Reputation
Elizabeth Blevins, Michael Ko, Julie Cachia
9/26/2019
Prepare data
Read in data Files
# read in part 1 data
Part1 <- read.csv("../../data/one_shot_trust_long.csv")
# read in part 2 data
Part2 <- read.csv("../../data/reputation_study_part2.csv")
#Exclude pilot data from Part 2
N <- dim(Part2)[1]
Part2 <- Part2[39:N,] #Take out what we know are test trials##Resolve pID/WelcomeCode discrepancies discovered in the Random Trial Bonus script (in same folder)
#Convert emails and welcome code to character in order to perform operation below
Part2$Email <- as.character(Part2$Email)
Part2$WelcomeCode <- as.character(Part2$WelcomeCode)
#Manually replace Part 2 Welcome Code with correct code from Part 1 pID
Part2$WelcomeCode[Part2$Email == "theresaslater@gmail.com"] <- as.character(Part1$pID[Part1$email == "theresaslater@gmail.com"])## Warning in Part2$WelcomeCode[Part2$Email == "theresaslater@gmail.com"] <-
## as.character(Part1$pID[Part1$email == : number of items to replace is not a
## multiple of replacement lengthPart2$WelcomeCode[Part2$Email == "tammrafoster@gmail.com"] <- as.character(Part1$pID[Part1$email == "tammrafoster@gmail.com"])## Warning in Part2$WelcomeCode[Part2$Email == "tammrafoster@gmail.com"] <-
## as.character(Part1$pID[Part1$email == : number of items to replace is not a
## multiple of replacement lengthPart2$WelcomeCode[Part2$Email == "jrtravagline@gmail.com"] <- as.character(Part1$pID[Part1$email == "jrtravagline@gmail.com"])## Warning in Part2$WelcomeCode[Part2$Email == "jrtravagline@gmail.com"] <-
## as.character(Part1$pID[Part1$email == : number of items to replace is not a
## multiple of replacement lengthPart2$WelcomeCode[Part2$Email == "mollyki@stanford.edu"] <- as.character(Part1$pID[Part1$email == "mollyki@stanford.edu"])## Warning in Part2$WelcomeCode[Part2$Email == "mollyki@stanford.edu"] <-
## as.character(Part1$pID[Part1$email == : number of items to replace is not a
## multiple of replacement length# keep relevant columns from Part 2
Part2 <- Part2 %>%
dplyr::select("WelcomeCode",
"Age",
"Gender",
"Education",
contains("SES"),
contains("Trust"),
contains("Image"),
contains("RepStability"),
contains("SchwartzValues"),
# select actual and ideal affect variables using regex
# first character is "a" or "i", number 1-4 as second character, and underscore
matches("^[a1-4\\_\\]{2}"), matches("^[i1-4\\_\\]{2}"))Join Part 1 and Part 2 data into “Data” dataframe
# turn pID into characters (WelcomeCode from Part 2 has already been done above)
Part1$pID <- as.character(Part1$pID)
Data <- left_join(Part1, Part2, by = c("pID" = "WelcomeCode")) # combine part 1 and part 2 by pID/WelcomeCodeClean data
cols.num <- c("Age",
"Trust1","Trust2", "Trust3", "Trust4", "Trust5",
"Trust6",
"SelfImage1", "SelfImage2", "SelfImage3", "SelfImage4", "SelfImage5", "SelfImage6",
"OtherImage1", "OtherImage2", "OtherImage3", "OtherImage4", "OtherImage5", "OtherImage6",
"RepStability1", "RepStability2", "RepStability3", "RepStability4", "RepStability5",
"SchwartzValues_1", "SchwartzValues_2", "SchwartzValues_3", "SchwartzValues_4", "SchwartzValues_5", "SchwartzValues_6", "SchwartzValues_7", "SchwartzValues_8", "SchwartzValues_9", "SchwartzValues_10")
Data[cols.num] <- sapply(Data[cols.num],as.character)
Data[cols.num] <- sapply(Data[cols.num],as.numeric)## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercionData <- Data %>%
# select participants 30 years old and younger
filter(Age <= 30 & Age >= 18) %>%
# create factors
mutate(Gender = factor(Gender,
levels = c(1, 2),
labels = c("Male", "Female")),
Education = factor(Education,
levels = c(1:9),
labels = c("Less than high school",
"High School/GED",
"Some college (not currently enrolled)",
"Some college (currently enrolled)",
"Associates degree",
"BA/BS degree",
"Master's Degree",
"Doctoral Degree",
"Professional Degree")),
FamSES2 = factor(FamSES2,
levels = c(1:8),
labels = c("<10K",
"10-20K",
"20-30K",
"30-40K",
"40-50K",
"50-75K",
"75-100K",
">100K")),
reputation = factor(reputation,
levels = c("low", "mod", "high")),
expression = factor(expression,
levels = c("neut", "calm", "exci")),
race = factor(race,
levels = c("asian", "white")),
sex = factor(sex,
levels = c("female", "male")))
Data <- Data %>%
group_by(pID) %>%
# calculate SD in amount given
mutate(responseSD = sd(response, na.rm = T)) %>%
# if SD = 0, make responseNoVar equal to 1 else 0
mutate(responseNoVar = ifelse(responseSD == 0, 1, 0)) #if SD for all trials was 0, make the responseNoVar equal to 1. If not 0, then make it equal to 0 Compute AVI
# i1_1 enthusiastic
# i1_7 excited
# i1_9 elated
# i2_9 euphoric
# i1_6 relaxed
# i3_3 calm
# i3_9 peaceful
# i3_10 serene
# Compute actual and ideal affect scores
Data <- Data %>%
mutate_at(vars(a1_1:i4_9), list(~ as.numeric(as.character(.)))) %>% #Turn affect variables from factor to numeric
group_by(pID) %>%
rowwise() %>%
mutate(aHAP = mean(c(a1_1, a1_7, a1_9, a2_9), na.rm = T),
aLAP = mean(c(a1_6, a3_3, a3_9, a3_10), na.rm = T),
iHAP = mean(c(i1_1, i1_7, i1_9, i2_9), na.rm = T),
iLAP = mean(c(i1_6, i3_3, i3_9, i3_10), na.rm = T))
#Ipsatize actual and ideal (separately)
Ipsatized_Actual <- Data %>%
dplyr::select(pID, a1_1:a4_9) %>% #Only select actual affect variables
ungroup() %>%
mutate(aSD = apply(.[,2:ncol(.)], na.rm = T, 1, sd)) %>% #Compute rowwise SD
mutate(aMean = rowMeans(dplyr::select(., contains("_")), na.rm = T)) %>% #Compute rowwise means
mutate(a_enthus_i = (a1_1 - aMean)/aSD, #Ipsatize enthusiastic
a_excited_i = (a1_7 - aMean)/aSD, #Ipsatize excited
a_elated_i = (a1_9 - aMean)/aSD, #Ipsatize elated
a_euphoric_i = (a2_9 - aMean)/aSD, #Ipsatize euphoric
a_relaxed_i = (a1_6 - aMean)/aSD, #Ipsatize relaxed
a_calm_i = (a3_3 - aMean)/aSD, #Ipsatize calm
a_peaceful_i = (a3_9 - aMean)/aSD, #Ipsatize peaceful
a_serene_i = (a3_10 - aMean)/aSD) %>% #Ipsatize serene
rowwise %>%
mutate(aHAP_i = mean(c(a_enthus_i, a_excited_i, a_elated_i, a_euphoric_i), na.rm = T), #Compute ipsatized HAP
aLAP_i = mean(c(a_relaxed_i, a_calm_i, a_peaceful_i, a_serene_i), na.rm = T)) %>% #Compute ipsatized LAP
dplyr::select(pID, aHAP_i, aLAP_i) %>% #Keep only the ipsatized HAP and LAP scores
distinct()
Ipsatized_Ideal <- Data %>%
dplyr::select(pID, i1_1:i4_9) %>% #Only select ideal affect variables
ungroup() %>%
mutate(iSD = apply(.[,2:ncol(.)], na.rm = T, 1, sd)) %>% #Compute rowwise SD
mutate(iMean = rowMeans(dplyr::select(., contains("_")), na.rm = T)) %>% #Compute rowwise means
mutate(i_enthus_i = (i1_1 - iMean)/iSD, #Ipsatize enthusiastic
i_excited_i = (i1_7 - iMean)/iSD, #Ipsatize excited
i_elated_i = (i1_9 - iMean)/iSD, #Ipsatize elated
i_euphoric_i = (i2_9 - iMean)/iSD, #Ipsatize euphoric
i_relaxed_i = (i1_6 - iMean)/iSD, #Ipsatize relaxed
i_calm_i = (i3_3 - iMean)/iSD, #Ipsatize calm
i_peaceful_i = (i3_9 - iMean)/iSD, #Ipsatize peaceful
i_serene_i = (i3_10 - iMean)/iSD) %>% #Ipsatize serene
rowwise %>%
mutate(iHAP_i = mean(c(i_enthus_i, i_excited_i, i_elated_i, i_euphoric_i), na.rm = T), #Compute ipsatized HAP
iLAP_i = mean(c(i_relaxed_i, i_calm_i, i_peaceful_i, i_serene_i), na.rm = T)) %>% #Compute ipsatized LAP
dplyr::select(pID, iHAP_i, iLAP_i) %>% #Keep only the ipsatized HAP and LAP scores
distinct()
Data <- left_join(Data, Ipsatized_Actual, by = "pID") #Add ipsatized actual affect scores to original dataframe
Data <- left_join(Data, Ipsatized_Ideal, by = "pID") #Add ipsatized ideal affect scores to original dataframeCompute other scales
#General Trust Scale
Data <- Data %>%
ungroup() %>%
mutate(GeneralTrust = rowMeans(dplyr::select(., starts_with("Trust")), na.rm = T)) #Compute mean
#Importance of Social Image - Self
Data <- Data %>%
ungroup() %>%
mutate(SelfImage = rowMeans(dplyr::select(., starts_with("SelfImage")), na.rm = T)) #Compute mean
#Importance of Social Image - Other
Data <- Data %>%
ungroup() %>%
mutate(OtherImage = rowMeans(dplyr::select(., starts_with("OtherImage")), na.rm = T)) #Compute mean
#Reputation Stability Mindset
Data <- Data %>%
ungroup() %>%
mutate(RepStability = rowMeans(dplyr::select(., starts_with("RepStability")), na.rm = T)) #Compute meanDescriptives
Sample
No variation
| responseNoVar | n |
|---|---|
| 0 | 106 |
| 1 | 6 |
| pID | response |
|---|---|
| 0m6kq8tbb8mp | 10 |
| 1sv9s4ko8o9i | 5 |
| 2mivlx9sx17 | 10 |
| 9tpuiq3jo39 | 10 |
| m0ce8ox9id | 3 |
| ql9isp4x0hl | 10 |
Total sample size (before excluding no variation) = 112.
Total sample size (after excluding no variation) = 106.
Age
| Age_Mean | Age_SD |
|---|---|
| 25.28 | 3.595 |
Gender
| Gender | n |
|---|---|
| Male | 43 |
| Female | 64 |
| NA | 5 |
Education
| Education | n |
|---|---|
| High School/GED | 2 |
| Some college (not currently enrolled) | 9 |
| Some college (currently enrolled) | 21 |
| Associates degree | 3 |
| BA/BS degree | 56 |
| Master’s Degree | 21 |
SES
| FamSES2 | n |
|---|---|
| <10K | 1 |
| 20-30K | 18 |
| 30-40K | 6 |
| 40-50K | 10 |
| 50-75K | 19 |
| 75-100K | 21 |
| >100K | 36 |
| NA | 1 |
AVI: Ideal
| iHAP_Mean | iHAP_SD | iLAP_Mean | iLAP_SD | iHAPi_Mean | iHAPi_SD | iLAPi_Mean |
|---|---|---|---|---|---|---|
| 3.42 | 0.7647 | 3.92 | 0.743 | 0.6492 | 0.3682 | 1.018 |
| iLAPi_SD |
|---|
| 0.4154 |
AVI: Actual
| aHAP_Mean | aHAP_SD | aLAP_Mean | aLAP_SD | aHAPi_Mean | aHAPi_SD | aLAPi_Mean |
|---|---|---|---|---|---|---|
| 2.71 | 0.7722 | 2.975 | 0.7934 | 0.1138 | 0.5882 | 0.3607 |
| aLAPi_SD |
|---|
| 0.6639 |
General Trust Scale
| trust_mean | trust_sd |
|---|---|
| 3.614 | 0.6806 |
Importance of Social Image - Self
| selfImage_mean | selfImage_sd |
|---|---|
| 5.012 | 1.249 |
Importance of Social Image - Other
| otherImage_mean | otherImage_sd |
|---|---|
| 4.225 | 1.384 |
Reputation Stability Mindset (higher values indicate more stability)
| repStability_mean | repStability_sd |
|---|---|
| 3.394 | 1.012 |
Cronbach’s alpha for AVI
Ideal HAP: 0.76
Ideal LAP: 0.8
Actual HAP: 0.85
Actual LAP: 0.84
Visualiations
Offers
Mean offers only
| give_Mean | give_SD |
|---|---|
| 4.279 | 2.122 |
Offers by Expression and Reputation
| expression | give_Mean | give_SD |
|---|---|---|
| neut | 4.042 | 2.129 |
| calm | 4.113 | 2.06 |
| exci | 4.049 | 2.04 |
| reputation | give_Mean | give_SD |
|---|---|---|
| low | 2.021 | 0.02726 |
| mod | 4.012 | 0.1274 |
| high | 6.171 | 0.06332 |
| expression | reputation | mean | stdv |
|---|---|---|---|
| neut | low | 1.989 | 0.6827 |
| neut | mod | 3.896 | 0.8138 |
| neut | high | 6.241 | 0.6913 |
| calm | low | 2.035 | 0.7117 |
| calm | mod | 4.149 | 0.6803 |
| calm | high | 6.156 | 0.6094 |
| exci | low | 2.038 | 0.667 |
| exci | mod | 3.992 | 0.7104 |
| exci | high | 6.117 | 0.7236 |
Offers by Expression, Reptutation and Race
Across all reference groups:
High repuation > Moderate reputation > Low reputation (all p < .001)
Higher SES predicted more giving (all p < .001)
Low vs. Mod Reputation:
The difference between low and moderate reputation is higher for calm than for excited targets. (p = .02)
The difference between low and moderate reputation is higher for asian than for white targets. (p = .01)
Three-factor interaction: The effect modReputation:expressionExcited is different for white compared to asian recipients. (p = .03)
Mod vs. High Reputation:
The difference between moderate and high reputation is higher for excited than for calm targets. (p = .008)
Three-factor interaction: The effect highReputation:expressionExcited is different for white compared to asian recipients. (p = .004)
Within high reputation:
The difference between excited and calm recipients is higher for asian than for white targets. (p = .007)
Offers by Expression, Reputation, Race and Sex
Confirmatory analysis
Preparation
# subset data to just expression levels "calm" vs. "excited" (without "neutral")
Data_noNeut <- Data_noVar %>%
filter(expression != "neut") %>%
# refactor to exclude neutral
mutate(expression = factor(expression, levels = c("calm", "exci")))
# check levels
contrasts(Data_noNeut$expression)## exci
## calm 0
## exci 1# convert SES from factor into numeric
Data_noNeut$FamSES2num <- as.numeric(Data_noNeut$FamSES2)
Data$FamSES2num <- as.numeric(Data$FamSES2)Model comparison for global effects:
Adding reputation first, then expression
#Just the intercept
model1 <- lmer(data = Data_noNeut, response ~ 1 + (1|pID), REML=FALSE)
summary(model1)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ 1 + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 23193.4 23213.0 -11593.7 23187.4 5085
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2452 -0.5599 -0.0419 0.6286 2.9512
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.383 1.839
## Residual 5.191 2.278
## Number of obs: 5088, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.0810 0.1815 106.0000 22.49 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Global effect of reputation
model2 <- lmer(data = Data_noNeut, response ~ 1 + reputation + (1|pID), REML=FALSE)
summary(model2)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ 1 + reputation + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 19207.4 19240.1 -9598.7 19197.4 5083
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6803 -0.6089 -0.0365 0.5811 4.5554
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.443 1.856
## Residual 2.331 1.527
## Number of obs: 5088, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.037e+00 1.840e-01 1.120e+02 11.07 <2e-16 ***
## reputationmod 2.034e+00 5.242e-02 4.982e+03 38.79 <2e-16 ***
## reputationhigh 4.100e+00 5.242e-02 4.982e+03 78.20 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm
## reputatinmd -0.142
## reputatnhgh -0.142 0.500#Model comparison for gloabl effect of reputation
anova(model1, model2)## Data: Data_noNeut
## Models:
## model1: response ~ 1 + (1 | pID)
## model2: response ~ 1 + reputation + (1 | pID)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## model1 3 23193 23213 -11593.7 23187
## model2 5 19207 19240 -9598.7 19197 3990 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Adding effect of expression
model3 <- lmer(data = Data_noNeut, response ~ 1 + reputation + expression + (1|pID), REML=FALSE)
summary(model3)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ 1 + reputation + expression + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 19207.2 19246.4 -9597.6 19195.2 5082
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7025 -0.6147 -0.0421 0.5813 4.5353
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.443 1.856
