735 0.395 -0.436 0.227
## S_GB_I.G:V_1P:C__S 0.375 -0.698 0.228 -0.438
## S_GB_I.G:V_1P:C__W
## Smp_GrB_I.G
## Valnc_dm1Ps
## Cndtn_c_ffW
## Cndtn_c_ffS
## Sm_GB_I.G:V_1P
## S_GB_I.G:C__W
## S_GB_I.G:C__S
## Vln_1P:C__W
## Vln_1P:C__S
## S_GB_I.G:V_1P:C__W
## S_GB_I.G:V_1P:C__S -0.514
Model 4[sampling]: Positive first samples – holding Samp group (in/out) and condition constant
#here we effects code group and condition and dummy code valence
collapsed.sampling.4 <- glmer(n_trials~Samp_GroupB_eff*Condition_c_eff*Valence_dum+ (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.4)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: n_trials ~ Samp_GroupB_eff * Condition_c_eff * Valence_dum +
## (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 8689.7 8761.3 -4331.8 8663.7 1815
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8005 -0.3255 -0.0033 0.2619 4.4771
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3545 0.5954
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 1.40467
## Samp_GroupB_effIn.Group 0.04595
## Condition_c_effWorse 0.06110
## Condition_c_effSame -0.01441
## Valence_dumNeg -0.03304
## Samp_GroupB_effIn.Group:Condition_c_effWorse -0.01391
## Samp_GroupB_effIn.Group:Condition_c_effSame 0.03888
## Samp_GroupB_effIn.Group:Valence_dumNeg 0.01082
## Condition_c_effWorse:Valence_dumNeg -0.13878
## Condition_c_effSame:Valence_dumNeg 0.07568
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dumNeg 0.02608
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dumNeg -0.04557
## Std. Error
## (Intercept) 0.03291
## Samp_GroupB_effIn.Group 0.01487
## Condition_c_effWorse 0.04553
## Condition_c_effSame 0.04670
## Valence_dumNeg 0.04596
## Samp_GroupB_effIn.Group:Condition_c_effWorse 0.02043
## Samp_GroupB_effIn.Group:Condition_c_effSame 0.02147
## Samp_GroupB_effIn.Group:Valence_dumNeg 0.02114
## Condition_c_effWorse:Valence_dumNeg 0.06547
## Condition_c_effSame:Valence_dumNeg 0.06527
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dumNeg 0.03011
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dumNeg 0.02997
## z value
## (Intercept) 42.686
## Samp_GroupB_effIn.Group 3.091
## Condition_c_effWorse 1.342
## Condition_c_effSame -0.309
## Valence_dumNeg -0.719
## Samp_GroupB_effIn.Group:Condition_c_effWorse -0.681
## Samp_GroupB_effIn.Group:Condition_c_effSame 1.810
## Samp_GroupB_effIn.Group:Valence_dumNeg 0.512
## Condition_c_effWorse:Valence_dumNeg -2.120
## Condition_c_effSame:Valence_dumNeg 1.160
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dumNeg 0.866
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dumNeg -1.521
## Pr(>|z|)
## (Intercept) <2e-16 ***
## Samp_GroupB_effIn.Group 0.0020 **
## Condition_c_effWorse 0.1795
## Condition_c_effSame 0.7577
## Valence_dumNeg 0.4723
## Samp_GroupB_effIn.Group:Condition_c_effWorse 0.4959
## Samp_GroupB_effIn.Group:Condition_c_effSame 0.0702 .
## Samp_GroupB_effIn.Group:Valence_dumNeg 0.6087
## Condition_c_effWorse:Valence_dumNeg 0.0340 *
## Condition_c_effSame:Valence_dumNeg 0.2462
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dumNeg 0.3864
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dumNeg 0.1284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Cnd__W Cnd__S Vlnc_N Sm_GB_I.G:C__W
## Smp_GrB_I.G -0.021
## Cndtn_c_ffW -0.032 0.006
## Cndtn_c_ffS 0.036 -0.014 -0.503
## Valenc_dmNg -0.702 0.015 0.024 -0.027
## Sm_GB_I.G:C__W 0.006 -0.081 -0.018 0.017 -0.004
## Sm_GB_I.G:C__S -0.014 0.060 0.017 -0.031 0.010 -0.491
## S_GB_I.G:V_ 0.015 -0.703 -0.004 0.010 -0.024 0.057
## Cndt__W:V_N 0.023 -0.004 -0.695 0.349 0.021 0.013
## Cndt__S:V_N -0.026 0.010 0.360 -0.715 0.012 -0.012
## S_GB_I.G:C__W: -0.004 0.055 0.012 -0.011 -0.001 -0.679
## S_GB_I.G:C__S: 0.010 -0.043 -0.012 0.022 -0.005 0.352
## Sm_GB_I.G:C__S S_GB_I.G:V C__W:V C__S:V S_GB_I.G:C__W:
## Smp_GrB_I.G
## Cndtn_c_ffW
## Cndtn_c_ffS
## Valenc_dmNg
## Sm_GB_I.G:C__W
## Sm_GB_I.G:C__S
## S_GB_I.G:V_ -0.042
## Cndt__W:V_N -0.011 -0.001
## Cndt__S:V_N 0.022 -0.005 -0.517
## S_GB_I.G:C__W: 0.333 0.020 -0.024 0.016
## S_GB_I.G:C__S: -0.717 0.007 0.016 -0.028 -0.514
Model 5[sampling]: Negative first samples – holding Samp group (in/out) and condition constant?
#here we effects code group and condition and dummy code valence
collapsed.sampling.5 <- glmer(n_trials~Samp_GroupB_eff*Condition_c_eff*Valence_dum1+ (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.5)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: n_trials ~ Samp_GroupB_eff * Condition_c_eff * Valence_dum1 +
## (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 8689.7 8761.3 -4331.8 8663.7 1815
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8005 -0.3255 -0.0033 0.2619 4.4771
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3545 0.5954
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 1.371634
## Samp_GroupB_effIn.Group 0.056768
## Condition_c_effWorse -0.077676
## Condition_c_effSame 0.061276
## Valence_dum1Pos 0.033037
## Samp_GroupB_effIn.Group:Condition_c_effWorse 0.012166
## Samp_GroupB_effIn.Group:Condition_c_effSame -0.006693
## Samp_GroupB_effIn.Group:Valence_dum1Pos -0.010821
## Condition_c_effWorse:Valence_dum1Pos 0.138780
## Condition_c_effSame:Valence_dum1Pos -0.075686
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dum1Pos -0.026078
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dum1Pos 0.045570
## Std. Error
## (Intercept) 0.032742
## Samp_GroupB_effIn.Group 0.015028
## Condition_c_effWorse 0.047055
## Condition_c_effSame 0.045605
## Valence_dum1Pos 0.045959
## Samp_GroupB_effIn.Group:Condition_c_effWorse 0.022117
## Samp_GroupB_effIn.Group:Condition_c_effSame 0.020906
## Samp_GroupB_effIn.Group:Valence_dum1Pos 0.021139
## Condition_c_effWorse:Valence_dum1Pos 0.065473
## Condition_c_effSame:Valence_dum1Pos 0.065271
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dum1Pos 0.030109
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dum1Pos 0.029969
## z value
## (Intercept) 41.892
## Samp_GroupB_effIn.Group 3.777
## Condition_c_effWorse -1.651
## Condition_c_effSame 1.344
## Valence_dum1Pos 0.719
## Samp_GroupB_effIn.Group:Condition_c_effWorse 0.550
## Samp_GroupB_effIn.Group:Condition_c_effSame -0.320
## Samp_GroupB_effIn.Group:Valence_dum1Pos -0.512
## Condition_c_effWorse:Valence_dum1Pos 2.120
## Condition_c_effSame:Valence_dum1Pos -1.160
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dum1Pos -0.866
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dum1Pos 1.521
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Samp_GroupB_effIn.Group 0.000158 ***
