wsdg analyses
Dani Grant
2023-04-13
1. descriptives
a. stimui
table.means <- aggregate(cbind(money, donate, propDonate, need, diff) ~ stimulus, data = d, FUN = mean)
table.means[2:6] <- round(table.means[2:6], 2)
table.means## stimulus money donate propDonate need diff
## 1 A Home Within 0.55 0.17 0.32 4.80 3.12
## 2 Andrea 0.57 0.17 0.31 4.59 2.84
## 3 anonymous 0.52 0.12 0.22 3.34 2.31
## 4 Asia 0.57 0.18 0.33 4.58 3.01
## 5 Community Forward Holiday Gift Giving 0.56 0.17 0.31 4.82 3.07
## 6 Community Table Food Pantry 0.58 0.18 0.32 4.83 3.00
## 7 Covid-19 Emergency Response Fund 0.59 0.20 0.32 4.74 3.05
## 8 Jay 0.56 0.18 0.32 4.63 2.97
## 9 John 0.57 0.19 0.33 4.51 2.92
## 10 Kids In Need of Desks (KIND) 0.56 0.19 0.32 4.86 3.18
## 11 Lighthouse 0.58 0.18 0.31 4.69 3.02
## 12 Lisa 0.58 0.19 0.33 4.61 2.95
## 13 Motor City Transportation Fund 0.60 0.19 0.32 4.86 3.06
## 14 Nancy 0.59 0.20 0.32 4.54 2.91
## 15 Nate 0.56 0.18 0.33 4.67 2.96
## 16 Project Homeless Connect 0.59 0.18 0.31 4.80 3.03
## 17 Stacey 0.60 0.20 0.34 4.61 2.94
## 18 Stanley 0.58 0.19 0.32 4.58 2.93
## 19 Tamekia 0.59 0.19 0.33 4.55 2.91
## 20 U-Inspire 0.59 0.19 0.32 4.74 3.12
## 21 Wildfire Relief Fund 0.57 0.18 0.32 4.75 3.02table.sd <- aggregate(cbind(money, donate, propDonate, need, diff) ~ stimulus, data = d, FUN = sd)
table.sd[2:6] <- round(table.sd[2:6], 2)
table.sd## stimulus money donate propDonate need diff
## 1 A Home Within 0.27 0.22 0.36 1.81 1.91
## 2 Andrea 0.27 0.23 0.36 1.79 1.80
## 3 anonymous 0.26 0.15 0.25 1.58 1.58
## 4 Asia 0.27 0.23 0.35 1.75 1.87
## 5 Community Forward Holiday Gift Giving 0.27 0.22 0.35 1.74 1.93
## 6 Community Table Food Pantry 0.27 0.25 0.37 1.72 1.88
## 7 Covid-19 Emergency Response Fund 0.27 0.26 0.36 1.80 1.92
## 8 Jay 0.27 0.23 0.36 1.71 1.83
## 9 John 0.27 0.25 0.37 1.78 1.79
## 10 Kids In Need of Desks (KIND) 0.28 0.25 0.36 1.77 1.94
## 11 Lighthouse 0.28 0.24 0.36 1.77 1.85
## 12 Lisa 0.28 0.26 0.36 1.76 1.85
## 13 Motor City Transportation Fund 0.27 0.24 0.35 1.72 1.89
## 14 Nancy 0.28 0.26 0.36 1.84 1.82
## 15 Nate 0.27 0.23 0.36 1.78 1.89
## 16 Project Homeless Connect 0.27 0.23 0.35 1.77 1.88
## 17 Stacey 0.28 0.26 0.37 1.85 1.85
## 18 Stanley 0.27 0.25 0.36 1.78 1.82
## 19 Tamekia 0.26 0.24 0.35 1.79 1.86
## 20 U-Inspire 0.27 0.25 0.35 1.77 1.93
## 21 Wildfire Relief Fund 0.27 0.24 0.36 1.79 1.89b. condition
table.means <- aggregate(cbind(money, donate, propDonate, need, diff) ~ condition, data = d, FUN = mean)
table.means[2:6] <- round(table.means[2:6], 2)
table.means## condition money donate propDonate need diff
## 1 charity 0.58 0.18 0.32 4.79 3.07
## 2 individual 0.58 0.19 0.33 4.59 2.93
## 3 practice 0.52 0.12 0.22 3.34 2.31table.sd <- aggregate(cbind(money, donate, propDonate, need, diff) ~ condition, data = d, FUN = sd)
table.sd[2:6] <- round(table.sd[2:6], 2)
table.sd## condition money donate propDonate need diff
## 1 charity 0.27 0.24 0.35 1.76 1.90
## 2 individual 0.27 0.24 0.36 1.78 1.84
## 3 practice 0.26 0.15 0.25 1.58 1.581. does condition predict difference or need?
singular random intercepts for stimulus
summary(m1 <- lmer(need ~ indiv_1 + charity_1 + (indiv_1 + charity_1 | participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: need ~ indiv_1 + charity_1 + (indiv_1 + charity_1 | participant)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 33266.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8292 -0.4541 0.0383 0.5284 4.5838
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 1.917 1.385
## indiv_1 1.463 1.209 -0.56
## charity_1 1.662 1.289 -0.55 0.78
## Residual 1.360 1.166
## Number of obs: 9827, groups: participant, 378
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.33907 0.07531 44.34
## indiv_1 1.24797 0.06949 17.96
## charity_1 1.44930 0.07320 19.80
##
## Correlation of Fixed Effects:
## (Intr) indv_1
## indiv_1 -0.590
## charity_1 -0.584 0.753summary(m2 <- lmer(diff ~ indiv_1 + charity_1 + (indiv_1 + charity_1 | participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: diff ~ indiv_1 + charity_1 + (indiv_1 + charity_1 | participant)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 29722.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9340 -0.4237 -0.0409 0.4092 5.3215
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 1.9329 1.3903
## indiv_1 1.2910 1.1362 -0.26
## charity_1 1.4443 1.2018 -0.25 0.82
## Residual 0.9105 0.9542
## Number of obs: 9827, groups: participant, 378
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.30908 0.07426 31.093
## indiv_1 0.62531 0.06370 9.817
## charity_1 0.75760 0.06681 11.340
##
## Correlation of Fixed Effects:
## (Intr) indv_1
## indiv_1 -0.310
## charity_1 -0.301 0.791tab_model(m1, m2,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| need | diff | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 3.34 | 0.08 | 3.19 – 3.49 | 44.34 | <0.001 | 2.31 | 0.07 | 2.16 – 2.45 | 31.09 | <0.001 |
| indiv_1 | 1.25 | 0.07 | 1.11 – 1.38 | 17.96 | <0.001 | 0.63 | 0.06 | 0.50 – 0.75 | 9.82 | <0.001 |
| charity_1 | 1.45 | 0.07 | 1.31 – 1.59 | 19.80 | <0.001 | 0.76 | 0.07 | 0.63 – 0.89 | 11.34 | <0.001 |
| Random Effects | ||||||||||
| σ2 | 1.36 | 0.91 | ||||||||
| τ00 | 1.92 participant | 1.93 participant | ||||||||
| τ11 | 1.46 participant.indiv_1 | 1.29 participant.indiv_1 | ||||||||
| 1.66 participant.charity_1 | 1.44 participant.charity_1 | |||||||||
| ρ01 | -0.56 | -0.26 | ||||||||
| -0.55 | -0.25 | |||||||||
| ICC | 0.55 | 0.72 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| Observations | 9827 | 9827 | ||||||||
| Marginal R2 / Conditional R2 | 0.099 / 0.591 | 0.026 / 0.729 | ||||||||
- difference plot
ggplot(data = d[(!is.na(d$condition)) & !is.na(d$diff),]) + #exclude NA pts on DV + IV manip
geom_point(shape = 16, #scatter points
size = .5,
position = position_jitterdodge( #spreads scatter points out
dodge.width = 0.9,
jitter.height = 0.8),
aes(y = diff, x = condition, #categorical IV
