study 1-4 analyses
Dani Grant
6/28/2022
log transform response time in descriptive section (done)
means and CI for faces, blackboxes, blur faces
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## logit0. descriptives
a. how often is cued stim trusted?
round(prop.table(table(d$cuedFaceTrusted_1[d$study == 1])), 3)##
## 0 1
## 0.484 0.516round(prop.table(table(d$cuedFaceTrusted_1[d$study == 2])), 3)##
## 0 1
## 0.486 0.514round(prop.table(table(d$cuedFaceTrusted_1[d$study == 3])), 3)##
## 0 1
## 0.496 0.504round(prop.table(table(d$cuedFaceTrusted_1[d$study == 4])), 3)##
## 0 1
## 0.484 0.516b. participants in trust vs. distrust conditions
vector <- data.frame(d$participant, d$age, d$pt_gender, d$ethnicity, d$study, d$trustCondition)
vw <- vector %>%
group_by(d.participant) %>%
slice(1)
vw$d.age <- as.numeric(vw$d.age)
vw$d.study <- as.numeric(vw$d.study)
length(vw$d.participant[vw$d.trustCondition == "trust"]) #990## [1] 990length(vw$d.participant[vw$d.trustCondition == "distrust"]) #295## [1] 295c. age of participants?
# merged - age
describe(vw$d.age)# by study
describeBy(vw$d.age, vw$d.study, na.rm = T)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 202 18.89 1.41 18 18.6 0 18 28 10 3.08 13.79 0.1
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 220 19.31 1.63 19 19.03 1.48 18 31 13 3.58 18.47 0.11
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 400 18.89 1.93 18 18.53 0 18 45 27 7.46 86.81 0.1
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 462 19.3 2.31 19 18.97 1.48 18 51 33 8 91.32 0.11d. gender across/within studies
# merged - gender
table(vw$d.pt_gender)##
## female male
## 881 404prop.table(table(vw$d.pt_gender))##
## female male
## 0.6856031 0.3143969# study 1
table(vw$d.pt_gender[vw$d.study == 1])##
## female male
## 142 61prop.table(table(vw$d.pt_gender[vw$d.study == 1]))##
## female male
## 0.6995074 0.3004926# study 2
table(vw$d.pt_gender[vw$d.study == 2])##
## female male
## 139 81prop.table(table(vw$d.pt_gender[vw$d.study == 2]))##
## female male
## 0.6318182 0.3681818# study 3
table(vw$d.pt_gender[vw$d.study == 3])##
## female male
## 288 112prop.table(table(vw$d.pt_gender[vw$d.study == 3]))##
## female male
## 0.72 0.28# study 4
table(vw$d.pt_gender[vw$d.study == 4])##
## female male
## 312 150prop.table(table(vw$d.pt_gender[vw$d.study == 4]))##
## female male
## 0.6753247 0.3246753e. ethnicity across/within studies
# merged - ethnicity
table(vw$d.ethnicity)##
## Asian / Asian-American Asian/Asian-American
## 99 14
## Black / African-American Black/African-American
## 27 7
## Hispanic / Latin-American Hispanic/Latin-American
## 106 19
## middle eastern Middle Eastern
## 1 8
## Mix Mix Asian/White
## 1 5
## Mix Black/White Mix Latina/White
## 1 1
## mixed Mixed
## 4 8
## Native-Pacific Islander Native / Pacific-Islander
## 1 3
## North African Other
## 1 24
## Persian White / Asian
## 1 3
## White / Caucasian-American White/Caucasian-American
## 794 157round(prop.table(table(vw$d.ethnicity)), 4)##
## Asian / Asian-American Asian/Asian-American
## 0.0770 0.0109
## Black / African-American Black/African-American
## 0.0210 0.0054
## Hispanic / Latin-American Hispanic/Latin-American
## 0.0825 0.0148
## middle eastern Middle Eastern
## 0.0008 0.0062
## Mix Mix Asian/White
## 0.0008 0.0039
## Mix Black/White Mix Latina/White
## 0.0008 0.0008
## mixed Mixed
## 0.0031 0.0062
## Native-Pacific Islander Native / Pacific-Islander
## 0.0008 0.0023
## North African Other
## 0.0008 0.0187
## Persian White / Asian
## 0.0008 0.0023
## White / Caucasian-American White/Caucasian-American
## 0.6179 0.1222# study 1
table(vw$d.ethnicity[vw$d.study == 1])##
## Asian/Asian-American Black/African-American Hispanic/Latin-American
## 14 7 19
## Native-Pacific Islander Other White/Caucasian-American
## 1 5 157round(prop.table(table(vw$d.ethnicity[vw$d.study == 1])), 3)##
## Asian/Asian-American Black/African-American Hispanic/Latin-American
## 0.069 0.034 0.094
## Native-Pacific Islander Other White/Caucasian-American
## 0.005 0.025 0.773# study 2
table(vw$d.ethnicity[vw$d.study == 2])##
## Asian / Asian-American Black / African-American
## 24 3
## Hispanic / Latin-American Other
## 24 14
## White / Caucasian-American
## 155round(prop.table(table(vw$d.ethnicity[vw$d.study == 2])),3)##
## Asian / Asian-American Black / African-American
## 0.109 0.014
## Hispanic / Latin-American Other
## 0.109 0.064
## White / Caucasian-American
## 0.705# study 3
table(vw$d.ethnicity[vw$d.study == 3])##
## Asian / Asian-American Black / African-American
## 35 9
## Hispanic / Latin-American Middle Eastern
## 36 3
## Mix Mix Asian/White
## 1 5
## Mix Black/White Mix Latina/White
## 1 1
## Native / Pacific-Islander North African
## 2 1
## Other Persian
## 2 1
## White / Caucasian-American
## 303round(prop.table(table(vw$d.ethnicity[vw$d.study == 3])),3)##
## Asian / Asian-American Black / African-American
## 0.088 0.022
## Hispanic / Latin-American Middle Eastern
## 0.090 0.007
## Mix Mix Asian/White
## 0.002 0.013
## Mix Black/White Mix Latina/White
## 0.002 0.002
## Native / Pacific-Islander North African
## 0.005 0.002
## Other Persian
## 0.005 0.002
## White / Caucasian-American
## 0.757# study 4
table(vw$d.ethnicity[vw$d.study == 4])##
## Asian / Asian-American Black / African-American
## 40 15
## Hispanic / Latin-American middle eastern
## 46 1
## Middle Eastern mixed
## 5 4
## Mixed Native / Pacific-Islander
## 8 1
## Other White / Asian
## 3 3
## White / Caucasian-American
## 336round(prop.table(table(vw$d.ethnicity[vw$d.study == 4])),3)##
## Asian / Asian-American Black / African-American
## 0.087 0.032
## Hispanic / Latin-American middle eastern
## 0.100 0.002
## Middle Eastern mixed
## 0.011 0.009
## Mixed Native / Pacific-Islander
## 0.017 0.002
## Other White / Asian
## 0.006 0.006
## White / Caucasian-American
## 0.727ANALYSES
1. contrast code model
#don't include stimulus gender because of black boxes
m1 <- glmer(cuedFaceTrusted_1 ~ trust_0 + faceVother + blurVblackbox + pt_female_.5 +
(1 | participant) + (1 | joined.CU),
family = binomial("logit"), data = d)
summary(m1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trust_0 + faceVother + blurVblackbox + pt_female_.5 +
## (1 | participant) + (1 | joined.CU)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 43384.5 43443.1 -21685.2 43370.5 32108
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3291 -0.8839 0.5374 0.8511 2.1936
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.4992 0.7066
## participant (Intercept) 0.1201 0.3465
## Number of obs: 32115, groups: joined.CU, 9081; participant, 1285
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.08235 0.02531 3.254 0.00114 **