## Condition_c_effWorse 0.098793 .
## Condition_c_effSame 0.179073
## Valence_dum1Pos 0.472240
## Samp_GroupB_effIn.Group:Condition_c_effWorse 0.582248
## Samp_GroupB_effIn.Group:Condition_c_effSame 0.748841
## Samp_GroupB_effIn.Group:Valence_dum1Pos 0.608732
## Condition_c_effWorse:Valence_dum1Pos 0.034035 *
## Condition_c_effSame:Valence_dum1Pos 0.246224
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dum1Pos 0.386412
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dum1Pos 0.128369
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Cnd__W Cnd__S Vln_1P Sm_GB_I.G:C__W
## Smp_GrB_I.G -0.026
## Cndtn_c_ffW 0.074 -0.007
## Cndtn_c_ffS -0.015 0.004 -0.532
## Valnc_dm1Ps -0.698 0.019 -0.052 0.010
## Sm_GB_I.G:C__W -0.007 0.113 -0.030 0.016 0.005
## Sm_GB_I.G:C__S 0.004 -0.047 0.016 -0.025 -0.003 -0.538
## S_GB_I.G:V_ 0.019 -0.711 0.005 -0.003 -0.024 -0.080
## Cnd__W:V_1P -0.052 0.005 -0.719 0.382 0.021 0.022
## Cnd__S:V_1P 0.010 -0.003 0.372 -0.699 0.012 -0.011
## S_GB_I.G:C__W: 0.005 -0.083 0.022 -0.011 -0.001 -0.735
## S_GB_I.G:C__S: -0.003 0.033 -0.011 0.017 -0.005 0.375
## Sm_GB_I.G:C__S S_GB_I.G:V C__W:V C__S:V S_GB_I.G:C__W:
## Smp_GrB_I.G
## Cndtn_c_ffW
## Cndtn_c_ffS
## Valnc_dm1Ps
## Sm_GB_I.G:C__W
## Sm_GB_I.G:C__S
## S_GB_I.G:V_ 0.033
## Cnd__W:V_1P -0.011 -0.001
## Cnd__S:V_1P 0.017 -0.005 -0.517
## S_GB_I.G:C__W: 0.395 0.020 -0.024 0.016
## S_GB_I.G:C__S: -0.698 0.007 0.016 -0.028 -0.514
Model 6[sampling]: Contrast coding. Better/Worse = 0; Same = 1
collapsed.sampling.6 <- glmer(n_trials~Samp_GroupB_dum*Condition_contr_eff+ (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.6)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## n_trials ~ Samp_GroupB_dum * Condition_contr_eff + (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 8681.2 8708.8 -4335.6 8671.2 1823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8485 -0.3084 -0.0294 0.2605 4.5436
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3574 0.5978
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1.341218 0.027603
## Samp_GroupB_dumIn.Group 0.108605 0.022393
## Condition_contr_effSame 0.004474 0.027225
## Samp_GroupB_dumIn.Group:Condition_contr_effSame 0.024095 0.022393
## z value Pr(>|z|)
## (Intercept) 48.589 < 2e-16 ***
## Samp_GroupB_dumIn.Group 4.850 1.24e-06 ***
## Condition_contr_effSame 0.164 0.869
## Samp_GroupB_dumIn.Group:Condition_contr_effSame 1.076 0.282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Cnd__S
## Smp_GrB_I.G -0.429
## Cndtn_cnt_S 0.343 -0.154
## S_GB_I.G:C_ -0.152 0.344 -0.435
Model 7[sampling]: What is happening in the better condition?
collapsed.sampling.7 <- glmer(n_trials~Samp_GroupB_eff*Condition_d_dum*Valence_eff+ (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.7)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: n_trials ~ Samp_GroupB_eff * Condition_d_dum * Valence_eff +
## (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 8689.7 8761.3 -4331.8 8663.7 1815
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8005 -0.3255 -0.0033 0.2619 4.4771
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3545 0.5954
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 1.379867
## Samp_GroupB_effIn.Group 0.050485
## Condition_d_dumSame 0.031718
## Condition_d_dumBetter -0.006863
## Valence_effNeg -0.085908
## Samp_GroupB_effIn.Group:Condition_d_dumSame 0.016964
## Samp_GroupB_effIn.Group:Condition_d_dumBetter -0.014346
## Samp_GroupB_effIn.Group:Valence_effNeg 0.018450
## Condition_d_dumSame:Valence_effNeg 0.107232
## Condition_d_dumBetter:Valence_effNeg 0.100937
## Samp_GroupB_effIn.Group:Condition_d_dumSame:Valence_effNeg -0.035824
## Samp_GroupB_effIn.Group:Condition_d_dumBetter:Valence_effNeg -0.003293
## Std. Error
## (Intercept) 0.040683
## Samp_GroupB_effIn.Group 0.018567
## Condition_d_dumSame 0.056940
## Condition_d_dumBetter 0.056049
## Valence_effNeg 0.040383
## Samp_GroupB_effIn.Group:Condition_d_dumSame 0.026138
## Samp_GroupB_effIn.Group:Condition_d_dumBetter 0.025827
## Samp_GroupB_effIn.Group:Valence_effNeg 0.018567
## Condition_d_dumSame:Valence_effNeg 0.056938
## Condition_d_dumBetter:Valence_effNeg 0.056048
## Samp_GroupB_effIn.Group:Condition_d_dumSame:Valence_effNeg 0.026138
## Samp_GroupB_effIn.Group:Condition_d_dumBetter:Valence_effNeg 0.025827
## z value
## (Intercept) 33.917
## Samp_GroupB_effIn.Group 2.719
## Condition_d_dumSame 0.557
## Condition_d_dumBetter -0.122
## Valence_effNeg -2.127
## Samp_GroupB_effIn.Group:Condition_d_dumSame 0.649
## Samp_GroupB_effIn.Group:Condition_d_dumBetter -0.555
## Samp_GroupB_effIn.Group:Valence_effNeg 0.994
## Condition_d_dumSame:Valence_effNeg 1.883
## Condition_d_dumBetter:Valence_effNeg 1.801
## Samp_GroupB_effIn.Group:Condition_d_dumSame:Valence_effNeg -1.371
## Samp_GroupB_effIn.Group:Condition_d_dumBetter:Valence_effNeg -0.128
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Samp_GroupB_effIn.Group 0.00655 **