color = condition, #for third var add here
fill = condition), #for third var add here
alpha = .2) +
geom_bar(stat = 'summary',
fun.y = 'mean',
position = 'dodge',
aes(y = diff,
x = condition,
color = condition,
fill = condition),
alpha = .3) + #data point transparency
scale_color_manual(values = c("darkseagreen", "darkorchid1", "coral1")) + #aesthetic
scale_fill_manual(values = c("darkseagreen", "darkorchid1", "coral1")) + #aesthetic
ylab("By sending money, how much of a \ndifference do you think you could make?") + #aesthetic
theme(panel.grid = element_blank(), #aesthetic
legend.title = element_blank(),
panel.background = element_rect(fill = 'white'),
axis.text.x = element_text(size = 14),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 14),
axis.text.y = element_text(size = 14)) +
coord_cartesian(ylim = c(1, 7)) + #aesthetic
stat_summary(fun.data = mean_se, # adds SE bars--may need to do some trial and error to get SE colors to match the chart bars
geom = "errorbar",
position = position_dodge(.9),
width = .3, aes(y = diff, x = condition, color = condition,
fill = condition),
colour = c("darkseagreen4", "darkorchid3", "coral3")) +
geom_hline(yintercept = 0, color = 'gray70') ## Warning: Ignoring unknown parameters: fun.y## Warning: Ignoring unknown aesthetics: fill## No summary function supplied, defaulting to `mean_se()`
#+ facet_grid(~variablename) -- if want to add 4th variable- need plot
ggplot(data = d[(!is.na(d$condition)) & !is.na(d$need),]) + #exclude NA pts on DV + IV manip
geom_point(shape = 16, #scatter points
size = .5,
position = position_jitterdodge( #spreads scatter points out
dodge.width = 0.9,
jitter.height = 0.8),
aes(y = need, x = condition, #categorical IV
color = condition, #for third var add here
fill = condition), #for third var add here
alpha = .2) +
geom_bar(stat = 'summary',
fun.y = 'mean',
position = 'dodge',
aes(y = need,
x = condition,
color = condition,
fill = condition),
alpha = .3) + #data point transparency
scale_color_manual(values = c("darkseagreen", "darkorchid1", "coral1")) + #aesthetic
scale_fill_manual(values = c("darkseagreen", "darkorchid1", "coral1")) + #aesthetic
ylab("How much do you think ____ is in\n genuine need?") + #aesthetic
theme(panel.grid = element_blank(), #aesthetic
legend.title = element_blank(),
panel.background = element_rect(fill = 'white'),
axis.text.x = element_text(size = 14),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 14),
axis.text.y = element_text(size = 14)) +
coord_cartesian(ylim = c(1, 7)) + #aesthetic
stat_summary(fun.data = mean_se, # adds SE bars--may need to do some trial and error to get SE colors to match the chart bars
geom = "errorbar",
position = position_dodge(.9),
width = .3, aes(y = need, x = condition, color = condition,
fill = condition),
colour = c("darkseagreen4", "darkorchid3", "coral3")) +
geom_hline(yintercept = 0, color = 'gray70') ## Warning: Ignoring unknown parameters: fun.y## Warning: Ignoring unknown aesthetics: fill## No summary function supplied, defaulting to `mean_se()`
#+ facet_grid(~variablename) -- if want to add 4th variable1. does condition predict donations?
a. raw donated
summary(m1 <- lmer(donate ~ indiv_1 + charity_1 + (indiv_1 + charity_1 | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ indiv_1 + charity_1 + (indiv_1 + charity_1 | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: -6459
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0574 -0.4435 -0.0785 0.2429 5.7846
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 9.628e-03 0.098123
## indiv_1 7.087e-03 0.084183 0.77
## charity_1 7.300e-03 0.085441 0.67 0.91
## stimulus (Intercept) 4.279e-05 0.006541
## Residual 2.633e-02 0.162251
## Number of obs: 9828, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.115520 0.008937 12.926
## indiv_1 0.072178 0.009186 7.857
## charity_1 0.066646 0.009217 7.231
##
## Correlation of Fixed Effects:
## (Intr) indv_1
## indiv_1 -0.458
## charity_1 -0.480 0.848summary(m2 <- lmer(donate ~ pract_1 + charity_1 + (pract_1 + charity_1 | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ pract_1 + charity_1 + (pract_1 + charity_1 | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: -6459
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0574 -0.4435 -0.0785 0.2429 5.7846
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 2.942e-02 0.171515
## pract_1 7.087e-03 0.084183 -0.93
## charity_1 1.257e-03 0.035452 -0.21 0.17
## stimulus (Intercept) 4.279e-05 0.006541
## Residual 2.633e-02 0.162251
## Number of obs: 9828, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.187698 0.009438 19.888
## pract_1 -0.072178 0.009186 -7.857
## charity_1 -0.005532 0.005080 -1.089
##
## Correlation of Fixed Effects:
## (Intr) prct_1
## pract_1 -0.540
## charity_1 -0.304 0.270summary(m3 <- lmer(donate ~ pract_1 + indiv_1 + (indiv_1 + pract_1 | participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ pract_1 + indiv_1 + (indiv_1 + pract_1 | participant)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: -6456.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0330 -0.4495 -0.0798 0.2411 5.7928
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.028166 0.16783
## indiv_1 0.001250 0.03536 0.00
## pract_1 0.007291 0.08539 -0.90 0.24
## Residual 0.026359 0.16235
## Number of obs: 9828, groups: participant, 378
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.182167 0.009027 20.180
## pract_1 -0.066646 0.006155 -10.828
## indiv_1 0.005532 0.004154 1.332
##
## Correlation of Fixed Effects:
## (Intr) prct_1
## pract_1 -0.741
## indiv_1 -0.186 0.348tab_model(m1, m2, m3,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| donate | donate | donate | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.12 | 0.01 | 0.10 – 0.13 | 12.93 | <0.001 | 0.19 | 0.01 | 0.17 – 0.21 | 19.89 | <0.001 | 0.18 | 0.01 | 0.16 – 0.20 | 20.18 | <0.001 |
| indiv_1 | 0.07 | 0.01 | 0.05 – 0.09 | 7.86 | <0.001 | 0.01 | 0.00 | -0.00 – 0.01 | 1.33 | 0.183 | |||||