## trust_0 -0.08331 0.03937 -2.116 0.03433 *
## faceVother 0.04089 0.04258 0.960 0.33684
## blurVblackbox 0.11462 0.06927 1.655 0.09802 .
## pt_female_.5 0.04002 0.03383 1.183 0.23690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trst_0 fcVthr blrVbl
## trust_0 -0.140
## faceVother 0.553 0.248
## blurVblckbx 0.095 0.000 0.090
## pt_femal_.5 -0.248 0.004 -0.006 0.029tab_model(m1)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.09 | 1.03 – 1.14 | 0.001 |
| trust 0 | 0.92 | 0.85 – 0.99 | 0.034 |
| faceVother | 1.04 | 0.96 – 1.13 | 0.337 |
| blurVblackbox | 1.12 | 0.98 – 1.28 | 0.098 |
| pt female 5 | 1.04 | 0.97 – 1.11 | 0.237 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 joined.CU | 0.50 | ||
| τ00 participant | 0.12 | ||
| ICC | 0.16 | ||
| N participant | 1285 | ||
| N joined.CU | 9081 | ||
| Observations | 32115 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.159 | ||
a. black boxes simple effects
m1bb <- glmer(cuedFaceTrusted_1 ~ faces_1 + blur_1 + pt_female_.5 +
(1 | participant) + (1 | joined.CU),
family = binomial("logit"), data = d[d$trustCondition == "trust",])
summary(m1bb)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ faces_1 + blur_1 + pt_female_.5 + (1 | participant) +
## (1 | joined.CU)
## Data: d[d$trustCondition == "trust", ]
##
## AIC BIC logLik deviance df.resid
## 33530.4 33579.1 -16759.2 33518.4 24763
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1383 -0.8766 0.5450 0.8397 2.0110
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.513 0.7162
## participant (Intercept) 0.162 0.4025
## Number of obs: 24769, groups: joined.CU, 8858; participant, 991
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.15008 0.05529 2.714 0.00664 **
## faces_1 -0.10565 0.06089 -1.735 0.08274 .
## blur_1 -0.11867 0.07365 -1.611 0.10712
## pt_female_.5 0.07523 0.04127 1.823 0.06829 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) facs_1 blur_1
## faces_1 -0.894
## blur_1 -0.736 0.673
## pt_femal_.5 -0.117 -0.016 -0.034tab_model(m1bb)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.16 | 1.04 – 1.29 | 0.007 |
| faces 1 | 0.90 | 0.80 – 1.01 | 0.083 |
| blur 1 | 0.89 | 0.77 – 1.03 | 0.107 |
| pt female 5 | 1.08 | 0.99 – 1.17 | 0.068 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 joined.CU | 0.51 | ||
| τ00 participant | 0.16 | ||
| ICC | 0.17 | ||
| N participant | 991 | ||
| N joined.CU | 8858 | ||
| Observations | 24769 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.171 | ||
b. blurred faces simple effects
m1bf <- glmer(cuedFaceTrusted_1 ~ faces_1 + blackboxes_1 + pt_female_.5 +
(1 | participant) + (1 | joined.CU),
family = binomial("logit"),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
data = d[d$trustCondition == "trust",])
summary(m1bf)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ faces_1 + blackboxes_1 + pt_female_.5 + (1 |
## participant) + (1 | joined.CU)
## Data: d[d$trustCondition == "trust", ]
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 33530.4 33579.1 -16759.2 33518.4 24763
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1383 -0.8766 0.5450 0.8397 2.0110
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.513 0.7162
## participant (Intercept) 0.162 0.4025
## Number of obs: 24769, groups: joined.CU, 8858; participant, 991
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.03141 0.04984 0.630 0.5286
## faces_1 0.01302 0.05566 0.234 0.8150
## blackboxes_1 0.11868 0.07365 1.611 0.1071
## pt_female_.5 0.07523 0.04127 1.823 0.0683 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) facs_1 blck_1
## faces_1 -0.871
## blackboxs_1 -0.661 0.587
## pt_femal_.5 -0.180 0.027 0.034tab_model(m1bf)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.03 | 0.94 – 1.14 | 0.529 |
| faces 1 | 1.01 | 0.91 – 1.13 | 0.815 |
| blackboxes 1 | 1.13 | 0.97 – 1.30 | 0.107 |
| pt female 5 | 1.08 | 0.99 – 1.17 | 0.068 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 joined.CU | 0.51 | ||
| τ00 participant | 0.16 | ||
| ICC | 0.17 | ||
| N participant | 991 | ||
| N joined.CU | 8858 | ||
| Observations | 24769 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.171 | ||
c. faces simple effects
m1ft <- glmer(cuedFaceTrusted_1 ~ blackboxes_1 + blur_1 + pt_female_.5 +
(1 | participant) + (1 | joined.CU),
family = binomial("logit"),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
data = d[d$trustCondition == "trust",])
summary(m1ft)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ blackboxes_1 + blur_1 + pt_female_.5 + (1 |
## participant) + (1 | joined.CU)
## Data: d[d$trustCondition == "trust", ]
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 33530.4 33579.1 -16759.2 33518.4 24763
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1383 -0.8766 0.5450 0.8397 2.0110