## Residual 2.330 1.526
## Number of obs: 5088, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.06879 0.18524 115.02053 11.168 <2e-16 ***
## reputationmod 2.03361 0.05241 4982.00000 38.800 <2e-16 ***
## reputationhigh 4.09965 0.05241 4982.00000 78.218 <2e-16 ***
## expressionexci -0.06447 0.04279 4982.00000 -1.506 0.132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm rpttnh
## reputatinmd -0.141
## reputatnhgh -0.141 0.500
## expressinxc -0.116 0.000 0.000#Model comparison for global effect of expression
anova(model2, model3)## Data: Data_noNeut
## Models:
## model2: response ~ 1 + reputation + (1 | pID)
## model3: response ~ 1 + reputation + expression + (1 | pID)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## model2 5 19207 19240 -9598.7 19197
## model3 6 19207 19246 -9597.6 19195 2.2687 1 0.132#Adding interaction between reputation & expression
model4 <- lmer(data = Data_noNeut, response ~ 1 + reputation*expression + (1|pID), REML=FALSE)
summary(model4)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ 1 + reputation * expression + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 19208.7 19261.0 -9596.3 19192.7 5080
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6953 -0.6084 -0.0365 0.5902 4.5584
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.443 1.856
## Residual 2.328 1.526
## Number of obs: 5088, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.035e+00 1.877e-01 1.212e+02 10.844
## reputationmod 2.113e+00 7.411e-02 4.982e+03 28.517
## reputationhigh 4.120e+00 7.411e-02 4.982e+03 55.601
## expressionexci 2.358e-03 7.411e-02 4.982e+03 0.032
## reputationmod:expressionexci -1.592e-01 1.048e-01 4.982e+03 -1.519
## reputationhigh:expressionexci -4.127e-02 1.048e-01 4.982e+03 -0.394
## Pr(>|t|)
## (Intercept) <2e-16 ***
## reputationmod <2e-16 ***
## reputationhigh <2e-16 ***
## expressionexci 0.975
## reputationmod:expressionexci 0.129
## reputationhigh:expressionexci 0.694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm rpttnh exprss rpttnm:
## reputatinmd -0.197
## reputatnhgh -0.197 0.500
## expressinxc -0.197 0.500 0.500
## rpttnmd:xpr 0.140 -0.707 -0.354 -0.707
## rpttnhgh:xp 0.140 -0.354 -0.707 -0.707 0.500#Model comparison for interaction between reputation & expression
anova(model3, model4)## Data: Data_noNeut
## Models:
## model3: response ~ 1 + reputation + expression + (1 | pID)
## model4: response ~ 1 + reputation * expression + (1 | pID)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## model3 6 19207 19246 -9597.6 19195
## model4 8 19209 19261 -9596.3 19193 2.4853 2 0.2886Adding reputation first, then expression
#Just the intercept
model1 <- lmer(data = Data_noNeut, response ~ 1 + (1|pID), REML=FALSE)
summary(model1)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ 1 + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 23193.4 23213.0 -11593.7 23187.4 5085
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2452 -0.5599 -0.0419 0.6286 2.9512
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.383 1.839
## Residual 5.191 2.278
## Number of obs: 5088, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.0810 0.1815 106.0000 22.49 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Main effect of expression
model2 <- lmer(data = Data_noNeut, response ~ 1 + expression + (1|pID), REML=FALSE)
summary(model2)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ 1 + expression + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 23194.4 23220.5 -11593.2 23186.4 5084
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2597 -0.5599 -0.0460 0.6322 2.9657
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.383 1.839
## Residual 5.190 2.278
## Number of obs: 5088, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.11321 0.18428 112.66507 22.320 <2e-16 ***
## expressionexci -0.06447 0.06388 4982.00000 -1.009 0.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## expressinxc -0.173#Model comparison for main effect of expression
anova(model1, model2)## Data: Data_noNeut
## Models:
## model1: response ~ 1 + (1 | pID)
## model2: response ~ 1 + expression + (1 | pID)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## model1 3 23193 23213 -11594 23187
## model2 4 23194 23220 -11593 23186 1.0183 1 0.3129#Adding effect of reputation
model3 <- lmer(data = Data_noNeut, response ~ 1 + reputation + expression + (1|pID), REML=FALSE)
summary(model3)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ 1 + reputation + expression + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 19207.2 19246.4 -9597.6 19195.2 5082
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7025 -0.6147 -0.0421 0.5813 4.5353
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.443 1.856
## Residual 2.330 1.526
## Number of obs: 5088, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.06879 0.18524 115.02053 11.168 <2e-16 ***
## reputationmod 2.03361 0.05241 4982.00000 38.800 <2e-16 ***
## reputationhigh 4.09965 0.05241 4982.00000 78.218 <2e-16 ***
## expressionexci -0.06447 0.04279 4982.00000 -1.506 0.132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm rpttnh
## reputatinmd -0.141
## reputatnhgh -0.141 0.500
## expressinxc -0.116 0.000 0.000#Model comparison for main effect of reputation
anova(model2, model3)## Data: Data_noNeut
## Models:
## model2: response ~ 1 + expression + (1 | pID)
## model3: response ~ 1 + reputation + expression + (1 | pID)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## model2 4 23194 23220 -11593.2 23186
## model3 6 19207 19246 -9597.6 19195 3991.2 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear Mixed Effects
Model 1: Offer ~ Reputation
# reputation only
# participant gender and ses are included as covariates
model1 <- lmer(data = Data_noNeut, response ~ reputation + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model1)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + Gender + FamSES2num + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18477.7 18523.2 -9231.9 18463.7 4889
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6429 -0.6330 -0.0508 0.5732 4.5275
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.342 1.530
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.05482 0.55439 102.63765 -1.903 0.0599 .
## reputationmod 2.00858 0.05357 4794.00010 37.493 < 2e-16 ***
## reputationhigh 4.03370 0.05357 4794.00010 75.295 < 2e-16 ***
## GenderFemale 0.55005 0.38192 101.99973 1.440 0.1529
## FamSES2num 0.45376 0.10109 101.99972 4.489 1.89e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm rpttnh GndrFm
## reputatinmd -0.048
## reputatnhgh -0.048 0.500
## GenderFemal 0.173 0.000 0.000
## FamSES2num -0.889 0.000 0.000 -0.533# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "mod")
model1 <- lmer(data = Data_noNeut, response ~ reputation + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model1)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + Gender + FamSES2num + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18477.7 18523.2 -9231.9 18463.7 4889
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6429 -0.6330 -0.0508 0.5732 4.5275
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.342 1.530
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.95376 0.55439 102.63765 1.720 0.0884 .
## reputationlow -2.00858 0.05357 4794.00010 -37.493 < 2e-16 ***
## reputationhigh 2.02512 0.05357 4794.00010 37.802 < 2e-16 ***
## GenderFemale 0.55005 0.38192 101.99972 1.440 0.1529
## FamSES2num 0.45376 0.10109 101.99972 4.489 1.89e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnl rpttnh GndrFm
## reputatinlw -0.048
## reputatnhgh -0.048 0.500
## GenderFemal 0.173 0.000 0.000
## FamSES2num -0.889 0.000 0.000 -0.533# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "high")
model1 <- lmer(data = Data_noNeut, response ~ reputation + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model1)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + Gender + FamSES2num + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18477.7 18523.2 -9231.9 18463.7 4889
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6429 -0.6330 -0.0508 0.5732 4.5275
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.342 1.530
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.97888 0.55439 102.63765 5.373 4.86e-07 ***
## reputationmod -2.02512 0.05357 4794.00009 -37.802 < 2e-16 ***
## reputationlow -4.03370 0.05357 4794.00009 -75.295 < 2e-16 ***
## GenderFemale 0.55005 0.38192 101.99973 1.440 0.153
## FamSES2num 0.45376 0.10109 101.99972 4.489 1.89e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm rpttnl GndrFm
## reputatinmd -0.048
## reputatinlw -0.048 0.500
## GenderFemal 0.173 0.000 0.000
## FamSES2num -0.889 0.000 0.000 -0.533Model 2: Offer ~ Reputation + Expression
# reputation and expression (additive)
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "low")
model2 <- lmer(data = Data_noNeut, response ~ reputation + expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model2)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + expression + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18477.4 18529.4 -9230.7 18461.4 4888
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6655 -0.6216 -0.0457 0.5803 4.5071
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.341 1.530
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.02193 0.55482 102.95689 -1.842 0.0684 .
## reputationhigh 4.03370 0.05356 4794.00010 75.313 < 2e-16 ***
## reputationmod 2.00858 0.05356 4794.00010 37.502 < 2e-16 ***
## expressionexci -0.06577 0.04373 4794.00009 -1.504 0.1327
## GenderFemale 0.55005 0.38192 101.99972 1.440 0.1529
## FamSES2num 0.45376 0.10109 101.99971 4.489 1.89e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnh rpttnm exprss GndrFm
## reputatnhgh -0.048
## reputatinmd -0.048 0.500
## expressinxc -0.039 0.000 0.000
## GenderFemal 0.173 0.000 0.000 0.000
## FamSES2num -0.888 0.000 0.000 0.000 -0.533# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "mod")
model2 <- lmer(data = Data_noNeut, response ~ reputation + expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model2)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + expression + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18477.4 18529.4 -9230.7 18461.4 4888
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6655 -0.6216 -0.0457 0.5803 4.5071
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.341 1.530
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.98665 0.55482 102.95691 1.778 0.0783 .
## reputationlow -2.00858 0.05356 4794.00009 -37.502 < 2e-16 ***
## reputationhigh 2.02512 0.05356 4794.00009 37.811 < 2e-16 ***
## expressionexci -0.06577 0.04373 4794.00009 -1.504 0.1327
## GenderFemale 0.55005 0.38192 101.99972 1.440 0.1529
## FamSES2num 0.45376 0.10109 101.99973 4.489 1.89e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnl rpttnh exprss GndrFm
## reputatinlw -0.048
## reputatnhgh -0.048 0.500
## expressinxc -0.039 0.000 0.000
## GenderFemal 0.173 0.000 0.000 0.000
## FamSES2num -0.888 0.000 0.000 0.000 -0.533# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "high")
model2 <- lmer(data = Data_noNeut, response ~ reputation + expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model2)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + expression + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18477.4 18529.4 -9230.7 18461.4 4888
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6655 -0.6216 -0.0457 0.5803 4.5071
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.341 1.530
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.01177 0.55482 102.95691 5.428 3.80e-07 ***
## reputationmod -2.02512 0.05356 4794.00010 -37.811 < 2e-16 ***
## reputationlow -4.03370 0.05356 4794.00010 -75.313 < 2e-16 ***
## expressionexci -0.06577 0.04373 4794.00009 -1.504 0.133
## GenderFemale 0.55005 0.38192 101.99972 1.440 0.153
## FamSES2num 0.45376 0.10109 101.99973 4.489 1.89e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm rpttnl exprss GndrFm
## reputatinmd -0.048
## reputatinlw -0.048 0.500
## expressinxc -0.039 0.000 0.000
## GenderFemal 0.173 0.000 0.000 0.000
## FamSES2num -0.888 0.000 0.000 0.000 -0.533# model comparison
anova(model1, model2)## Data: Data_noNeut
## Models:
## model1: response ~ reputation + Gender + FamSES2num + (1 | pID)
## model2: response ~ reputation + expression + Gender + FamSES2num + (1 |
## model2: pID)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## model1 7 18478 18523 -9231.9 18464
## model2 8 18477 18529 -9230.7 18461 2.2612 1 0.1326Model 3: Offer ~ Reputation * Expression
# reputation and expression (interactive)
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "low")
model3 <- lmer(data = Data_noNeut, response ~ reputation * expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model3)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation * expression + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18478.4 18543.3 -9229.2 18458.4 4886
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6595 -0.6017 -0.0431 0.5755 4.5348
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.339 1.529
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.06217 0.55568 103.59642 -1.911
## reputationhigh 4.06250 0.07572 4794.00009 53.652
## reputationmod 2.10049 0.07572 4794.00009 27.740
## expressionexci 0.01471 0.07572 4794.00010 0.194
## GenderFemale 0.55005 0.38192 101.99972 1.440
## FamSES2num 0.45376 0.10109 101.99973 4.489
## reputationhigh:expressionexci -0.05760 0.10708 4794.00010 -0.538
## reputationmod:expressionexci -0.18382 0.10708 4794.00010 -1.717
## Pr(>|t|)
## (Intercept) 0.0587 .
## reputationhigh < 2e-16 ***
## reputationmod < 2e-16 ***
## expressionexci 0.8460
## GenderFemale 0.1529
## FamSES2num 1.89e-05 ***
## reputationhigh:expressionexci 0.5907
## reputationmod:expressionexci 0.0861 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnh rpttnm exprss GndrFm FmSES2 rpttnh:
## reputatnhgh -0.068
## reputatinmd -0.068 0.500
## expressinxc -0.068 0.500 0.500
## GenderFemal 0.173 0.000 0.000 0.000
## FamSES2num -0.887 0.000 0.000 0.000 -0.533
## rpttnhgh:xp 0.048 -0.707 -0.354 -0.707 0.000 0.000
## rpttnmd:xpr 0.048 -0.354 -0.707 -0.707 0.000 0.000 0.500# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "mod")
model3 <- lmer(data = Data_noNeut, response ~ reputation * expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model3)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation * expression + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18478.4 18543.3 -9229.2 18458.4 4886
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6595 -0.6017 -0.0431 0.5755 4.5348
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.339 1.529
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.03832 0.55568 103.59641 1.869
