Reputation
Elizabeth Blevins
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")Join Part 1 and Part 2 data into “Data” dataframe
# turn pID and WelcomeCode into characters
Part1$pID <- as.character(Part1$pID)
Part2$WelcomeCode <- as.character(Part2$WelcomeCode)
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 | 104 |
| 1 | 6 |
| pID | response |
|---|---|
| 0m6kq8tbb8mp | 10 |
| 1sv9s4ko8o9i | 5 |
| 2mivlx9sx17 | 10 |
| 9tpuiq3jo39 | 10 |
| m0ce8ox9id | 3 |
| ql9isp4x0hl | 10 |
Total sample size (before excluding no variation) = 110.
Total sample size (after excluding no variation) = 104.
Age
| Age_Mean | Age_SD |
|---|---|
| 25.35 | 3.59 |
Gender
| Gender | n |
|---|---|
| Male | 43 |
| Female | 63 |
| NA | 4 |
Education
| Education | n |
|---|---|
| High School/GED | 2 |
| Some college (not currently enrolled) | 9 |
| Some college (currently enrolled) | 20 |
| Associates degree | 3 |
| BA/BS degree | 55 |
| 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 | 34 |
| NA | 1 |
AVI: Ideal
| iHAP_Mean | iHAP_SD | iLAP_Mean | iLAP_SD | iHAPi_Mean | iHAPi_SD | iLAPi_Mean |
|---|---|---|---|---|---|---|
| 3.414 | 0.7636 | 3.934 | 0.7401 | 0.6442 | 0.3659 | 1.028 |
| iLAPi_SD |
|---|
| 0.411 |
AVI: Actual
| aHAP_Mean | aHAP_SD | aLAP_Mean | aLAP_SD |
|---|---|---|---|
| 2.7 | 0.775 | 2.993 | 0.7895 |
General Trust Scale
| trust_mean | trust_sd |
|---|---|
| 4.526 | 0.9015 |
Importance of Social Image - Self
| selfImage_mean | selfImage_sd |
|---|---|
| 4.999 | 1.256 |
Importance of Social Image - Other
| otherImage_mean | otherImage_sd |
|---|---|
| 4.23 | 1.396 |
Reputation Stability Mindset (higher values indicate more stability)
| repStability_mean | repStability_sd |
|---|---|
| 3.398 | 1.019 |
Cronbach’s alpha for AVI
Ideal HAP: 0.76
Ideal LAP: 0.8
Actual HAP: 0.85
Actual LAP: 0.83
Visualiations
Offers
Mean offers only
| give_Mean | give_SD |
|---|---|
| 4.274 | 2.14 |
Offers by Expression and Reputation
| expression | give_Mean | give_SD |
|---|---|---|
| neut | 4.034 | 2.126 |
| calm | 4.105 | 2.057 |
| exci | 4.038 | 2.036 |
| reputation | give_Mean | give_SD |
|---|---|---|
| low | 2.015 | 0.02786 |
| mod | 4.004 | 0.1271 |
| high | 6.159 | 0.06336 |
| expression | reputation | mean | stdv |
|---|---|---|---|
| neut | low | 1.983 | 0.6818 |
| neut | mod | 3.892 | 0.821 |
| neut | high | 6.228 | 0.7001 |
| calm | low | 2.029 | 0.7124 |
| calm | mod | 4.142 | 0.6831 |
| calm | high | 6.143 | 0.6123 |
| exci | low | 2.034 | 0.6668 |
| exci | mod | 3.977 | 0.7152 |
| exci | high | 6.105 | 0.7272 |
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)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
## 22776.9 22796.4 -11385.4 22770.9 4989
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2393 -0.5598 -0.0411 0.6097 2.9457
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.443 1.856
## Residual 5.212 2.283
## Number of obs: 4992, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.0715 0.1848 104.0000 22.03 <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
## 18910.2 18942.7 -9450.1 18900.2 4987
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6482 -0.6306 -0.0329 0.5828 4.5234
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.502 1.871
## Residual 2.361 1.537
## Number of obs: 4992, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.031e+00 1.873e-01 1.098e+02 10.84 <2e-16 ***
## reputationmod 2.028e+00 5.327e-02 4.888e+03 38.08 <2e-16 ***
## reputationhigh 4.093e+00 5.327e-02 4.888e+03 76.83 <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 22777 22796 -11385.4 22771
## model2 5 18910 18943 -9450.1 18900 3870.7 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
## 18909.8 18948.9 -9448.9 18897.8 4986
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6709 -0.6266 -0.0414 0.5799 4.5030
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.502 1.871
## Residual 2.360 1.536
## Number of obs: 4992, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.06430 0.18860 112.81277 10.95 <2e-16 ***
## reputationmod 2.02825 0.05326 4888.00000 38.08 <2e-16 ***
## reputationhigh 4.09255 0.05326 4888.00000 76.84 <2e-16 ***
## expressionexci -0.06611 0.04348 4888.00000 -1.52 0.129
## ---
## 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 -0.115 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 18910 18943 -9450.1 18900
## model3 6 18910 18949 -9448.9 18898 2.3105 1 0.1285#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
## 18911.1 18963.2 -9447.6 18895.1 4984
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6632 -0.6114 -0.0331 0.5905 4.5273
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.503 1.871
## Residual 2.358 1.536
## Number of obs: 4992, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.029e+00 1.911e-01 1.189e+02 10.618
## reputationmod 2.113e+00 7.530e-02 4.888e+03 28.062
## reputationhigh 4.114e+00 7.530e-02 4.888e+03 54.640
## expressionexci 4.808e-03 7.530e-02 4.888e+03 0.064
## reputationmod:expressionexci -1.695e-01 1.065e-01 4.888e+03 -1.592
## reputationhigh:expressionexci -4.327e-02 1.065e-01 4.888e+03 -0.406
## Pr(>|t|)
## (Intercept) <2e-16 ***
## reputationmod <2e-16 ***
## reputationhigh <2e-16 ***
## expressionexci 0.949
## reputationmod:expressionexci 0.112
## reputationhigh:expressionexci 0.685
## ---
## 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 0.139 -0.707 -0.354 -0.707
## rpttnhgh:xp 0.139 -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 18910 18949 -9448.9 18898
## model4 8 18911 18963 -9447.6 18895 2.7343 2 0.2548Adding 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
## 22776.9 22796.4 -11385.4 22770.9 4989
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2393 -0.5598 -0.0411 0.6097 2.9457
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.443 1.856
## Residual 5.212 2.283
## Number of obs: 4992, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.0715 0.1848 104.0000 22.03 <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
## 22777.9 22803.9 -11384.9 22769.9 4988
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2541 -0.5616 -0.0460 0.6242 2.9605
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.443 1.856
## Residual 5.211 2.283
## Number of obs: 4992, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.10457 0.18760 110.45259 21.879 <2e-16 ***
## expressionexci -0.06611 0.06462 4888.00000 -1.023 0.306
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## expressinxc -0.172#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 22777 22796 -11385 22771
## model2 4 22778 22804 -11385 22770 1.0465 1 0.3063#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
## 18909.8 18948.9 -9448.9 18897.8 4986
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6709 -0.6266 -0.0414 0.5799 4.5030
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.502 1.871
## Residual 2.360 1.536
## Number of obs: 4992, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.06430 0.18860 112.81277 10.95 <2e-16 ***
## reputationmod 2.02825 0.05326 4888.00000 38.08 <2e-16 ***
## reputationhigh 4.09255 0.05326 4888.00000 76.84 <2e-16 ***
## expressionexci -0.06611 0.04348 4888.00000 -1.52 0.129
## ---
## 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 -0.115 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 22778 22804 -11384.9 22770
## model3 6 18910 18949 -9448.9 18898 3872 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
## 18340.4 18385.8 -9163.2 18326.4 4841
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6236 -0.6376 -0.0450 0.5736 4.5100
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.363 1.537
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.07979 0.55734 101.63661 -1.937 0.0555 .