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.513 0.7162
## participant (Intercept) 0.162 0.4025
## Number of obs: 24769, groups: joined.CU, 8858; participant, 991
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.04443 0.02735 1.624 0.1043
## blackboxes_1 0.10565 0.06089 1.735 0.0827 .
## blur_1 -0.01302 0.05566 -0.234 0.8150
## pt_female_.5 0.07523 0.04127 1.823 0.0683 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) blck_1 blur_1
## blackboxs_1 -0.420
## blur_1 -0.447 0.204
## pt_femal_.5 -0.274 0.016 -0.027tab_model(m1ft)| cuedFaceTrusted_1 | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.05 | 0.99 – 1.10 | 0.104 |
| blackboxes 1 | 1.11 | 0.99 – 1.25 | 0.083 |
| blur 1 | 0.99 | 0.89 – 1.10 | 0.815 |
| pt female 5 | 1.08 | 0.99 – 1.17 | 0.068 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 joined.CU | 0.51 | ||
| τ00 participant | 0.16 | ||
| ICC | 0.17 | ||
| N participant | 991 | ||
| N joined.CU | 8858 | ||
| Observations | 24769 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.171 | ||
2. does difference in trustworthiness interact with avg trustworthiness
a. faces
d1 <- d[d$stimCondition == "faces",]
m2CC <- glmer(cuedFaceTrusted_1 ~ trust_.5 * trustDiff.CUC * trustAvg.CUC + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
family = binomial("logit"),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
data = d1)## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 1 from bobyqa: bobyqa -- maximum number of function
## evaluations exceeded## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0904896 (tol = 0.002, component 1)summary(m2CC)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 * trustDiff.CUC * trustAvg.CUC +
## pt_female_.5 + stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d1
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 32738.8 32836.2 -16357.4 32714.8 24661
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6388 -0.8633 0.4885 0.8465 2.2385
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.47377 0.6883
## participant (Intercept) 0.08277 0.2877
## Number of obs: 24673, groups: joined.CU, 4595; participant, 993
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.137323 0.178080 0.771 0.44063
## trust_.5 -0.066074 0.212013 -0.312 0.75531
## trustDiff.CUC -1.795594 0.362510 -4.953 7.3e-07 ***
## trustAvg.CUC -0.034030 0.055135 -0.617 0.53709
## pt_female_.5 0.004725 0.036126 0.131 0.89593
## stim_female_.5 -0.049539 0.047735 -1.038 0.29936
## trust_.5:trustDiff.CUC -0.292167 0.589606 -0.496 0.62023
## trust_.5:trustAvg.CUC 0.047727 0.066159 0.721 0.47067
## trustDiff.CUC:trustAvg.CUC 0.334817 0.113616 2.947 0.00321 **
## trust_.5:trustDiff.CUC:trustAvg.CUC 0.129066 0.183760 0.702 0.48245
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trs_.5 trD.CUC tA.CUC pt__.5 st__.5 tr_.5:D.CUC t_.5:A
## trust_.5 -0.241
## trstDff.CUC -0.022 0.022
## trstAvg.CUC -0.990 0.240 0.022
## pt_femal_.5 -0.041 -0.006 -0.002 0.005
## stim_fml_.5 -0.482 -0.019 0.000 0.443 0.001
## tr_.5:D.CUC 0.018 -0.032 -0.514 -0.019 0.004 0.000
## tr_.5:A.CUC 0.237 -0.985 -0.023 -0.245 0.006 0.021 0.033
## tD.CUC:A.CU 0.022 -0.023 -0.993 -0.022 0.001 -0.001 0.508 0.024
## t_.5:D.CUC: -0.018 0.033 0.512 0.019 -0.004 0.000 -0.992 -0.034
## tD.CUC:
## trust_.5
## trstDff.CUC
## trstAvg.CUC
## pt_femal_.5
## stim_fml_.5
## tr_.5:D.CUC
## tr_.5:A.CUC
## tD.CUC:A.CU
## t_.5:D.CUC: -0.515
## optimizer (bobyqa) convergence code: 1 (bobyqa -- maximum number of function evaluations exceeded)
## Model failed to converge with max|grad| = 0.0904896 (tol = 0.002, component 1)i. trust
d2 <- d[d$stimCondition == "faces" & d$trustCondition == "trust",]
mft <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d2)
summary(mft)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 22967.2 23029.3 -11475.6 22951.2 17319
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5056 -0.8454 0.4876 0.8220 2.1534
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.5220 0.7225
## participant (Intercept) 0.1233 0.3511
## Number of obs: 17327, groups: joined.CU, 4372; participant, 699
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.10777 0.19423 0.555 0.578988
## trustDiff.CUC -1.92561 0.33952 -5.672 1.42e-08 ***
## trustAvg.CUC -0.01338 0.05998 -0.223 0.823488
## pt_female_.5 0.03624 0.04655 0.779 0.436228
## stim_female_.5 -0.06568 0.05298 -1.240 0.215090
## trustDiff.CUC:trustAvg.CUC 0.38996 0.10614 3.674 0.000239 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC tA.CUC pt__.5 st__.5
## trstDff.CUC -0.010
## trstAvg.CUC -0.989 0.009
## pt_femal_.5 -0.052 0.002 0.009
## stim_fml_.5 -0.502 0.000 0.467 0.002
## tD.CUC:A.CU 0.009 -0.994 -0.008 -0.003 0.000- hi/low diff
m3ft.hi <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC.plus.sd * trustAvg.CUC + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d2)
summary(m3ft.hi)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC.plus.sd * trustAvg.CUC + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 22967.2 23029.3 -11475.6 22951.2 17319
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5056 -0.8454 0.4876 0.8220 2.1534
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.5220 0.7225
## participant (Intercept) 0.1233 0.3511
## Number of obs: 17327, groups: joined.CU, 4372; participant, 699
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.93306 0.26580 -3.510 0.000448 ***