## Condition_d_dumSame 0.57750
## Condition_d_dumBetter 0.90255
## Valence_effNeg 0.03339 *
## Samp_GroupB_effIn.Group:Condition_d_dumSame 0.51632
## Samp_GroupB_effIn.Group:Condition_d_dumBetter 0.57857
## Samp_GroupB_effIn.Group:Valence_effNeg 0.32039
## Condition_d_dumSame:Valence_effNeg 0.05966 .
## Condition_d_dumBetter:Valence_effNeg 0.07172 .
## Samp_GroupB_effIn.Group:Condition_d_dumSame:Valence_effNeg 0.17051
## Samp_GroupB_effIn.Group:Condition_d_dumBetter:Valence_effNeg 0.89854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Cnd__S Cnd__B Vlnc_N Sm_GB_I.G:C__S
## Smp_GrB_I.G -0.024
## Cndtn_d_dmS -0.705 0.017
## Cndtn_d_dmB -0.716 0.018 0.511
## Valenc_ffNg 0.069 -0.012 -0.050 -0.051
## Sm_GB_I.G:C__S 0.017 -0.710 -0.028 -0.012 0.008
## Sm_GB_I.G:C__B 0.017 -0.719 -0.012 -0.021 0.009 0.511
## S_GB_I.G:V_ -0.012 0.146 0.008 0.009 -0.024 -0.103
## Cndt__S:V_N -0.049 0.008 0.014 0.036 -0.709 -0.001
## Cndt__B:V_N -0.050 0.009 0.036 0.014 -0.721 -0.006
## S_GB_I.G:C__S: 0.008 -0.103 -0.001 -0.006 0.017 0.042
## S_GB_I.G:C__B: 0.008 -0.105 -0.006 -0.009 0.018 0.074
## Sm_GB_I.G:C__B S_GB_I.G:V C__S:V C__B:V S_GB_I.G:C__S:
## Smp_GrB_I.G
## Cndtn_d_dmS
## Cndtn_d_dmB
## Valenc_ffNg
## Sm_GB_I.G:C__S
## Sm_GB_I.G:C__B
## S_GB_I.G:V_ -0.105
## Cndt__S:V_N -0.006 0.017
## Cndt__B:V_N -0.009 0.018 0.511
## S_GB_I.G:C__S: 0.074 -0.710 -0.028 -0.012
## S_GB_I.G:C__B: 0.049 -0.719 -0.012 -0.021 0.511
Linear mixed models for for point-estimates
Histogram for DV
hist(Evaluation.in.out$P.Estimates)
Model 1[point-estimates]: In group biases holding valence and condition constant.
#
collapsed.evaluation.1 <- lmer(P.Estimates~Evaluated.Group_dum*Condition_c_eff*Valence_eff*+ (1|Participant), data = Evaluation.in.out)
summary(collapsed.evaluation.1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Evaluated.Group_dum * Condition_c_eff * Valence_eff *
## +(1 | Participant)
## Data: Evaluation.in.out
##
## REML criterion at convergence: 13680.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0290 -0.4631 0.0658 0.4837 3.8493