## reputationmod 2.01547 0.05408 4747.00012 37.268 < 2e-16 ***
## reputationhigh 4.04084 0.05408 4747.00012 74.718 < 2e-16 ***
## GenderFemale 0.55667 0.38335 100.99965 1.452 0.1496
## FamSES2num 0.45792 0.10168 100.99965 4.503 1.8e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm rpttnh GndrFm
## reputatinmd -0.049
## reputatnhgh -0.049 0.500
## GenderFemal 0.171 0.000 0.000
## FamSES2num -0.889 0.000 0.000 -0.529# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "mod")
model1 <- lmer(data = Data_noNeut, response ~ reputation + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model1)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + Gender + FamSES2num + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18340.4 18385.8 -9163.2 18326.4 4841
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6236 -0.6376 -0.0450 0.5736 4.5100
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.363 1.537
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.93568 0.55734 101.63661 1.679 0.0963 .
## reputationlow -2.01547 0.05408 4747.00012 -37.268 < 2e-16 ***
## reputationhigh 2.02537 0.05408 4747.00012 37.451 < 2e-16 ***
## GenderFemale 0.55667 0.38335 100.99965 1.452 0.1496
## FamSES2num 0.45792 0.10168 100.99966 4.503 1.8e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnl rpttnh GndrFm
## reputatinlw -0.049
## reputatnhgh -0.049 0.500
## GenderFemal 0.171 0.000 0.000
## FamSES2num -0.889 0.000 0.000 -0.529# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "high")
model1 <- lmer(data = Data_noNeut, response ~ reputation + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model1)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + Gender + FamSES2num + (1 | pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18340.4 18385.8 -9163.2 18326.4 4841
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6236 -0.6376 -0.0450 0.5736 4.5100
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.363 1.537
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.96105 0.55734 101.63660 5.313 6.4e-07 ***
## reputationmod -2.02537 0.05408 4747.00011 -37.451 < 2e-16 ***
## reputationlow -4.04084 0.05408 4747.00011 -74.718 < 2e-16 ***
## GenderFemale 0.55667 0.38335 100.99965 1.452 0.15
## FamSES2num 0.45792 0.10168 100.99964 4.503 1.8e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm rpttnl GndrFm
## reputatinmd -0.049
## reputatinlw -0.049 0.500
## GenderFemal 0.171 0.000 0.000
## FamSES2num -0.889 0.000 0.000 -0.529Model 2: Offer ~ Reputation + Expression
# reputation and expression (additive)
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "low")
model2 <- lmer(data = Data_noNeut, response ~ reputation + expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model2)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + expression + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18340.1 18392.0 -9162.0 18324.1 4840
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6465 -0.6323 -0.0446 0.5843 4.4893
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.362 1.537
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.04637 0.55778 101.95536 -1.876 0.0635 .
## reputationhigh 4.04084 0.05407 4747.00012 74.736 < 2e-16 ***
## reputationmod 2.01547 0.05407 4747.00012 37.277 < 2e-16 ***
## expressionexci -0.06683 0.04415 4747.00012 -1.514 0.1301
## GenderFemale 0.55667 0.38335 100.99965 1.452 0.1496
## FamSES2num 0.45792 0.10168 100.99965 4.503 1.8e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnh rpttnm exprss GndrFm
## reputatnhgh -0.048
## reputatinmd -0.048 0.500
## expressinxc -0.040 0.000 0.000
## GenderFemal 0.171 0.000 0.000 0.000
## FamSES2num -0.889 0.000 0.000 0.000 -0.529# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "mod")
model2 <- lmer(data = Data_noNeut, response ~ reputation + expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model2)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + expression + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18340.1 18392.0 -9162.0 18324.1 4840
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6465 -0.6323 -0.0446 0.5843 4.4893
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.362 1.537
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.96910 0.55778 101.95536 1.737 0.0853 .
## reputationlow -2.01547 0.05407 4747.00012 -37.277 < 2e-16 ***
## reputationhigh 2.02537 0.05407 4747.00012 37.460 < 2e-16 ***
## expressionexci -0.06683 0.04415 4747.00012 -1.514 0.1301
## GenderFemale 0.55667 0.38335 100.99965 1.452 0.1496
## FamSES2num 0.45792 0.10168 100.99965 4.503 1.8e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnl rpttnh exprss GndrFm
## reputatinlw -0.048
## reputatnhgh -0.048 0.500
## expressinxc -0.040 0.000 0.000
## GenderFemal 0.171 0.000 0.000 0.000
## FamSES2num -0.889 0.000 0.000 0.000 -0.529# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "high")
model2 <- lmer(data = Data_noNeut, response ~ reputation + expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model2)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation + expression + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18340.1 18392.0 -9162.0 18324.1 4840
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6465 -0.6323 -0.0446 0.5843 4.4893
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.362 1.537
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.99447 0.55778 101.95535 5.369 5.01e-07 ***
## reputationmod -2.02537 0.05407 4747.00012 -37.460 < 2e-16 ***
## reputationlow -4.04084 0.05407 4747.00012 -74.736 < 2e-16 ***
## expressionexci -0.06683 0.04415 4747.00012 -1.514 0.13
## GenderFemale 0.55667 0.38335 100.99965 1.452 0.15
## FamSES2num 0.45792 0.10168 100.99965 4.503 1.80e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnm rpttnl exprss GndrFm
## reputatinmd -0.048
## reputatinlw -0.048 0.500
## expressinxc -0.040 0.000 0.000
## GenderFemal 0.171 0.000 0.000 0.000
## FamSES2num -0.889 0.000 0.000 0.000 -0.529# model comparison
anova(model1, model2)## Data: Data_noNeut
## Models:
## model1: response ~ reputation + Gender + FamSES2num + (1 | pID)
## model2: response ~ reputation + expression + Gender + FamSES2num + (1 |
## model2: pID)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## model1 7 18340 18386 -9163.2 18326
## model2 8 18340 18392 -9162.0 18324 2.2912 1 0.1301Model 3: Offer ~ Reputation * Expression
# reputation and expression (interactive)
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "low")
model3 <- lmer(data = Data_noNeut, response ~ reputation * expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model3)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation * expression + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18341.0 18405.9 -9160.5 18321.0 4838
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6403 -0.6320 -0.0404 0.5821 4.5169
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.361 1.536
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.08659 0.55865 102.59400 -1.945
## reputationhigh 4.06931 0.07644 4747.00008 53.236
## reputationmod 2.10767 0.07644 4747.00008 27.573
## expressionexci 0.01361 0.07644 4747.00008 0.178
## GenderFemale 0.55667 0.38335 100.99975 1.452
## FamSES2num 0.45792 0.10168 100.99974 4.503
## reputationhigh:expressionexci -0.05693 0.10810 4747.00008 -0.527
## reputationmod:expressionexci -0.18441 0.10810 4747.00008 -1.706
## Pr(>|t|)
## (Intercept) 0.0545 .
## reputationhigh < 2e-16 ***
## reputationmod < 2e-16 ***
## expressionexci 0.8587
## GenderFemale 0.1496
## FamSES2num 1.8e-05 ***
## reputationhigh:expressionexci 0.5985
## reputationmod:expressionexci 0.0881 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnh rpttnm exprss GndrFm FmSES2 rpttnh:
## reputatnhgh -0.068
## reputatinmd -0.068 0.500
## expressinxc -0.068 0.500 0.500
## GenderFemal 0.171 0.000 0.000 0.000
## FamSES2num -0.887 0.000 0.000 0.000 -0.529
## rpttnhgh:xp 0.048 -0.707 -0.354 -0.707 0.000 0.000
## rpttnmd:xpr 0.048 -0.354 -0.707 -0.707 0.000 0.000 0.500# to see all contrasts, re-level reputation and run again
Data_noNeut$reputation <- relevel(Data_noNeut$reputation, ref = "mod")
model3 <- lmer(data = Data_noNeut, response ~ reputation * expression + Gender + FamSES2num + (1 | pID), REML=FALSE)
summary(model3)## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: response ~ reputation * expression + Gender + FamSES2num + (1 |
## pID)
## Data: Data_noNeut
##
## AIC BIC logLik deviance df.resid
## 18341.0 18405.9 -9160.5 18321.0 4838
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6403 -0.6320 -0.0404 0.5821 4.5169
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.361 1.536
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.02108 0.55865 102.59401 1.828