## trustDiff.CUC.plus.sd -1.92560 0.33939 -5.674 1.4e-08 ***
## trustAvg.CUC 0.19740 0.08262 2.389 0.016877 *
## pt_female_.5 0.03624 0.04655 0.779 0.436238
## stim_female_.5 -0.06568 0.05298 -1.240 0.215087
## trustDiff.CUC.plus.sd:trustAvg.CUC 0.38996 0.10610 3.675 0.000238 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC.. tA.CUC pt__.5 st__.5
## trstD.CUC.. 0.683
## trstAvg.CUC -0.991 -0.683
## pt_femal_.5 -0.036 0.002 0.004
## stim_fml_.5 -0.367 0.000 0.339 0.002
## tD.CUC..:A. -0.679 -0.994 0.688 -0.003 0.000m3ft.low <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC.min.sd * trustAvg.CUC + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d2)
summary(m3ft.low)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC.min.sd * trustAvg.CUC + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 22967.2 23029.3 -11475.6 22951.2 17319
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5056 -0.8454 0.4876 0.8220 2.1534
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.5220 0.7225
## participant (Intercept) 0.1233 0.3511
## Number of obs: 17327, groups: joined.CU, 4372; participant, 699
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.14864 0.26858 4.277 1.90e-05 ***
## trustDiff.CUC.min.sd -1.92564 0.33968 -5.669 1.44e-08 ***
## trustAvg.CUC -0.22417 0.08337 -2.689 0.00717 **
## pt_female_.5 0.03624 0.04655 0.779 0.43624
## stim_female_.5 -0.06568 0.05298 -1.240 0.21511
## trustDiff.CUC.min.sd:trustAvg.CUC 0.38997 0.10619 3.672 0.00024 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC.. tA.CUC pt__.5 st__.5
## trstD.CUC.. -0.691
## trstAvg.CUC -0.992 0.691
## pt_femal_.5 -0.039 0.002 0.008
## stim_fml_.5 -0.363 -0.001 0.336 0.002
## tD.CUC..:A. 0.686 -0.994 -0.694 -0.003 0.000tab_model(mft, m3ft.hi, m3ft.low)| cuedFaceTrusted_1 | cuedFaceTrusted_1 | cuedFaceTrusted_1 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p |
| (Intercept) | 1.11 | 0.76 – 1.63 | 0.579 | 0.39 | 0.23 – 0.66 | <0.001 | 3.15 | 1.86 – 5.34 | <0.001 |
| trustDiff CUC | 0.15 | 0.07 – 0.28 | <0.001 | ||||||
| trustAvg CUC | 0.99 | 0.88 – 1.11 | 0.823 | 1.22 | 1.04 – 1.43 | 0.017 | 0.80 | 0.68 – 0.94 | 0.007 |
| pt female 5 | 1.04 | 0.95 – 1.14 | 0.436 | 1.04 | 0.95 – 1.14 | 0.436 | 1.04 | 0.95 – 1.14 | 0.436 |
| stim female 5 | 0.94 | 0.84 – 1.04 | 0.215 | 0.94 | 0.84 – 1.04 | 0.215 | 0.94 | 0.84 – 1.04 | 0.215 |
|
trustDiff CUC × trustAvg CUC |
1.48 | 1.20 – 1.82 | <0.001 | ||||||
| trustDiff CUC plus sd | 0.15 | 0.07 – 0.28 | <0.001 | ||||||
|
trustDiff CUC plus sd × trustAvg CUC |
1.48 | 1.20 – 1.82 | <0.001 | ||||||
| trustDiff CUC min sd | 0.15 | 0.07 – 0.28 | <0.001 | ||||||
|
trustDiff CUC min sd × trustAvg CUC |
1.48 | 1.20 – 1.82 | <0.001 | ||||||
| Random Effects | |||||||||
| σ2 | 3.29 | 3.29 | 3.29 | ||||||
| τ00 | 0.52 joined.CU | 0.52 joined.CU | 0.52 joined.CU | ||||||
| 0.12 participant | 0.12 participant | 0.12 participant | |||||||
| ICC | 0.16 | 0.16 | 0.16 | ||||||
| N | 699 participant | 699 participant | 699 participant | ||||||
| 4372 joined.CU | 4372 joined.CU | 4372 joined.CU | |||||||
| Observations | 17327 | 17327 | 17327 | ||||||
| Marginal R2 / Conditional R2 | 0.037 / 0.195 | 0.037 / 0.195 | 0.037 / 0.195 | ||||||
plot_models(mft, m3ft.hi, m3ft.low)
- hi/low avg
m4.hi <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.plus.sd + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d2)
summary(m4.hi)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.plus.sd + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 22967.2 23029.3 -11475.6 22951.2 17319
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5056 -0.8454 0.4876 0.8220 2.1534
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.5220 0.7225
## participant (Intercept) 0.1233 0.3511
## Number of obs: 17327, groups: joined.CU, 4372; participant, 699
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.10181 0.16779 0.607 0.543999
## trustDiff.CUC -1.75161 0.29234 -5.992 2.08e-09 ***
## trustAvg.CUC.plus.sd -0.01338 0.05997 -0.223 0.823445
## pt_female_.5 0.03624 0.04655 0.779 0.436234
## stim_female_.5 -0.06568 0.05298 -1.240 0.215078
## trustDiff.CUC:trustAvg.CUC.plus.sd 0.38996 0.10608 3.676 0.000237 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC tA.CUC pt__.5 st__.5
## trstDff.CUC -0.010
## trstA.CUC.. -0.986 0.009
## pt_femal_.5 -0.059 0.002 0.009
## stim_fml_.5 -0.506 0.000 0.467 0.002
## tD.CUC:A.CU 0.009 -0.992 -0.008 -0.003 0.000m4.low <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.min.sd + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d2)
summary(m4.low)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.min.sd + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d2
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 22967.2 23029.3 -11475.6 22951.2 17319
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5056 -0.8454 0.4876 0.8220 2.1534
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.5220 0.7225
## participant (Intercept) 0.1233 0.3511
## Number of obs: 17327, groups: joined.CU, 4372; participant, 699
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.11377 0.22062 0.516 0.606077