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 11.18 3.343
## Residual 94.29 9.710
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 61.09276
## Evaluated.Group_dumIn.Group 3.20740
## Condition_c_effWorse 2.11325
## Condition_c_effSame -0.01159
## Valence_effNeg -0.49591
## Evaluated.Group_dumIn.Group:Condition_c_effWorse -3.45815
## Evaluated.Group_dumIn.Group:Condition_c_effSame -1.15820
## Evaluated.Group_dumIn.Group:Valence_effNeg -0.58329
## Condition_c_effWorse:Valence_effNeg 0.04167
## Condition_c_effSame:Valence_effNeg -0.24110
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg -0.31490
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg 0.34577
## Std. Error
## (Intercept) 0.34023
## Evaluated.Group_dumIn.Group 0.45495
## Condition_c_effWorse 0.48418
## Condition_c_effSame 0.48404
## Valence_effNeg 0.34023
## Evaluated.Group_dumIn.Group:Condition_c_effWorse 0.64743
## Evaluated.Group_dumIn.Group:Condition_c_effSame 0.64724
## Evaluated.Group_dumIn.Group:Valence_effNeg 0.45495
## Condition_c_effWorse:Valence_effNeg 0.48418
## Condition_c_effSame:Valence_effNeg 0.48404
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg 0.64743
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg 0.64724
## df
## (Intercept) 1795.82964
## Evaluated.Group_dumIn.Group 907.99994
## Condition_c_effWorse 1795.82964
## Condition_c_effSame 1795.82964
## Valence_effNeg 1795.82964
## Evaluated.Group_dumIn.Group:Condition_c_effWorse 907.99994
## Evaluated.Group_dumIn.Group:Condition_c_effSame 907.99994
## Evaluated.Group_dumIn.Group:Valence_effNeg 907.99994
## Condition_c_effWorse:Valence_effNeg 1795.82964
## Condition_c_effSame:Valence_effNeg 1795.82964
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg 907.99994
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg 907.99994
## t value
## (Intercept) 179.562
## Evaluated.Group_dumIn.Group 7.050
## Condition_c_effWorse 4.365
## Condition_c_effSame -0.024
## Valence_effNeg -1.458
## Evaluated.Group_dumIn.Group:Condition_c_effWorse -5.341
## Evaluated.Group_dumIn.Group:Condition_c_effSame -1.789
## Evaluated.Group_dumIn.Group:Valence_effNeg -1.282
## Condition_c_effWorse:Valence_effNeg 0.086
## Condition_c_effSame:Valence_effNeg -0.498
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg -0.486
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg 0.534
## Pr(>|t|)
## (Intercept) < 2e-16
## Evaluated.Group_dumIn.Group 3.54e-12
## Condition_c_effWorse 1.35e-05
## Condition_c_effSame 0.9809
## Valence_effNeg 0.1451
## Evaluated.Group_dumIn.Group:Condition_c_effWorse 1.17e-07
## Evaluated.Group_dumIn.Group:Condition_c_effSame 0.0739
## Evaluated.Group_dumIn.Group:Valence_effNeg 0.2001
## Condition_c_effWorse:Valence_effNeg 0.9314
## Condition_c_effSame:Valence_effNeg 0.6185
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg 0.6268
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg 0.5933
##
## (Intercept) ***
## Evaluated.Group_dumIn.Group ***
## Condition_c_effWorse ***
## Condition_c_effSame
## Valence_effNeg
## Evaluated.Group_dumIn.Group:Condition_c_effWorse ***
## Evaluated.Group_dumIn.Group:Condition_c_effSame .
## Evaluated.Group_dumIn.Group:Valence_effNeg
## Condition_c_effWorse:Valence_effNeg
## Condition_c_effSame:Valence_effNeg
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ev.G_I.G Cnd__W Cnd__S Vlnc_N Ev.G_I.G:C__W
## Evltd.G_I.G -0.669
## Cndtn_c_ffW 0.018 -0.012
## Cndtn_c_ffS 0.017 -0.011 -0.518
## Valenc_ffNg -0.009 0.006 0.043 -0.020
## Ev.G_I.G:C__W -0.012 0.018 -0.669 0.346 -0.029
## Ev.G_I.G:C__S -0.011 0.017 0.346 -0.669 0.013 -0.518
## E.G_I.G:V_N 0.006 -0.009 -0.029 0.013 -0.669 0.043
## Cndt__W:V_N 0.043 -0.029 0.021 -0.011 0.018 -0.014
## Cndt__S:V_N -0.020 0.013 -0.011 -0.023 0.017 0.008
## E.G_I.G:C__W: -0.029 0.043 -0.014 0.008 -0.012 0.021
## E.G_I.G:C__S: 0.013 -0.020 0.008 0.016 -0.011 -0.011
## Ev.G_I.G:C__S E.G_I.G:V C__W:V C__S:V E.G_I.G:C__W:
## Evltd.G_I.G
## Cndtn_c_ffW
## Cndtn_c_ffS
## Valenc_ffNg
## Ev.G_I.G:C__W
## Ev.G_I.G:C__S
## E.G_I.G:V_N -0.020
## Cndt__W:V_N 0.008 -0.012
## Cndt__S:V_N 0.016 -0.011 -0.518
## E.G_I.G:C__W: -0.011 0.018 -0.669 0.346
## E.G_I.G:C__S: -0.023 0.017 0.346 -0.669 -0.518
Model 2[point-estimates]: Effect of positive first sample valence holding group and condition constant
collapsed.evaluation.2 <- lmer(P.Estimates~Valence_dum1*Condition_c_eff*Evaluated.Group_eff+ (1|Participant), data = Evaluation.in.out)
summary(collapsed.evaluation.2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Valence_dum1 * Condition_c_eff * Evaluated.Group_eff +
## (1 | Participant)
## Data: Evaluation.in.out
##
## REML criterion at convergence: 13680.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0290 -0.4631 0.0658 0.4837 3.8493
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 11.18 3.343
## Residual 94.29 9.710
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 61.9089
## Valence_dum1Pos 1.5751
## Condition_c_effWorse 0.2684
## Condition_c_effSame -0.6589
## Evaluated.Group_effIn.Group 1.3121
## Valence_dum1Pos:Condition_c_effWorse 0.2316
## Valence_dum1Pos:Condition_c_effSame 0.1364
## Valence_dum1Pos:Evaluated.Group_effIn.Group 0.5833
## Condition_c_effWorse:Evaluated.Group_effIn.Group -1.8865
## Condition_c_effSame:Evaluated.Group_effIn.Group -0.4062
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group 0.3149
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group -0.3458
## Std. Error
## (Intercept) 0.3561
## Valence_dum1Pos 0.5060
## Condition_c_effWorse 0.5145
## Condition_c_effSame 0.5031
## Evaluated.Group_effIn.Group 0.3202
## Valence_dum1Pos:Condition_c_effWorse 0.7201
## Valence_dum1Pos:Condition_c_effSame 0.7199
## Valence_dum1Pos:Evaluated.Group_effIn.Group 0.4550
## Condition_c_effWorse:Evaluated.Group_effIn.Group 0.4626
## Condition_c_effSame:Evaluated.Group_effIn.Group 0.4523
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group 0.6474
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group 0.6472
## df
## (Intercept) 908.0000
## Valence_dum1Pos 908.0000
## Condition_c_effWorse 908.0000
## Condition_c_effSame 908.0000
## Evaluated.Group_effIn.Group 907.9999
## Valence_dum1Pos:Condition_c_effWorse 908.0000
## Valence_dum1Pos:Condition_c_effSame 908.0000
## Valence_dum1Pos:Evaluated.Group_effIn.Group 907.9999
## Condition_c_effWorse:Evaluated.Group_effIn.Group 907.9999
## Condition_c_effSame:Evaluated.Group_effIn.Group 907.9999
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group 907.9999
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group 907.9999
## t value
## (Intercept) 173.829
## Valence_dum1Pos 3.113
## Condition_c_effWorse 0.522
## Condition_c_effSame -1.310
## Evaluated.Group_effIn.Group 4.098
## Valence_dum1Pos:Condition_c_effWorse 0.322
## Valence_dum1Pos:Condition_c_effSame 0.190
## Valence_dum1Pos:Evaluated.Group_effIn.Group 1.282
## Condition_c_effWorse:Evaluated.Group_effIn.Group -4.078
## Condition_c_effSame:Evaluated.Group_effIn.Group -0.898
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group 0.486
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group -0.534
## Pr(>|t|)
## (Intercept) < 2e-16
## Valence_dum1Pos 0.00191
## Condition_c_effWorse 0.60203
## Condition_c_effSame 0.19061
## Evaluated.Group_effIn.Group 4.55e-05
## Valence_dum1Pos:Condition_c_effWorse 0.74786
## Valence_dum1Pos:Condition_c_effSame 0.84974
## Valence_dum1Pos:Evaluated.Group_effIn.Group 0.20014
## Condition_c_effWorse:Evaluated.Group_effIn.Group 4.94e-05
## Condition_c_effSame:Evaluated.Group_effIn.Group 0.36937
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group 0.62682
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group 0.59331
##
## (Intercept) ***
## Valence_dum1Pos **
## Condition_c_effWorse
## Condition_c_effSame
## Evaluated.Group_effIn.Group ***
## Valence_dum1Pos:Condition_c_effWorse
## Valence_dum1Pos:Condition_c_effSame
## Valence_dum1Pos:Evaluated.Group_effIn.Group
## Condition_c_effWorse:Evaluated.Group_effIn.Group ***
## Condition_c_effSame:Evaluated.Group_effIn.Group
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Vln_1P Cnd__W Cnd__S E.G_I. Vl_1P:C__W Vl_1P:C__S