| charity_1 | 0.07 | 0.01 | 0.05 – 0.08 | 7.23 | <0.001 | -0.01 | 0.01 | -0.02 – 0.00 | -1.09 | 0.276 | |||||
| pract_1 | -0.07 | 0.01 | -0.09 – -0.05 | -7.86 | <0.001 | -0.07 | 0.01 | -0.08 – -0.05 | -10.83 | <0.001 | |||||
| Random Effects | |||||||||||||||
| σ2 | 0.03 | 0.03 | 0.03 | ||||||||||||
| τ00 | 0.01 participant | 0.03 participant | 0.03 participant | ||||||||||||
| 0.00 stimulus | 0.00 stimulus | ||||||||||||||
| τ11 | 0.01 participant.indiv_1 | 0.01 participant.pract_1 | 0.00 participant.indiv_1 | ||||||||||||
| 0.01 participant.charity_1 | 0.00 participant.charity_1 | 0.01 participant.pract_1 | |||||||||||||
| ρ01 | 0.77 participant.indiv_1 | -0.93 participant.pract_1 | -0.00 | ||||||||||||
| 0.67 participant.charity_1 | -0.21 participant.charity_1 | -0.90 | |||||||||||||
| ICC | 0.48 | 0.48 | 0.48 | ||||||||||||
| N | 378 participant | 378 participant | 378 participant | ||||||||||||
| 21 stimulus | 21 stimulus | ||||||||||||||
| Observations | 9828 | 9828 | 9828 | ||||||||||||
| Marginal R2 / Conditional R2 | 0.017 / 0.490 | 0.017 / 0.490 | 0.017 / 0.489 | ||||||||||||
2. proportion donated
singular random intercepts for stimulus
summary(m4 <- lmer(propDonate ~ indiv_1 + charity_1 + (indiv_1 + charity_1 | participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ indiv_1 + charity_1 + (indiv_1 + charity_1 | participant)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: -3361.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7629 -0.3035 -0.0273 0.2403 5.0872
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.04671 0.2161
## indiv_1 0.03234 0.1798 0.16
## charity_1 0.03400 0.1844 0.10 0.82
## Residual 0.03240 0.1800
## Number of obs: 9828, groups: participant, 378
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.22262 0.01174 18.960
## indiv_1 0.10333 0.01041 9.924
## charity_1 0.09376 0.01062 8.828
##
## Correlation of Fixed Effects:
## (Intr) indv_1
## indiv_1 0.016
## charity_1 -0.028 0.780summary(m5 <- lmer(propDonate ~ pract_1 + charity_1 + (pract_1 + charity_1 | participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ pract_1 + charity_1 + (pract_1 + charity_1 | participant)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: -3361.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7629 -0.3035 -0.0273 0.2403 5.0872
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.09129 0.3021
## pract_1 0.03234 0.1798 -0.71
## charity_1 0.01189 0.1091 -0.22 0.26
## Residual 0.03240 0.1800
## Number of obs: 9828, groups: participant, 378
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.325947 0.015814 20.611
## pract_1 -0.103330 0.010412 -9.924
## charity_1 -0.009566 0.006972 -1.372
##
## Correlation of Fixed Effects:
## (Intr) prct_1
## pract_1 -0.670
## charity_1 -0.249 0.304summary(m6 <- lmer(propDonate ~ pract_1 + indiv_1 + (indiv_1 + pract_1 | participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ pract_1 + indiv_1 + (indiv_1 + pract_1 | participant)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: -3361.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7629 -0.3035 -0.0273 0.2403 5.0872
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.08892 0.2982
## indiv_1 0.01189 0.1091 -0.15
## pract_1 0.03400 0.1844 -0.69 0.34
## Residual 0.03240 0.1800
## Number of obs: 9828, groups: participant, 378
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.316381 0.015615 20.262
## pract_1 -0.093764 0.010621 -8.828
## indiv_1 0.009566 0.006972 1.372
##
## Correlation of Fixed Effects:
## (Intr) prct_1
## pract_1 -0.660
## indiv_1 -0.194 0.358tab_model(m4, m5, m6,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| propDonate | propDonate | propDonate | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.22 | 0.01 | 0.20 – 0.25 | 18.96 | <0.001 | 0.33 | 0.02 | 0.29 – 0.36 | 20.61 | <0.001 | 0.32 | 0.02 | 0.29 – 0.35 | 20.26 | <0.001 |
| indiv_1 | 0.10 | 0.01 | 0.08 – 0.12 | 9.92 | <0.001 | 0.01 | 0.01 | -0.00 – 0.02 | 1.37 | 0.170 | |||||
| charity_1 | 0.09 | 0.01 | 0.07 – 0.11 | 8.83 | <0.001 | -0.01 | 0.01 | -0.02 – 0.00 | -1.37 | 0.170 | |||||
| pract_1 | -0.10 | 0.01 | -0.12 – -0.08 | -9.92 | <0.001 | -0.09 | 0.01 | -0.11 – -0.07 | -8.83 | <0.001 | |||||
| Random Effects | |||||||||||||||
| σ2 | 0.03 | 0.03 | 0.03 | ||||||||||||
| τ00 | 0.05 participant | 0.09 participant | 0.09 participant | ||||||||||||
| τ11 | 0.03 participant.indiv_1 | 0.03 participant.pract_1 | 0.01 participant.indiv_1 | ||||||||||||
| 0.03 participant.charity_1 | 0.01 participant.charity_1 | 0.03 participant.pract_1 | |||||||||||||
| ρ01 | 0.16 | -0.71 | -0.15 | ||||||||||||
| 0.10 | -0.22 | -0.69 | |||||||||||||
| ICC | 0.71 | 0.71 | 0.71 | ||||||||||||
| N | 378 participant | 378 participant | 378 participant | ||||||||||||
| Observations | 9828 | 9828 | 9828 | ||||||||||||
| Marginal R2 / Conditional R2 | 0.015 / 0.716 | 0.015 / 0.716 | 0.015 / 0.716 | ||||||||||||
3. do people donate more when allocated more money?
a. raw donated
singular with money.c random slopes
summary(m7 <- lmer(donate ~ (indiv_1 + charity_1) * money.c + (indiv_1 + charity_1 | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (indiv_1 + charity_1) * money.c + (indiv_1 + charity_1 |
## participant) + (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -9298.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0749 -0.5785 -0.0534 0.4756 5.7576
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 1.086e-02 0.104196
## indiv_1 9.053e-03 0.095146 0.52
## charity_1 9.266e-03 0.096261 0.44 0.85
## stimulus (Intercept) 1.844e-05 0.004294
## Residual 1.895e-02 0.137671
## Number of obs: 9828, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.125271 0.007467 16.777
## indiv_1 0.058189 0.007606 7.650
## charity_1 0.052753 0.007643 6.902
## money.c 0.222396 0.011108 20.022
## indiv_1:money.c 0.099772 0.013830 7.214
## charity_1:money.c 0.092507 0.013832 6.688
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 mony.c in_1:.