## reputationlow -2.10049 0.07572 4794.00010 -27.740
## reputationhigh 1.96201 0.07572 4794.00010 25.911
## expressionexci -0.16912 0.07572 4794.00010 -2.233
## GenderFemale 0.55005 0.38192 101.99972 1.440
## FamSES2num 0.45376 0.10109 101.99972 4.489
## reputationlow:expressionexci 0.18382 0.10708 4794.00010 1.717
## reputationhigh:expressionexci 0.12623 0.10708 4794.00010 1.179
## Pr(>|t|)
## (Intercept) 0.0645 .
## reputationlow < 2e-16 ***
## reputationhigh < 2e-16 ***
## expressionexci 0.0256 *
## GenderFemale 0.1529
## FamSES2num 1.89e-05 ***
## reputationlow:expressionexci 0.0861 .
## reputationhigh:expressionexci 0.2386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnl rpttnh exprss GndrFm FmSES2 rpttnl:
## reputatinlw -0.068
## reputatnhgh -0.068 0.500
## expressinxc -0.068 0.500 0.500
## GenderFemal 0.173 0.000 0.000 0.000
## FamSES2num -0.887 0.000 0.000 0.000 -0.533
## rpttnlw:xpr 0.048 -0.707 -0.354 -0.707 0.000 0.000
## rpttnhgh:xp 0.048 -0.354 -0.707 -0.707 0.000 0.000 0.500# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "high")
model3 <- lmer(data = Data_noNeut, response ~ reputation * expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model3)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation * expression + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18478.4 18543.3 -9229.2 18458.4 4886
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6595 -0.6017 -0.0431 0.5755 4.5348
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.339 1.529
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.00033 0.55568 103.59641 5.399
## reputationmod -1.96201 0.07572 4794.00009 -25.911
## reputationlow -4.06250 0.07572 4794.00009 -53.652
## expressionexci -0.04289 0.07572 4794.00009 -0.566
## GenderFemale 0.55005 0.38192 101.99972 1.440
## FamSES2num 0.45376 0.10109 101.99972 4.489
## reputationmod:expressionexci -0.12623 0.10708 4794.00009 -1.179
## reputationlow:expressionexci 0.05760 0.10708 4794.00009 0.538
## Pr(>|t|)
## (Intercept) 4.27e-07 ***
## reputationmod < 2e-16 ***
## reputationlow < 2e-16 ***
## expressionexci 0.571
## GenderFemale 0.153
## FamSES2num 1.89e-05 ***
## reputationmod:expressionexci 0.239
## reputationlow:expressionexci 0.591
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm rpttnl exprss GndrFm FmSES2 rpttnm:
## reputatinmd -0.068
## reputatinlw -0.068 0.500
## expressinxc -0.068 0.500 0.500
## GenderFemal 0.173 0.000 0.000 0.000
## FamSES2num -0.887 0.000 0.000 0.000 -0.533
## rpttnmd:xpr 0.048 -0.707 -0.354 -0.707 0.000 0.000
## rpttnlw:xpr 0.048 -0.354 -0.707 -0.707 0.000 0.000 0.500# model comparison
anova(model2, model3)## Data: Data_noNeut
## Models:
## model2: response ~ reputation + expression + Gender + FamSES2num + (1 |
## model2: pID)
## model3: response ~ reputation * expression + Gender + FamSES2num + (1 |
## model3: pID)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## model2 8 18477 18529 -9230.7 18461
## model3 10 18478 18543 -9229.2 18458 3.0827 2 0.2141Model 4: Offer ~ Reputation * Expression + Race
# reputation, expression, and race (additive)
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "low")
model4 <- lmer(data = Data_noNeut, response ~ reputation * expression + race + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model4)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression + race + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18480.3 18551.8 -9229.2 18458.3 4885
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6616 -0.6035 -0.0438 0.5763 4.5368
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.339 1.529
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.059e+00 5.561e-01 1.039e+02 -1.904
## reputationhigh 4.062e+00 7.572e-02 4.794e+03 53.652
## reputationmod 2.100e+00 7.572e-02 4.794e+03 27.740
## expressionexci 1.471e-02 7.572e-02 4.794e+03 0.194
## racewhite -6.127e-03 4.372e-02 4.794e+03 -0.140
## GenderFemale 5.500e-01 3.819e-01 1.020e+02 1.440
## FamSES2num 4.538e-01 1.011e-01 1.020e+02 4.489
## reputationhigh:expressionexci -5.760e-02 1.071e-01 4.794e+03 -0.538
## reputationmod:expressionexci -1.838e-01 1.071e-01 4.794e+03 -1.717
## Pr(>|t|)
## (Intercept) 0.0596 .
## reputationhigh < 2e-16 ***
## reputationmod < 2e-16 ***
## expressionexci 0.8460
## racewhite 0.8885
## GenderFemale 0.1529
## FamSES2num 1.89e-05 ***
## reputationhigh:expressionexci 0.5907
## reputationmod:expressionexci 0.0861 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnh rpttnm exprss racwht GndrFm FmSES2 rpttnh:
## reputatnhgh -0.068
## reputatinmd -0.068 0.500
## expressinxc -0.068 0.500 0.500
## racewhite -0.039 0.000 0.000 0.000
## GenderFemal 0.173 0.000 0.000 0.000 0.000
## FamSES2num -0.886 0.000 0.000 0.000 0.000 -0.533
## rpttnhgh:xp 0.048 -0.707 -0.354 -0.707 0.000 0.000 0.000
## rpttnmd:xpr 0.048 -0.354 -0.707 -0.707 0.000 0.000 0.000 0.500# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "mod")
model4 <- lmer(data = Data_noNeut, response ~ reputation * expression + race + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model4)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression + race + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18480.3 18551.8 -9229.2 18458.3 4885
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6616 -0.6035 -0.0438 0.5763 4.5368
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.339 1.529
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.041e+00 5.561e-01 1.039e+02 1.873
## reputationlow -2.100e+00 7.572e-02 4.794e+03 -27.740
## reputationhigh 1.962e+00 7.572e-02 4.794e+03 25.911
## expressionexci -1.691e-01 7.572e-02 4.794e+03 -2.233
## racewhite -6.127e-03 4.372e-02 4.794e+03 -0.140
## GenderFemale 5.500e-01 3.819e-01 1.020e+02 1.440
## FamSES2num 4.538e-01 1.011e-01 1.020e+02 4.489
## reputationlow:expressionexci 1.838e-01 1.071e-01 4.794e+03 1.717
## reputationhigh:expressionexci 1.262e-01 1.071e-01 4.794e+03 1.179
## Pr(>|t|)
## (Intercept) 0.0639 .
## reputationlow < 2e-16 ***
## reputationhigh < 2e-16 ***
## expressionexci 0.0256 *
## racewhite 0.8885
## GenderFemale 0.1529
## FamSES2num 1.89e-05 ***
## reputationlow:expressionexci 0.0861 .
## reputationhigh:expressionexci 0.2386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnl rpttnh exprss racwht GndrFm FmSES2 rpttnl:
## reputatinlw -0.068
## reputatnhgh -0.068 0.500
## expressinxc -0.068 0.500 0.500
## racewhite -0.039 0.000 0.000 0.000
## GenderFemal 0.173 0.000 0.000 0.000 0.000
## FamSES2num -0.886 0.000 0.000 0.000 0.000 -0.533
## rpttnlw:xpr 0.048 -0.707 -0.354 -0.707 0.000 0.000 0.000
## rpttnhgh:xp 0.048 -0.354 -0.707 -0.707 0.000 0.000 0.000 0.500# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "high")
model4 <- lmer(data = Data_noNeut, response ~ reputation * expression + race + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model4)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression + race + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18480.3 18551.8 -9229.2 18458.3 4885
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6616 -0.6035 -0.0438 0.5763 4.5368
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.339 1.529
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.003e+00 5.561e-01 1.039e+02 5.401
## reputationmod -1.962e+00 7.572e-02 4.794e+03 -25.911
## reputationlow -4.062e+00 7.572e-02 4.794e+03 -53.652
## expressionexci -4.289e-02 7.572e-02 4.794e+03 -0.566
## racewhite -6.127e-03 4.372e-02 4.794e+03 -0.140
## GenderFemale 5.500e-01 3.819e-01 1.020e+02 1.440
## FamSES2num 4.538e-01 1.011e-01 1.020e+02 4.489
## reputationmod:expressionexci -1.262e-01 1.071e-01 4.794e+03 -1.179
## reputationlow:expressionexci 5.760e-02 1.071e-01 4.794e+03 0.538
## Pr(>|t|)
## (Intercept) 4.23e-07 ***
## reputationmod < 2e-16 ***
## reputationlow < 2e-16 ***
## expressionexci 0.571
## racewhite 0.889
## GenderFemale 0.153
## FamSES2num 1.89e-05 ***
## reputationmod:expressionexci 0.239
## reputationlow:expressionexci 0.591
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm rpttnl exprss racwht GndrFm FmSES2 rpttnm:
## reputatinmd -0.068
## reputatinlw -0.068 0.500
## expressinxc -0.068 0.500 0.500
## racewhite -0.039 0.000 0.000 0.000
## GenderFemal 0.173 0.000 0.000 0.000 0.000
## FamSES2num -0.886 0.000 0.000 0.000 0.000 -0.533
## rpttnmd:xpr 0.048 -0.707 -0.354 -0.707 0.000 0.000 0.000
## rpttnlw:xpr 0.048 -0.354 -0.707 -0.707 0.000 0.000 0.000 0.500Model 5: Offer ~ Reputation * Expression * Race
# reputation, expression, and race (interactive)
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "low")
model5 <- lmer(data = Data_noNeut, response ~ reputation * expression * race + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model5)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18464.2 18568.2 -9216.1 18432.2 4880
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6121 -0.6041 -0.0336 0.5876 4.4812
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.327 1.525
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -1.16266 0.55823 105.50940
## reputationhigh 4.07108 0.10679 4794.00000
## reputationmod 2.31618 0.10679 4794.00000
## expressionexci 0.13971 0.10679 4794.00000
## racewhite 0.20098 0.10679 4794.00000
## GenderFemale 0.55005 0.38192 101.99999
## FamSES2num 0.45376 0.10109 101.99999
## reputationhigh:expressionexci 0.08578 0.15103 4794.00000
## reputationmod:expressionexci -0.52941 0.15103 4794.00000
## reputationhigh:racewhite -0.01716 0.15103 4794.00000
## reputationmod:racewhite -0.43137 0.15103 4794.00000
## expressionexci:racewhite -0.25000 0.15103 4794.00000
## reputationhigh:expressionexci:racewhite -0.28676 0.21359 4794.00000
## reputationmod:expressionexci:racewhite 0.69118 0.21359 4794.00000
## t value Pr(>|t|)
## (Intercept) -2.083 0.03969 *
## reputationhigh 38.121 < 2e-16 ***
## reputationmod 21.688 < 2e-16 ***
## expressionexci 1.308 0.19087
## racewhite 1.882 0.05990 .
## GenderFemale 1.440 0.15287
## FamSES2num 4.489 1.89e-05 ***
## reputationhigh:expressionexci 0.568 0.57006
## reputationmod:expressionexci -3.505 0.00046 ***
## reputationhigh:racewhite -0.114 0.90956
## reputationmod:racewhite -2.856 0.00431 **
## expressionexci:racewhite -1.655 0.09793 .
## reputationhigh:expressionexci:racewhite -1.343 0.17946
## reputationmod:expressionexci:racewhite 3.236 0.00122 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "mod")
model5 <- lmer(data = Data_noNeut, response ~ reputation * expression * race + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model5)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18464.2 18568.2 -9216.1 18432.2 4880
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6121 -0.6041 -0.0336 0.5876 4.4812
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.327 1.525
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.1535 0.5582 105.5094
## reputationlow -2.3162 0.1068 4794.0000
## reputationhigh 1.7549 0.1068 4794.0000
## expressionexci -0.3897 0.1068 4794.0000
## racewhite -0.2304 0.1068 4794.0000
## GenderFemale 0.5500 0.3819 102.0000
## FamSES2num 0.4538 0.1011 102.0000
## reputationlow:expressionexci 0.5294 0.1510 4794.0000
## reputationhigh:expressionexci 0.6152 0.1510 4794.0000
## reputationlow:racewhite 0.4314 0.1510 4794.0000
## reputationhigh:racewhite 0.4142 0.1510 4794.0000
## expressionexci:racewhite 0.4412 0.1510 4794.0000
## reputationlow:expressionexci:racewhite -0.6912 0.2136 4794.0000
## reputationhigh:expressionexci:racewhite -0.9779 0.2136 4794.0000
## t value Pr(>|t|)
## (Intercept) 2.066 0.041239 *
## reputationlow -21.688 < 2e-16 ***
## reputationhigh 16.433 < 2e-16 ***
## expressionexci -3.649 0.000266 ***
## racewhite -2.157 0.031026 *
## GenderFemale 1.440 0.152867
## FamSES2num 4.489 1.89e-05 ***
## reputationlow:expressionexci 3.505 0.000460 ***
## reputationhigh:expressionexci 4.073 4.71e-05 ***
## reputationlow:racewhite 2.856 0.004305 **
## reputationhigh:racewhite 2.743 0.006117 **
## expressionexci:racewhite 2.921 0.003504 **
## reputationlow:expressionexci:racewhite -3.236 0.001220 **
## reputationhigh:expressionexci:racewhite -4.579 4.80e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "high")
model5 <- lmer(data = Data_noNeut, response ~ reputation * expression * race + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model5)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18464.2 18568.2 -9216.1 18432.2 4880
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6121 -0.6041 -0.0336 0.5876 4.4812
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.327 1.525
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.90842 0.55823 105.50941
## reputationmod -1.75490 0.10679 4794.00000
## reputationlow -4.07108 0.10679 4794.00000
## expressionexci 0.22549 0.10679 4794.00000
## racewhite 0.18382 0.10679 4794.00000
## GenderFemale 0.55005 0.38192 102.00000
## FamSES2num 0.45376 0.10109 102.00000
## reputationmod:expressionexci -0.61520 0.15103 4794.00000
## reputationlow:expressionexci -0.08578 0.15103 4794.00000
## reputationmod:racewhite -0.41422 0.15103 4794.00000
## reputationlow:racewhite 0.01716 0.15103 4794.00000
## expressionexci:racewhite -0.53676 0.15103 4794.00000
## reputationmod:expressionexci:racewhite 0.97794 0.21359 4794.00000
## reputationlow:expressionexci:racewhite 0.28676 0.21359 4794.00000
## t value Pr(>|t|)
## (Intercept) 5.210 9.41e-07 ***
## reputationmod -16.433 < 2e-16 ***
## reputationlow -38.121 < 2e-16 ***
## expressionexci 2.111 0.034784 *
## racewhite 1.721 0.085260 .
## GenderFemale 1.440 0.152867
## FamSES2num 4.489 1.89e-05 ***
## reputationmod:expressionexci -4.073 4.71e-05 ***
## reputationlow:expressionexci -0.568 0.570060
## reputationmod:racewhite -2.743 0.006117 **
## reputationlow:racewhite 0.114 0.909559
## expressionexci:racewhite -3.554 0.000383 ***
## reputationmod:expressionexci:racewhite 4.579 4.80e-06 ***
## reputationlow:expressionexci:racewhite 1.343 0.179459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itModel 6: Offer ~ Reputation * Expression * Race + Sex
# (adding sex)
# reputation, expression, race, and sex (additive)
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "low")
model6 <- lmer(data = Data_noNeut, response ~ reputation * expression * race + sex +
Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model6)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + sex + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18459.9 18570.3 -9212.9 18425.9 4879
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6512 -0.5932 -0.0318 0.5846 4.4481