## Valnc_dm1Ps -0.704
## Cndtn_c_ffW 0.060 -0.042
## Cndtn_c_ffS -0.003 0.002 -0.530
## Evltd.G_I.G 0.000 0.000 0.000 0.000
## Vln_1P:C__W -0.043 0.018 -0.714 0.379 0.000
## Vln_1P:C__S 0.002 0.017 0.371 -0.699 0.000 -0.518
## V_1P:E.G_I. 0.000 0.000 0.000 0.000 -0.704 0.000 0.000
## C__W:E.G_I. 0.000 0.000 0.000 0.000 0.060 0.000 0.000
## C__S:E.G_I. 0.000 0.000 0.000 0.000 -0.003 0.000 0.000
## V_1P:C__W:E 0.000 0.000 0.000 0.000 -0.043 0.000 0.000
## V_1P:C__S:E 0.000 0.000 0.000 0.000 0.002 0.000 0.000
## V_1P:E C__W:E C__S:E V_1P:C__W:
## Valnc_dm1Ps
## Cndtn_c_ffW
## Cndtn_c_ffS
## Evltd.G_I.G
## Vln_1P:C__W
## Vln_1P:C__S
## V_1P:E.G_I.
## C__W:E.G_I. -0.042
## C__S:E.G_I. 0.002 -0.530
## V_1P:C__W:E 0.018 -0.714 0.379
## V_1P:C__S:E 0.017 0.371 -0.699 -0.518
Model 3[point-estimates] Effective of negative first sample valence holding group and condition constant
collapsed.evaluation.3 <- lmer(P.Estimates~Evaluated.Group_eff*Valence_dum*Condition_c_eff+ (1|Participant), data = Evaluation.in.out)
summary(collapsed.evaluation.3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Evaluated.Group_eff * Valence_dum * Condition_c_eff +
## (1 | Participant)
## Data: Evaluation.in.out
##
## REML criterion at convergence: 13680.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0290 -0.4631 0.0658 0.4837 3.8493
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 11.18 3.343
## Residual 94.29 9.710
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 63.4840
## Evaluated.Group_effIn.Group 1.8953
## Valence_dumNeg -1.5751
## Condition_c_effWorse 0.5000
## Condition_c_effSame -0.5225
## Evaluated.Group_effIn.Group:Valence_dumNeg -0.5833
## Evaluated.Group_effIn.Group:Condition_c_effWorse -1.5716
## Evaluated.Group_effIn.Group:Condition_c_effSame -0.7520
## Valence_dumNeg:Condition_c_effWorse -0.2316
## Valence_dumNeg:Condition_c_effSame -0.1364
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effWorse -0.3149
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effSame 0.3458
## Std. Error
## (Intercept) 0.3595
## Evaluated.Group_effIn.Group 0.3232
## Valence_dumNeg 0.5060
## Condition_c_effWorse 0.5038
## Condition_c_effSame 0.5149
## Evaluated.Group_effIn.Group:Valence_dumNeg 0.4550
## Evaluated.Group_effIn.Group:Condition_c_effWorse 0.4530
## Evaluated.Group_effIn.Group:Condition_c_effSame 0.4630
## Valence_dumNeg:Condition_c_effWorse 0.7201
## Valence_dumNeg:Condition_c_effSame 0.7199
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effWorse 0.6474
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effSame 0.6472
## df
## (Intercept) 908.0000
## Evaluated.Group_effIn.Group 907.9999
## Valence_dumNeg 908.0000
## Condition_c_effWorse 908.0000
## Condition_c_effSame 908.0000
## Evaluated.Group_effIn.Group:Valence_dumNeg 907.9999
## Evaluated.Group_effIn.Group:Condition_c_effWorse 907.9999
## Evaluated.Group_effIn.Group:Condition_c_effSame 907.9999
## Valence_dumNeg:Condition_c_effWorse 908.0000
## Valence_dumNeg:Condition_c_effSame 908.0000
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effWorse 907.9999
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effSame 907.9999
## t value
## (Intercept) 176.610
## Evaluated.Group_effIn.Group 5.865
## Valence_dumNeg -3.113
## Condition_c_effWorse 0.992
## Condition_c_effSame -1.015
## Evaluated.Group_effIn.Group:Valence_dumNeg -1.282
## Evaluated.Group_effIn.Group:Condition_c_effWorse -3.470
## Evaluated.Group_effIn.Group:Condition_c_effSame -1.624
## Valence_dumNeg:Condition_c_effWorse -0.322
## Valence_dumNeg:Condition_c_effSame -0.190
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effWorse -0.486
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effSame 0.534
## Pr(>|t|)
## (Intercept) < 2e-16
## Evaluated.Group_effIn.Group 6.3e-09
## Valence_dumNeg 0.001911
## Condition_c_effWorse 0.321295
## Condition_c_effSame 0.310545
## Evaluated.Group_effIn.Group:Valence_dumNeg 0.200139
## Evaluated.Group_effIn.Group:Condition_c_effWorse 0.000546
## Evaluated.Group_effIn.Group:Condition_c_effSame 0.104667
## Valence_dumNeg:Condition_c_effWorse 0.747862
## Valence_dumNeg:Condition_c_effSame 0.849738
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effWorse 0.626815
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effSame 0.593313
##
## (Intercept) ***
## Evaluated.Group_effIn.Group ***
## Valence_dumNeg **
## Condition_c_effWorse
## Condition_c_effSame
## Evaluated.Group_effIn.Group:Valence_dumNeg
## Evaluated.Group_effIn.Group:Condition_c_effWorse ***
## Evaluated.Group_effIn.Group:Condition_c_effSame
## Valence_dumNeg:Condition_c_effWorse
## Valence_dumNeg:Condition_c_effSame
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effWorse
## Evaluated.Group_effIn.Group:Valence_dumNeg:Condition_c_effSame
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ev.G_I.G Vlnc_N Cnd__W Cnd__S Ev.G_I.G:V_N
## Evltd.G_I.G 0.000
## Valenc_dmNg -0.710 0.000
## Cndtn_c_ffW -0.025 0.000 0.018
## Cndtn_c_ffS 0.036 0.000 -0.026 -0.506
## Ev.G_I.G:V_N 0.000 -0.710 0.000 0.000 0.000
## E.G_I.G:C__W 0.000 -0.025 0.000 0.000 0.000 0.018
## E.G_I.G:C__S 0.000 0.036 0.000 0.000 0.000 -0.026
## Vlnc_N:C__W 0.018 0.000 0.018 -0.700 0.354 0.000
## Vlnc_N:C__S -0.026 0.000 0.017 0.362 -0.715 0.000
## E.G_I.G:V_N:C__W 0.000 0.018 0.000 0.000 0.000 0.018
## E.G_I.G:V_N:C__S 0.000 -0.026 0.000 0.000 0.000 0.017
## E.G_I.G:C__W E.G_I.G:C__S V_N:C__W V_N:C__S
## Evltd.G_I.G
## Valenc_dmNg
## Cndtn_c_ffW
## Cndtn_c_ffS
## Ev.G_I.G:V_N
## E.G_I.G:C__W
## E.G_I.G:C__S -0.506
## Vlnc_N:C__W 0.000 0.000
## Vlnc_N:C__S 0.000 0.000 -0.518
## E.G_I.G:V_N:C__W -0.700 0.354 0.000 0.000
## E.G_I.G:V_N:C__S 0.362 -0.715 0.000 0.000
## E.G_I.G:V_N:C__W
## Evltd.G_I.G
## Valenc_dmNg
## Cndtn_c_ffW
## Cndtn_c_ffS
## Ev.G_I.G:V_N
## E.G_I.G:C__W
## E.G_I.G:C__S
## Vlnc_N:C__W
## Vlnc_N:C__S
## E.G_I.G:V_N:C__W
## E.G_I.G:V_N:C__S -0.518
Model 4[point-estimates]: Effect of out group + positive first sample effects holding condition constant