## indiv_1 -0.237
## charity_1 -0.270 0.820
## money.c 0.065 -0.064 -0.064
## indv_1:mny. -0.052 0.043 0.051 -0.803
## chrty_1:mn. -0.052 0.051 0.043 -0.803 0.645summary(m8 <- lmer(donate ~ (pract_1 + charity_1) * money.c + (pract_1 + charity_1 | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (pract_1 + charity_1) * money.c + (pract_1 + charity_1 |
## participant) + (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -9298.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0749 -0.5785 -0.0534 0.4756 5.7576
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 3.015e-02 0.173651
## pract_1 9.053e-03 0.095146 -0.86
## charity_1 2.731e-03 0.052262 -0.22 0.25
## stimulus (Intercept) 1.844e-05 0.004294
## Residual 1.895e-02 0.137671
## Number of obs: 9828, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.183461 0.009308 19.709
## pract_1 -0.058189 0.007606 -7.650
## charity_1 -0.005436 0.004579 -1.187
## money.c 0.322168 0.008240 39.097
## pract_1:money.c -0.099772 0.013830 -7.214
## charity_1:money.c -0.007265 0.011655 -0.623
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 mony.c pr_1:.
## pract_1 -0.627
## charity_1 -0.285 0.293
## money.c -0.012 0.014 0.024
## prct_1:mny. 0.007 0.043 -0.014 -0.596
## chrty_1:mn. 0.008 -0.010 -0.033 -0.707 0.421tab_model(m7, m8,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| donate | donate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.13 | 0.01 | 0.11 – 0.14 | 16.78 | <0.001 | 0.18 | 0.01 | 0.17 – 0.20 | 19.71 | <0.001 |
| indiv_1 | 0.06 | 0.01 | 0.04 – 0.07 | 7.65 | <0.001 | |||||
| charity_1 | 0.05 | 0.01 | 0.04 – 0.07 | 6.90 | <0.001 | -0.01 | 0.00 | -0.01 – 0.00 | -1.19 | 0.235 |
| money.c | 0.22 | 0.01 | 0.20 – 0.24 | 20.02 | <0.001 | 0.32 | 0.01 | 0.31 – 0.34 | 39.10 | <0.001 |
| indiv_1 * money.c | 0.10 | 0.01 | 0.07 – 0.13 | 7.21 | <0.001 | |||||
| charity_1 * money.c | 0.09 | 0.01 | 0.07 – 0.12 | 6.69 | <0.001 | -0.01 | 0.01 | -0.03 – 0.02 | -0.62 | 0.533 |
| pract_1 | -0.06 | 0.01 | -0.07 – -0.04 | -7.65 | <0.001 | |||||
| pract_1 * money.c | -0.10 | 0.01 | -0.13 – -0.07 | -7.21 | <0.001 | |||||
| Random Effects | ||||||||||
| σ2 | 0.02 | 0.02 | ||||||||
| τ00 | 0.01 participant | 0.03 participant | ||||||||
| 0.00 stimulus | 0.00 stimulus | |||||||||
| τ11 | 0.01 participant.indiv_1 | 0.01 participant.pract_1 | ||||||||
| 0.01 participant.charity_1 | 0.00 participant.charity_1 | |||||||||
| ρ01 | 0.52 participant.indiv_1 | -0.86 participant.pract_1 | ||||||||
| 0.44 participant.charity_1 | -0.22 participant.charity_1 | |||||||||
| ICC | 0.57 | 0.57 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| 21 stimulus | 21 stimulus | |||||||||
| Observations | 9828 | 9828 | ||||||||
| Marginal R2 / Conditional R2 | 0.143 / 0.633 | 0.143 / 0.633 | ||||||||
4.
b. proportion donated
random int for stimulus is singular for both models
summary(m9 <- lmer(propDonate ~ (indiv_1 + charity_1) * money.c + (indiv_1 + charity_1 + money.c | participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (indiv_1 + charity_1) * money.c + (indiv_1 + charity_1 +
## money.c | participant)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -3360.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7916 -0.3035 -0.0321 0.2403 5.1241
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.046946 0.21667
## indiv_1 0.032676 0.18076 0.15
## charity_1 0.034262 0.18510 0.10 0.82
## money.c 0.005801 0.07616 0.11 -0.15 -0.10
## Residual 0.031937 0.17871
## Number of obs: 9828, groups: participant, 378
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.222461 0.011776 18.891
## indiv_1 0.103552 0.010459 9.901
## charity_1 0.093988 0.010658 8.819
## money.c -0.003551 0.014941 -0.238
## indiv_1:money.c -0.001497 0.017951 -0.083
## charity_1:money.c -0.001661 0.017951 -0.093
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 mony.c in_1:.
## indiv_1 0.008
## charity_1 -0.034 0.782
## money.c 0.080 -0.093 -0.080
## indv_1:mny. -0.043 0.041 0.048 -0.775
## chrty_1:mn. -0.043 0.049 0.040 -0.775 0.645summary(m10 <- lmer(propDonate ~ (pract_1 + charity_1) * money.c + (pract_1 + charity_1 + money.c | participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (pract_1 + charity_1) * money.c + (pract_1 + charity_1 +
## money.c | participant)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -3360.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7916 -0.3035 -0.0321 0.2403 5.1241
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.091341 0.30223
## pract_1 0.032676 0.18076 -0.71
## charity_1 0.011988 0.10949 -0.22 0.26
## money.c 0.005801 0.07616 -0.01 0.15 0.08
## Residual 0.031937 0.17871
## Number of obs: 9828, groups: participant, 378
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.3260133 0.0158149 20.614
## pract_1 -0.1035520 0.0104591 -9.901
## charity_1 -0.0095639 0.0069750 -1.371
## money.c -0.0050480 0.0113887 -0.443
## pract_1:money.c 0.0014966 0.0179514 0.083
## charity_1:money.c -0.0001642 0.0151233 -0.011
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 mony.c pr_1:.
## pract_1 -0.668
## charity_1 -0.249 0.305
## money.c -0.011 0.058 0.042
## prct_1:mny. 0.005 0.041 -0.012 -0.559
## chrty_1:mn. 0.006 -0.010 -0.029 -0.664 0.421tab_model(m9, m10,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| propDonate | propDonate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.22 | 0.01 | 0.20 – 0.25 | 18.89 | <0.001 | 0.33 | 0.02 | 0.30 – 0.36 | 20.61 | <0.001 |
| indiv_1 | 0.10 | 0.01 | 0.08 – 0.12 | 9.90 | <0.001 | |||||
| charity_1 | 0.09 | 0.01 | 0.07 – 0.11 | 8.82 | <0.001 | -0.01 | 0.01 | -0.02 – 0.00 | -1.37 | 0.170 |
| money.c | -0.00 | 0.01 | -0.03 – 0.03 | -0.24 | 0.812 | -0.01 | 0.01 | -0.03 – 0.02 | -0.44 | 0.658 |
| indiv_1 * money.c | -0.00 | 0.02 | -0.04 – 0.03 | -0.08 | 0.934 | |||||
| charity_1 * money.c | -0.00 | 0.02 | -0.04 – 0.03 | -0.09 | 0.926 | -0.00 | 0.02 | -0.03 – 0.03 | -0.01 | 0.991 |
| pract_1 | -0.10 | 0.01 | -0.12 – -0.08 | -9.90 | <0.001 | |||||
| pract_1 * money.c | 0.00 | 0.02 | -0.03 – 0.04 | 0.08 | 0.934 | |||||
| Random Effects | ||||||||||
| σ2 | 0.03 | 0.03 | ||||||||
| τ00 | 0.05 participant | 0.09 participant | ||||||||
| τ11 | 0.03 participant.indiv_1 | 0.03 participant.pract_1 | ||||||||
| 0.03 participant.charity_1 | 0.01 participant.charity_1 | |||||||||
| 0.01 participant.money.c | 0.01 participant.money.c | |||||||||
| ρ01 | 0.15 | -0.71 | ||||||||
| 0.10 | -0.22 | |||||||||
| 0.11 | -0.01 | |||||||||
| ICC | 0.72 | 0.72 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| Observations | 9828 | 9828 | ||||||||
| Marginal R2 / Conditional R2 | 0.015 / 0.720 | 0.015 / 0.720 | ||||||||
5. do people donate more when stimluli are in greater need?