## trustDiff.CUC -2.09957 0.38608 -5.438 5.38e-08 ***
## trustAvg.CUC.min.sd -0.01339 0.05995 -0.223 0.823292
## pt_female_.5 0.03624 0.04655 0.779 0.436243
## stim_female_.5 -0.06568 0.05297 -1.240 0.214984
## trustDiff.CUC:trustAvg.CUC.min.sd 0.38995 0.10599 3.679 0.000234 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC tA.CUC pt__.5 st__.5
## trstDff.CUC -0.009
## trstA.CUC.. -0.992 0.009
## pt_femal_.5 -0.047 0.002 0.009
## stim_fml_.5 -0.498 0.000 0.467 0.002
## tD.CUC:A.CU 0.009 -0.995 -0.008 -0.003 0.000tab_model(mft, m4.hi, m4.low)| cuedFaceTrusted_1 | cuedFaceTrusted_1 | cuedFaceTrusted_1 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p |
| (Intercept) | 1.11 | 0.76 – 1.63 | 0.579 | 1.11 | 0.80 – 1.54 | 0.544 | 1.12 | 0.73 – 1.73 | 0.606 |
| trustDiff CUC | 0.15 | 0.07 – 0.28 | <0.001 | 0.17 | 0.10 – 0.31 | <0.001 | 0.12 | 0.06 – 0.26 | <0.001 |
| trustAvg CUC | 0.99 | 0.88 – 1.11 | 0.823 | ||||||
| pt female 5 | 1.04 | 0.95 – 1.14 | 0.436 | 1.04 | 0.95 – 1.14 | 0.436 | 1.04 | 0.95 – 1.14 | 0.436 |
| stim female 5 | 0.94 | 0.84 – 1.04 | 0.215 | 0.94 | 0.84 – 1.04 | 0.215 | 0.94 | 0.84 – 1.04 | 0.215 |
|
trustDiff CUC × trustAvg CUC |
1.48 | 1.20 – 1.82 | <0.001 | ||||||
| trustAvg CUC plus sd | 0.99 | 0.88 – 1.11 | 0.823 | ||||||
|
trustDiff CUC × trustAvg CUC plus sd |
1.48 | 1.20 – 1.82 | <0.001 | ||||||
| trustAvg CUC min sd | 0.99 | 0.88 – 1.11 | 0.823 | ||||||
|
trustDiff CUC × trustAvg CUC min sd |
1.48 | 1.20 – 1.82 | <0.001 | ||||||
| Random Effects | |||||||||
| σ2 | 3.29 | 3.29 | 3.29 | ||||||
| τ00 | 0.52 joined.CU | 0.52 joined.CU | 0.52 joined.CU | ||||||
| 0.12 participant | 0.12 participant | 0.12 participant | |||||||
| ICC | 0.16 | 0.16 | 0.16 | ||||||
| N | 699 participant | 699 participant | 699 participant | ||||||
| 4372 joined.CU | 4372 joined.CU | 4372 joined.CU | |||||||
| Observations | 17327 | 17327 | 17327 | ||||||
| Marginal R2 / Conditional R2 | 0.037 / 0.195 | 0.037 / 0.195 | 0.037 / 0.195 | ||||||
plot_models(mft, m4.hi, m4.low)
ii. distrust
d3 <- d[d$stimCondition == "faces" & d$trustCondition == "distrust",]
m0CC <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
family = binomial("logit"),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
data = d3)## boundary (singular) fit: see help('isSingular')summary(m0CC)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d3
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 9949.1 10004.3 -4966.5 9933.1 7338
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9244 -0.9033 -0.5544 0.9080 1.7156
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.3352 0.5789
## participant (Intercept) 0.0000 0.0000
## Number of obs: 7346, groups: joined.CU, 2140; participant, 295
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.014144 0.237879 0.059 0.95259
## trustDiff.CUC -1.832866 0.568789 -3.222 0.00127 **
## trustAvg.CUC -0.003963 0.073777 -0.054 0.95716
## pt_female_.5 -0.046912 0.054304 -0.864 0.38766
## stim_female_.5 -0.007598 0.071160 -0.107 0.91497
## trustDiff.CUC:trustAvg.CUC 0.331806 0.178003 1.864 0.06231 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC tA.CUC pt__.5 st__.5
## trstDff.CUC -0.030
## trstAvg.CUC -0.990 0.033
## pt_femal_.5 -0.039 -0.007 -0.002
## stim_fml_.5 -0.488 -0.005 0.452 0.000
## tD.CUC:A.CU 0.032 -0.992 -0.034 0.006 0.004
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')- hi/low diff
m0.hi <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC.plus.sd * trustAvg.CUC + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d3)## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 1 from bobyqa: bobyqa -- maximum number of function
## evaluations exceeded## boundary (singular) fit: see help('isSingular')summary(m0.hi)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC.plus.sd * trustAvg.CUC + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d3
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 9949.1 10004.3 -4966.5 9933.1 7338
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9244 -0.9033 -0.5544 0.9080 1.7156
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.3352 0.5789
## participant (Intercept) 0.0000 0.0000
## Number of obs: 7346, groups: joined.CU, 2140; participant, 295
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.976534 0.382872 -2.551 0.01076 *
## trustDiff.CUC.plus.sd -1.832786 0.568672 -3.223 0.00127 **
## trustAvg.CUC 0.175376 0.119238 1.471 0.14134
## pt_female_.5 -0.046914 0.054304 -0.864 0.38764
## stim_female_.5 -0.007598 0.071158 -0.107 0.91497
## trustDiff.CUC.plus.sd:trustAvg.CUC 0.331781 0.177966 1.864 0.06228 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC.. tA.CUC pt__.5 st__.5
## trstD.CUC.. 0.784
## trstAvg.CUC -0.991 -0.780
## pt_femal_.5 -0.030 -0.007 0.004
## stim_fml_.5 -0.307 -0.005 0.283 0.000
## tD.CUC..:A. -0.777 -0.992 0.786 0.006 0.004
## optimizer (bobyqa) convergence code: 1 (bobyqa -- maximum number of function evaluations exceeded)
## boundary (singular) fit: see help('isSingular')m0.low <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC.min.sd * trustAvg.CUC + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d3)## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 1 from bobyqa: bobyqa -- maximum number of function
## evaluations exceeded## boundary (singular) fit: see help('isSingular')summary(m0.low)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC.min.sd * trustAvg.CUC + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d3
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 9949.1 10004.3 -4966.5 9933.1 7338
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9244 -0.9033 -0.5544 0.9080 1.7156