collapsed.evaluation.4 <- lmer(P.Estimates~Evaluated.Group_dum1*Valence_dum1*Condition_c_eff+ (1|Participant), data = Evaluation.in.out)
summary(collapsed.evaluation.4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Evaluated.Group_dum1 * Valence_dum1 * Condition_c_eff +
## (1 | Participant)
## Data: Evaluation.in.out
##
## REML criterion at convergence: 13672.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0290 -0.4631 0.0658 0.4837 3.8493
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 11.18 3.343
## Residual 94.29 9.710
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 63.2210
## Evaluated.Group_dum1Out.Group -2.6241
## Valence_dum1Pos 2.1584
## Condition_c_effWorse -1.6181
## Condition_c_effSame -1.0651
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos -1.1666
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse 3.7730
## Evaluated.Group_dum1Out.Group:Condition_c_effSame 0.8124
## Valence_dum1Pos:Condition_c_effWorse 0.5465
## Valence_dum1Pos:Condition_c_effSame -0.2094
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effWorse -0.6298
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effSame 0.6915
## Std. Error
## (Intercept) 0.4789
## Evaluated.Group_dum1Out.Group 0.6404
## Valence_dum1Pos 0.6805
## Condition_c_effWorse 0.6919
## Condition_c_effSame 0.6765
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos 0.9099
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse 0.9252
## Evaluated.Group_dum1Out.Group:Condition_c_effSame 0.9046
## Valence_dum1Pos:Condition_c_effWorse 0.9684
## Valence_dum1Pos:Condition_c_effSame 0.9681
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effWorse 1.2949
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effSame 1.2945
## df
## (Intercept) 1795.8296
## Evaluated.Group_dum1Out.Group 907.9999
## Valence_dum1Pos 1795.8296
## Condition_c_effWorse 1795.8296
## Condition_c_effSame 1795.8296
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos 907.9999
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse 907.9999
## Evaluated.Group_dum1Out.Group:Condition_c_effSame 907.9999
## Valence_dum1Pos:Condition_c_effWorse 1795.8296
## Valence_dum1Pos:Condition_c_effSame 1795.8296
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effWorse 907.9999
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effSame 907.9999
## t value
## (Intercept) 132.004
## Evaluated.Group_dum1Out.Group -4.098
## Valence_dum1Pos 3.172
## Condition_c_effWorse -2.339
## Condition_c_effSame -1.574
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos -1.282
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse 4.078
## Evaluated.Group_dum1Out.Group:Condition_c_effSame 0.898
## Valence_dum1Pos:Condition_c_effWorse 0.564
## Valence_dum1Pos:Condition_c_effSame -0.216
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effWorse -0.486
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effSame 0.534
## Pr(>|t|)
## (Intercept) < 2e-16
## Evaluated.Group_dum1Out.Group 4.55e-05
## Valence_dum1Pos 0.00154
## Condition_c_effWorse 0.01946
## Condition_c_effSame 0.11556
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos 0.20014
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse 4.94e-05
## Evaluated.Group_dum1Out.Group:Condition_c_effSame 0.36937
## Valence_dum1Pos:Condition_c_effWorse 0.57261
## Valence_dum1Pos:Condition_c_effSame 0.82881
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effWorse 0.62682
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effSame 0.59331
##
## (Intercept) ***
## Evaluated.Group_dum1Out.Group ***
## Valence_dum1Pos **
## Condition_c_effWorse *
## Condition_c_effSame
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse ***
## Evaluated.Group_dum1Out.Group:Condition_c_effSame
## Valence_dum1Pos:Condition_c_effWorse
## Valence_dum1Pos:Condition_c_effSame
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effWorse
## Evaluated.Group_dum1Out.Group:Valence_dum1Pos:Condition_c_effSame
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ev.G_1O.G Vln_1P Cnd__W Cnd__S Ev.G_1O.G:V_1P
## Evlt.G_1O.G -0.669
## Valnc_dm1Ps -0.704 0.471
## Cndtn_c_ffW 0.060 -0.040 -0.042
## Cndtn_c_ffS -0.003 0.002 0.002 -0.530
## Ev.G_1O.G:V_1P 0.471 -0.704 -0.669 0.028 -0.002
## E.G_1O.G:C__W -0.040 0.060 0.028 -0.669 0.355 -0.042
## E.G_1O.G:C__S 0.002 -0.003 -0.002 0.355 -0.669 0.002
## Vln_1P:C__W -0.043 0.029 0.018 -0.714 0.379 -0.012
## Vln_1P:C__S 0.002 -0.002 0.017 0.371 -0.699 -0.011
## E.G_1O.G:V_1P:C__W 0.029 -0.043 -0.012 0.478 -0.253 0.018
## E.G_1O.G:V_1P:C__S -0.002 0.002 -0.011 -0.248 0.467 0.017
## E.G_1O.G:C__W E.G_1O.G:C__S V_1P:C__W V_1P:C__S
## Evlt.G_1O.G
## Valnc_dm1Ps
## Cndtn_c_ffW
## Cndtn_c_ffS
## Ev.G_1O.G:V_1P
## E.G_1O.G:C__W
## E.G_1O.G:C__S -0.530
## Vln_1P:C__W 0.478 -0.253
## Vln_1P:C__S -0.248 0.467 -0.518
## E.G_1O.G:V_1P:C__W -0.714 0.379 -0.669 0.346
## E.G_1O.G:V_1P:C__S 0.371 -0.699 0.346 -0.669
## E.G_1O.G:V_1P:C__W
## Evlt.G_1O.G
## Valnc_dm1Ps
## Cndtn_c_ffW
## Cndtn_c_ffS
## Ev.G_1O.G:V_1P
## E.G_1O.G:C__W
## E.G_1O.G:C__S
## Vln_1P:C__W
## Vln_1P:C__S
## E.G_1O.G:V_1P:C__W
## E.G_1O.G:V_1P:C__S -0.518
Model 5[point-estimates]: in group bias with valence and condition constant
#
collapsed.evaluation.5 <- lmer(P.Estimates~Evaluated.Group_dum1*Condition_c_eff*Valence_eff*+ (1|Participant), data = Evaluation.in.out)
summary(collapsed.evaluation.5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Evaluated.Group_dum1 * Condition_c_eff * Valence_eff *
## +(1 | Participant)
## Data: Evaluation.in.out
##
## REML criterion at convergence: 13680.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0290 -0.4631 0.0658 0.4837 3.8493