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.323 1.524
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -1.21760 0.55864 105.82619
## reputationhigh 4.07108 0.10672 4793.99999
## reputationmod 2.31618 0.10672 4793.99999
## expressionexci 0.13971 0.10672 4793.99998
## racewhite 0.20098 0.10672 4793.99998
## sexmale 0.10989 0.04357 4794.00000
## GenderFemale 0.55005 0.38192 101.99999
## FamSES2num 0.45376 0.10109 101.99999
## reputationhigh:expressionexci 0.08578 0.15093 4793.99999
## reputationmod:expressionexci -0.52941 0.15093 4793.99999
## reputationhigh:racewhite -0.01716 0.15093 4793.99999
## reputationmod:racewhite -0.43137 0.15093 4793.99999
## expressionexci:racewhite -0.25000 0.15093 4793.99999
## reputationhigh:expressionexci:racewhite -0.28676 0.21344 4794.00000
## reputationmod:expressionexci:racewhite 0.69118 0.21344 4794.00000
## t value Pr(>|t|)
## (Intercept) -2.180 0.031506 *
## reputationhigh 38.146 < 2e-16 ***
## reputationmod 21.703 < 2e-16 ***
## expressionexci 1.309 0.190577
## racewhite 1.883 0.059733 .
## sexmale 2.522 0.011698 *
## GenderFemale 1.440 0.152867
## FamSES2num 4.489 1.89e-05 ***
## reputationhigh:expressionexci 0.568 0.569805
## reputationmod:expressionexci -3.508 0.000456 ***
## reputationhigh:racewhite -0.114 0.909500
## reputationmod:racewhite -2.858 0.004280 **
## expressionexci:racewhite -1.656 0.097703 .
## reputationhigh:expressionexci:racewhite -1.344 0.179171
## reputationmod:expressionexci:racewhite 3.238 0.001211 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "mod")
model6 <- lmer(data = Data_noNeut, response ~ reputation * expression * race + sex +
Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model6)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + sex + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18459.9 18570.3 -9212.9 18425.9 4879
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6512 -0.5932 -0.0318 0.5846 4.4481
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.323 1.524
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.09857 0.55864 105.82615
## reputationlow -2.31618 0.10672 4794.00002
## reputationhigh 1.75490 0.10672 4794.00002
## expressionexci -0.38971 0.10672 4794.00002
## racewhite -0.23039 0.10672 4794.00002
## sexmale 0.10989 0.04357 4794.00002
## GenderFemale 0.55005 0.38192 101.99995
## FamSES2num 0.45376 0.10109 101.99995
## reputationlow:expressionexci 0.52941 0.15093 4794.00002
## reputationhigh:expressionexci 0.61520 0.15093 4794.00002
## reputationlow:racewhite 0.43137 0.15093 4794.00002
## reputationhigh:racewhite 0.41422 0.15093 4794.00002
## expressionexci:racewhite 0.44118 0.15093 4794.00002
## reputationlow:expressionexci:racewhite -0.69118 0.21344 4794.00002
## reputationhigh:expressionexci:racewhite -0.97794 0.21344 4794.00002
## t value Pr(>|t|)
## (Intercept) 1.967 0.051859 .
## reputationlow -21.703 < 2e-16 ***
## reputationhigh 16.444 < 2e-16 ***
## expressionexci -3.652 0.000263 ***
## racewhite -2.159 0.030915 *
## sexmale 2.522 0.011698 *
## GenderFemale 1.440 0.152867
## FamSES2num 4.489 1.89e-05 ***
## reputationlow:expressionexci 3.508 0.000456 ***
## reputationhigh:expressionexci 4.076 4.65e-05 ***
## reputationlow:racewhite 2.858 0.004280 **
## reputationhigh:racewhite 2.744 0.006084 **
## expressionexci:racewhite 2.923 0.003482 **
## reputationlow:expressionexci:racewhite -3.238 0.001211 **
## reputationhigh:expressionexci:racewhite -4.582 4.73e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "high")
model6 <- lmer(data = Data_noNeut, response ~ reputation * expression * race + sex +
Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model6)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + sex + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18459.9 18570.3 -9212.9 18425.9 4879
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6512 -0.5932 -0.0318 0.5846 4.4481
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.323 1.524
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.85348 0.55864 105.82615
## reputationmod -1.75490 0.10672 4794.00002
## reputationlow -4.07108 0.10672 4794.00002
## expressionexci 0.22549 0.10672 4794.00002
## racewhite 0.18382 0.10672 4794.00002
## sexmale 0.10989 0.04357 4794.00002
## GenderFemale 0.55005 0.38192 101.99995
## FamSES2num 0.45376 0.10109 101.99995
## reputationmod:expressionexci -0.61520 0.15093 4794.00002
## reputationlow:expressionexci -0.08578 0.15093 4794.00002
## reputationmod:racewhite -0.41422 0.15093 4794.00002
## reputationlow:racewhite 0.01716 0.15093 4794.00002
## expressionexci:racewhite -0.53676 0.15093 4794.00002
## reputationmod:expressionexci:racewhite 0.97794 0.21344 4794.00002
## reputationlow:expressionexci:racewhite 0.28676 0.21344 4794.00002
## t value Pr(>|t|)
## (Intercept) 5.108 1.45e-06 ***
## reputationmod -16.444 < 2e-16 ***
## reputationlow -38.146 < 2e-16 ***
## expressionexci 2.113 0.03466 *
## racewhite 1.722 0.08505 .
## sexmale 2.522 0.01170 *
## GenderFemale 1.440 0.15287
## FamSES2num 4.489 1.89e-05 ***
## reputationmod:expressionexci -4.076 4.65e-05 ***
## reputationlow:expressionexci -0.568 0.56980
## reputationmod:racewhite -2.744 0.00608 **
## reputationlow:racewhite 0.114 0.90950
## expressionexci:racewhite -3.556 0.00038 ***
## reputationmod:expressionexci:racewhite 4.582 4.73e-06 ***
## reputationlow:expressionexci:racewhite 1.344 0.17917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itModel 7: Offer ~ Reputation * Expression * Race * Sex
# reputation, expression, race, and sex (interactive)
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "low")
model7 <- lmer(data = Data_noNeut, response ~ reputation * expression * race * sex +
Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model7)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race * sex + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18468.8 18650.7 -9206.4 18412.8 4868
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6601 -0.5946 -0.0273 0.5822 4.4661
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.317 1.522
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) -1.26315 0.56327
## reputationhigh 4.11275 0.15072
## reputationmod 2.47549 0.15072
## expressionexci 0.12255 0.15072
## racewhite 0.26471 0.15072
## sexmale 0.20098 0.15072
## GenderFemale 0.55005 0.38192
## FamSES2num 0.45376 0.10109
## reputationhigh:expressionexci 0.19118 0.21315
## reputationmod:expressionexci -0.50980 0.21315
## reputationhigh:racewhite -0.17157 0.21315
## reputationmod:racewhite -0.51961 0.21315
## expressionexci:racewhite -0.35784 0.21315
## reputationhigh:sexmale -0.08333 0.21315
## reputationmod:sexmale -0.31863 0.21315
## expressionexci:sexmale 0.03431 0.21315
## racewhite:sexmale -0.12745 0.21315
## reputationhigh:expressionexci:racewhite -0.21078 0.30144
## reputationmod:expressionexci:racewhite 0.63725 0.30144
## reputationhigh:expressionexci:sexmale -0.21078 0.30144
## reputationmod:expressionexci:sexmale -0.03922 0.30144
## reputationhigh:racewhite:sexmale 0.30882 0.30144
## reputationmod:racewhite:sexmale 0.17647 0.30144
## expressionexci:racewhite:sexmale 0.21569 0.30144
## reputationhigh:expressionexci:racewhite:sexmale -0.15196 0.42631
## reputationmod:expressionexci:racewhite:sexmale 0.10784 0.42631
## df t value
## (Intercept) 109.37344 -2.243
## reputationhigh 4794.00001 27.287
## reputationmod 4794.00001 16.424
## expressionexci 4794.00001 0.813
## racewhite 4794.00001 1.756
## sexmale 4794.00001 1.333
## GenderFemale 101.99999 1.440
## FamSES2num 101.99999 4.489
## reputationhigh:expressionexci 4794.00001 0.897
## reputationmod:expressionexci 4794.00001 -2.392
## reputationhigh:racewhite 4794.00001 -0.805
## reputationmod:racewhite 4794.00001 -2.438
## expressionexci:racewhite 4794.00001 -1.679
## reputationhigh:sexmale 4794.00001 -0.391
## reputationmod:sexmale 4794.00001 -1.495
## expressionexci:sexmale 4794.00001 0.161
## racewhite:sexmale 4794.00001 -0.598
## reputationhigh:expressionexci:racewhite 4794.00000 -0.699
## reputationmod:expressionexci:racewhite 4794.00000 2.114
## reputationhigh:expressionexci:sexmale 4794.00000 -0.699
## reputationmod:expressionexci:sexmale 4794.00001 -0.130
## reputationhigh:racewhite:sexmale 4794.00000 1.024
## reputationmod:racewhite:sexmale 4794.00001 0.585
## expressionexci:racewhite:sexmale 4794.00000 0.716
## reputationhigh:expressionexci:racewhite:sexmale 4794.00000 -0.356
## reputationmod:expressionexci:racewhite:sexmale 4794.00000 0.253
## Pr(>|t|)
## (Intercept) 0.0269 *
## reputationhigh < 2e-16 ***
## reputationmod < 2e-16 ***
## expressionexci 0.4162
## racewhite 0.0791 .
## sexmale 0.1824
## GenderFemale 0.1529
## FamSES2num 1.89e-05 ***
## reputationhigh:expressionexci 0.3698
## reputationmod:expressionexci 0.0168 *
## reputationhigh:racewhite 0.4209
## reputationmod:racewhite 0.0148 *
## expressionexci:racewhite 0.0933 .
## reputationhigh:sexmale 0.6958
## reputationmod:sexmale 0.1350
## expressionexci:sexmale 0.8721
## racewhite:sexmale 0.5499
## reputationhigh:expressionexci:racewhite 0.4844
## reputationmod:expressionexci:racewhite 0.0346 *
## reputationhigh:expressionexci:sexmale 0.4844
## reputationmod:expressionexci:sexmale 0.8965
## reputationhigh:racewhite:sexmale 0.3057
## reputationmod:racewhite:sexmale 0.5583
## expressionexci:racewhite:sexmale 0.4743
## reputationhigh:expressionexci:racewhite:sexmale 0.7215
## reputationmod:expressionexci:racewhite:sexmale 0.8003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 26 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "mod")
model7 <- lmer(data = Data_noNeut, response ~ reputation * expression * race * sex +
Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model7)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race * sex + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18468.8 18650.7 -9206.4 18412.8 4868
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6601 -0.5946 -0.0273 0.5822 4.4661