## reputationlow -2.10767 0.07644 4747.00008 -27.573
## reputationhigh 1.96163 0.07644 4747.00008 25.663
## expressionexci -0.17079 0.07644 4747.00008 -2.234
## GenderFemale 0.55667 0.38335 100.99975 1.452
## FamSES2num 0.45792 0.10168 100.99975 4.503
## reputationlow:expressionexci 0.18441 0.10810 4747.00008 1.706
## reputationhigh:expressionexci 0.12748 0.10810 4747.00008 1.179
## Pr(>|t|)
## (Intercept) 0.0705 .
## reputationlow < 2e-16 ***
## reputationhigh < 2e-16 ***
## expressionexci 0.0255 *
## GenderFemale 0.1496
## FamSES2num 1.8e-05 ***
## reputationlow:expressionexci 0.0881 .
## reputationhigh:expressionexci 0.2384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttnl rpttnh exprss GndrFm FmSES2 rpttnl:
## reputatinlw -0.068
## reputatnhgh -0.068 0.500
## expressinxc -0.068 0.500 0.500
## GenderFemal 0.171 0.000 0.000 0.000
## FamSES2num -0.887 0.000 0.000 0.000 -0.529
## rpttnlw:xpr 0.048 -0.707 -0.354 -0.707 0.000 0.000
## rpttnhgh:xp 0.048 -0.354 -0.707 -0.707 0.000 0.000 0.500# to see all contrasts, re-level reputation and run again
Data_noNeut$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
## 18341.0 18405.9 -9160.5 18321.0 4838
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6403 -0.6320 -0.0404 0.5821 4.5169
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.361 1.536
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.98271 0.55865 102.59403 5.339
## reputationmod -1.96163 0.07644 4747.00008 -25.663
## reputationlow -4.06931 0.07644 4747.00008 -53.236
## expressionexci -0.04332 0.07644 4747.00008 -0.567
## GenderFemale 0.55667 0.38335 100.99976 1.452
## FamSES2num 0.45792 0.10168 100.99976 4.503
## reputationmod:expressionexci -0.12748 0.10810 4747.00008 -1.179
## reputationlow:expressionexci 0.05693 0.10810 4747.00008 0.527
## Pr(>|t|)
## (Intercept) 5.63e-07 ***
## reputationmod < 2e-16 ***
## reputationlow < 2e-16 ***
## expressionexci 0.571
## GenderFemale 0.150
## FamSES2num 1.80e-05 ***
## reputationmod:expressionexci 0.238
## reputationlow:expressionexci 0.598
## ---
## 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 0.171 0.000 0.000 0.000
## FamSES2num -0.887 0.000 0.000 0.000 -0.529
## 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 18340 18392 -9162.0 18324
## model3 10 18341 18406 -9160.5 18321 3.0509 2 0.2175Model 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
## 18343.0 18414.4 -9160.5 18321.0 4837
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6425 -0.6299 -0.0416 0.5801 4.5191
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.361 1.536
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.083e+00 5.591e-01 1.029e+02 -1.938
## reputationhigh 4.069e+00 7.644e-02 4.747e+03 53.236
## reputationmod 2.108e+00 7.644e-02 4.747e+03 27.573
## expressionexci 1.361e-02 7.644e-02 4.747e+03 0.178
## racewhite -6.601e-03 4.413e-02 4.747e+03 -0.150
## GenderFemale 5.567e-01 3.833e-01 1.010e+02 1.452
## FamSES2num 4.579e-01 1.017e-01 1.010e+02 4.503
## reputationhigh:expressionexci -5.693e-02 1.081e-01 4.747e+03 -0.527
## reputationmod:expressionexci -1.844e-01 1.081e-01 4.747e+03 -1.706
## Pr(>|t|)
## (Intercept) 0.0554 .
## reputationhigh < 2e-16 ***
## reputationmod < 2e-16 ***
## expressionexci 0.8587
## racewhite 0.8811
## GenderFemale 0.1496
## FamSES2num 1.8e-05 ***
## reputationhigh:expressionexci 0.5985
## reputationmod:expressionexci 0.0881 .
## ---
## 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 0.171 0.000 0.000 0.000 0.000
## FamSES2num -0.887 0.000 0.000 0.000 0.000 -0.529
## 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
## 18343.0 18414.4 -9160.5 18321.0 4837
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6425 -0.6299 -0.0416 0.5801 4.5191
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.361 1.536
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.024e+00 5.591e-01 1.029e+02 1.832
## reputationlow -2.108e+00 7.644e-02 4.747e+03 -27.573
## reputationhigh 1.962e+00 7.644e-02 4.747e+03 25.663
## expressionexci -1.708e-01 7.644e-02 4.747e+03 -2.234
## racewhite -6.601e-03 4.413e-02 4.747e+03 -0.150
## GenderFemale 5.567e-01 3.833e-01 1.010e+02 1.452
## FamSES2num 4.579e-01 1.017e-01 1.010e+02 4.503
## reputationlow:expressionexci 1.844e-01 1.081e-01 4.747e+03 1.706
## reputationhigh:expressionexci 1.275e-01 1.081e-01 4.747e+03 1.179
## Pr(>|t|)
## (Intercept) 0.0698 .
## reputationlow < 2e-16 ***
## reputationhigh < 2e-16 ***
## expressionexci 0.0255 *
## racewhite 0.8811
## GenderFemale 0.1496
## FamSES2num 1.8e-05 ***
## reputationlow:expressionexci 0.0881 .
## reputationhigh:expressionexci 0.2384
## ---
## 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 0.171 0.000 0.000 0.000 0.000
## FamSES2num -0.887 0.000 0.000 0.000 0.000 -0.529
## 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
## 18343.0 18414.4 -9160.5 18321.0 4837
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6425 -0.6299 -0.0416 0.5801 4.5191
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.361 1.536
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.986e+00 5.591e-01 1.029e+02 5.341
## reputationmod -1.962e+00 7.644e-02 4.747e+03 -25.663
## reputationlow -4.069e+00 7.644e-02 4.747e+03 -53.236
## expressionexci -4.332e-02 7.644e-02 4.747e+03 -0.567
## racewhite -6.601e-03 4.413e-02 4.747e+03 -0.150
## GenderFemale 5.567e-01 3.833e-01 1.010e+02 1.452
## FamSES2num 4.579e-01 1.017e-01 1.010e+02 4.503
## reputationmod:expressionexci -1.275e-01 1.081e-01 4.747e+03 -1.179
## reputationlow:expressionexci 5.693e-02 1.081e-01 4.747e+03 0.527
## Pr(>|t|)
## (Intercept) 5.56e-07 ***
## reputationmod < 2e-16 ***
## reputationlow < 2e-16 ***
## expressionexci 0.571
## racewhite 0.881
## GenderFemale 0.150
## FamSES2num 1.80e-05 ***
## reputationmod:expressionexci 0.238
## reputationlow:expressionexci 0.598
## ---
## 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 0.171 0.000 0.000 0.000 0.000
## FamSES2num -0.887 0.000 0.000 0.000 0.000 -0.529
## 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
## 18327.0 18430.8 -9147.5 18295.0 4832
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5926 -0.6097 -0.0305 0.5920 4.4639