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 11.18 3.343
## Residual 94.29 9.710
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 64.3002
## Evaluated.Group_dum1Out.Group -3.2074
## Condition_c_effWorse -1.3449
## Condition_c_effSame -1.1698
## Valence_effNeg -1.0792
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse 3.4581
## Evaluated.Group_dum1Out.Group:Condition_c_effSame 1.1582
## Evaluated.Group_dum1Out.Group:Valence_effNeg 0.5833
## Condition_c_effWorse:Valence_effNeg -0.2732
## Condition_c_effSame:Valence_effNeg 0.1047
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse:Valence_effNeg 0.3149
## Evaluated.Group_dum1Out.Group:Condition_c_effSame:Valence_effNeg -0.3458
## Std. Error
## (Intercept) 0.3402
## Evaluated.Group_dum1Out.Group 0.4550
## Condition_c_effWorse 0.4842
## Condition_c_effSame 0.4840
## Valence_effNeg 0.3402
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse 0.6474
## Evaluated.Group_dum1Out.Group:Condition_c_effSame 0.6472
## Evaluated.Group_dum1Out.Group:Valence_effNeg 0.4550
## Condition_c_effWorse:Valence_effNeg 0.4842
## Condition_c_effSame:Valence_effNeg 0.4840
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse:Valence_effNeg 0.6474
## Evaluated.Group_dum1Out.Group:Condition_c_effSame:Valence_effNeg 0.6472
## df
## (Intercept) 1795.8296
## Evaluated.Group_dum1Out.Group 907.9999
## Condition_c_effWorse 1795.8296
## Condition_c_effSame 1795.8296
## Valence_effNeg 1795.8296
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse 907.9999
## Evaluated.Group_dum1Out.Group:Condition_c_effSame 907.9999
## Evaluated.Group_dum1Out.Group:Valence_effNeg 907.9999
## Condition_c_effWorse:Valence_effNeg 1795.8296
## Condition_c_effSame:Valence_effNeg 1795.8296
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse:Valence_effNeg 907.9999
## Evaluated.Group_dum1Out.Group:Condition_c_effSame:Valence_effNeg 907.9999
## t value
## (Intercept) 188.989
## Evaluated.Group_dum1Out.Group -7.050
## Condition_c_effWorse -2.778
## Condition_c_effSame -2.417
## Valence_effNeg -3.172
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse 5.341
## Evaluated.Group_dum1Out.Group:Condition_c_effSame 1.789
## Evaluated.Group_dum1Out.Group:Valence_effNeg 1.282
## Condition_c_effWorse:Valence_effNeg -0.564
## Condition_c_effSame:Valence_effNeg 0.216
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse:Valence_effNeg 0.486
## Evaluated.Group_dum1Out.Group:Condition_c_effSame:Valence_effNeg -0.534
## Pr(>|t|)
## (Intercept) < 2e-16
## Evaluated.Group_dum1Out.Group 3.54e-12
## Condition_c_effWorse 0.00553
## Condition_c_effSame 0.01576
## Valence_effNeg 0.00154
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse 1.17e-07
## Evaluated.Group_dum1Out.Group:Condition_c_effSame 0.07388
## Evaluated.Group_dum1Out.Group:Valence_effNeg 0.20014
## Condition_c_effWorse:Valence_effNeg 0.57261
## Condition_c_effSame:Valence_effNeg 0.82881
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse:Valence_effNeg 0.62682
## Evaluated.Group_dum1Out.Group:Condition_c_effSame:Valence_effNeg 0.59331
##
## (Intercept) ***
## Evaluated.Group_dum1Out.Group ***
## Condition_c_effWorse **
## Condition_c_effSame *
## Valence_effNeg **
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse ***
## Evaluated.Group_dum1Out.Group:Condition_c_effSame .
## Evaluated.Group_dum1Out.Group:Valence_effNeg
## Condition_c_effWorse:Valence_effNeg
## Condition_c_effSame:Valence_effNeg
## Evaluated.Group_dum1Out.Group:Condition_c_effWorse:Valence_effNeg
## Evaluated.Group_dum1Out.Group:Condition_c_effSame:Valence_effNeg
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ev.G_1O.G Cnd__W Cnd__S Vlnc_N Ev.G_1O.G:C__W
## Evlt.G_1O.G -0.669
## Cndtn_c_ffW 0.018 -0.012
## Cndtn_c_ffS 0.017 -0.011 -0.518
## Valenc_ffNg -0.009 0.006 0.043 -0.020
## Ev.G_1O.G:C__W -0.012 0.018 -0.669 0.346 -0.029
## Ev.G_1O.G:C__S -0.011 0.017 0.346 -0.669 0.013 -0.518
## E.G_1O.G:V_ 0.006 -0.009 -0.029 0.013 -0.669 0.043
## Cndt__W:V_N 0.043 -0.029 0.021 -0.011 0.018 -0.014
## Cndt__S:V_N -0.020 0.013 -0.011 -0.023 0.017 0.008
## E.G_1O.G:C__W: -0.029 0.043 -0.014 0.008 -0.012 0.021
## E.G_1O.G:C__S: 0.013 -0.020 0.008 0.016 -0.011 -0.011
## Ev.G_1O.G:C__S E.G_1O.G:V C__W:V C__S:V E.G_1O.G:C__W:
## Evlt.G_1O.G
## Cndtn_c_ffW
## Cndtn_c_ffS
## Valenc_ffNg
## Ev.G_1O.G:C__W
## Ev.G_1O.G:C__S
## E.G_1O.G:V_ -0.020
## Cndt__W:V_N 0.008 -0.012
## Cndt__S:V_N 0.016 -0.011 -0.518
## E.G_1O.G:C__W: -0.011 0.018 -0.669 0.346
## E.G_1O.G:C__S: -0.023 0.017 0.346 -0.669 -0.518
Model 6[point-estimates] Out group + Negative first sample
collapsed.evaluation.6 <- lmer(P.Estimates~Evaluated.Group_dum*Valence_dum1*Condition_c_eff+ (1|Participant), data = Evaluation.in.out)
summary(collapsed.evaluation.6)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Evaluated.Group_dum * Valence_dum1 * Condition_c_eff +
## (1 | Participant)
## Data: Evaluation.in.out
##
## REML criterion at convergence: 13672.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0290 -0.4631 0.0658 0.4837 3.8493
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 11.18 3.343
## Residual 94.29 9.710
## Number of obs: 1828, groups: Participant, 914
##
## Fixed effects:
## Estimate
## (Intercept) 60.59685
## Evaluated.Group_dumIn.Group 2.62411
## Valence_dum1Pos 0.99183
## Condition_c_effWorse 2.15493
## Condition_c_effSame -0.25269
## Evaluated.Group_dumIn.Group:Valence_dum1Pos 1.16657
## Evaluated.Group_dumIn.Group:Condition_c_effWorse -3.77305
## Evaluated.Group_dumIn.Group:Condition_c_effSame -0.81242
## Valence_dum1Pos:Condition_c_effWorse -0.08334
## Valence_dum1Pos:Condition_c_effSame 0.48220
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effWorse 0.62980
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effSame -0.69155
## Std. Error
## (Intercept) 0.47893
## Evaluated.Group_dumIn.Group 0.64042
## Valence_dum1Pos 0.68047
## Condition_c_effWorse 0.69189
## Condition_c_effSame 0.67650
## Evaluated.Group_dumIn.Group:Valence_dum1Pos 0.90990
## Evaluated.Group_dumIn.Group:Condition_c_effWorse 0.92517
## Evaluated.Group_dumIn.Group:Condition_c_effSame 0.90461
## Valence_dum1Pos:Condition_c_effWorse 0.96836
## Valence_dum1Pos:Condition_c_effSame 0.96807
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effWorse 1.29487
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effSame 1.29448
## df
## (Intercept) 1795.82964
## Evaluated.Group_dumIn.Group 907.99994
## Valence_dum1Pos 1795.82964
## Condition_c_effWorse 1795.82964
## Condition_c_effSame 1795.82964
## Evaluated.Group_dumIn.Group:Valence_dum1Pos 907.99994
## Evaluated.Group_dumIn.Group:Condition_c_effWorse 907.99994
## Evaluated.Group_dumIn.Group:Condition_c_effSame 907.99994
## Valence_dum1Pos:Condition_c_effWorse 1795.82964
## Valence_dum1Pos:Condition_c_effSame 1795.82964
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effWorse 907.99994