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.3352 0.5789
## participant (Intercept) 0.0000 0.0000
## Number of obs: 7346, groups: joined.CU, 2140; participant, 295
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.004723 0.393904 2.551 0.01075 *
## trustDiff.CUC.min.sd -1.832645 0.567953 -3.227 0.00125 **
## trustAvg.CUC -0.183271 0.123036 -1.490 0.13634
## pt_female_.5 -0.046910 0.054304 -0.864 0.38767
## stim_female_.5 -0.007597 0.071158 -0.107 0.91497
## trustDiff.CUC.min.sd:trustAvg.CUC 0.331737 0.177745 1.866 0.06199 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC.. tA.CUC pt__.5 st__.5
## trstD.CUC.. -0.797
## trstAvg.CUC -0.992 0.794
## pt_femal_.5 -0.018 -0.007 -0.006
## stim_fml_.5 -0.291 -0.005 0.268 0.000
## tD.CUC..:A. 0.792 -0.992 -0.801 0.006 0.004
## optimizer (bobyqa) convergence code: 1 (bobyqa -- maximum number of function evaluations exceeded)
## boundary (singular) fit: see help('isSingular')tab_model(m0CC, m0.hi, m0.low)| cuedFaceTrusted_1 | cuedFaceTrusted_1 | cuedFaceTrusted_1 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p |
| (Intercept) | 1.01 | 0.64 – 1.62 | 0.953 | 0.38 | 0.18 – 0.80 | 0.011 | 2.73 | 1.26 – 5.91 | 0.011 |
| trustDiff CUC | 0.16 | 0.05 – 0.49 | 0.001 | ||||||
| trustAvg CUC | 1.00 | 0.86 – 1.15 | 0.957 | 1.19 | 0.94 – 1.51 | 0.141 | 0.83 | 0.65 – 1.06 | 0.136 |
| pt female 5 | 0.95 | 0.86 – 1.06 | 0.388 | 0.95 | 0.86 – 1.06 | 0.388 | 0.95 | 0.86 – 1.06 | 0.388 |
| stim female 5 | 0.99 | 0.86 – 1.14 | 0.915 | 0.99 | 0.86 – 1.14 | 0.915 | 0.99 | 0.86 – 1.14 | 0.915 |
|
trustDiff CUC × trustAvg CUC |
1.39 | 0.98 – 1.98 | 0.062 | ||||||
| trustDiff CUC plus sd | 0.16 | 0.05 – 0.49 | 0.001 | ||||||
|
trustDiff CUC plus sd × trustAvg CUC |
1.39 | 0.98 – 1.98 | 0.062 | ||||||
| trustDiff CUC min sd | 0.16 | 0.05 – 0.49 | 0.001 | ||||||
|
trustDiff CUC min sd × trustAvg CUC |
1.39 | 0.98 – 1.97 | 0.062 | ||||||
| Random Effects | |||||||||
| σ2 | 3.29 | 3.29 | 3.29 | ||||||
| τ00 | 0.34 joined.CU | 0.34 joined.CU | 0.34 joined.CU | ||||||
| 0.00 participant | 0.00 participant | 0.00 participant | |||||||
| N | 295 participant | 295 participant | 295 participant | ||||||
| 2140 joined.CU | 2140 joined.CU | 2140 joined.CU | |||||||
| Observations | 7346 | 7346 | 7346 | ||||||
| Marginal R2 / Conditional R2 | 0.029 / NA | 0.029 / NA | 0.029 / NA | ||||||
plot_models(m0CC, m0.hi, m0.low)
- hi/low avg
m5.hi <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.plus.sd + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d3)## boundary (singular) fit: see help('isSingular')summary(m5.hi)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.plus.sd + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d3
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 9949.1 10004.3 -4966.5 9933.1 7338
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9244 -0.9033 -0.5544 0.9080 1.7156
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.3352 0.5789
## participant (Intercept) 0.0000 0.0000
## Number of obs: 7346, groups: joined.CU, 2140; participant, 295
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.012371 0.205334 0.060 0.951960
## trustDiff.CUC -1.684796 0.490034 -3.438 0.000586 ***
## trustAvg.CUC.plus.sd -0.003961 0.073776 -0.054 0.957185
## pt_female_.5 -0.046913 0.054305 -0.864 0.387651
## stim_female_.5 -0.007598 0.071160 -0.107 0.914971
## trustDiff.CUC:trustAvg.CUC.plus.sd 0.331800 0.177981 1.864 0.062287 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC tA.CUC pt__.5 st__.5
## trstDff.CUC -0.030
## trstA.CUC.. -0.987 0.032
## pt_femal_.5 -0.046 -0.008 -0.002
## stim_fml_.5 -0.493 -0.005 0.452 0.000
## tD.CUC:A.CU 0.031 -0.989 -0.033 0.006 0.004
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m5.low <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.min.sd + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d3)## boundary (singular) fit: see help('isSingular')summary(m5.low)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.min.sd + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d3
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 9949.1 10004.3 -4966.5 9933.1 7338
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9244 -0.9033 -0.5544 0.9080 1.7156
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.3352 0.5789
## participant (Intercept) 0.0000 0.0000
## Number of obs: 7346, groups: joined.CU, 2140; participant, 295
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.015916 0.270515 0.059 0.95308
## trustDiff.CUC -1.980856 0.647762 -3.058 0.00223 **
## trustAvg.CUC.min.sd -0.003964 0.073777 -0.054 0.95715
## pt_female_.5 -0.046911 0.054305 -0.864 0.38767
## stim_female_.5 -0.007600 0.071160 -0.107 0.91494
## trustDiff.CUC:trustAvg.CUC.min.sd 0.331789 0.178031 1.864 0.06237 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC tA.CUC pt__.5 st__.5
## trstDff.CUC -0.031
## trstA.CUC.. -0.992 0.032
## pt_femal_.5 -0.034 -0.007 -0.002
## stim_fml_.5 -0.484 -0.005 0.452 0.000
## tD.CUC:A.CU 0.032 -0.994 -0.033 0.006 0.004
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')tab_model(m0CC, m5.hi, m5.low)| cuedFaceTrusted_1 | cuedFaceTrusted_1 | cuedFaceTrusted_1 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p |
| (Intercept) | 1.01 | 0.64 – 1.62 | 0.953 | 1.01 | 0.68 – 1.51 | 0.952 | 1.02 | 0.60 – 1.73 | 0.953 |