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.317 1.522
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1.212e+00 5.633e-01
## reputationlow -2.475e+00 1.507e-01
## reputationhigh 1.637e+00 1.507e-01
## expressionexci -3.873e-01 1.507e-01
## racewhite -2.549e-01 1.507e-01
## sexmale -1.176e-01 1.507e-01
## GenderFemale 5.500e-01 3.819e-01
## FamSES2num 4.538e-01 1.011e-01
## reputationlow:expressionexci 5.098e-01 2.132e-01
## reputationhigh:expressionexci 7.010e-01 2.132e-01
## reputationlow:racewhite 5.196e-01 2.132e-01
## reputationhigh:racewhite 3.480e-01 2.132e-01
## expressionexci:racewhite 2.794e-01 2.132e-01
## reputationlow:sexmale 3.186e-01 2.132e-01
## reputationhigh:sexmale 2.353e-01 2.132e-01
## expressionexci:sexmale -4.902e-03 2.132e-01
## racewhite:sexmale 4.902e-02 2.132e-01
## reputationlow:expressionexci:racewhite -6.373e-01 3.014e-01
## reputationhigh:expressionexci:racewhite -8.480e-01 3.014e-01
## reputationlow:expressionexci:sexmale 3.922e-02 3.014e-01
## reputationhigh:expressionexci:sexmale -1.716e-01 3.014e-01
## reputationlow:racewhite:sexmale -1.765e-01 3.014e-01
## reputationhigh:racewhite:sexmale 1.324e-01 3.014e-01
## expressionexci:racewhite:sexmale 3.235e-01 3.014e-01
## reputationlow:expressionexci:racewhite:sexmale -1.078e-01 4.263e-01
## reputationhigh:expressionexci:racewhite:sexmale -2.598e-01 4.263e-01
## df t value
## (Intercept) 1.094e+02 2.152
## reputationlow 4.794e+03 -16.424
## reputationhigh 4.794e+03 10.863
## expressionexci 4.794e+03 -2.569
## racewhite 4.794e+03 -1.691
## sexmale 4.794e+03 -0.781
## GenderFemale 1.020e+02 1.440
## FamSES2num 1.020e+02 4.489
## reputationlow:expressionexci 4.794e+03 2.392
## reputationhigh:expressionexci 4.794e+03 3.289
## reputationlow:racewhite 4.794e+03 2.438
## reputationhigh:racewhite 4.794e+03 1.633
## expressionexci:racewhite 4.794e+03 1.311
## reputationlow:sexmale 4.794e+03 1.495
## reputationhigh:sexmale 4.794e+03 1.104
## expressionexci:sexmale 4.794e+03 -0.023
## racewhite:sexmale 4.794e+03 0.230
## reputationlow:expressionexci:racewhite 4.794e+03 -2.114
## reputationhigh:expressionexci:racewhite 4.794e+03 -2.813
## reputationlow:expressionexci:sexmale 4.794e+03 0.130
## reputationhigh:expressionexci:sexmale 4.794e+03 -0.569
## reputationlow:racewhite:sexmale 4.794e+03 -0.585
## reputationhigh:racewhite:sexmale 4.794e+03 0.439
## expressionexci:racewhite:sexmale 4.794e+03 1.073
## reputationlow:expressionexci:racewhite:sexmale 4.794e+03 -0.253
## reputationhigh:expressionexci:racewhite:sexmale 4.794e+03 -0.609
## Pr(>|t|)
## (Intercept) 0.03357 *
## reputationlow < 2e-16 ***
## reputationhigh < 2e-16 ***
## expressionexci 0.01022 *
## racewhite 0.09086 .
## sexmale 0.43510
## GenderFemale 0.15287
## FamSES2num 1.89e-05 ***
## reputationlow:expressionexci 0.01681 *
## reputationhigh:expressionexci 0.00101 **
## reputationlow:racewhite 0.01482 *
## reputationhigh:racewhite 0.10257
## expressionexci:racewhite 0.18997
## reputationlow:sexmale 0.13502
## reputationhigh:sexmale 0.26970
## expressionexci:sexmale 0.98165
## racewhite:sexmale 0.81812
## reputationlow:expressionexci:racewhite 0.03457 *
## reputationhigh:expressionexci:racewhite 0.00492 **
## reputationlow:expressionexci:sexmale 0.89650
## reputationhigh:expressionexci:sexmale 0.56928
## reputationlow:racewhite:sexmale 0.55829
## reputationhigh:racewhite:sexmale 0.66064
## expressionexci:racewhite:sexmale 0.28321
## reputationlow:expressionexci:racewhite:sexmale 0.80030
## reputationhigh:expressionexci:racewhite:sexmale 0.54227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 26 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "high")
model7 <- lmer(data = Data_noNeut, response ~ reputation * expression * race * sex +
Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model7)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race * sex + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18468.8 18650.7 -9206.4 18412.8 4868
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6601 -0.5946 -0.0273 0.5822 4.4661
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.317 1.522
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.84960 0.56327
## reputationmod -1.63725 0.15072
## reputationlow -4.11275 0.15072
## expressionexci 0.31373 0.15072
## racewhite 0.09314 0.15072
## sexmale 0.11765 0.15072
## GenderFemale 0.55005 0.38192
## FamSES2num 0.45376 0.10109
## reputationmod:expressionexci -0.70098 0.21315
## reputationlow:expressionexci -0.19118 0.21315
## reputationmod:racewhite -0.34804 0.21315
## reputationlow:racewhite 0.17157 0.21315
## expressionexci:racewhite -0.56863 0.21315
## reputationmod:sexmale -0.23529 0.21315
## reputationlow:sexmale 0.08333 0.21315
## expressionexci:sexmale -0.17647 0.21315
## racewhite:sexmale 0.18137 0.21315
## reputationmod:expressionexci:racewhite 0.84804 0.30144
## reputationlow:expressionexci:racewhite 0.21078 0.30144
## reputationmod:expressionexci:sexmale 0.17157 0.30144
## reputationlow:expressionexci:sexmale 0.21078 0.30144
## reputationmod:racewhite:sexmale -0.13235 0.30144
## reputationlow:racewhite:sexmale -0.30882 0.30144
## expressionexci:racewhite:sexmale 0.06373 0.30144
## reputationmod:expressionexci:racewhite:sexmale 0.25980 0.42631
## reputationlow:expressionexci:racewhite:sexmale 0.15196 0.42631
## df t value Pr(>|t|)
## (Intercept) 109.37344 5.059 1.71e-06
## reputationmod 4793.99997 -10.863 < 2e-16
## reputationlow 4793.99997 -27.287 < 2e-16
## expressionexci 4793.99995 2.081 0.03744
## racewhite 4793.99995 0.618 0.53664
## sexmale 4793.99995 0.781 0.43510
## GenderFemale 101.99999 1.440 0.15287
## FamSES2num 101.99999 4.489 1.89e-05
## reputationmod:expressionexci 4793.99997 -3.289 0.00101
## reputationlow:expressionexci 4793.99997 -0.897 0.36982
## reputationmod:racewhite 4793.99997 -1.633 0.10257
## reputationlow:racewhite 4793.99997 0.805 0.42091
## expressionexci:racewhite 4793.99995 -2.668 0.00766
## reputationmod:sexmale 4793.99997 -1.104 0.26970
## reputationlow:sexmale 4793.99997 0.391 0.69585
## expressionexci:sexmale 4793.99995 -0.828 0.40776
## racewhite:sexmale 4793.99995 0.851 0.39487
## reputationmod:expressionexci:racewhite 4793.99998 2.813 0.00492
## reputationlow:expressionexci:racewhite 4793.99998 0.699 0.48443
## reputationmod:expressionexci:sexmale 4793.99998 0.569 0.56928
## reputationlow:expressionexci:sexmale 4793.99997 0.699 0.48443
## reputationmod:racewhite:sexmale 4793.99998 -0.439 0.66064
## reputationlow:racewhite:sexmale 4793.99997 -1.024 0.30566
## expressionexci:racewhite:sexmale 4793.99996 0.211 0.83258
## reputationmod:expressionexci:racewhite:sexmale 4793.99998 0.609 0.54227
## reputationlow:expressionexci:racewhite:sexmale 4793.99998 0.356 0.72151
##
## (Intercept) ***
## reputationmod ***
## reputationlow ***
## expressionexci *
## racewhite
## sexmale
## GenderFemale
## FamSES2num ***
## reputationmod:expressionexci **
## reputationlow:expressionexci
## reputationmod:racewhite
## reputationlow:racewhite
## expressionexci:racewhite **
## reputationmod:sexmale
## reputationlow:sexmale
## expressionexci:sexmale
## racewhite:sexmale
## reputationmod:expressionexci:racewhite **
## reputationlow:expressionexci:racewhite
## reputationmod:expressionexci:sexmale
## reputationlow:expressionexci:sexmale
## reputationmod:racewhite:sexmale
## reputationlow:racewhite:sexmale
## expressionexci:racewhite:sexmale
## reputationmod:expressionexci:racewhite:sexmale
## reputationlow:expressionexci:racewhite:sexmale
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 26 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itWithin moderate reputation only
# look only at moderate reputation to see whether there is a significant interaction
Data_noNeut_mod <- Data_noNeut %>%
filter(reputation == "mod")
# expression only
model1_mod <- lmer(data = Data_noNeut_mod, response ~ expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model1_mod)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ expression + Gender + FamSES2num + (1 | pID)
## Data: Data_noNeut_mod
##
## AIC BIC logLik deviance df.resid
## 4889.5 4921.9 -2438.7 4877.5 1626
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6769 -0.3676 -0.0067 0.3857 9.3964
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.951 1.9877
## Residual 0.890 0.9434
## Number of obs: 1632, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.11075 0.69552 102.23034 1.597 0.113353
## expressionexci -0.16912 0.04671 1530.00001 -3.621 0.000303 ***
## GenderFemale 0.52893 0.47962 101.99998 1.103 0.272704
## FamSES2num 0.44399 0.12695 101.99999 3.497 0.000698 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) exprss GndrFm
## expressinxc -0.034
## GenderFemal 0.174 0.000
## FamSES2num -0.890 0.000 -0.533# full model
model2_mod <- lmer(data = Data_noNeut_mod, response ~ expression * race * sex + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model2_mod)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ expression * race * sex + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut_mod
##
## AIC BIC logLik deviance df.resid
## 4867.8 4932.5 -2421.9 4843.8 1620
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6651 -0.4023 0.0012 0.3882 9.3153
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.9521 1.9880
## Residual 0.8706 0.9331
## Number of obs: 1632, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.285e+00 6.978e-01 1.036e+02 1.841
## expressionexci -3.873e-01 9.239e-02 1.530e+03 -4.192
## racewhite -2.549e-01 9.239e-02 1.530e+03 -2.759
## sexmale -1.176e-01 9.239e-02 1.530e+03 -1.273
## GenderFemale 5.289e-01 4.796e-01 1.020e+02 1.103
## FamSES2num 4.440e-01 1.270e-01 1.020e+02 3.497
## expressionexci:racewhite 2.794e-01 1.307e-01 1.530e+03 2.139
## expressionexci:sexmale -4.902e-03 1.307e-01 1.530e+03 -0.038
## racewhite:sexmale 4.902e-02 1.307e-01 1.530e+03 0.375
## expressionexci:racewhite:sexmale 3.235e-01 1.848e-01 1.530e+03 1.751
## Pr(>|t|)
## (Intercept) 0.068464 .
## expressionexci 2.93e-05 ***
## racewhite 0.005865 **
## sexmale 0.203062
## GenderFemale 0.272705
## FamSES2num 0.000698 ***
## expressionexci:racewhite 0.032629 *
## expressionexci:sexmale 0.970076
## racewhite:sexmale 0.707574
## expressionexci:racewhite:sexmale 0.080154 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) exprss racwht sexmal GndrFm FmSES2 exprssnxc:r
## expressinxc -0.066
## racewhite -0.066 0.500
## sexmale -0.066 0.500 0.500
## GenderFemal 0.173 0.000 0.000 0.000
## FamSES2num -0.887 0.000 0.000 0.000 -0.533
## exprssnxc:r 0.047 -0.707 -0.707 -0.354 0.000 0.000
## exprssnxc:s 0.047 -0.707 -0.354 -0.707 0.000 0.000 0.500
## racwht:sxml 0.047 -0.354 -0.707 -0.707 0.000 0.000 0.500
## exprssnxc:: -0.033 0.500 0.500 0.500 0.000 0.000 -0.707
## exprssnxc:s rcwht:
## expressinxc
## racewhite
## sexmale
## GenderFemal
## FamSES2num
## exprssnxc:r
## exprssnxc:s
## racwht:sxml 0.500
## exprssnxc:: -0.707 -0.707Individual differences in ideal affect
##For now, we only have European American data, so we will test whether there are individual differences in ideal affect:
#Again, only look within moderate reputation ("mod") because that's where it seems ideal affect matters
#Model 1: Player 2 expression, race, and ideal HAP as predictors for amount given by participants, controlling for actual HAP
model1 <- lmer(data = Data_noNeut_mod, response ~ expression * race * scale(iHAP, scale = F) + scale(aHAP, scale = F) + (1|pID), REML=FALSE)
summary(model1)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ expression * race * scale(iHAP, scale = F) + scale(aHAP,
## scale = F) + (1 | pID)
## Data: Data_noNeut_mod
##
## AIC BIC logLik deviance df.resid
## 5108.1 5167.9 -2543.1 5086.1 1685
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5876 -0.4063 0.0032 0.4020 9.2432
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 4.5984 2.1444
## Residual 0.8908 0.9438
## Number of obs: 1696, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 4.26651 0.21327
## expressionexci -0.36557 0.06482
## racewhite -0.23585 0.06482
## scale(iHAP, scale = F) 0.35605 0.33388
## scale(aHAP, scale = F) -0.63323 0.31457
## expressionexci:racewhite 0.41745 0.09167
## expressionexci:scale(iHAP, scale = F) -0.06058 0.09291
## racewhite:scale(iHAP, scale = F) -0.10238 0.09291
## expressionexci:racewhite:scale(iHAP, scale = F) 0.04653 0.13139
## df t value
## (Intercept) 113.73511 20.006
## expressionexci 1590.00000 -5.639
## racewhite 1590.00000 -3.638
## scale(iHAP, scale = F) 112.42813 1.066
## scale(aHAP, scale = F) 106.00000 -2.013
## expressionexci:racewhite 1590.00000 4.554
## expressionexci:scale(iHAP, scale = F) 1590.00000 -0.652
## racewhite:scale(iHAP, scale = F) 1590.00000 -1.102
## expressionexci:racewhite:scale(iHAP, scale = F) 1590.00000 0.354
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## expressionexci 2.02e-08 ***
## racewhite 0.000283 ***
## scale(iHAP, scale = F) 0.288528
## scale(aHAP, scale = F) 0.046652 *
## expressionexci:racewhite 5.67e-06 ***
## expressionexci:scale(iHAP, scale = F) 0.514442
## racewhite:scale(iHAP, scale = F) 0.270640
## expressionexci:racewhite:scale(iHAP, scale = F) 0.723293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) exprss racwht scale(iHAP,scl=F) scale(aHAP,scl=F)
## expressinxc -0.152
## racewhite -0.152 0.500
## scale(iHAP,scl=F) 0.000 0.000 0.000
## scale(aHAP,scl=F) 0.000 0.000 0.000 -0.402
## exprssnxc:r 0.107 -0.707 -0.707 0.000 0.000
## e:(HAP,s=F) 0.000 0.000 0.000 -0.139 0.000
## r:(HAP,s=F) 0.000 0.000 0.000 -0.139 0.000
## e::(HAP,s=F 0.000 0.000 0.000 0.098 0.000
## exprs: e:(s=F r:(s=F
## expressinxc
## racewhite
## scale(iHAP,scl=F)
## scale(aHAP,scl=F)
## exprssnxc:r
## e:(HAP,s=F) 0.000
## r:(HAP,s=F) 0.000 0.500
## e::(HAP,s=F 0.000 -0.707 -0.707#Model 2: Player 2 expression, race, and ideal LAP as predictors for amount given by participants, controlling for actual LAP
model2 <- lmer(data = Data_noNeut_mod, response ~ expression * race * scale(iLAP, scale = F) + scale(aLAP, scale = F) + (1|pID), REML=FALSE)
summary(model2)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ expression * race * scale(iLAP, scale = F) + scale(aLAP,
## scale = F) + (1 | pID)
## Data: Data_noNeut_mod
##
## AIC BIC logLik deviance df.resid
## 5110.7 5170.5 -2544.3 5088.7 1685
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6637 -0.4146 0.0196 0.4086 9.3228
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 4.7640 2.1827
## Residual 0.8902 0.9435
## Number of obs: 1696, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 4.26651 0.21689
## expressionexci -0.36557 0.06480
## racewhite -0.23585 0.06480
## scale(iLAP, scale = F) 0.11418 0.31713
## scale(aLAP, scale = F) -0.06162 0.27220
## expressionexci:racewhite 0.41745 0.09164
## expressionexci:scale(iLAP, scale = F) 0.11659 0.09465
## racewhite:scale(iLAP, scale = F) 0.06418 0.09465
## expressionexci:racewhite:scale(iLAP, scale = F) -0.21630 0.13385
## df t value
## (Intercept) 113.45971 19.671
## expressionexci 1590.00000 -5.642
## racewhite 1590.00000 -3.640
## scale(iLAP, scale = F) 113.44372 0.360
## scale(aLAP, scale = F) 105.99999 -0.226
## expressionexci:racewhite 1590.00000 4.555
## expressionexci:scale(iLAP, scale = F) 1590.00000 1.232
## racewhite:scale(iLAP, scale = F) 1590.00000 0.678
## expressionexci:racewhite:scale(iLAP, scale = F) 1590.00000 -1.616
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## expressionexci 1.99e-08 ***
## racewhite 0.000282 ***
## scale(iLAP, scale = F) 0.719487
## scale(aLAP, scale = F) 0.821356
## expressionexci:racewhite 5.63e-06 ***
## expressionexci:scale(iLAP, scale = F) 0.218215
## racewhite:scale(iLAP, scale = F) 0.497820
## expressionexci:racewhite:scale(iLAP, scale = F) 0.106310
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) exprss racwht scale(iLAP,scl=F) scale(aLAP,scl=F)
## expressinxc -0.149
## racewhite -0.149 0.500
## scale(iLAP,scl=F) 0.000 0.000 0.000
## scale(aLAP,scl=F) 0.000 0.000 0.000 -0.045
## exprssnxc:r 0.106 -0.707 -0.707 0.000 0.000
## e:(LAP,s=F) 0.000 0.000 0.000 -0.149 0.000
## r:(LAP,s=F) 0.000 0.000 0.000 -0.149 0.000
## e::(LAP,s=F 0.000 0.000 0.000 0.106 0.000
## exprs: e:(s=F r:(s=F
## expressinxc
## racewhite
## scale(iLAP,scl=F)
## scale(aLAP,scl=F)
## exprssnxc:r
## e:(LAP,s=F) 0.000
## r:(LAP,s=F) 0.000 0.500
## e::(LAP,s=F 0.000 -0.707 -0.707#Plot
expr_colors=c("#94B3D7","#D99594") #Set correct colors for "expression" variable for the jittered points in the graphs below
#iHAP
Data_noNeut_mod %>%
ggplot(aes(x = iHAP, y = response, fill = expression)) +
facet_grid(~race) +
geom_jitter(aes(colour = expression, alpha=0.01), width = 0.2, height = 0.4) +
geom_smooth(method=lm, color='#2C3E50') +
guides(fill=guide_legend(title="Player 2 Expression")) +
scale_fill_manual(values=c("#94B3D7", "#D99594")) + #Set correct ribbon colors
scale_color_manual(values = expr_colors) + #Set correct jitter (point) colors
xlab("Participant ideal HAP") +
ylab("Amount Given ($)") +
ylim(c(0,10))## Warning: Removed 78 rows containing missing values (geom_point).
#iLAP
Data_noNeut_mod %>%
ggplot(aes(x = iLAP, y = response, fill = expression)) +
facet_grid(~race) +
geom_jitter(aes(colour = expression, alpha=0.01), width = 0.2, height = 0.4) +
geom_smooth(method=lm, color='#2C3E50') +
guides(fill=guide_legend(title="Player 2 Expression")) +
scale_fill_manual(values=c("#94B3D7", "#D99594")) + #Set correct ribbon colors
scale_color_manual(values = expr_colors) + #Set correct jitter (point) colors
xlab("Participant ideal LAP") +
ylab("Amount Given ($)") +
ylim(c(0,10))## Warning: Removed 77 rows containing missing values (geom_point).