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effSame 907.99994
## t value
## (Intercept) 126.525
## Evaluated.Group_dumIn.Group 4.098
## Valence_dum1Pos 1.458
## Condition_c_effWorse 3.115
## Condition_c_effSame -0.374
## Evaluated.Group_dumIn.Group:Valence_dum1Pos 1.282
## Evaluated.Group_dumIn.Group:Condition_c_effWorse -4.078
## Evaluated.Group_dumIn.Group:Condition_c_effSame -0.898
## Valence_dum1Pos:Condition_c_effWorse -0.086
## Valence_dum1Pos:Condition_c_effSame 0.498
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effWorse 0.486
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effSame -0.534
## Pr(>|t|)
## (Intercept) < 2e-16
## Evaluated.Group_dumIn.Group 4.55e-05
## Valence_dum1Pos 0.14513
## Condition_c_effWorse 0.00187
## Condition_c_effSame 0.70880
## Evaluated.Group_dumIn.Group:Valence_dum1Pos 0.20014
## Evaluated.Group_dumIn.Group:Condition_c_effWorse 4.94e-05
## Evaluated.Group_dumIn.Group:Condition_c_effSame 0.36937
## Valence_dum1Pos:Condition_c_effWorse 0.93142
## Valence_dum1Pos:Condition_c_effSame 0.61847
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effWorse 0.62682
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effSame 0.59331
##
## (Intercept) ***
## Evaluated.Group_dumIn.Group ***
## Valence_dum1Pos
## Condition_c_effWorse **
## Condition_c_effSame
## Evaluated.Group_dumIn.Group:Valence_dum1Pos
## Evaluated.Group_dumIn.Group:Condition_c_effWorse ***
## Evaluated.Group_dumIn.Group:Condition_c_effSame
## Valence_dum1Pos:Condition_c_effWorse
## Valence_dum1Pos:Condition_c_effSame
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effWorse
## Evaluated.Group_dumIn.Group:Valence_dum1Pos:Condition_c_effSame
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ev.G_I.G Vln_1P Cnd__W Cnd__S Ev.G_I.G:V_1P
## Evltd.G_I.G -0.669
## Valnc_dm1Ps -0.704 0.471
## Cndtn_c_ffW 0.060 -0.040 -0.042
## Cndtn_c_ffS -0.003 0.002 0.002 -0.530
## Ev.G_I.G:V_1P 0.471 -0.704 -0.669 0.028 -0.002
## E.G_I.G:C__W -0.040 0.060 0.028 -0.669 0.355 -0.042
## E.G_I.G:C__S 0.002 -0.003 -0.002 0.355 -0.669 0.002
## Vln_1P:C__W -0.043 0.029 0.018 -0.714 0.379 -0.012
## Vln_1P:C__S 0.002 -0.002 0.017 0.371 -0.699 -0.011
## E.G_I.G:V_1P:C__W 0.029 -0.043 -0.012 0.478 -0.253 0.018
## E.G_I.G:V_1P:C__S -0.002 0.002 -0.011 -0.248 0.467 0.017
## E.G_I.G:C__W E.G_I.G:C__S V_1P:C__W V_1P:C__S
## Evltd.G_I.G
## Valnc_dm1Ps
## Cndtn_c_ffW
## Cndtn_c_ffS
## Ev.G_I.G:V_1P
## E.G_I.G:C__W
## E.G_I.G:C__S -0.530
## Vln_1P:C__W 0.478 -0.253
## Vln_1P:C__S -0.248 0.467 -0.518
## E.G_I.G:V_1P:C__W -0.714 0.379 -0.669 0.346
## E.G_I.G:V_1P:C__S 0.371 -0.699 0.346 -0.669
## E.G_I.G:V_1P:C__W
## Evltd.G_I.G
## Valnc_dm1Ps
## Cndtn_c_ffW
## Cndtn_c_ffS
## Ev.G_I.G:V_1P
## E.G_I.G:C__W
## E.G_I.G:C__S
## Vln_1P:C__W
## Vln_1P:C__S
## E.G_I.G:V_1P:C__W
## E.G_I.G:V_1P:C__S -0.518
Model 7: Effect of condition. Worse only
collapsed.evaluation.7 <- lmer(P.Estimates~Evaluated.Group_dum1*Valence_eff+ (1|Participant), data = worse.only)
## boundary (singular) fit: see ?isSingular
summary(collapsed.evaluation.7)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Evaluated.Group_dum1 * Valence_eff + (1 | Participant)
## Data: worse.only
##
## REML criterion at convergence: 4528.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8373 -0.3910 0.0628 0.4886 3.3811
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.0 0.00
## Residual 121.4 11.02
## Number of obs: 594, groups: Participant, 297
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 62.9553 0.6401 590.0000
## Evaluated.Group_dum1Out.Group 0.2508 0.9052 590.0000
## Valence_effNeg -1.3524 0.6401 590.0000
## Evaluated.Group_dum1Out.Group:Valence_effNeg 0.8982 0.9052 590.0000
## t value Pr(>|t|)
## (Intercept) 98.357 <2e-16 ***
## Evaluated.Group_dum1Out.Group 0.277 0.782
## Valence_effNeg -2.113 0.035 *
## Evaluated.Group_dum1Out.Group:Valence_effNeg 0.992 0.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ev.G_1O.G Vlnc_N
## Evlt.G_1O.G -0.707
## Valenc_ffNg 0.051 -0.036
## E.G_1O.G:V_ -0.036 0.051 -0.707
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Model 8: Effect of condition. Same only
collapsed.evaluation.8 <- lmer(P.Estimates~Evaluated.Group_dum*Valence_eff1+ (1|Participant), data = same.only)
summary(collapsed.evaluation.8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Evaluated.Group_dum * Valence_eff1 + (1 | Participant)
## Data: same.only
##
## REML criterion at convergence: 4396.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5032 -0.5176 0.0620 0.4883 3.2973
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 22.21 4.713
## Residual 77.34 8.794
## Number of obs: 594, groups: Participant, 297
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 61.0812 0.5793 562.0201
## Evaluated.Group_dumIn.Group 2.0492 0.7222 295.0000
## Valence_eff1Pos 0.7370 0.5793 562.0201
## Evaluated.Group_dumIn.Group:Valence_eff1Pos 0.2375 0.7222 295.0000
## t value Pr(>|t|)
## (Intercept) 105.431 < 2e-16 ***
## Evaluated.Group_dumIn.Group 2.838 0.00486 **
## Valence_eff1Pos 1.272 0.20385
## Evaluated.Group_dumIn.Group:Valence_eff1Pos 0.329 0.74247
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ev.G_I.G Vln_1P
## Evltd.G_I.G -0.623
## Valnc_ff1Ps 0.037 -0.023
## E.G_I.G:V_1 -0.023 0.037 -0.623
Model 9: Effect of condition. Better only
collapsed.evaluation.9 <- lmer(P.Estimates~Evaluated.Group_dum*Valence_eff1+ (1|Participant), data = better.only)
summary(collapsed.evaluation.9)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Evaluated.Group_dum * Valence_eff1 + (1 | Participant)
## Data: better.only
##
## REML criterion at convergence: 4723.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.9033 -0.4593 0.0911 0.4962 3.4328
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 15.51 3.938
## Residual 80.70 8.983
## Number of obs: 640, groups: Participant, 320
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 58.9911 0.5488 619.8889
## Evaluated.Group_dumIn.Group 7.8237 0.7109 318.0000
## Valence_eff1Pos 0.2965 0.5488 619.8889
## Evaluated.Group_dumIn.Group:Valence_eff1Pos 0.6142 0.7109 318.0000
## t value Pr(>|t|)
## (Intercept) 107.484 <2e-16 ***
## Evaluated.Group_dumIn.Group 11.006 <2e-16 ***
## Valence_eff1Pos 0.540 0.589
## Evaluated.Group_dumIn.Group:Valence_eff1Pos 0.864 0.388
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ev.G_I.G Vln_1P
## Evltd.G_I.G -0.648
## Valnc_ff1Ps 0.044 -0.028
## E.G_I.G:V_1 -0.028 0.044 -0.648
###Power simulations
#sim <- powerSim(collapsed.sampling.1, fixed("Samp_GroupB_dumIn.Group", "z"), seed = 5, nsim = 800, alpha = .05)
#sim