a. raw donated
singular with need.c random slopes
summary(m11 <- lmer(donate ~ (indiv_1 + charity_1) * need.c + (indiv_1 + charity_1 | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (indiv_1 + charity_1) * need.c + (indiv_1 + charity_1 |
## participant) + (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -7471.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3465 -0.5027 -0.0947 0.3439 5.4512
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 6.659e-03 0.081604
## indiv_1 4.804e-03 0.069311 0.90
## charity_1 5.192e-03 0.072059 0.89 0.93
## stimulus (Intercept) 6.345e-05 0.007965
## Residual 2.401e-02 0.154961
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.147272 0.009843 14.963
## indiv_1 0.031420 0.010244 3.067
## charity_1 0.017850 0.010320 1.730
## need.c 0.030606 0.002204 13.889
## indiv_1:need.c 0.012175 0.002647 4.600
## charity_1:need.c 0.010775 0.002679 4.022
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 need.c in_1:.
## indiv_1 -0.653
## charity_1 -0.645 0.868
## need.c 0.232 -0.226 -0.228
## indv_1:nd.c -0.180 0.154 0.168 -0.775
## chrty_1:nd. -0.178 0.169 0.130 -0.765 0.673summary(m12 <- lmer(donate ~ (pract_1 + charity_1) * need.c + (pract_1 + charity_1 | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (pract_1 + charity_1) * need.c + (pract_1 + charity_1 |
## participant) + (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -7471.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3465 -0.5027 -0.0947 0.3439 5.4512
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 2.163e-02 0.147087
## pract_1 4.804e-03 0.069311 -0.97
## charity_1 7.476e-04 0.027343 0.00 0.09
## stimulus (Intercept) 6.345e-05 0.007965
## Residual 2.401e-02 0.154961
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.178692 0.008370 21.349
## pract_1 -0.031420 0.010244 -3.067
## charity_1 -0.013570 0.005283 -2.569
## need.c 0.042780 0.001680 25.459
## pract_1:need.c -0.012175 0.002647 -4.600
## charity_1:need.c -0.001400 0.002155 -0.650
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 need.c pr_1:.
## pract_1 -0.456
## charity_1 -0.289 0.243
## need.c -0.042 0.055 0.040
## prct_1:nd.c 0.024 0.154 -0.030 -0.559
## chrty_1:nd. 0.026 -0.021 -0.129 -0.617 0.392tab_model(m11, m12,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| donate | donate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.15 | 0.01 | 0.13 – 0.17 | 14.96 | <0.001 | 0.18 | 0.01 | 0.16 – 0.20 | 21.35 | <0.001 |
| indiv_1 | 0.03 | 0.01 | 0.01 – 0.05 | 3.07 | 0.002 | |||||
| charity_1 | 0.02 | 0.01 | -0.00 – 0.04 | 1.73 | 0.084 | -0.01 | 0.01 | -0.02 – -0.00 | -2.57 | 0.010 |
| need.c | 0.03 | 0.00 | 0.03 – 0.03 | 13.89 | <0.001 | 0.04 | 0.00 | 0.04 – 0.05 | 25.46 | <0.001 |
| indiv_1 * need.c | 0.01 | 0.00 | 0.01 – 0.02 | 4.60 | <0.001 | |||||
| charity_1 * need.c | 0.01 | 0.00 | 0.01 – 0.02 | 4.02 | <0.001 | -0.00 | 0.00 | -0.01 – 0.00 | -0.65 | 0.516 |
| pract_1 | -0.03 | 0.01 | -0.05 – -0.01 | -3.07 | 0.002 | |||||
| pract_1 * need.c | -0.01 | 0.00 | -0.02 – -0.01 | -4.60 | <0.001 | |||||
| Random Effects | ||||||||||
| σ2 | 0.02 | 0.02 | ||||||||
| τ00 | 0.01 participant | 0.02 participant | ||||||||
| 0.00 stimulus | 0.00 stimulus | |||||||||
| τ11 | 0.00 participant.indiv_1 | 0.00 participant.pract_1 | ||||||||
| 0.01 participant.charity_1 | 0.00 participant.charity_1 | |||||||||
| ρ01 | 0.90 participant.indiv_1 | -0.97 participant.pract_1 | ||||||||
| 0.89 participant.charity_1 | -0.00 participant.charity_1 | |||||||||
| ICC | 0.44 | 0.44 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| 21 stimulus | 21 stimulus | |||||||||
| Observations | 9827 | 9827 | ||||||||
| Marginal R2 / Conditional R2 | 0.118 / 0.502 | 0.118 / 0.502 | ||||||||
mplot <- lmer(donate ~ condition * need.c + (condition | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d)
(p <- plot_model(mplot, type = "pred",
terms = c("need.c", "condition")) +
ggtitle("") +
ylab("proportion donated") +
xlab("mean-centered need rating") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
legend.position = c(.5, .8),
legend.direction = "horizontal",
legend.box = "horizontal",
legend.background = element_rect(fill = "white", color = "white"),
legend.title = element_blank()) +
scale_color_manual(
labels = c("charity","individual", "practice"),
values = c("blue", "red", "purple")) +
scale_fill_manual(values = c("blue", "red", "purple")) +
scale_x_continuous(breaks = c(-2.5, -2, -1.5, -1, -.5, 0, .5, 1, 1.5, 2, 2.5),
limits = c(-2.5, 3)) +
scale_y_continuous(breaks = c(0, .10, .20, .30, .40),
limits = c(0, .4)))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.## Warning: Removed 3 row(s) containing missing values (geom_path).
b. proportion donated
summary(m13 <- lmer(propDonate ~ (indiv_1 + charity_1) * need.c + (indiv_1 + charity_1 + need.c | participant)+ (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (indiv_1 + charity_1) * need.c + (indiv_1 + charity_1 +
## need.c | participant) + (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -7240.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0573 -0.3088 -0.0311 0.2570 6.3962
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 5.290e-02 0.229991
## indiv_1 1.510e-02 0.122883 -0.09
## charity_1 1.912e-02 0.138289 -0.16 0.75
## need.c 3.388e-03 0.058205 0.62 0.06 -0.11
## stimulus (Intercept) 2.158e-05 0.004645
## Residual 2.031e-02 0.142513
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.266624 0.013647 19.537
## indiv_1 0.034138 0.009694 3.522
## charity_1 0.010896 0.010265 1.061
## need.c 0.050044 0.004611 10.854
## indiv_1:need.c 0.021589 0.003778 5.714
## charity_1:need.c 0.024938 0.003871 6.443
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 need.c in_1:.