Contrast Coding
# purpose: to check for main effect of reputation
# check default coding
contrasts(Data_noNeut$reputation)## mod low
## high 0 0
## mod 1 0
## low 0 1# use effect coding
contrasts(Data_noNeut$reputation) <- cbind(Linear = c(1, 0, -1),
Quad = c(-1, 2, -1))
contrasts(Data_noNeut$reputation)## Linear Quad
## high 1 -1
## mod 0 2
## low -1 -1# when using effect coding, intercept is grand mean
model1_effect <- lmer(data = Data_noNeut, response ~ reputation + (1 | pID), REML=FALSE)
summary(model1_effect)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 19207.4 19240.1 -9598.7 19197.4 5083
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6803 -0.6089 -0.0365 0.5811 4.5554
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.443 1.856
## Residual 2.331 1.527
## Number of obs: 5088, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.081e+00 1.815e-01 1.060e+02 22.486 <2e-16 ***
## reputationLinear 2.050e+00 2.621e-02 4.982e+03 78.200 <2e-16 ***
## reputationQuad -5.405e-03 1.513e-02 4.982e+03 -0.357 0.721
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnL
## reputatnLnr 0.000
## reputatinQd 0.000 0.000# check grand mean
grandmean <- Data_noNeut %>%
group_by(pID) %>%
summarise(mean = mean(response)) %>%
summarise(mean = mean(mean))
grandmean## # A tibble: 1 x 1
## mean
## <dbl>
## 1 4.08# check means for each reputation condition mean
means <- Data_noNeut %>%
group_by(pID, reputation) %>%
summarise(mean = mean(response)) %>%
group_by(reputation) %>%
summarise(mean = mean(mean))
means## # A tibble: 3 x 2
## reputation mean
## <fct> <dbl>
## 1 high 6.14
## 2 mod 4.07
## 3 low 2.04Gender analyses
# separate dataframe into males and females only
Data_Males <- Data_noNeut %>%
filter(Gender == "Male")
Data_Females <- Data_noNeut %>%
filter(Gender == "Female")
# plot descriptives
summary <- Data_noVar %>%
# calculate for each subject
ungroup() %>%
group_by(pID, expression, reputation, Gender, race, sex) %>%
summarize(n = n(),
mean = mean(response, na.rm = T),
stdv = sd(response),
sem = stdv / sqrt(n)) %>%
# calculate across subjects
group_by(expression, reputation, Gender, race, sex) %>%
summarize(n = n(),
mean = mean(mean, na.rm = T),
stdv = mean(stdv, na.rm = T),
sem = mean(sem, na.rm = T))## Warning: Factor `Gender` contains implicit NA, consider using
## `forcats::fct_explicit_na`
## Warning: Factor `Gender` contains implicit NA, consider using
## `forcats::fct_explicit_na`genderSummary <- summary %>%
filter(expression != "neut") %>%
filter(Gender == "Male" | Gender == "Female")
ggplot(genderSummary, aes(x = reputation, y = mean, fill = expression)) +
geom_bar(stat = "identity", position="dodge") +
geom_errorbar(width = .33, position = position_dodge(.9), aes(ymin = (mean-sem), ymax = (mean+sem))) +
scale_fill_manual(values=c("#94b3d7", "#d99594")) +
facet_grid(~ Gender * race * sex) +
xlab("Target Reputation") +
ylab("Mean Offer ($)") +
scale_y_continuous(limits = c(0, 10))
# males only
# full model
Data_Males$reputation <- relevel(Data_Males$reputation, ref = "low")
model_males <- lmer(data = Data_Males, response ~ reputation * expression * race * sex +
FamSES2num + (1|pID), REML=FALSE)
summary(model_males)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation * expression * race * sex + FamSES2num +
## (1 | pID)
## Data: Data_Males
##
## AIC BIC logLik deviance df.resid
## 7263.2 7413.4 -3604.6 7209.2 1893
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1010 -0.6709 -0.0355 0.6717 3.9634
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 1.871 1.368
## Residual 2.317 1.522
## Number of obs: 1920, groups: pID, 40
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) -1.240e+00 6.031e-01
## reputationhigh 2.662e+00 2.407e-01
## reputationmod 1.587e+00 2.407e-01
## expressionexci 1.500e-01 2.407e-01
## racewhite 6.250e-02 2.407e-01
## sexmale 5.000e-02 2.407e-01
## FamSES2num 6.185e-01 1.101e-01
## reputationhigh:expressionexci -1.250e-02 3.404e-01
## reputationmod:expressionexci -3.875e-01 3.404e-01
## reputationhigh:racewhite -1.000e-01 3.404e-01
## reputationmod:racewhite -2.250e-01 3.404e-01
## expressionexci:racewhite -2.875e-01 3.404e-01
## reputationhigh:sexmale -1.625e-01 3.404e-01
## reputationmod:sexmale -2.500e-02 3.404e-01
## expressionexci:sexmale 4.626e-14 3.404e-01
## racewhite:sexmale 1.625e-01 3.404e-01
## reputationhigh:expressionexci:racewhite -2.500e-02 4.813e-01
## reputationmod:expressionexci:racewhite 3.875e-01 4.813e-01
## reputationhigh:expressionexci:sexmale 7.500e-02 4.813e-01
## reputationmod:expressionexci:sexmale -3.000e-01 4.813e-01
## reputationhigh:racewhite:sexmale 2.375e-01 4.813e-01
## reputationmod:racewhite:sexmale -2.375e-01 4.813e-01
## expressionexci:racewhite:sexmale 1.000e-01 4.813e-01
## reputationhigh:expressionexci:racewhite:sexmale -3.875e-01 6.807e-01
## reputationmod:expressionexci:racewhite:sexmale 5.750e-01 6.807e-01
## df t value
## (Intercept) 4.688e+01 -2.057
## reputationhigh 1.880e+03 11.063
## reputationmod 1.880e+03 6.596
## expressionexci 1.880e+03 0.623
## racewhite 1.880e+03 0.260
## sexmale 1.880e+03 0.208
## FamSES2num 4.000e+01 5.619
## reputationhigh:expressionexci 1.880e+03 -0.037
## reputationmod:expressionexci 1.880e+03 -1.139
## reputationhigh:racewhite 1.880e+03 -0.294
## reputationmod:racewhite 1.880e+03 -0.661
## expressionexci:racewhite 1.880e+03 -0.845
## reputationhigh:sexmale 1.880e+03 -0.477
## reputationmod:sexmale 1.880e+03 -0.073
## expressionexci:sexmale 1.880e+03 0.000
## racewhite:sexmale 1.880e+03 0.477
## reputationhigh:expressionexci:racewhite 1.880e+03 -0.052
## reputationmod:expressionexci:racewhite 1.880e+03 0.805
## reputationhigh:expressionexci:sexmale 1.880e+03 0.156
## reputationmod:expressionexci:sexmale 1.880e+03 -0.623
## reputationhigh:racewhite:sexmale 1.880e+03 0.493
## reputationmod:racewhite:sexmale 1.880e+03 -0.493
## expressionexci:racewhite:sexmale 1.880e+03 0.208
## reputationhigh:expressionexci:racewhite:sexmale 1.880e+03 -0.569
## reputationmod:expressionexci:racewhite:sexmale 1.880e+03 0.845
## Pr(>|t|)
## (Intercept) 0.0453 *
## reputationhigh < 2e-16 ***
## reputationmod 5.47e-11 ***
## expressionexci 0.5332
## racewhite 0.7951
## sexmale 0.8354
## FamSES2num 1.62e-06 ***
## reputationhigh:expressionexci 0.9707
## reputationmod:expressionexci 0.2551
## reputationhigh:racewhite 0.7689
## reputationmod:racewhite 0.5086
## expressionexci:racewhite 0.3984
## reputationhigh:sexmale 0.6331
## reputationmod:sexmale 0.9415
## expressionexci:sexmale 1.0000
## racewhite:sexmale 0.6331
## reputationhigh:expressionexci:racewhite 0.9586
## reputationmod:expressionexci:racewhite 0.4209
## reputationhigh:expressionexci:sexmale 0.8762
## reputationmod:expressionexci:sexmale 0.5332
## reputationhigh:racewhite:sexmale 0.6218
## reputationmod:racewhite:sexmale 0.6218
## expressionexci:racewhite:sexmale 0.8354
## reputationhigh:expressionexci:racewhite:sexmale 0.5693
## reputationmod:expressionexci:racewhite:sexmale 0.3984
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 25 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# females only
# full model
Data_Females$reputation <- relevel(Data_Females$reputation, ref = "low")
model_females <- lmer(data = Data_Females, response ~ reputation * expression * race * sex +
FamSES2num + (1|pID), REML=FALSE)
summary(model_females)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation * expression * race * sex + FamSES2num +
## (1 | pID)
## Data: Data_Females
##
## AIC BIC logLik deviance df.resid
## 10693.4 10855.3 -5319.7 10639.4 2949
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0477 -0.5425 -0.0331 0.5330 4.7713
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.710 1.646
## Residual 1.914 1.383
## Number of obs: 2976, groups: pID, 62
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.463e-01 1.227e+00
## reputationhigh 5.048e+00 1.757e-01
## reputationmod 3.048e+00 1.757e-01
## expressionexci 1.048e-01 1.757e-01
## racewhite 3.952e-01 1.757e-01
## sexmale 2.984e-01 1.757e-01
## FamSES2num 1.645e-01 1.746e-01
## reputationhigh:expressionexci 3.226e-01 2.485e-01
## reputationmod:expressionexci -5.887e-01 2.485e-01
## reputationhigh:racewhite -2.177e-01 2.485e-01
## reputationmod:racewhite -7.097e-01 2.485e-01
## expressionexci:racewhite -4.032e-01 2.485e-01
## reputationhigh:sexmale -3.226e-02 2.485e-01
## reputationmod:sexmale -5.081e-01 2.485e-01
## expressionexci:sexmale 5.645e-02 2.485e-01
## racewhite:sexmale -3.145e-01 2.485e-01
## reputationhigh:expressionexci:racewhite -3.306e-01 3.514e-01
## reputationmod:expressionexci:racewhite 7.984e-01 3.514e-01
## reputationhigh:expressionexci:sexmale -3.952e-01 3.514e-01
## reputationmod:expressionexci:sexmale 1.290e-01 3.514e-01
## reputationhigh:racewhite:sexmale 3.548e-01 3.514e-01
## reputationmod:racewhite:sexmale 4.435e-01 3.514e-01
## expressionexci:racewhite:sexmale 2.903e-01 3.514e-01
## reputationhigh:expressionexci:racewhite:sexmale 7.322e-14 4.969e-01
## reputationmod:expressionexci:racewhite:sexmale -1.935e-01 4.969e-01
## df t value
## (Intercept) 6.324e+01 0.608
## reputationhigh 2.914e+03 28.736
## reputationmod 2.914e+03 17.352
## expressionexci 2.914e+03 0.597
## racewhite 2.914e+03 2.249
## sexmale 2.914e+03 1.698
## FamSES2num 6.200e+01 0.942
## reputationhigh:expressionexci 2.914e+03 1.298
## reputationmod:expressionexci 2.914e+03 -2.370
## reputationhigh:racewhite 2.914e+03 -0.876
## reputationmod:racewhite 2.914e+03 -2.856
## expressionexci:racewhite 2.914e+03 -1.623
## reputationhigh:sexmale 2.914e+03 -0.130
## reputationmod:sexmale 2.914e+03 -2.045
## expressionexci:sexmale 2.914e+03 0.227
## racewhite:sexmale 2.914e+03 -1.266
## reputationhigh:expressionexci:racewhite 2.914e+03 -0.941
## reputationmod:expressionexci:racewhite 2.914e+03 2.272
## reputationhigh:expressionexci:sexmale 2.914e+03 -1.125
## reputationmod:expressionexci:sexmale 2.914e+03 0.367
## reputationhigh:racewhite:sexmale 2.914e+03 1.010
## reputationmod:racewhite:sexmale 2.914e+03 1.262
## expressionexci:racewhite:sexmale 2.914e+03 0.826
## reputationhigh:expressionexci:racewhite:sexmale 2.914e+03 0.000
## reputationmod:expressionexci:racewhite:sexmale 2.914e+03 -0.390
## Pr(>|t|)
## (Intercept) 0.54517
## reputationhigh < 2e-16 ***
## reputationmod < 2e-16 ***
## expressionexci 0.55072
## racewhite 0.02457 *
## sexmale 0.08953 .
## FamSES2num 0.34989
## reputationhigh:expressionexci 0.19427
## reputationmod:expressionexci 0.01788 *
## reputationhigh:racewhite 0.38089
## reputationmod:racewhite 0.00432 **
## expressionexci:racewhite 0.10471
## reputationhigh:sexmale 0.89670
## reputationmod:sexmale 0.04095 *
## expressionexci:sexmale 0.82027
## racewhite:sexmale 0.20565
## reputationhigh:expressionexci:racewhite 0.34677
## reputationmod:expressionexci:racewhite 0.02314 *
## reputationhigh:expressionexci:sexmale 0.26083
## reputationmod:expressionexci:sexmale 0.71347
## reputationhigh:racewhite:sexmale 0.31263
## reputationmod:racewhite:sexmale 0.20692
## expressionexci:racewhite:sexmale 0.40872
## reputationhigh:expressionexci:racewhite:sexmale 1.00000
## reputationmod:expressionexci:racewhite:sexmale 0.69693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 25 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itOrthogonal contrasts
response ~ reputation * expression + race + sex + (1|pID)
# check default coding
contrasts(Data_noVar$reputation)## mod high
## low 0 0
## mod 1 0
## high 0 1contrasts(Data_noVar$expression)## calm exci
## neut 0 0
## calm 1 0
## exci 0 1contrasts(Data_noVar$sex)## male
## female 0
## male 1contrasts(Data_noVar$race)## white
## asian 0
## white 1#recode
contrasts(Data_noVar$reputation) <- cbind(line = c(-1, 0, 1),
quad = c(-1, 2, -1))
contrasts(Data_noVar$expression) <- cbind(NeutVOther = c(2, -1, -1),
ExciVCalm = c(0, -1, 1))
contrasts(Data_noVar$sex) <- cbind(MaleVFemale = c(-1, 1))
contrasts(Data_noVar$race) <- cbind(WhiteVAsian = c(-1, 1))
#when using effect coding, intercept is grand mean
model <- lmer(data = Data_noVar, response ~ reputation * expression + race + sex + (1|pID), REML = FALSE)
summary(model)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation * expression + race + sex + (1 | pID)
## Data: Data_noVar
##
## AIC BIC logLik deviance df.resid
## 28865.4 28955.7 -14419.7 28839.4 7619
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1414 -0.6070 -0.0268 0.5977 5.0525