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.348 1.532
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -1.18684 0.56123 104.50377
## reputationhigh 4.07673 0.10781 4747.00009
## reputationmod 2.32426 0.10781 4747.00009
## expressionexci 0.13861 0.10781 4747.00009
## racewhite 0.20050 0.10781 4747.00009
## GenderFemale 0.55667 0.38335 100.99973
## FamSES2num 0.45792 0.10168 100.99973
## reputationhigh:expressionexci 0.08911 0.15246 4747.00009
## reputationmod:expressionexci -0.53218 0.15246 4747.00009
## reputationhigh:racewhite -0.01485 0.15246 4747.00009
## reputationmod:racewhite -0.43317 0.15246 4747.00009
## expressionexci:racewhite -0.25000 0.15246 4747.00009
## reputationhigh:expressionexci:racewhite -0.29208 0.21561 4747.00009
## reputationmod:expressionexci:racewhite 0.69554 0.21561 4747.00009
## t value Pr(>|t|)
## (Intercept) -2.115 0.036833 *
## reputationhigh 37.816 < 2e-16 ***
## reputationmod 21.560 < 2e-16 ***
## expressionexci 1.286 0.198582
## racewhite 1.860 0.062977 .
## GenderFemale 1.452 0.149569
## FamSES2num 4.503 1.8e-05 ***
## reputationhigh:expressionexci 0.584 0.558928
## reputationmod:expressionexci -3.491 0.000486 ***
## reputationhigh:racewhite -0.097 0.922403
## reputationmod:racewhite -2.841 0.004514 **
## expressionexci:racewhite -1.640 0.101117
## reputationhigh:expressionexci:racewhite -1.355 0.175590
## reputationmod:expressionexci:racewhite 3.226 0.001264 **
## ---
## 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
## 18327.0 18430.8 -9147.5 18295.0 4832
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5926 -0.6097 -0.0305 0.5920 4.4639
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.348 1.532
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.1374 0.5612 104.5038
## reputationlow -2.3243 0.1078 4747.0001
## reputationhigh 1.7525 0.1078 4747.0001
## expressionexci -0.3936 0.1078 4747.0001
## racewhite -0.2327 0.1078 4747.0001
## GenderFemale 0.5567 0.3833 100.9997
## FamSES2num 0.4579 0.1017 100.9997
## reputationlow:expressionexci 0.5322 0.1525 4747.0001
## reputationhigh:expressionexci 0.6213 0.1525 4747.0001
## reputationlow:racewhite 0.4332 0.1525 4747.0001
## reputationhigh:racewhite 0.4183 0.1525 4747.0001
## expressionexci:racewhite 0.4455 0.1525 4747.0001
## reputationlow:expressionexci:racewhite -0.6955 0.2156 4747.0001
## reputationhigh:expressionexci:racewhite -0.9876 0.2156 4747.0001
## t value Pr(>|t|)
## (Intercept) 2.027 0.045247 *
## reputationlow -21.560 < 2e-16 ***
## reputationhigh 16.256 < 2e-16 ***
## expressionexci -3.651 0.000264 ***
## racewhite -2.158 0.030956 *
## GenderFemale 1.452 0.149569
## FamSES2num 4.503 1.80e-05 ***
## reputationlow:expressionexci 3.491 0.000486 ***
## reputationhigh:expressionexci 4.075 4.67e-05 ***
## reputationlow:racewhite 2.841 0.004514 **
## reputationhigh:racewhite 2.744 0.006096 **
## expressionexci:racewhite 2.922 0.003490 **
## reputationlow:expressionexci:racewhite -3.226 0.001264 **
## reputationhigh:expressionexci:racewhite -4.581 4.76e-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
## 18327.0 18430.8 -9147.5 18295.0 4832
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5926 -0.6097 -0.0305 0.5920 4.4639
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.348 1.532
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.88989 0.56123 104.50376
## reputationmod -1.75248 0.10781 4747.00009
## reputationlow -4.07673 0.10781 4747.00009
## expressionexci 0.22772 0.10781 4747.00010
## racewhite 0.18564 0.10781 4747.00010
## GenderFemale 0.55667 0.38335 100.99973
## FamSES2num 0.45792 0.10168 100.99972
## reputationmod:expressionexci -0.62129 0.15246 4747.00009
## reputationlow:expressionexci -0.08911 0.15246 4747.00009
## reputationmod:racewhite -0.41832 0.15246 4747.00009
## reputationlow:racewhite 0.01485 0.15246 4747.00009
## expressionexci:racewhite -0.54208 0.15246 4747.00009
## reputationmod:expressionexci:racewhite 0.98762 0.21561 4747.00009
## reputationlow:expressionexci:racewhite 0.29208 0.21561 4747.00009
## t value Pr(>|t|)
## (Intercept) 5.149 1.24e-06 ***
## reputationmod -16.256 < 2e-16 ***
## reputationlow -37.816 < 2e-16 ***
## expressionexci 2.112 0.034708 *
## racewhite 1.722 0.085129 .
## GenderFemale 1.452 0.149569
## FamSES2num 4.503 1.80e-05 ***
## reputationmod:expressionexci -4.075 4.67e-05 ***
## reputationlow:expressionexci -0.584 0.558928
## reputationmod:racewhite -2.744 0.006096 **
## reputationlow:racewhite 0.097 0.922403
## expressionexci:racewhite -3.556 0.000381 ***
## reputationmod:expressionexci:racewhite 4.581 4.76e-06 ***
## reputationlow:expressionexci:racewhite 1.355 0.175590
## ---
## 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
## 18322.7 18432.9 -9144.3 18288.7 4831
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6317 -0.5958 -0.0301 0.5870 4.4307
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.345 1.531
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -1.24212 0.56166 104.82008
## reputationhigh 4.07673 0.10773 4747.00010
## reputationmod 2.32426 0.10773 4747.00010
## expressionexci 0.13861 0.10773 4747.00010
## racewhite 0.20050 0.10773 4747.00010
## sexmale 0.11056 0.04398 4747.00010
## GenderFemale 0.55667 0.38335 100.99972
## FamSES2num 0.45792 0.10168 100.99972
## reputationhigh:expressionexci 0.08911 0.15236 4747.00010
## reputationmod:expressionexci -0.53218 0.15236 4747.00010
## reputationhigh:racewhite -0.01485 0.15236 4747.00010
## reputationmod:racewhite -0.43317 0.15236 4747.00010
## expressionexci:racewhite -0.25000 0.15236 4747.00010
## reputationhigh:expressionexci:racewhite -0.29208 0.21547 4747.00010
## reputationmod:expressionexci:racewhite 0.69554 0.21547 4747.00010
## t value Pr(>|t|)
## (Intercept) -2.212 0.029170 *
## reputationhigh 37.841 < 2e-16 ***
## reputationmod 21.574 < 2e-16 ***
## expressionexci 1.287 0.198284
## racewhite 1.861 0.062802 .
## sexmale 2.514 0.011977 *
## GenderFemale 1.452 0.149569
## FamSES2num 4.503 1.8e-05 ***
## reputationhigh:expressionexci 0.585 0.558666
## reputationmod:expressionexci -3.493 0.000482 ***
## reputationhigh:racewhite -0.097 0.922351
## reputationmod:racewhite -2.843 0.004487 **
## expressionexci:racewhite -1.641 0.100891
## reputationhigh:expressionexci:racewhite -1.356 0.175302
## reputationmod:expressionexci:racewhite 3.228 0.001255 **
## ---
## 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
## 18322.7 18432.9 -9144.3 18288.7 4831
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6317 -0.5958 -0.0301 0.5870 4.4307