## indiv_1 -0.387
## charity_1 -0.412 0.800
## need.c 0.545 -0.260 -0.322
## indv_1:nd.c -0.232 0.310 0.306 -0.660
## chrty_1:nd. -0.234 0.320 0.266 -0.649 0.800summary(m14 <- lmer(propDonate ~ (pract_1 + charity_1) * need.c + (pract_1 + charity_1 + need.c | participant)+ (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (pract_1 + charity_1) * need.c + (pract_1 + charity_1 +
## need.c | participant) + (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -7240.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0573 -0.3088 -0.0311 0.2570 6.3962
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 6.314e-02 0.251280
## pract_1 1.510e-02 0.122883 -0.41
## charity_1 8.773e-03 0.093663 -0.21 0.21
## need.c 3.388e-03 0.058205 0.60 -0.06 -0.25
## stimulus (Intercept) 2.158e-05 0.004645
## Residual 2.031e-02 0.142513
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.300762 0.013339 22.548
## pract_1 -0.034138 0.009694 -3.522
## charity_1 -0.023242 0.006338 -3.667
## need.c 0.071632 0.003540 20.233
## pract_1:need.c -0.021589 0.003778 -5.714
## charity_1:need.c 0.003349 0.002419 1.385
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 need.c pr_1:.
## pract_1 -0.331
## charity_1 -0.248 0.234
## need.c 0.467 0.008 -0.137
## prct_1:nd.c 0.012 0.310 -0.022 -0.208
## chrty_1:nd. 0.008 -0.027 -0.127 -0.311 0.281tab_model(m13, m14,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| propDonate | propDonate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.27 | 0.01 | 0.24 – 0.29 | 19.54 | <0.001 | 0.30 | 0.01 | 0.27 – 0.33 | 22.55 | <0.001 |
| indiv_1 | 0.03 | 0.01 | 0.02 – 0.05 | 3.52 | <0.001 | |||||
| charity_1 | 0.01 | 0.01 | -0.01 – 0.03 | 1.06 | 0.288 | -0.02 | 0.01 | -0.04 – -0.01 | -3.67 | <0.001 |
| need.c | 0.05 | 0.00 | 0.04 – 0.06 | 10.85 | <0.001 | 0.07 | 0.00 | 0.06 – 0.08 | 20.23 | <0.001 |
| indiv_1 * need.c | 0.02 | 0.00 | 0.01 – 0.03 | 5.71 | <0.001 | |||||
| charity_1 * need.c | 0.02 | 0.00 | 0.02 – 0.03 | 6.44 | <0.001 | 0.00 | 0.00 | -0.00 – 0.01 | 1.38 | 0.166 |
| pract_1 | -0.03 | 0.01 | -0.05 – -0.02 | -3.52 | <0.001 | |||||
| pract_1 * need.c | -0.02 | 0.00 | -0.03 – -0.01 | -5.71 | <0.001 | |||||
| Random Effects | ||||||||||
| σ2 | 0.02 | 0.02 | ||||||||
| τ00 | 0.05 participant | 0.06 participant | ||||||||
| 0.00 stimulus | 0.00 stimulus | |||||||||
| τ11 | 0.02 participant.indiv_1 | 0.02 participant.pract_1 | ||||||||
| 0.02 participant.charity_1 | 0.01 participant.charity_1 | |||||||||
| 0.00 participant.need.c | 0.00 participant.need.c | |||||||||
| ρ01 | -0.09 participant.indiv_1 | -0.41 participant.pract_1 | ||||||||
| -0.16 participant.charity_1 | -0.21 participant.charity_1 | |||||||||
| 0.62 participant.need.c | 0.60 participant.need.c | |||||||||
| ICC | 0.78 | 0.78 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| 21 stimulus | 21 stimulus | |||||||||
| Observations | 9827 | 9827 | ||||||||
| Marginal R2 / Conditional R2 | 0.149 / 0.812 | 0.149 / 0.812 | ||||||||
mplot <- lmer(propDonate ~ condition * need.c + (condition + need.c | participant)+ (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d)
(p <- plot_model(mplot, type = "pred",
terms = c("need.c", "condition")) +
ggtitle("") +
ylab("proportion donated") +
xlab("mean-centered need rating") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
legend.position = c(.5, .2),
legend.direction = "horizontal",
legend.box = "horizontal",
legend.background = element_rect(fill = "white", color = "white"),
legend.title = element_blank()) +
scale_color_manual(
labels = c("charity","individual", "practice"),
values = c("blue", "red", "purple")) +
scale_fill_manual(values = c("blue", "red", "purple")) +
scale_x_continuous(breaks = c(-2.5, -2, -1.5, -1, -.5, 0, .5, 1, 1.5, 2, 2.5),
limits = c(-2.5, 3)) +
scale_y_continuous(breaks = c(0, .10, .20, .30, .40, .50, .6),
limits = c(0, .6)))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.## Warning: Removed 3 row(s) containing missing values (geom_path).
6. do people donate more when givig makes a greater difference?
a. raw donated
summary(m15 <- lmer(donate ~ (indiv_1 + charity_1) * diff.c + (indiv_1 + charity_1 + diff.c | participant)+ (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (indiv_1 + charity_1) * diff.c + (indiv_1 + charity_1 +
## diff.c | participant) + (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -7527.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4423 -0.4053 -0.0706 0.2046 6.2076
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 8.882e-03 0.094246
## indiv_1 5.815e-03 0.076259 0.75
## charity_1 5.567e-03 0.074611 0.69 0.95
## diff.c 1.510e-03 0.038864 0.58 0.04 0.03
## stimulus (Intercept) 4.913e-05 0.007009
## Residual 2.296e-02 0.151521
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.145034 0.009469 15.316
## indiv_1 0.046275 0.009444 4.900
## charity_1 0.034665 0.009427 3.677
## diff.c 0.044844 0.003243 13.828
## indiv_1:diff.c 0.002311 0.002797 0.826
## charity_1:diff.c 0.001500 0.002791 0.537
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 diff.c in_1:.
## indiv_1 -0.537
## charity_1 -0.557 0.864
## diff.c 0.356 -0.104 -0.109
## indv_1:dff. -0.123 0.107 0.108 -0.509
## chrty_1:df. -0.127 0.116 0.098 -0.529 0.738summary(m16 <- lmer(donate ~ (pract_1 + charity_1) * diff.c + (pract_1 + charity_1 + diff.c | participant)+ (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (pract_1 + charity_1) * diff.c + (pract_1 + charity_1 +
## diff.c | participant) + (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -7527.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4423 -0.4053 -0.0706 0.2046 6.2076
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 2.553e-02 0.159778
## pract_1 5.815e-03 0.076259 -0.92
## charity_1 6.003e-04 0.024500 -0.26 0.23
## diff.c 1.510e-03 0.038864 0.36 -0.04 -0.04
## stimulus (Intercept) 4.913e-05 0.007009
## Residual 2.296e-02 0.151521
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.1913096 0.0090967 21.031
## pract_1 -0.0462754 0.0094444 -4.900
## charity_1 -0.0116101 0.0049291 -2.355
## diff.c 0.0471555 0.0030183 15.623
## pract_1:diff.c -0.0023111 0.0027970 -0.826
## charity_1:diff.c -0.0008115 0.0020229 -0.401
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 diff.c pr_1:.