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.381 1.839
## Residual 2.403 1.550
## Number of obs: 7632, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 4.068e+00 1.795e-01 1.060e+02
## reputationline 2.075e+00 2.173e-02 7.526e+03
## reputationquad -2.791e-02 1.255e-02 7.526e+03
## expressionNeutVOther -1.297e-02 1.255e-02 7.526e+03
## expressionExciVCalm -3.223e-02 2.173e-02 7.526e+03
## raceWhiteVAsian 2.267e-02 1.774e-02 7.526e+03
## sexMaleVFemale 3.105e-02 1.774e-02 7.526e+03
## reputationline:expressionNeutVOther 2.526e-02 1.537e-02 7.526e+03
## reputationquad:expressionNeutVOther -2.250e-02 8.872e-03 7.526e+03
## reputationline:expressionExciVCalm -1.032e-02 2.661e-02 7.526e+03
## reputationquad:expressionExciVCalm -2.309e-02 1.537e-02 7.526e+03
## t value Pr(>|t|)
## (Intercept) 22.668 <2e-16 ***
## reputationline 95.490 <2e-16 ***
## reputationquad -2.224 0.0261 *
## expressionNeutVOther -1.034 0.3012
## expressionExciVCalm -1.483 0.1380
## raceWhiteVAsian 1.278 0.2014
## sexMaleVFemale 1.750 0.0801 .
## reputationline:expressionNeutVOther 1.644 0.1003
## reputationquad:expressionNeutVOther -2.537 0.0112 *
## reputationline:expressionExciVCalm -0.388 0.6983
## reputationquad:expressionExciVCalm -1.503 0.1329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnl rpttnq expNVO expEVC rcWhVA sxMlVF rpttnl:NVO
## reputatinln 0.000
## reputatinqd 0.000 0.000
## exprssnNtVO 0.000 0.000 0.000
## exprssnExVC 0.000 0.000 0.000 0.000
## raceWhtVAsn 0.000 0.000 0.000 0.000 0.000
## sexMaleVFml 0.000 0.000 0.000 0.000 0.000 0.000
## rpttnln:NVO 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## rpttnqd:NVO 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## rpttnln:EVC 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## rpttnqd:EVC 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## rpttnq:NVO rpttnl:EVC
## reputatinln
## reputatinqd
## exprssnNtVO
## exprssnExVC
## raceWhtVAsn
## sexMaleVFml
## rpttnln:NVO
## rpttnqd:NVO
## rpttnln:EVC 0.000
## rpttnqd:EVC 0.000 0.000Omit neutral expression (just calm vs. excited)
#use Data_noNeut instead of Data_noVar
#recode
contrasts(Data_noNeut$reputation) <- cbind(line = c(-1, 0, 1),
quad = c(-1, 2, -1))
contrasts(Data_noNeut$expression) <- cbind(ExciVCalm = c(-1, 1))
contrasts(Data_noNeut$sex) <- cbind(MaleVFemale = c(-1, 1))
contrasts(Data_noNeut$race) <- cbind(WhiteVAsian = c(-1, 1))
#when using effect coding, intercept is grand mean
model <- lmer(data = Data_noNeut, response ~ reputation * expression + race + sex + (1|pID), REML = FALSE)
summary(model)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation * expression + race + sex + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 19206.9 19272.2 -9593.4 19186.9 5078
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7360 -0.6062 -0.0421 0.5863 4.5319
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.443 1.856
## Residual 2.326 1.525
## Number of obs: 5088, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 4.081e+00 1.815e-01 1.060e+02
## reputationline -2.050e+00 2.618e-02 4.982e+03
## reputationquad -5.405e-03 1.512e-02 4.982e+03
## expressionExciVCalm -3.223e-02 2.138e-02 4.982e+03
## raceWhiteVAsian -6.682e-03 2.138e-02 4.982e+03
## sexMaleVFemale 5.110e-02 2.138e-02 4.982e+03
## reputationline:expressionExciVCalm 1.032e-02 2.618e-02 4.982e+03
## reputationquad:expressionExciVCalm -2.309e-02 1.512e-02 4.982e+03
## t value Pr(>|t|)
## (Intercept) 22.486 <2e-16 ***
## reputationline -78.283 <2e-16 ***
## reputationquad -0.358 0.7207
## expressionExciVCalm -1.508 0.1317
## raceWhiteVAsian -0.313 0.7546
## sexMaleVFemale 2.390 0.0169 *
## reputationline:expressionExciVCalm 0.394 0.6936
## reputationquad:expressionExciVCalm -1.528 0.1267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnl rpttnq expEVC rcWhVA sxMlVF rpttnl:EVC
## reputatinln 0.000
## reputatinqd 0.000 0.000
## exprssnExVC 0.000 0.000 0.000
## raceWhtVAsn 0.000 0.000 0.000 0.000
## sexMaleVFml 0.000 0.000 0.000 0.000 0.000
## rpttnln:EVC 0.000 0.000 0.000 0.000 0.000 0.000
## rpttnqd:EVC 0.000 0.000 0.000 0.000 0.000 0.000 0.000Include Gender and SES as additional covariates
# check default coding
contrasts(Data_noNeut$Gender)## Female
## Male 0
## Female 1#recode
contrasts(Data_noNeut$Gender) <- cbind(FemaleVMale = c(-1,1))
#when using effect coding, intercept is grand mean
model <- lmer(data = Data_noNeut, response ~ reputation * expression + race + sex + Gender + FamSES2num + (1|pID), REML = FALSE)
summary(model)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression + race + sex + Gender + FamSES2num +
## (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18476 18554 -9226 18452 4884
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7006 -0.6123 -0.0433 0.5801 4.5039
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.492 1.579
## Residual 2.336 1.528
## Number of obs: 4896, groups: pID, 102
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.234e+00 6.161e-01 1.020e+02
## reputationline -2.017e+00 2.675e-02 4.794e+03
## reputationquad -2.757e-03 1.545e-02 4.794e+03
## expressionExciVCalm -3.288e-02 2.184e-02 4.794e+03
## raceWhiteVAsian -3.064e-03 2.184e-02 4.794e+03
## sexMaleVFemale 5.494e-02 2.184e-02 4.794e+03
## GenderFemaleVMale 2.750e-01 1.910e-01 1.020e+02
## FamSES2num 4.538e-01 1.011e-01 1.020e+02
## reputationline:expressionExciVCalm 1.440e-02 2.675e-02 4.794e+03
## reputationquad:expressionExciVCalm -2.584e-02 1.545e-02 4.794e+03
## t value Pr(>|t|)
## (Intercept) 2.003 0.0478 *
## reputationline -75.387 < 2e-16 ***
## reputationquad -0.179 0.8583
## expressionExciVCalm -1.505 0.1323
## raceWhiteVAsian -0.140 0.8885
## sexMaleVFemale 2.515 0.0119 *
## GenderFemaleVMale 1.440 0.1529
## FamSES2num 4.489 1.89e-05 ***
## reputationline:expressionExciVCalm 0.538 0.5904
## reputationquad:expressionExciVCalm -1.673 0.0944 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnl rpttnq expEVC rcWhVA sxMlVF GndFVM FmSES2
## reputatinln 0.000
## reputatinqd 0.000 0.000
## exprssnExVC 0.000 0.000 0.000
## raceWhtVAsn 0.000 0.000 0.000 0.000
## sexMaleVFml 0.000 0.000 0.000 0.000 0.000
## GendrFmlVMl 0.466 0.000 0.000 0.000 0.000 0.000
## FamSES2num -0.965 0.000 0.000 0.000 0.000 0.000 -0.533
## rpttnln:EVC 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## rpttnqd:EVC 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## rpttnl:EVC
## reputatinln
## reputatinqd
## exprssnExVC
## raceWhtVAsn
## sexMaleVFml
## GendrFmlVMl
## FamSES2num
## rpttnln:EVC
## rpttnqd:EVC 0.000### Check pairwise comparisons to compare "giving to excited vs. calm for each race target for each condition (e.g., compare giving to excited vs. calm asian target in low reputation, excited vs. calm asian target in moderate, etc.)" (Jeanne's request, 11/10/2019):
contrasts(Data$expression) <- cbind(ExcitVsCalm = c(0, -1, 1),
CalmVsNeut = c(-1, 1, 0))
#For White, low reputation
Data$race <- relevel(Data$race, ref = "white")
Data$reputation <- relevel(Data$reputation, ref = "low")
model2_effect <- lmer(data = Data, response ~ reputation * expression * race + Gender + FamSES2num + (1|pID), REML=FALSE)
summary(model2_effect)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + Gender + FamSES2num +
## (1 | pID)
## Data: Data
##
## AIC BIC logLik deviance df.resid
## 28929.1 29081.8 -14442.6 28885.1 7610
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0536 -0.6142 -0.0257 0.5833 4.9249
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.914 1.707
## Residual 2.422 1.556
## Number of obs: 7632, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) -1.178e+00 5.933e-01
## reputationmod 1.851e+00 6.171e-02
## reputationhigh 3.861e+00 6.171e-02
## expressionExcitVsCalm -6.132e-02 6.171e-02
## expressionCalmVsNeut -1.651e-02 6.171e-02
## raceasian -1.211e-01 6.171e-02
## GenderFemale 6.964e-02 3.919e-01
## FamSES2num 5.577e-01 1.057e-01
## reputationmod:expressionExcitVsCalm 1.179e-01 8.728e-02
## reputationhigh:expressionExcitVsCalm -1.533e-01 8.728e-02
## reputationmod:expressionCalmVsNeut 8.019e-02 8.728e-02
## reputationhigh:expressionCalmVsNeut -1.132e-01 8.728e-02
## reputationmod:raceasian 8.176e-02 8.728e-02
## reputationhigh:raceasian 1.415e-01 8.728e-02
## expressionExcitVsCalm:raceasian 1.682e-01 8.728e-02
## expressionCalmVsNeut:raceasian 9.591e-02 8.728e-02
## reputationmod:expressionExcitVsCalm:raceasian -3.318e-01 1.234e-01
## reputationhigh:expressionExcitVsCalm:raceasian 1.509e-01 1.234e-01
## reputationmod:expressionCalmVsNeut:raceasian 1.572e-03 1.234e-01
## reputationhigh:expressionCalmVsNeut:raceasian 2.594e-02 1.234e-01
## df t value Pr(>|t|)
## (Intercept) 1.070e+02 -1.985 0.04967
## reputationmod 7.526e+03 29.999 < 2e-16
## reputationhigh 7.526e+03 62.559 < 2e-16
## expressionExcitVsCalm 7.526e+03 -0.994 0.32044
## expressionCalmVsNeut 7.526e+03 -0.268 0.78908
## raceasian 7.526e+03 -1.962 0.04983
## GenderFemale 1.060e+02 0.178 0.85928
## FamSES2num 1.060e+02 5.277 7e-07
## reputationmod:expressionExcitVsCalm 7.526e+03 1.351 0.17669
## reputationhigh:expressionExcitVsCalm 7.526e+03 -1.756 0.07905
## reputationmod:expressionCalmVsNeut 7.526e+03 0.919 0.35824
## reputationhigh:expressionCalmVsNeut 7.526e+03 -1.297 0.19464
## reputationmod:raceasian 7.526e+03 0.937 0.34890
## reputationhigh:raceasian 7.526e+03 1.621 0.10498
## expressionExcitVsCalm:raceasian 7.526e+03 1.928 0.05394
## expressionCalmVsNeut:raceasian 7.526e+03 1.099 0.27184
## reputationmod:expressionExcitVsCalm:raceasian 7.526e+03 -2.688 0.00721
## reputationhigh:expressionExcitVsCalm:raceasian 7.526e+03 1.223 0.22140
## reputationmod:expressionCalmVsNeut:raceasian 7.526e+03 0.013 0.98984
## reputationhigh:expressionCalmVsNeut:raceasian 7.526e+03 0.210 0.83353
##
## (Intercept) *
## reputationmod ***
## reputationhigh ***
## expressionExcitVsCalm
## expressionCalmVsNeut
## raceasian *
## GenderFemale
## FamSES2num ***
## reputationmod:expressionExcitVsCalm
## reputationhigh:expressionExcitVsCalm .
## reputationmod:expressionCalmVsNeut
## reputationhigh:expressionCalmVsNeut
## reputationmod:raceasian
## reputationhigh:raceasian
## expressionExcitVsCalm:raceasian .
## expressionCalmVsNeut:raceasian
## reputationmod:expressionExcitVsCalm:raceasian **
## reputationhigh:expressionExcitVsCalm:raceasian
## reputationmod:expressionCalmVsNeut:raceasian
## reputationhigh:expressionCalmVsNeut:raceasian
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it#For White, mod reputation
Data$reputation <- relevel(Data$reputation, ref = "mod")
model2_effect <- lmer(data = Data, response ~ reputation * expression * race + Gender + FamSES2num + (1|pID), REML=FALSE)
summary(model2_effect)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + Gender + FamSES2num +
## (1 | pID)
## Data: Data
##
## AIC BIC logLik deviance df.resid
## 28929.1 29081.8 -14442.6 28885.1 7610
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0536 -0.6142 -0.0257 0.5833 4.9249