| trustDiff CUC | 0.16 | 0.05 – 0.49 | 0.001 | 0.19 | 0.07 – 0.48 | 0.001 | 0.14 | 0.04 – 0.49 | 0.002 |
| trustAvg CUC | 1.00 | 0.86 – 1.15 | 0.957 | ||||||
| pt female 5 | 0.95 | 0.86 – 1.06 | 0.388 | 0.95 | 0.86 – 1.06 | 0.388 | 0.95 | 0.86 – 1.06 | 0.388 |
| stim female 5 | 0.99 | 0.86 – 1.14 | 0.915 | 0.99 | 0.86 – 1.14 | 0.915 | 0.99 | 0.86 – 1.14 | 0.915 |
|
trustDiff CUC × trustAvg CUC |
1.39 | 0.98 – 1.98 | 0.062 | ||||||
| trustAvg CUC plus sd | 1.00 | 0.86 – 1.15 | 0.957 | ||||||
|
trustDiff CUC × trustAvg CUC plus sd |
1.39 | 0.98 – 1.98 | 0.062 | ||||||
| trustAvg CUC min sd | 1.00 | 0.86 – 1.15 | 0.957 | ||||||
|
trustDiff CUC × trustAvg CUC min sd |
1.39 | 0.98 – 1.98 | 0.062 | ||||||
| Random Effects | |||||||||
| σ2 | 3.29 | 3.29 | 3.29 | ||||||
| τ00 | 0.34 joined.CU | 0.34 joined.CU | 0.34 joined.CU | ||||||
| 0.00 participant | 0.00 participant | 0.00 participant | |||||||
| N | 295 participant | 295 participant | 295 participant | ||||||
| 2140 joined.CU | 2140 joined.CU | 2140 joined.CU | |||||||
| Observations | 7346 | 7346 | 7346 | ||||||
| Marginal R2 / Conditional R2 | 0.029 / NA | 0.029 / NA | 0.029 / NA | ||||||
plot_models(m0CC, m5.hi, m5.low)
b. blurry faces
d4 <- d[d$stimCondition == "blurryFaces",]
m6bft <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d4)
summary(m6bft)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d4
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 5368.9 5419.0 -2676.4 5352.9 3886
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4749 -0.9465 0.6758 0.9178 1.2974
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.1608 0.4011
## participant (Intercept) 0.1267 0.3559
## Number of obs: 3894, groups: joined.CU, 2370; participant, 163
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.42354 0.34555 -1.226 0.2203
## trustDiff.CUC -1.11743 0.53541 -2.087 0.0369 *
## trustAvg.CUC 0.14401 0.10638 1.354 0.1758
## pt_female_.5 0.08133 0.09743 0.835 0.4038
## stim_female_.5 -0.10067 0.08035 -1.253 0.2103
## trustDiff.CUC:trustAvg.CUC 0.35704 0.16635 2.146 0.0319 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC tA.CUC pt__.5 st__.5
## trstDff.CUC 0.019
## trstAvg.CUC -0.990 -0.020
## pt_femal_.5 -0.059 -0.014 -0.002
## stim_fml_.5 -0.495 -0.004 0.488 -0.001
## tD.CUC:A.CU -0.021 -0.995 0.021 0.014 0.005- hi/low diff
m6bft.hi <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC.plus.sd * trustAvg.CUC + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d4)## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 1 from bobyqa: bobyqa -- maximum number of function
## evaluations exceededsummary(m6bft.hi)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC.plus.sd * trustAvg.CUC + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d4
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 5368.9 5419.0 -2676.4 5352.9 3886
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4749 -0.9465 0.6758 0.9178 1.2974
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.1609 0.4011
## participant (Intercept) 0.1267 0.3559
## Number of obs: 3894, groups: joined.CU, 2370; participant, 163
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.02749 0.45494 -2.258 0.0239 *
## trustDiff.CUC.plus.sd -1.11735 0.53547 -2.087 0.0369 *
## trustAvg.CUC 0.33698 0.14074 2.394 0.0167 *
## pt_female_.5 0.08133 0.09743 0.835 0.4038
## stim_female_.5 -0.10068 0.08035 -1.253 0.2102
## trustDiff.CUC.plus.sd:trustAvg.CUC 0.35702 0.16637 2.146 0.0319 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC.. tA.CUC pt__.5 st__.5
## trstD.CUC.. 0.651
## trstAvg.CUC -0.992 -0.651
## pt_femal_.5 -0.054 -0.014 0.007
## stim_fml_.5 -0.379 -0.004 0.372 -0.001
## tD.CUC..:A. -0.649 -0.995 0.655 0.014 0.005
## optimizer (bobyqa) convergence code: 1 (bobyqa -- maximum number of function evaluations exceeded)m6bft.low <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC.min.sd * trustAvg.CUC + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d4)
summary(m6bft.low)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC.min.sd * trustAvg.CUC + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d4
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 5368.9 5419.0 -2676.4 5352.9 3886
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4749 -0.9465 0.6758 0.9178 1.2974
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.1608 0.4011
## participant (Intercept) 0.1267 0.3559
## Number of obs: 3894, groups: joined.CU, 2370; participant, 163
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.18045 0.44648 0.404 0.6861
## trustDiff.CUC.min.sd -1.11744 0.53552 -2.087 0.0369 *
## trustAvg.CUC -0.04898 0.13783 -0.355 0.7223
## pt_female_.5 0.08133 0.09743 0.835 0.4038
## stim_female_.5 -0.10067 0.08036 -1.253 0.2103
## trustDiff.CUC.min.sd:trustAvg.CUC 0.35704 0.16639 2.146 0.0319 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC.. tA.CUC pt__.5 st__.5
## trstD.CUC.. -0.633
## trstAvg.CUC -0.992 0.634
## pt_femal_.5 -0.037 -0.014 -0.011
## stim_fml_.5 -0.381 -0.004 0.373 -0.001
## tD.CUC..:A. 0.629 -0.995 -0.636 0.014 0.005tab_model(m6bft, m6bft.hi, m6bft.low)| cuedFaceTrusted_1 | cuedFaceTrusted_1 | cuedFaceTrusted_1 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p |
| (Intercept) | 0.65 | 0.33 – 1.29 | 0.220 | 0.36 | 0.15 – 0.87 | 0.024 | 1.20 | 0.50 – 2.87 | 0.686 |
| trustDiff CUC | 0.33 | 0.11 – 0.93 | 0.037 | ||||||