## pract_1 -0.479
## charity_1 -0.311 0.265
## diff.c 0.267 0.012 -0.010
## prct_1:dff. 0.017 0.107 -0.001 -0.380
## chrty_1:df. 0.007 -0.013 -0.050 -0.366 0.364tab_model(m15, m16,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| donate | donate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.15 | 0.01 | 0.13 – 0.16 | 15.32 | <0.001 | 0.19 | 0.01 | 0.17 – 0.21 | 21.03 | <0.001 |
| indiv_1 | 0.05 | 0.01 | 0.03 – 0.06 | 4.90 | <0.001 | |||||
| charity_1 | 0.03 | 0.01 | 0.02 – 0.05 | 3.68 | <0.001 | -0.01 | 0.00 | -0.02 – -0.00 | -2.36 | 0.019 |
| diff.c | 0.04 | 0.00 | 0.04 – 0.05 | 13.83 | <0.001 | 0.05 | 0.00 | 0.04 – 0.05 | 15.62 | <0.001 |
| indiv_1 * diff.c | 0.00 | 0.00 | -0.00 – 0.01 | 0.83 | 0.409 | |||||
| charity_1 * diff.c | 0.00 | 0.00 | -0.00 – 0.01 | 0.54 | 0.591 | -0.00 | 0.00 | -0.00 – 0.00 | -0.40 | 0.688 |
| pract_1 | -0.05 | 0.01 | -0.06 – -0.03 | -4.90 | <0.001 | |||||
| pract_1 * diff.c | -0.00 | 0.00 | -0.01 – 0.00 | -0.83 | 0.409 | |||||
| Random Effects | ||||||||||
| σ2 | 0.02 | 0.02 | ||||||||
| τ00 | 0.01 participant | 0.03 participant | ||||||||
| 0.00 stimulus | 0.00 stimulus | |||||||||
| τ11 | 0.01 participant.indiv_1 | 0.01 participant.pract_1 | ||||||||
| 0.01 participant.charity_1 | 0.00 participant.charity_1 | |||||||||
| 0.00 participant.diff.c | 0.00 participant.diff.c | |||||||||
| ρ01 | 0.75 participant.indiv_1 | -0.92 participant.pract_1 | ||||||||
| 0.69 participant.charity_1 | -0.26 participant.charity_1 | |||||||||
| 0.58 participant.diff.c | 0.36 participant.diff.c | |||||||||
| ICC | 0.53 | 0.53 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| 21 stimulus | 21 stimulus | |||||||||
| Observations | 9827 | 9827 | ||||||||
| Marginal R2 / Conditional R2 | 0.139 / 0.598 | 0.139 / 0.598 | ||||||||
mplot <- lmer(donate ~ condition * diff.c + (condition + diff.c | participant)+ (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d)
(p <- plot_model(mplot, type = "pred",
terms = c("condition")) +
ggtitle("") +
ylab("donated") +
xlab("condition") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
legend.position = c(.5, .2),
legend.direction = "horizontal",
legend.box = "horizontal",
legend.background = element_rect(fill = "white", color = "white"),
legend.title = element_blank()) +
scale_fill_manual(values = c("blue", "red", "purple")) +
scale_y_continuous(breaks = c(0, .10, .20, .30, .40, .5),
limits = c(0, .5)))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
- proportion donated random int for stimulus was singular
summary(m17 <- lmer(propDonate ~ (indiv_1 + charity_1) * diff.c + (indiv_1 + charity_1 + diff.c | participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (indiv_1 + charity_1) * diff.c + (indiv_1 + charity_1 +
## diff.c | participant)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -6224.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4664 -0.3022 -0.0244 0.2402 6.2612
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.051698 0.22737
## indiv_1 0.028345 0.16836 0.11
## charity_1 0.028681 0.16936 0.06 0.84
## diff.c 0.007507 0.08664 0.50 0.07 0.06
## Residual 0.022087 0.14862
## Number of obs: 9827, groups: participant, 378
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.273383 0.012806 21.349
## indiv_1 0.068203 0.009917 6.877
## charity_1 0.047568 0.009989 4.762
## diff.c 0.063897 0.005980 10.684
## indiv_1:diff.c 0.026399 0.004132 6.389
## charity_1:diff.c 0.024129 0.004162 5.797
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 diff.c in_1:.
## indiv_1 -0.040
## charity_1 -0.079 0.811
## diff.c 0.508 -0.090 -0.094
## indv_1:dff. -0.144 0.175 0.178 -0.484
## chrty_1:df. -0.149 0.186 0.168 -0.495 0.790summary(m18 <- lmer(propDonate ~ (pract_1 + charity_1) * diff.c + (pract_1 + charity_1 + diff.c | participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (pract_1 + charity_1) * diff.c + (pract_1 + charity_1 +
## diff.c | participant)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -6224.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4664 -0.3022 -0.0244 0.2402 6.2612
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.088465 0.29743
## pract_1 0.028345 0.16836 -0.65
## charity_1 0.009191 0.09587 -0.22 0.27
## diff.c 0.007507 0.08664 0.42 -0.07 -0.01
## Residual 0.022087 0.14862
## Number of obs: 9827, groups: participant, 378
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.341586 0.015878 21.514
## pract_1 -0.068203 0.009917 -6.877
## charity_1 -0.020635 0.006114 -3.375
## diff.c 0.090296 0.005379 16.786
## pract_1:diff.c -0.026399 0.004132 -6.389
## charity_1:diff.c -0.002270 0.002688 -0.844
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 diff.c pr_1:.