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.345 1.531
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.08213 0.56166 104.82008
## reputationlow -2.32426 0.10773 4747.00010
## reputationhigh 1.75248 0.10773 4747.00010
## expressionexci -0.39356 0.10773 4747.00010
## racewhite -0.23267 0.10773 4747.00010
## sexmale 0.11056 0.04398 4747.00010
## GenderFemale 0.55667 0.38335 100.99972
## FamSES2num 0.45792 0.10168 100.99972
## reputationlow:expressionexci 0.53218 0.15236 4747.00010
## reputationhigh:expressionexci 0.62129 0.15236 4747.00010
## reputationlow:racewhite 0.43317 0.15236 4747.00010
## reputationhigh:racewhite 0.41832 0.15236 4747.00010
## expressionexci:racewhite 0.44554 0.15236 4747.00010
## reputationlow:expressionexci:racewhite -0.69554 0.21547 4747.00010
## reputationhigh:expressionexci:racewhite -0.98762 0.21547 4747.00010
## t value Pr(>|t|)
## (Intercept) 1.927 0.056727 .
## reputationlow -21.574 < 2e-16 ***
## reputationhigh 16.267 < 2e-16 ***
## expressionexci -3.653 0.000262 ***
## racewhite -2.160 0.030845 *
## sexmale 2.514 0.011977 *
## GenderFemale 1.452 0.149569
## FamSES2num 4.503 1.80e-05 ***
## reputationlow:expressionexci 3.493 0.000482 ***
## reputationhigh:expressionexci 4.078 4.62e-05 ***
## reputationlow:racewhite 2.843 0.004487 **
## reputationhigh:racewhite 2.746 0.006063 **
## expressionexci:racewhite 2.924 0.003468 **
## reputationlow:expressionexci:racewhite -3.228 0.001255 **
## reputationhigh:expressionexci:racewhite -4.584 4.69e-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
## 18322.7 18432.9 -9144.3 18288.7 4831
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6317 -0.5958 -0.0301 0.5870 4.4307
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.345 1.531
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.83461 0.56166 104.82007
## reputationmod -1.75248 0.10773 4747.00009
## reputationlow -4.07673 0.10773 4747.00009
## expressionexci 0.22772 0.10773 4747.00009
## racewhite 0.18564 0.10773 4747.00009
## sexmale 0.11056 0.04398 4747.00010
## GenderFemale 0.55667 0.38335 100.99972
## FamSES2num 0.45792 0.10168 100.99972
## reputationmod:expressionexci -0.62129 0.15236 4747.00009
## reputationlow:expressionexci -0.08911 0.15236 4747.00009
## reputationmod:racewhite -0.41832 0.15236 4747.00009
## reputationlow:racewhite 0.01485 0.15236 4747.00009
## expressionexci:racewhite -0.54208 0.15236 4747.00009
## reputationmod:expressionexci:racewhite 0.98762 0.21547 4747.00009
## reputationlow:expressionexci:racewhite 0.29208 0.21547 4747.00009
## t value Pr(>|t|)
## (Intercept) 5.047 1.90e-06 ***
## reputationmod -16.267 < 2e-16 ***
## reputationlow -37.841 < 2e-16 ***
## expressionexci 2.114 0.034588 *
## racewhite 1.723 0.084922 .
## sexmale 2.514 0.011977 *
## GenderFemale 1.452 0.149569
## FamSES2num 4.503 1.80e-05 ***
## reputationmod:expressionexci -4.078 4.62e-05 ***
## reputationlow:expressionexci -0.585 0.558666
## reputationmod:racewhite -2.746 0.006063 **
## reputationlow:racewhite 0.097 0.922351
## expressionexci:racewhite -3.558 0.000377 ***
## reputationmod:expressionexci:racewhite 4.584 4.69e-06 ***
## reputationlow:expressionexci:racewhite 1.356 0.175302
## ---
## 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
## 18331.7 18513.3 -9137.8 18275.7 4820
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6408 -0.5996 -0.0253 0.5816 4.4470