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.914 1.707
## Residual 2.422 1.556
## Number of obs: 7632, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 6.736e-01 5.933e-01
## reputationlow -1.851e+00 6.171e-02
## reputationhigh 2.009e+00 6.171e-02
## expressionExcitVsCalm 5.660e-02 6.171e-02
## expressionCalmVsNeut 6.368e-02 6.171e-02
## raceasian -3.931e-02 6.171e-02
## GenderFemale 6.964e-02 3.919e-01
## FamSES2num 5.577e-01 1.057e-01
## reputationlow:expressionExcitVsCalm -1.179e-01 8.728e-02
## reputationhigh:expressionExcitVsCalm -2.712e-01 8.728e-02
## reputationlow:expressionCalmVsNeut -8.019e-02 8.728e-02
## reputationhigh:expressionCalmVsNeut -1.934e-01 8.728e-02
## reputationlow:raceasian -8.176e-02 8.728e-02
## reputationhigh:raceasian 5.975e-02 8.728e-02
## expressionExcitVsCalm:raceasian -1.635e-01 8.728e-02
## expressionCalmVsNeut:raceasian 9.748e-02 8.728e-02
## reputationlow:expressionExcitVsCalm:raceasian 3.318e-01 1.234e-01
## reputationhigh:expressionExcitVsCalm:raceasian 4.827e-01 1.234e-01
## reputationlow:expressionCalmVsNeut:raceasian -1.572e-03 1.234e-01
## reputationhigh:expressionCalmVsNeut:raceasian 2.437e-02 1.234e-01
## df t value Pr(>|t|)
## (Intercept) 1.070e+02 1.135 0.25875
## reputationlow 7.526e+03 -29.999 < 2e-16
## reputationhigh 7.526e+03 32.560 < 2e-16
## expressionExcitVsCalm 7.526e+03 0.917 0.35908
## expressionCalmVsNeut 7.526e+03 1.032 0.30218
## raceasian 7.526e+03 -0.637 0.52419
## GenderFemale 1.060e+02 0.178 0.85928
## FamSES2num 1.060e+02 5.277 7.00e-07
## reputationlow:expressionExcitVsCalm 7.526e+03 -1.351 0.17669
## reputationhigh:expressionExcitVsCalm 7.526e+03 -3.108 0.00189
## reputationlow:expressionCalmVsNeut 7.526e+03 -0.919 0.35824
## reputationhigh:expressionCalmVsNeut 7.526e+03 -2.216 0.02673
## reputationlow:raceasian 7.526e+03 -0.937 0.34890
## reputationhigh:raceasian 7.526e+03 0.685 0.49363
## expressionExcitVsCalm:raceasian 7.526e+03 -1.874 0.06103
## expressionCalmVsNeut:raceasian 7.526e+03 1.117 0.26406
## reputationlow:expressionExcitVsCalm:raceasian 7.526e+03 2.688 0.00721
## reputationhigh:expressionExcitVsCalm:raceasian 7.526e+03 3.911 9.28e-05
## reputationlow:expressionCalmVsNeut:raceasian 7.526e+03 -0.013 0.98984
## reputationhigh:expressionCalmVsNeut:raceasian 7.526e+03 0.197 0.84348
##
## (Intercept)
## reputationlow ***
## reputationhigh ***
## expressionExcitVsCalm
## expressionCalmVsNeut
## raceasian
## GenderFemale
## FamSES2num ***
## reputationlow:expressionExcitVsCalm
## reputationhigh:expressionExcitVsCalm **
## reputationlow:expressionCalmVsNeut
## reputationhigh:expressionCalmVsNeut *
## reputationlow:raceasian
## reputationhigh:raceasian
## expressionExcitVsCalm:raceasian .
## expressionCalmVsNeut:raceasian
## reputationlow:expressionExcitVsCalm:raceasian **
## reputationhigh:expressionExcitVsCalm:raceasian ***
## reputationlow:expressionCalmVsNeut:raceasian
## reputationhigh:expressionCalmVsNeut:raceasian
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it#For White, high reputation
Data$reputation <- relevel(Data$reputation, ref = "high")
model2_effect <- lmer(data = Data, response ~ reputation * expression * race + Gender + FamSES2num + (1|pID), REML=FALSE)
summary(model2_effect)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + Gender + FamSES2num +
## (1 | pID)
## Data: Data
##
## AIC BIC logLik deviance df.resid
## 28929.1 29081.8 -14442.6 28885.1 7610
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0536 -0.6142 -0.0257 0.5833 4.9249
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.914 1.707
## Residual 2.422 1.556
## Number of obs: 7632, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.68302 0.59327
## reputationmod -2.00943 0.06171
## reputationlow -3.86085 0.06171
## expressionExcitVsCalm -0.21462 0.06171
## expressionCalmVsNeut -0.12972 0.06171
## raceasian 0.02044 0.06171
## GenderFemale 0.06964 0.39188
## FamSES2num 0.55772 0.10568
## reputationmod:expressionExcitVsCalm 0.27123 0.08728
## reputationlow:expressionExcitVsCalm 0.15330 0.08728
## reputationmod:expressionCalmVsNeut 0.19340 0.08728
## reputationlow:expressionCalmVsNeut 0.11321 0.08728
## reputationmod:raceasian -0.05975 0.08728
## reputationlow:raceasian -0.14151 0.08728
## expressionExcitVsCalm:raceasian 0.31918 0.08728
## expressionCalmVsNeut:raceasian 0.12186 0.08728
## reputationmod:expressionExcitVsCalm:raceasian -0.48270 0.12343
## reputationlow:expressionExcitVsCalm:raceasian -0.15094 0.12343
## reputationmod:expressionCalmVsNeut:raceasian -0.02437 0.12343
## reputationlow:expressionCalmVsNeut:raceasian -0.02594 0.12343
## df t value Pr(>|t|)
## (Intercept) 106.96231 4.522 1.59e-05
## reputationmod 7526.00002 -32.560 < 2e-16
## reputationlow 7526.00002 -62.559 < 2e-16
## expressionExcitVsCalm 7526.00002 -3.478 0.000509
## expressionCalmVsNeut 7526.00002 -2.102 0.035597
## raceasian 7526.00002 0.331 0.740499
## GenderFemale 105.99994 0.178 0.859282
## FamSES2num 105.99995 5.277 7.00e-07
## reputationmod:expressionExcitVsCalm 7526.00002 3.108 0.001893
## reputationlow:expressionExcitVsCalm 7526.00002 1.756 0.079048
## reputationmod:expressionCalmVsNeut 7526.00002 2.216 0.026731
## reputationlow:expressionCalmVsNeut 7526.00002 1.297 0.194640
## reputationmod:raceasian 7526.00002 -0.685 0.493633
## reputationlow:raceasian 7526.00002 -1.621 0.104981
## expressionExcitVsCalm:raceasian 7526.00002 3.657 0.000257
## expressionCalmVsNeut:raceasian 7526.00002 1.396 0.162703
## reputationmod:expressionExcitVsCalm:raceasian 7526.00002 -3.911 9.28e-05
## reputationlow:expressionExcitVsCalm:raceasian 7526.00002 -1.223 0.221402
## reputationmod:expressionCalmVsNeut:raceasian 7526.00002 -0.197 0.843482
## reputationlow:expressionCalmVsNeut:raceasian 7526.00002 -0.210 0.833527
##
## (Intercept) ***
## reputationmod ***
## reputationlow ***
## expressionExcitVsCalm ***
## expressionCalmVsNeut *
## raceasian
## GenderFemale
## FamSES2num ***
## reputationmod:expressionExcitVsCalm **
## reputationlow:expressionExcitVsCalm .
## reputationmod:expressionCalmVsNeut *
## reputationlow:expressionCalmVsNeut
## reputationmod:raceasian
## reputationlow:raceasian
## expressionExcitVsCalm:raceasian ***
## expressionCalmVsNeut:raceasian
## reputationmod:expressionExcitVsCalm:raceasian ***
## reputationlow:expressionExcitVsCalm:raceasian
## reputationmod:expressionCalmVsNeut:raceasian
## reputationlow:expressionCalmVsNeut:raceasian
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it#For Asian, low reputation
Data$race <- relevel(Data$race, ref = "asian")
Data$reputation <- relevel(Data$reputation, ref = "low")
model2_effect <- lmer(data = Data, response ~ reputation * expression * race + Gender + FamSES2num + (1|pID), REML=FALSE)
summary(model2_effect)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + Gender + FamSES2num +
## (1 | pID)
## Data: Data
##
## AIC BIC logLik deviance df.resid
## 28929.1 29081.8 -14442.6 28885.1 7610
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0536 -0.6142 -0.0257 0.5833 4.9249
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.914 1.707
## Residual 2.422 1.556
## Number of obs: 7632, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) -1.299e+00 5.933e-01
## reputationhigh 4.002e+00 6.171e-02
## reputationmod 1.933e+00 6.171e-02
## expressionExcitVsCalm 1.069e-01 6.171e-02
## expressionCalmVsNeut 7.940e-02 6.171e-02
## racewhite 1.211e-01 6.171e-02
## GenderFemale 6.964e-02 3.919e-01
## FamSES2num 5.577e-01 1.057e-01
## reputationhigh:expressionExcitVsCalm -2.358e-03 8.728e-02
## reputationmod:expressionExcitVsCalm -2.138e-01 8.728e-02
## reputationhigh:expressionCalmVsNeut -8.726e-02 8.728e-02
## reputationmod:expressionCalmVsNeut 8.176e-02 8.728e-02
## reputationhigh:racewhite -1.415e-01 8.728e-02
## reputationmod:racewhite -8.176e-02 8.728e-02
## expressionExcitVsCalm:racewhite -1.682e-01 8.728e-02
## expressionCalmVsNeut:racewhite -9.591e-02 8.728e-02
## reputationhigh:expressionExcitVsCalm:racewhite -1.509e-01 1.234e-01
## reputationmod:expressionExcitVsCalm:racewhite 3.318e-01 1.234e-01
## reputationhigh:expressionCalmVsNeut:racewhite -2.594e-02 1.234e-01
## reputationmod:expressionCalmVsNeut:racewhite -1.572e-03 1.234e-01
## df t value Pr(>|t|)
## (Intercept) 1.070e+02 -2.189 0.03074
## reputationhigh 7.526e+03 64.852 < 2e-16
## reputationmod 7.526e+03 31.324 < 2e-16
## expressionExcitVsCalm 7.526e+03 1.732 0.08323
## expressionCalmVsNeut 7.526e+03 1.287 0.19827
## racewhite 7.526e+03 1.962 0.04983
## GenderFemale 1.060e+02 0.178 0.85928
## FamSES2num 1.060e+02 5.277 7e-07
## reputationhigh:expressionExcitVsCalm 7.526e+03 -0.027 0.97844
## reputationmod:expressionExcitVsCalm 7.526e+03 -2.450 0.01431
## reputationhigh:expressionCalmVsNeut 7.526e+03 -1.000 0.31742
## reputationmod:expressionCalmVsNeut 7.526e+03 0.937 0.34890
## reputationhigh:racewhite 7.526e+03 -1.621 0.10498
## reputationmod:racewhite 7.526e+03 -0.937 0.34890
## expressionExcitVsCalm:racewhite 7.526e+03 -1.928 0.05394
## expressionCalmVsNeut:racewhite 7.526e+03 -1.099 0.27184
## reputationhigh:expressionExcitVsCalm:racewhite 7.526e+03 -1.223 0.22140
## reputationmod:expressionExcitVsCalm:racewhite 7.526e+03 2.688 0.00721
## reputationhigh:expressionCalmVsNeut:racewhite 7.526e+03 -0.210 0.83353
## reputationmod:expressionCalmVsNeut:racewhite 7.526e+03 -0.013 0.98984
##
## (Intercept) *
## reputationhigh ***
## reputationmod ***
## expressionExcitVsCalm .
## expressionCalmVsNeut
## racewhite *
## GenderFemale
## FamSES2num ***
## reputationhigh:expressionExcitVsCalm
## reputationmod:expressionExcitVsCalm *
## reputationhigh:expressionCalmVsNeut
## reputationmod:expressionCalmVsNeut
## reputationhigh:racewhite
## reputationmod:racewhite
## expressionExcitVsCalm:racewhite .
## expressionCalmVsNeut:racewhite
## reputationhigh:expressionExcitVsCalm:racewhite
## reputationmod:expressionExcitVsCalm:racewhite **
## reputationhigh:expressionCalmVsNeut:racewhite
## reputationmod:expressionCalmVsNeut:racewhite
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it#For Asian, mod reputation
Data$reputation <- relevel(Data$reputation, ref = "mod")
model2_effect <- lmer(data = Data, response ~ reputation * expression * race + Gender + FamSES2num + (1|pID), REML=FALSE)
summary(model2_effect)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + Gender + FamSES2num +
## (1 | pID)
## Data: Data
##
## AIC BIC logLik deviance df.resid
## 28929.1 29081.8 -14442.6 28885.1 7610
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0536 -0.6142 -0.0257 0.5833 4.9249
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.914 1.707
## Residual 2.422 1.556
## Number of obs: 7632, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 6.343e-01 5.933e-01
## reputationlow -1.933e+00 6.171e-02
## reputationhigh 2.069e+00 6.171e-02
## expressionExcitVsCalm -1.069e-01 6.171e-02
## expressionCalmVsNeut 1.612e-01 6.171e-02
## racewhite 3.931e-02 6.171e-02
## GenderFemale 6.964e-02 3.919e-01
## FamSES2num 5.577e-01 1.057e-01
## reputationlow:expressionExcitVsCalm 2.138e-01 8.728e-02
## reputationhigh:expressionExcitVsCalm 2.115e-01 8.728e-02
## reputationlow:expressionCalmVsNeut -8.176e-02 8.728e-02
## reputationhigh:expressionCalmVsNeut -1.690e-01 8.728e-02
## reputationlow:racewhite 8.176e-02 8.728e-02
## reputationhigh:racewhite -5.975e-02 8.728e-02
## expressionExcitVsCalm:racewhite 1.635e-01 8.728e-02
## expressionCalmVsNeut:racewhite -9.748e-02 8.728e-02
## reputationlow:expressionExcitVsCalm:racewhite -3.318e-01 1.234e-01
## reputationhigh:expressionExcitVsCalm:racewhite -4.827e-01 1.234e-01
## reputationlow:expressionCalmVsNeut:racewhite 1.572e-03 1.234e-01
## reputationhigh:expressionCalmVsNeut:racewhite -2.437e-02 1.234e-01
## df t value Pr(>|t|)
## (Intercept) 1.070e+02 1.069 0.28742
## reputationlow 7.526e+03 -31.324 < 2e-16
## reputationhigh 7.526e+03 33.528 < 2e-16
## expressionExcitVsCalm 7.526e+03 -1.732 0.08323
## expressionCalmVsNeut 7.526e+03 2.611 0.00903
## racewhite 7.526e+03 0.637 0.52419
## GenderFemale 1.060e+02 0.178 0.85928
## FamSES2num 1.060e+02 5.277 7.00e-07
## reputationlow:expressionExcitVsCalm 7.526e+03 2.450 0.01431
## reputationhigh:expressionExcitVsCalm 7.526e+03 2.423 0.01541
## reputationlow:expressionCalmVsNeut 7.526e+03 -0.937 0.34890
## reputationhigh:expressionCalmVsNeut 7.526e+03 -1.937 0.05283
## reputationlow:racewhite 7.526e+03 0.937 0.34890
## reputationhigh:racewhite 7.526e+03 -0.685 0.49363
## expressionExcitVsCalm:racewhite 7.526e+03 1.874 0.06103
## expressionCalmVsNeut:racewhite 7.526e+03 -1.117 0.26406
## reputationlow:expressionExcitVsCalm:racewhite 7.526e+03 -2.688 0.00721
## reputationhigh:expressionExcitVsCalm:racewhite 7.526e+03 -3.911 9.28e-05
## reputationlow:expressionCalmVsNeut:racewhite 7.526e+03 0.013 0.98984
## reputationhigh:expressionCalmVsNeut:racewhite 7.526e+03 -0.197 0.84348
##
## (Intercept)
## reputationlow ***
## reputationhigh ***
## expressionExcitVsCalm .
## expressionCalmVsNeut **
## racewhite
## GenderFemale
## FamSES2num ***
## reputationlow:expressionExcitVsCalm *
## reputationhigh:expressionExcitVsCalm *
## reputationlow:expressionCalmVsNeut
## reputationhigh:expressionCalmVsNeut .
## reputationlow:racewhite
## reputationhigh:racewhite
## expressionExcitVsCalm:racewhite .
## expressionCalmVsNeut:racewhite
## reputationlow:expressionExcitVsCalm:racewhite **
## reputationhigh:expressionExcitVsCalm:racewhite ***
## reputationlow:expressionCalmVsNeut:racewhite
## reputationhigh:expressionCalmVsNeut:racewhite
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it#For Asian, high reputation
Data$reputation <- relevel(Data$reputation, ref = "high")
model2_effect <- lmer(data = Data, response ~ reputation * expression * race + Gender + FamSES2num + (1|pID), REML=FALSE)
summary(model2_effect)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## response ~ reputation * expression * race + Gender + FamSES2num +
## (1 | pID)
## Data: Data
##
## AIC BIC logLik deviance df.resid
## 28929.1 29081.8 -14442.6 28885.1 7610
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0536 -0.6142 -0.0257 0.5833 4.9249
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.914 1.707
## Residual 2.422 1.556
## Number of obs: 7632, groups: pID, 106
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.703e+00 5.933e-01
## reputationmod -2.069e+00 6.171e-02
## reputationlow -4.002e+00 6.171e-02
## expressionExcitVsCalm 1.046e-01 6.171e-02
## expressionCalmVsNeut -7.862e-03 6.171e-02
## racewhite -2.044e-02 6.171e-02
## GenderFemale 6.964e-02 3.919e-01
## FamSES2num 5.577e-01 1.057e-01
## reputationmod:expressionExcitVsCalm -2.115e-01 8.728e-02
## reputationlow:expressionExcitVsCalm 2.358e-03 8.728e-02
## reputationmod:expressionCalmVsNeut 1.690e-01 8.728e-02
## reputationlow:expressionCalmVsNeut 8.726e-02 8.728e-02
## reputationmod:racewhite 5.975e-02 8.728e-02
## reputationlow:racewhite 1.415e-01 8.728e-02
## expressionExcitVsCalm:racewhite -3.192e-01 8.728e-02
## expressionCalmVsNeut:racewhite -1.219e-01 8.728e-02
## reputationmod:expressionExcitVsCalm:racewhite 4.827e-01 1.234e-01
## reputationlow:expressionExcitVsCalm:racewhite 1.509e-01 1.234e-01
## reputationmod:expressionCalmVsNeut:racewhite 2.437e-02 1.234e-01
## reputationlow:expressionCalmVsNeut:racewhite 2.594e-02 1.234e-01
## df t value Pr(>|t|)
## (Intercept) 1.070e+02 4.557 1.38e-05
## reputationmod 7.526e+03 -33.528 < 2e-16
## reputationlow 7.526e+03 -64.852 < 2e-16
## expressionExcitVsCalm 7.526e+03 1.694 0.090261
## expressionCalmVsNeut 7.526e+03 -0.127 0.898638
## racewhite 7.526e+03 -0.331 0.740499
## GenderFemale 1.060e+02 0.178 0.859282
## FamSES2num 1.060e+02 5.277 7.00e-07
## reputationmod:expressionExcitVsCalm 7.526e+03 -2.423 0.015415
## reputationlow:expressionExcitVsCalm 7.526e+03 0.027 0.978442
## reputationmod:expressionCalmVsNeut 7.526e+03 1.937 0.052828
## reputationlow:expressionCalmVsNeut 7.526e+03 1.000 0.317419
## reputationmod:racewhite 7.526e+03 0.685 0.493633
## reputationlow:racewhite 7.526e+03 1.621 0.104981
## expressionExcitVsCalm:racewhite 7.526e+03 -3.657 0.000257
## expressionCalmVsNeut:racewhite 7.526e+03 -1.396 0.162703
## reputationmod:expressionExcitVsCalm:racewhite 7.526e+03 3.911 9.28e-05
## reputationlow:expressionExcitVsCalm:racewhite 7.526e+03 1.223 0.221402
## reputationmod:expressionCalmVsNeut:racewhite 7.526e+03 0.197 0.843482
## reputationlow:expressionCalmVsNeut:racewhite 7.526e+03 0.210 0.833527
##
## (Intercept) ***
## reputationmod ***
## reputationlow ***
## expressionExcitVsCalm .
## expressionCalmVsNeut
## racewhite
## GenderFemale
## FamSES2num ***
## reputationmod:expressionExcitVsCalm *
## reputationlow:expressionExcitVsCalm
## reputationmod:expressionCalmVsNeut .
## reputationlow:expressionCalmVsNeut
## reputationmod:racewhite
## reputationlow:racewhite
## expressionExcitVsCalm:racewhite ***
## expressionCalmVsNeut:racewhite
## reputationmod:expressionExcitVsCalm:racewhite ***
## reputationlow:expressionExcitVsCalm:racewhite
## reputationmod:expressionCalmVsNeut:racewhite
## reputationlow:expressionCalmVsNeut:racewhite
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it