| trustAvg CUC | 1.15 | 0.94 – 1.42 | 0.176 | 1.40 | 1.06 – 1.85 | 0.017 | 0.95 | 0.73 – 1.25 | 0.722 |
| pt female 5 | 1.08 | 0.90 – 1.31 | 0.404 | 1.08 | 0.90 – 1.31 | 0.404 | 1.08 | 0.90 – 1.31 | 0.404 |
| stim female 5 | 0.90 | 0.77 – 1.06 | 0.210 | 0.90 | 0.77 – 1.06 | 0.210 | 0.90 | 0.77 – 1.06 | 0.210 |
|
trustDiff CUC × trustAvg CUC |
1.43 | 1.03 – 1.98 | 0.032 | ||||||
| trustDiff CUC plus sd | 0.33 | 0.11 – 0.93 | 0.037 | ||||||
|
trustDiff CUC plus sd × trustAvg CUC |
1.43 | 1.03 – 1.98 | 0.032 | ||||||
| trustDiff CUC min sd | 0.33 | 0.11 – 0.93 | 0.037 | ||||||
|
trustDiff CUC min sd × trustAvg CUC |
1.43 | 1.03 – 1.98 | 0.032 | ||||||
| Random Effects | |||||||||
| σ2 | 3.29 | 3.29 | 3.29 | ||||||
| τ00 | 0.16 joined.CU | 0.16 joined.CU | 0.16 joined.CU | ||||||
| 0.13 participant | 0.13 participant | 0.13 participant | |||||||
| ICC | 0.08 | 0.08 | 0.08 | ||||||
| N | 163 participant | 163 participant | 163 participant | ||||||
| 2370 joined.CU | 2370 joined.CU | 2370 joined.CU | |||||||
| Observations | 3894 | 3894 | 3894 | ||||||
| Marginal R2 / Conditional R2 | 0.004 / 0.084 | 0.004 / 0.084 | 0.004 / 0.084 | ||||||
plot_models(m6bft, m6bft.hi, m6bft.low)
- hi/low avg
m7bft.hi <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.plus.sd + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d4)
summary(m7bft.hi)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.plus.sd + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d4
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 5368.9 5419.0 -2676.4 5352.9 3886
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4749 -0.9465 0.6758 0.9178 1.2974
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.1608 0.4011
## participant (Intercept) 0.1267 0.3559
## Number of obs: 3894, groups: joined.CU, 2370; participant, 163
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.35929 0.29867 -1.203 0.2290
## trustDiff.CUC -0.95809 0.46166 -2.075 0.0380 *
## trustAvg.CUC.plus.sd 0.14401 0.10639 1.354 0.1759
## pt_female_.5 0.08133 0.09743 0.835 0.4038
## stim_female_.5 -0.10067 0.08036 -1.253 0.2103
## trustDiff.CUC:trustAvg.CUC.plus.sd 0.35703 0.16638 2.146 0.0319 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC tA.CUC pt__.5 st__.5
## trstDff.CUC 0.019
## trstA.CUC.. -0.986 -0.020
## pt_femal_.5 -0.069 -0.014 -0.002
## stim_fml_.5 -0.496 -0.004 0.488 -0.001
## tD.CUC:A.CU -0.021 -0.994 0.021 0.014 0.005m7bft.low <- glmer(cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.min.sd + pt_female_.5 + stim_female_.5 +
(1 | participant) + (1 | joined.CU),
control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000)),
family = binomial("logit"),
data = d4)
summary(m7bft.low)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cuedFaceTrusted_1 ~ trustDiff.CUC * trustAvg.CUC.min.sd + pt_female_.5 +
## stim_female_.5 + (1 | participant) + (1 | joined.CU)
## Data: d4
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2000))
##
## AIC BIC logLik deviance df.resid
## 5368.9 5419.0 -2676.4 5352.9 3886
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4749 -0.9465 0.6758 0.9178 1.2974
##
## Random effects:
## Groups Name Variance Std.Dev.
## joined.CU (Intercept) 0.1608 0.4011
## participant (Intercept) 0.1267 0.3559
## Number of obs: 3894, groups: joined.CU, 2370; participant, 163
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.48782 0.39259 -1.243 0.2140
## trustDiff.CUC -1.27673 0.60940 -2.095 0.0362 *
## trustAvg.CUC.min.sd 0.14401 0.10638 1.354 0.1758
## pt_female_.5 0.08133 0.09743 0.835 0.4038
## stim_female_.5 -0.10067 0.08035 -1.253 0.2103
## trustDiff.CUC:trustAvg.CUC.min.sd 0.35704 0.16637 2.146 0.0319 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trD.CUC tA.CUC pt__.5 st__.5
## trstDff.CUC 0.019
## trstA.CUC.. -0.992 -0.020
## pt_femal_.5 -0.052 -0.014 -0.002
## stim_fml_.5 -0.495 -0.004 0.488 -0.001
## tD.CUC:A.CU -0.021 -0.996 0.021 0.014 0.005tab_model(m6bft, m7bft.hi, m7bft.low)| cuedFaceTrusted_1 | cuedFaceTrusted_1 | cuedFaceTrusted_1 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p |
| (Intercept) | 0.65 | 0.33 – 1.29 | 0.220 | 0.70 | 0.39 – 1.25 | 0.229 | 0.61 | 0.28 – 1.33 | 0.214 |
| trustDiff CUC | 0.33 | 0.11 – 0.93 | 0.037 | 0.38 | 0.16 – 0.95 | 0.038 | 0.28 | 0.08 – 0.92 | 0.036 |
| trustAvg CUC | 1.15 | 0.94 – 1.42 | 0.176 | ||||||
| pt female 5 | 1.08 | 0.90 – 1.31 | 0.404 | 1.08 | 0.90 – 1.31 | 0.404 | 1.08 | 0.90 – 1.31 | 0.404 |
| stim female 5 | 0.90 | 0.77 – 1.06 | 0.210 | 0.90 | 0.77 – 1.06 | 0.210 | 0.90 | 0.77 – 1.06 | 0.210 |
|
trustDiff CUC × trustAvg CUC |
1.43 | 1.03 – 1.98 | 0.032 | ||||||
| trustAvg CUC plus sd | 1.15 | 0.94 – 1.42 | 0.176 | ||||||
|
trustDiff CUC × trustAvg CUC plus sd |
1.43 | 1.03 – 1.98 | 0.032 | ||||||
| trustAvg CUC min sd | 1.15 | 0.94 – 1.42 | 0.176 | ||||||
|
trustDiff CUC × trustAvg CUC min sd |
1.43 | 1.03 – 1.98 | 0.032 | ||||||
| Random Effects | |||||||||
| σ2 | 3.29 | 3.29 | 3.29 | ||||||
| τ00 | 0.16 joined.CU | 0.16 joined.CU | 0.16 joined.CU | ||||||
| 0.13 participant | 0.13 participant | 0.13 participant | |||||||
| ICC | 0.08 | 0.08 | 0.08 | ||||||
| N | 163 participant | 163 participant | 163 participant | ||||||
| 2370 joined.CU | 2370 joined.CU | 2370 joined.CU | |||||||
| Observations | 3894 | 3894 | 3894 | ||||||
| Marginal R2 / Conditional R2 | 0.004 / 0.084 | 0.004 / 0.084 | 0.004 / 0.084 | ||||||
plot_models(m6bft, m7bft.hi, m7bft.low)