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.509 1.584
## Residual 2.338 1.529
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) -1.28833 0.56635
## reputationhigh 4.11881 0.15215
## reputationmod 2.48515 0.15215
## expressionexci 0.12376 0.15215
## racewhite 0.26238 0.15215
## sexmale 0.20297 0.15215
## GenderFemale 0.55667 0.38335
## FamSES2num 0.45792 0.10168
## reputationhigh:expressionexci 0.19307 0.21517
## reputationmod:expressionexci -0.51485 0.21517
## reputationhigh:racewhite -0.16832 0.21517
## reputationmod:racewhite -0.51980 0.21517
## expressionexci:racewhite -0.35644 0.21517
## reputationhigh:sexmale -0.08416 0.21517
## reputationmod:sexmale -0.32178 0.21517
## expressionexci:sexmale 0.02970 0.21517
## racewhite:sexmale -0.12376 0.21517
## reputationhigh:expressionexci:racewhite -0.21782 0.30430
## reputationmod:expressionexci:racewhite 0.63861 0.30430
## reputationhigh:expressionexci:sexmale -0.20792 0.30430
## reputationmod:expressionexci:sexmale -0.03465 0.30430
## reputationhigh:racewhite:sexmale 0.30693 0.30430
## reputationmod:racewhite:sexmale 0.17327 0.30430
## expressionexci:racewhite:sexmale 0.21287 0.30430
## reputationhigh:expressionexci:racewhite:sexmale -0.14851 0.43035
## reputationmod:expressionexci:racewhite:sexmale 0.11386 0.43035
## df t value
## (Intercept) 108.36273 -2.275
## reputationhigh 4747.00002 27.071
## reputationmod 4747.00002 16.334
## expressionexci 4747.00002 0.813
## racewhite 4747.00002 1.724
## sexmale 4747.00002 1.334
## GenderFemale 100.99999 1.452
## FamSES2num 100.99999 4.503
## reputationhigh:expressionexci 4747.00001 0.897
## reputationmod:expressionexci 4747.00001 -2.393
## reputationhigh:racewhite 4747.00001 -0.782
## reputationmod:racewhite 4747.00001 -2.416
## expressionexci:racewhite 4747.00001 -1.657
## reputationhigh:sexmale 4747.00001 -0.391
## reputationmod:sexmale 4747.00001 -1.495
## expressionexci:sexmale 4747.00001 0.138
## racewhite:sexmale 4747.00001 -0.575
## reputationhigh:expressionexci:racewhite 4747.00000 -0.716
## reputationmod:expressionexci:racewhite 4747.00000 2.099
## reputationhigh:expressionexci:sexmale 4747.00000 -0.683
## reputationmod:expressionexci:sexmale 4747.00001 -0.114
## reputationhigh:racewhite:sexmale 4747.00000 1.009
## reputationmod:racewhite:sexmale 4747.00000 0.569
## expressionexci:racewhite:sexmale 4747.00000 0.700
## reputationhigh:expressionexci:racewhite:sexmale 4747.00000 -0.345
## reputationmod:expressionexci:racewhite:sexmale 4747.00000 0.265
## Pr(>|t|)
## (Intercept) 0.0249 *
## reputationhigh < 2e-16 ***
## reputationmod < 2e-16 ***
## expressionexci 0.4160
## racewhite 0.0847 .
## sexmale 0.1823
## GenderFemale 0.1496
## FamSES2num 1.8e-05 ***
## reputationhigh:expressionexci 0.3696
## reputationmod:expressionexci 0.0168 *
## reputationhigh:racewhite 0.4341
## reputationmod:racewhite 0.0157 *
## expressionexci:racewhite 0.0977 .
## reputationhigh:sexmale 0.6957
## reputationmod:sexmale 0.1349
## expressionexci:sexmale 0.8902
## racewhite:sexmale 0.5652
## reputationhigh:expressionexci:racewhite 0.4741
## reputationmod:expressionexci:racewhite 0.0359 *
## reputationhigh:expressionexci:sexmale 0.4945
## reputationmod:expressionexci:sexmale 0.9093
## reputationhigh:racewhite:sexmale 0.3132
## reputationmod:racewhite:sexmale 0.5691
## expressionexci:racewhite:sexmale 0.4842
## reputationhigh:expressionexci:racewhite:sexmale 0.7300
## reputationmod:expressionexci:racewhite:sexmale 0.7913
## ---
## 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
## 18331.7 18513.3 -9137.8 18275.7 4820
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6408 -0.5996 -0.0253 0.5816 4.4470
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.509 1.584
## Residual 2.338 1.529
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1.19682 0.56635
## reputationlow -2.48515 0.15215
## reputationhigh 1.63366 0.15215
## expressionexci -0.39109 0.15215
## racewhite -0.25743 0.15215
## sexmale -0.11881 0.15215
## GenderFemale 0.55667 0.38335
## FamSES2num 0.45792 0.10168
## reputationlow:expressionexci 0.51485 0.21517
## reputationhigh:expressionexci 0.70792 0.21517
## reputationlow:racewhite 0.51980 0.21517
## reputationhigh:racewhite 0.35148 0.21517
## expressionexci:racewhite 0.28218 0.21517
## reputationlow:sexmale 0.32178 0.21517
## reputationhigh:sexmale 0.23762 0.21517
## expressionexci:sexmale -0.00495 0.21517
## racewhite:sexmale 0.04951 0.21517
## reputationlow:expressionexci:racewhite -0.63861 0.30430
## reputationhigh:expressionexci:racewhite -0.85644 0.30430
## reputationlow:expressionexci:sexmale 0.03465 0.30430
## reputationhigh:expressionexci:sexmale -0.17327 0.30430
## reputationlow:racewhite:sexmale -0.17327 0.30430
## reputationhigh:racewhite:sexmale 0.13366 0.30430
## expressionexci:racewhite:sexmale 0.32673 0.30430
## reputationlow:expressionexci:racewhite:sexmale -0.11386 0.43035
## reputationhigh:expressionexci:racewhite:sexmale -0.26238 0.43035
## df t value
## (Intercept) 108.36274 2.113
## reputationlow 4747.00004 -16.334
## reputationhigh 4747.00004 10.737
## expressionexci 4747.00006 -2.570
## racewhite 4747.00006 -1.692
## sexmale 4747.00006 -0.781
## GenderFemale 100.99999 1.452
## FamSES2num 100.99999 4.503
## reputationlow:expressionexci 4747.00003 2.393
## reputationhigh:expressionexci 4747.00003 3.290
## reputationlow:racewhite 4747.00003 2.416
## reputationhigh:racewhite 4747.00003 1.634
## expressionexci:racewhite 4747.00004 1.311
## reputationlow:sexmale 4747.00003 1.495
## reputationhigh:sexmale 4747.00003 1.104
## expressionexci:sexmale 4747.00004 -0.023
## racewhite:sexmale 4747.00004 0.230
## reputationlow:expressionexci:racewhite 4747.00003 -2.099
## reputationhigh:expressionexci:racewhite 4747.00002 -2.814
## reputationlow:expressionexci:sexmale 4747.00003 0.114
## reputationhigh:expressionexci:sexmale 4747.00002 -0.569
## reputationlow:racewhite:sexmale 4747.00002 -0.569
## reputationhigh:racewhite:sexmale 4747.00002 0.439
## expressionexci:racewhite:sexmale 4747.00003 1.074
## reputationlow:expressionexci:racewhite:sexmale 4747.00002 -0.265
## reputationhigh:expressionexci:racewhite:sexmale 4747.00002 -0.610
## Pr(>|t|)
## (Intercept) 0.03688 *
## reputationlow < 2e-16 ***
## reputationhigh < 2e-16 ***
## expressionexci 0.01019 *
## racewhite 0.09073 .
## sexmale 0.43491
## GenderFemale 0.14957
## FamSES2num 1.8e-05 ***
## reputationlow:expressionexci 0.01676 *
## reputationhigh:expressionexci 0.00101 **
## reputationlow:racewhite 0.01574 *
## reputationhigh:racewhite 0.10243
## expressionexci:racewhite 0.18979
## reputationlow:sexmale 0.13486
## reputationhigh:sexmale 0.26950
## expressionexci:sexmale 0.98165
## racewhite:sexmale 0.81805
## reputationlow:expressionexci:racewhite 0.03590 *
## reputationhigh:expressionexci:racewhite 0.00491 **
## reputationlow:expressionexci:sexmale 0.90934
## reputationhigh:expressionexci:sexmale 0.56911
## reputationlow:racewhite:sexmale 0.56911
## reputationhigh:racewhite:sexmale 0.66050
## expressionexci:racewhite:sexmale 0.28300
## reputationlow:expressionexci:racewhite:sexmale 0.79134
## reputationhigh:expressionexci:racewhite:sexmale 0.54210
## ---
## 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
## 18331.7 18513.3 -9137.8 18275.7 4820
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6408 -0.5996 -0.0253 0.5816 4.4470
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.509 1.584
## Residual 2.338 1.529
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.83048 0.56635
## reputationmod -1.63366 0.15215
## reputationlow -4.11881 0.15215
## expressionexci 0.31683 0.15215
## racewhite 0.09406 0.15215
## sexmale 0.11881 0.15215
## GenderFemale 0.55667 0.38335
## FamSES2num 0.45792 0.10168
## reputationmod:expressionexci -0.70792 0.21517
## reputationlow:expressionexci -0.19307 0.21517
## reputationmod:racewhite -0.35149 0.21517
## reputationlow:racewhite 0.16832 0.21517
## expressionexci:racewhite -0.57426 0.21517
## reputationmod:sexmale -0.23762 0.21517
## reputationlow:sexmale 0.08416 0.21517
## expressionexci:sexmale -0.17822 0.21517
## racewhite:sexmale 0.18317 0.21517
## reputationmod:expressionexci:racewhite 0.85644 0.30430
## reputationlow:expressionexci:racewhite 0.21782 0.30430
## reputationmod:expressionexci:sexmale 0.17327 0.30430
## reputationlow:expressionexci:sexmale 0.20792 0.30430
## reputationmod:racewhite:sexmale -0.13366 0.30430
## reputationlow:racewhite:sexmale -0.30693 0.30430
## expressionexci:racewhite:sexmale 0.06436 0.30430
## reputationmod:expressionexci:racewhite:sexmale 0.26238 0.43035
## reputationlow:expressionexci:racewhite:sexmale 0.14851 0.43035
## df t value Pr(>|t|)
## (Intercept) 108.36269 4.998 2.24e-06
## reputationmod 4747.00002 -10.737 < 2e-16
## reputationlow 4747.00002 -27.071 < 2e-16
## expressionexci 4747.00003 2.082 0.03736
## racewhite 4747.00003 0.618 0.53647
## sexmale 4747.00003 0.781 0.43491
## GenderFemale 100.99995 1.452 0.14957
## FamSES2num 100.99995 4.503 1.80e-05
## reputationmod:expressionexci 4747.00002 -3.290 0.00101
## reputationlow:expressionexci 4747.00002 -0.897 0.36962
## reputationmod:racewhite 4747.00002 -1.634 0.10243
## reputationlow:racewhite 4747.00002 0.782 0.43411
## expressionexci:racewhite 4747.00003 -2.669 0.00764
## reputationmod:sexmale 4747.00002 -1.104 0.26950
## reputationlow:sexmale 4747.00002 0.391 0.69573
## expressionexci:sexmale 4747.00003 -0.828 0.40757
## racewhite:sexmale 4747.00003 0.851 0.39467
## reputationmod:expressionexci:racewhite 4747.00002 2.814 0.00491
## reputationlow:expressionexci:racewhite 4747.00002 0.716 0.47414
## reputationmod:expressionexci:sexmale 4747.00002 0.569 0.56911
## reputationlow:expressionexci:sexmale 4747.00002 0.683 0.49447
## reputationmod:racewhite:sexmale 4747.00002 -0.439 0.66050
## reputationlow:racewhite:sexmale 4747.00002 -1.009 0.31320
## expressionexci:racewhite:sexmale 4747.00003 0.211 0.83251
## reputationmod:expressionexci:racewhite:sexmale 4747.00002 0.610 0.54210
## reputationlow:expressionexci:racewhite:sexmale 4747.00002 0.345 0.73003
##
## (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
## 4857.2 4889.5 -2422.6 4845.2 1610
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6453 -0.3682 -0.0063 0.3907 9.3493
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.9777 1.994
## Residual 0.8987 0.948
## Number of obs: 1616, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.08690 0.69929 101.23011 1.554 0.123238
## expressionexci -0.17079 0.04717 1515.00000 -3.621 0.000303 ***
## GenderFemale 0.53699 0.48147 100.99999 1.115 0.267366
## FamSES2num 0.44905 0.12771 101.00000 3.516 0.000657 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) exprss GndrFm
## expressinxc -0.034
## GenderFemal 0.171 0.000
## FamSES2num -0.890 0.000 -0.529# 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
## 4835.4 4900.1 -2405.7 4811.4 1604
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6334 -0.4034 0.0020 0.3938 9.2678
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.9789 1.9947
## Residual 0.8789 0.9375
## Number of obs: 1616, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.26264 0.70162 102.57976 1.800
## expressionexci -0.39109 0.09329 1515.00015 -4.192
## racewhite -0.25743 0.09329 1515.00015 -2.760
## sexmale -0.11881 0.09329 1515.00015 -1.274
## GenderFemale 0.53699 0.48147 100.99954 1.115
## FamSES2num 0.44905 0.12771 100.99954 3.516
## expressionexci:racewhite 0.28218 0.13193 1515.00015 2.139
## expressionexci:sexmale -0.00495 0.13193 1515.00015 -0.038
## racewhite:sexmale 0.04951 0.13193 1515.00015 0.375
## expressionexci:racewhite:sexmale 0.32673 0.18657 1515.00015 1.751
## Pr(>|t|)
## (Intercept) 0.074860 .
## expressionexci 2.92e-05 ***
## racewhite 0.005859 **
## sexmale 0.202994
## GenderFemale 0.267366
## FamSES2num 0.000657 ***
## expressionexci:racewhite 0.032604 *
## expressionexci:sexmale 0.970072
## racewhite:sexmale 0.707532
## expressionexci:racewhite:sexmale 0.080110 .
## ---
## 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 0.171 0.000 0.000 0.000
## FamSES2num -0.887 0.000 0.000 0.000 -0.529
## 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
## 5025.5 5085.1 -2501.8 5003.5 1653
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5606 -0.4056 -0.0010 0.3990 9.2004
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 4.6550 2.1576
## Residual 0.8978 0.9475
## Number of obs: 1664, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 4.25962 0.21661
## expressionexci -0.37500 0.06570
## racewhite -0.23558 0.06570
## scale(iHAP, scale = F) 0.39709 0.34171
## scale(aHAP, scale = F) -0.67198 0.31981
## expressionexci:racewhite 0.42067 0.09291
## expressionexci:scale(iHAP, scale = F) -0.06464 0.09449
## racewhite:scale(iHAP, scale = F) -0.11235 0.09449
## expressionexci:racewhite:scale(iHAP, scale = F) 0.05732 0.13363
## df t value
## (Intercept) 111.55525 19.665
## expressionexci 1560.00000 -5.708
## racewhite 1560.00000 -3.586
## scale(iHAP, scale = F) 110.22410 1.162
## scale(aHAP, scale = F) 104.00000 -2.101
## expressionexci:racewhite 1560.00000 4.528
## expressionexci:scale(iHAP, scale = F) 1560.00000 -0.684
## racewhite:scale(iHAP, scale = F) 1560.00000 -1.189
## expressionexci:racewhite:scale(iHAP, scale = F) 1560.00000 0.429
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## expressionexci 1.37e-08 ***
## racewhite 0.000346 ***
## scale(iHAP, scale = F) 0.247725
## scale(aHAP, scale = F) 0.038038 *
## expressionexci:racewhite 6.42e-06 ***
## expressionexci:scale(iHAP, scale = F) 0.494011
## racewhite:scale(iHAP, scale = F) 0.234587
## expressionexci:racewhite:scale(iHAP, scale = F) 0.667988
## ---
## 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 -0.411
## exprssnxc:r 0.107 -0.707 -0.707 0.000 0.000
## e:(HAP,s=F) 0.000 0.000 0.000 -0.138 0.000
## r:(HAP,s=F) 0.000 0.000 0.000 -0.138 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
## 5028.1 5087.7 -2503.1 5006.1 1653
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6344 -0.4081 0.0189 0.4057 9.2896
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 4.839 2.1998
## Residual 0.897 0.9471
## Number of obs: 1664, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 4.25962 0.22065
## expressionexci -0.37500 0.06567
## racewhite -0.23558 0.06567
## scale(iLAP, scale = F) 0.13811 0.32487
## scale(aLAP, scale = F) -0.04216 0.27844
## expressionexci:racewhite 0.42067 0.09287
## expressionexci:scale(iLAP, scale = F) 0.14000 0.09667
## racewhite:scale(iLAP, scale = F) 0.06204 0.09667
## expressionexci:racewhite:scale(iLAP, scale = F) -0.22595 0.13671
## df t value
## (Intercept) 111.26016 19.305
## expressionexci 1560.00000 -5.710
## racewhite 1560.00000 -3.587
## scale(iLAP, scale = F) 111.25809 0.425
## scale(aLAP, scale = F) 103.99999 -0.151
## expressionexci:racewhite 1560.00000 4.530
## expressionexci:scale(iLAP, scale = F) 1560.00000 1.448
## racewhite:scale(iLAP, scale = F) 1560.00000 0.642
## expressionexci:racewhite:scale(iLAP, scale = F) 1560.00000 -1.653
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## expressionexci 1.35e-08 ***
## racewhite 0.000344 ***
## scale(iLAP, scale = F) 0.671568
## scale(aLAP, scale = F) 0.879930
## expressionexci:racewhite 6.36e-06 ***
## expressionexci:scale(iLAP, scale = F) 0.147752
## racewhite:scale(iLAP, scale = F) 0.521141
## expressionexci:racewhite:scale(iLAP, scale = F) 0.098595 .
## ---
## 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 -0.016
## exprssnxc:r 0.105 -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 0.105 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 76 rows containing missing values (geom_point).
#iLAP
Data_noNeut_mod %>%
ggplot(aes(x = iLAP, y = response, fill = expression)) +
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 ($)") +
ylim(c(0,10))## Warning: Removed 69 rows containing missing values (geom_point).
Contrast Coding
# purpose: to check for main effect of reputation
# check default coding
contrasts(Data_noNeut$reputation)## mod low
## high 0 0
## mod 1 0
## low 0 1# use effect coding
contrasts(Data_noNeut$reputation) <- cbind(LvMean = c(1, -1, 0),
HvMean = c(0, -1, 1))
contrasts(Data_noNeut$reputation)## LvMean HvMean
## high 1 0
## mod -1 -1
## low 0 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
## 18910.2 18942.7 -9450.1 18900.2 4987
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6482 -0.6306 -0.0329 0.5828 4.5234
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.502 1.871
## Residual 2.361 1.537
## Number of obs: 4992, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.07151 0.18480 104.00000 22.03 <2e-16 ***
## reputationLvMean 2.05228 0.03076 4888.00000 66.73 <2e-16 ***
## reputationHvMean -2.04026 0.03076 4888.00000 -66.34 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rpttLM
## reputtnLvMn 0.000
## reputtnHvMn 0.000 -0.500# 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 4.07# 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 6.12
## 2 mod 4.06
## 3 low 2.03Gender 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
## 10554.8 10716.4 -5250.4 10500.8 2901
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0243 -0.5411 -0.0281 0.5266 4.7570
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.749 1.658
## Residual 1.935 1.391
## Number of obs: 2928, groups: pID, 61
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 6.878e-01 1.241e+00
## reputationhigh 5.074e+00 1.781e-01
## reputationmod 3.074e+00 1.781e-01
## expressionexci 1.066e-01 1.781e-01
## racewhite 3.934e-01 1.781e-01
## sexmale 3.033e-01 1.781e-01
## FamSES2num 1.719e-01 1.771e-01
## reputationhigh:expressionexci 3.279e-01 2.519e-01
## reputationmod:expressionexci -5.984e-01 2.519e-01
## reputationhigh:racewhite -2.131e-01 2.519e-01
## reputationmod:racewhite -7.131e-01 2.519e-01
## expressionexci:racewhite -4.016e-01 2.519e-01
## reputationhigh:sexmale -3.279e-02 2.519e-01
## reputationmod:sexmale -5.164e-01 2.519e-01
## expressionexci:sexmale 4.918e-02 2.519e-01
## racewhite:sexmale -3.115e-01 2.519e-01
## reputationhigh:expressionexci:racewhite -3.443e-01 3.562e-01
## reputationmod:expressionexci:racewhite 8.033e-01 3.562e-01
## reputationhigh:expressionexci:sexmale -3.934e-01 3.562e-01
## reputationmod:expressionexci:sexmale 1.393e-01 3.562e-01
## reputationhigh:racewhite:sexmale 3.525e-01 3.562e-01
## reputationmod:racewhite:sexmale 4.426e-01 3.562e-01
## expressionexci:racewhite:sexmale 2.869e-01 3.562e-01
## reputationhigh:expressionexci:racewhite:sexmale 8.197e-03 5.038e-01
## reputationmod:expressionexci:racewhite:sexmale -1.885e-01 5.038e-01
## df t value
## (Intercept) 6.222e+01 0.554
## reputationhigh 2.867e+03 28.486
## reputationmod 2.867e+03 17.257
## expressionexci 2.867e+03 0.598
## racewhite 2.867e+03 2.209
## sexmale 2.867e+03 1.703
## FamSES2num 6.100e+01 0.971
## reputationhigh:expressionexci 2.867e+03 1.302
## reputationmod:expressionexci 2.867e+03 -2.375
## reputationhigh:racewhite 2.867e+03 -0.846
## reputationmod:racewhite 2.867e+03 -2.831
## expressionexci:racewhite 2.867e+03 -1.594
## reputationhigh:sexmale 2.867e+03 -0.130
## reputationmod:sexmale 2.867e+03 -2.050
## expressionexci:sexmale 2.867e+03 0.195
## racewhite:sexmale 2.867e+03 -1.237
## reputationhigh:expressionexci:racewhite 2.867e+03 -0.966
## reputationmod:expressionexci:racewhite 2.867e+03 2.255
## reputationhigh:expressionexci:sexmale 2.867e+03 -1.104
## reputationmod:expressionexci:sexmale 2.867e+03 0.391
## reputationhigh:racewhite:sexmale 2.867e+03 0.989
## reputationmod:racewhite:sexmale 2.867e+03 1.243
## expressionexci:racewhite:sexmale 2.867e+03 0.805
## reputationhigh:expressionexci:racewhite:sexmale 2.867e+03 0.016
## reputationmod:expressionexci:racewhite:sexmale 2.867e+03 -0.374
## Pr(>|t|)
## (Intercept) 0.58149
## reputationhigh < 2e-16 ***
## reputationmod < 2e-16 ***
## expressionexci 0.54972
## racewhite 0.02726 *
## sexmale 0.08873 .
## FamSES2num 0.33540
## reputationhigh:expressionexci 0.19315
## reputationmod:expressionexci 0.01759 *
## reputationhigh:racewhite 0.39759
## reputationmod:racewhite 0.00467 **
## expressionexci:racewhite 0.11093
## reputationhigh:sexmale 0.89645
## reputationmod:sexmale 0.04045 *
## expressionexci:sexmale 0.84522
## racewhite:sexmale 0.21636
## reputationhigh:expressionexci:racewhite 0.33392
## reputationmod:expressionexci:racewhite 0.02421 *
## reputationhigh:expressionexci:sexmale 0.26948
## reputationmod:expressionexci:sexmale 0.69570
## reputationhigh:racewhite:sexmale 0.32254
## reputationmod:racewhite:sexmale 0.21414
## expressionexci:racewhite:sexmale 0.42069
## reputationhigh:expressionexci:racewhite:sexmale 0.98702
## reputationmod:expressionexci:racewhite:sexmale 0.70827
## ---
## 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
## 28420.3 28510.3 -14197.1 28394.3 7475
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1158 -0.6176 -0.0251 0.6002 5.0140
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.440 1.855
## Residual 2.435 1.560
## Number of obs: 7488, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 4.059e+00 1.828e-01 1.040e+02
## reputationline 2.072e+00 2.208e-02 7.384e+03
## reputationquad -2.778e-02 1.275e-02 7.384e+03
## expressionNeutVOther -1.235e-02 1.275e-02 7.384e+03
## expressionExciVCalm -3.305e-02 2.208e-02 7.384e+03
## raceWhiteVAsian 2.364e-02 1.803e-02 7.384e+03
## sexMaleVFemale 3.592e-02 1.803e-02 7.384e+03
## reputationline:expressionNeutVOther 2.544e-02 1.562e-02 7.384e+03
## reputationquad:expressionNeutVOther -2.177e-02 9.016e-03 7.384e+03
## reputationline:expressionExciVCalm -1.082e-02 2.705e-02 7.384e+03
## reputationquad:expressionExciVCalm -2.464e-02 1.562e-02 7.384e+03
## t value Pr(>|t|)
## (Intercept) 22.210 <2e-16 ***
## reputationline 93.812 <2e-16 ***
## reputationquad -2.179 0.0294 *
## expressionNeutVOther -0.969 0.3326
## expressionExciVCalm -1.497 0.1345
## raceWhiteVAsian 1.311 0.1899
## sexMaleVFemale 1.992 0.0464 *
## reputationline:expressionNeutVOther 1.629 0.1033
## reputationquad:expressionNeutVOther -2.414 0.0158 *
## reputationline:expressionExciVCalm -0.400 0.6892
## reputationquad:expressionExciVCalm -1.578 0.1146
## ---
## 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
## 18908.3 18973.5 -9444.2 18888.3 4982
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7069 -0.6224 -0.0373 0.5874 4.4976
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 3.503 1.872
## Residual 2.355 1.535
## Number of obs: 4992, groups: pID, 104
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 4.072e+00 1.848e-01 1.040e+02
## reputationline -2.046e+00 2.660e-02 4.888e+03
## reputationquad -6.010e-03 1.536e-02 4.888e+03
## expressionExciVCalm -3.305e-02 2.172e-02 4.888e+03
## raceWhiteVAsian -5.809e-03 2.172e-02 4.888e+03
## sexMaleVFemale 5.629e-02 2.172e-02 4.888e+03
## reputationline:expressionExciVCalm 1.082e-02 2.660e-02 4.888e+03
## reputationquad:expressionExciVCalm -2.464e-02 1.536e-02 4.888e+03
## t value Pr(>|t|)
## (Intercept) 22.032 < 2e-16 ***
## reputationline -76.920 < 2e-16 ***
## reputationquad -0.391 0.69561
## expressionExciVCalm -1.522 0.12815
## raceWhiteVAsian -0.267 0.78913
## sexMaleVFemale 2.592 0.00958 **
## reputationline:expressionExciVCalm 0.407 0.68430
## reputationquad:expressionExciVCalm -1.604 0.10873
## ---
## 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
## 18338.7 18416.6 -9157.4 18314.7 4836
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6816 -0.6209 -0.0373 0.5867 4.4861
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 2.508 1.584
## Residual 2.357 1.535
## Number of obs: 4848, groups: pID, 101
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.21732 0.61884 100.99975
## reputationline -2.02042 0.02701 4747.00009
## reputationquad -0.00165 0.01559 4747.00009
## expressionExciVCalm -0.03342 0.02205 4747.00009
## raceWhiteVAsian -0.00330 0.02205 4747.00009
## sexMaleVFemale 0.05528 0.02205 4747.00009
## GenderFemaleVMale 0.27833 0.19168 100.99975
## FamSES2num 0.45792 0.10168 100.99975
## reputationline:expressionExciVCalm 0.01423 0.02701 4747.00009
## reputationquad:expressionExciVCalm -0.02599 0.01559 4747.00009
## t value Pr(>|t|)
## (Intercept) 1.967 0.0519 .
## reputationline -74.810 < 2e-16 ***
## reputationquad -0.106 0.9157
## expressionExciVCalm -1.515 0.1297
## raceWhiteVAsian -0.150 0.8810
## sexMaleVFemale 2.507 0.0122 *
## GenderFemaleVMale 1.452 0.1496
## FamSES2num 4.503 1.8e-05 ***
## reputationline:expressionExciVCalm 0.527 0.5982
## reputationquad:expressionExciVCalm -1.667 0.0956 .
## ---
## 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 0.464 0.000 0.000 0.000 0.000 0.000
## FamSES2num -0.965 0.000 0.000 0.000 0.000 0.000 -0.529
## 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