## pract_1 -0.592
## charity_1 -0.237 0.296
## diff.c 0.388 -0.035 -0.005
## prct_1:dff. 0.007 0.175 -0.007 -0.230
## chrty_1:df. 0.004 -0.019 -0.053 -0.266 0.314tab_model(m17, m18,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| propDonate | propDonate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.27 | 0.01 | 0.25 – 0.30 | 21.35 | <0.001 | 0.34 | 0.02 | 0.31 – 0.37 | 21.51 | <0.001 |
| indiv_1 | 0.07 | 0.01 | 0.05 – 0.09 | 6.88 | <0.001 | |||||
| charity_1 | 0.05 | 0.01 | 0.03 – 0.07 | 4.76 | <0.001 | -0.02 | 0.01 | -0.03 – -0.01 | -3.38 | 0.001 |
| diff.c | 0.06 | 0.01 | 0.05 – 0.08 | 10.68 | <0.001 | 0.09 | 0.01 | 0.08 – 0.10 | 16.79 | <0.001 |
| indiv_1 * diff.c | 0.03 | 0.00 | 0.02 – 0.03 | 6.39 | <0.001 | |||||
| charity_1 * diff.c | 0.02 | 0.00 | 0.02 – 0.03 | 5.80 | <0.001 | -0.00 | 0.00 | -0.01 – 0.00 | -0.84 | 0.398 |
| pract_1 | -0.07 | 0.01 | -0.09 – -0.05 | -6.88 | <0.001 | |||||
| pract_1 * diff.c | -0.03 | 0.00 | -0.03 – -0.02 | -6.39 | <0.001 | |||||
| Random Effects | ||||||||||
| σ2 | 0.02 | 0.02 | ||||||||
| τ00 | 0.05 participant | 0.09 participant | ||||||||
| τ11 | 0.03 participant.indiv_1 | 0.03 participant.pract_1 | ||||||||
| 0.03 participant.charity_1 | 0.01 participant.charity_1 | |||||||||
| 0.01 participant.diff.c | 0.01 participant.diff.c | |||||||||
| ρ01 | 0.11 | -0.65 | ||||||||
| 0.06 | -0.22 | |||||||||
| 0.50 | 0.42 | |||||||||
| ICC | 0.82 | 0.82 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| Observations | 9827 | 9827 | ||||||||
| Marginal R2 / Conditional R2 | 0.169 / 0.855 | 0.169 / 0.855 | ||||||||
mplot <- lmer(propDonate ~ condition * diff.c + (condition + diff.c | participant),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d)
(p <- plot_model(mplot, type = "pred",
terms = c("diff.c", "condition")) +
ggtitle("") +
ylab("proportion donated") +
xlab("mean-centered difference rating") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
legend.position = c(.5, .2),
legend.direction = "horizontal",
legend.box = "horizontal",
legend.background = element_rect(fill = "white", color = "white"),
legend.title = element_blank()) +
scale_color_manual(
labels = c("charity","individual", "practice"),
values = c("blue", "red", "purple")) +
scale_fill_manual(values = c("blue", "red", "purple")) +
scale_x_continuous(breaks = c(-2.5, -2, -1.5, -1, -.5, 0, .5, 1, 1.5, 2),
limits = c(-2, 2)) +
scale_y_continuous(breaks = c(0, .10, .20, .30, .40, .50, .6),
limits = c(0, .6)))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.## Warning: Removed 9 row(s) containing missing values (geom_path).
- is there an interaction with condition and money allocated while controlling need and difference made
- raw donated
summary(m15 <- lmer(donate ~ (indiv_1 + charity_1) * money.c + need.c + diff.c +
(indiv_1 + charity_1 + diff.c + money.c + need.c | participant)+ (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (indiv_1 + charity_1) * money.c + need.c + diff.c +
## (indiv_1 + charity_1 + diff.c + money.c + need.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -15834.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.6959 -0.3385 -0.0266 0.2692 7.7978
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 1.509e-02 0.122824
## indiv_1 4.554e-03 0.067484 0.04
## charity_1 5.370e-03 0.073281 -0.05 0.74
## diff.c 7.525e-04 0.027432 0.15 -0.15 -0.14
## money.c 7.548e-02 0.274741 0.83 0.42 0.33 -0.16
## need.c 7.201e-04 0.026835 0.71 0.13 -0.10 -0.11 0.62
## stimulus (Intercept) 1.051e-05 0.003241
## Residual 8.245e-03 0.090802
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.167435 0.007567 22.126
## indiv_1 0.010396 0.005629 1.847
## charity_1 -0.002888 0.005856 -0.493
## money.c 0.201364 0.015975 12.605
## need.c 0.028738 0.001708 16.829
## diff.c 0.019376 0.001950 9.935
## indiv_1:money.c 0.119052 0.009293 12.811
## charity_1:money.c 0.120054 0.009289 12.924
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 mony.c need.c diff.c in_1:.
## indiv_1 -0.347
## charity_1 -0.384 0.790
## money.c 0.631 0.207 0.167
## need.c 0.523 -0.017 -0.143 0.451
## diff.c 0.149 -0.074 -0.074 -0.130 -0.262
## indv_1:mny. -0.029 0.034 0.040 -0.374 -0.009 0.049
## chrty_1:mn. -0.028 0.040 0.030 -0.375 -0.007 0.051 0.645summary(m16 <- lmer(donate ~ (pract_1 + charity_1) * money.c + need.c + diff.c +
(pract_1 + charity_1 + diff.c + money.c + need.c | participant)+ (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (pract_1 + charity_1) * money.c + need.c + diff.c +
## (pract_1 + charity_1 + diff.c + money.c + need.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -15834.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.6959 -0.3385 -0.0266 0.2692 7.7978
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 2.030e-02 0.142473
## pract_1 4.554e-03 0.067484 -0.51
## charity_1 2.565e-03 0.050645 -0.23 0.26
## diff.c 7.525e-04 0.027432 0.06 0.15 0.00
## money.c 7.548e-02 0.274742 0.92 -0.42 -0.08 -0.16
## need.c 7.201e-04 0.026835 0.67 -0.13 -0.32 -0.11 0.62
## stimulus (Intercept) 1.051e-05 0.003241
## Residual 8.245e-03 0.090801
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.177831 0.007708 23.070
## pract_1 -0.010396 0.005629 -1.847
## charity_1 -0.013284 0.003726 -3.566
## money.c 0.320415 0.015181 21.107
## need.c 0.028738 0.001708 16.829
## diff.c 0.019376 0.001950 9.935
## pract_1:money.c -0.119052 0.009293 -12.811
## charity_1:money.c 0.001002 0.007827 0.128
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 mony.c need.c diff.c pr_1:.
## pract_1 -0.390
## charity_1 -0.274 0.269
## money.c 0.808 -0.238 -0.045
## need.c 0.501 0.017 -0.198 0.470
## diff.c 0.092 0.074 -0.004 -0.107 -0.262
## prct_1:mny. 0.004 0.034 -0.011 -0.218 0.009 -0.049
## chrty_1:mn. 0.007 -0.008 -0.029 -0.258 0.002 0.003 0.422tab_model(m15, m16,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| donate | donate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.17 | 0.01 | 0.15 – 0.18 | 22.13 | <0.001 | 0.18 | 0.01 | 0.16 – 0.19 | 23.07 | <0.001 |
| indiv_1 | 0.01 | 0.01 | -0.00 – 0.02 | 1.85 | 0.065 | |||||
| charity_1 | -0.00 | 0.01 | -0.01 – 0.01 | -0.49 | 0.622 | -0.01 | 0.00 | -0.02 – -0.01 | -3.57 | <0.001 |
| money.c | 0.20 | 0.02 | 0.17 – 0.23 | 12.60 | <0.001 | 0.32 | 0.02 | 0.29 – 0.35 | 21.11 | <0.001 |
| need.c | 0.03 | 0.00 | 0.03 – 0.03 | 16.83 | <0.001 | 0.03 | 0.00 | 0.03 – 0.03 | 16.83 | <0.001 |
| diff.c | 0.02 | 0.00 | 0.02 – 0.02 | 9.93 | <0.001 | 0.02 | 0.00 | 0.02 – 0.02 | 9.93 | <0.001 |
| indiv_1 * money.c | 0.12 | 0.01 | 0.10 – 0.14 | 12.81 | <0.001 | |||||
| charity_1 * money.c | 0.12 | 0.01 | 0.10 – 0.14 | 12.92 | <0.001 | 0.00 | 0.01 | -0.01 – 0.02 | 0.13 | 0.898 |
| pract_1 | -0.01 | 0.01 | -0.02 – 0.00 | -1.85 | 0.065 | |||||
| pract_1 * money.c | -0.12 | 0.01 | -0.14 – -0.10 | -12.81 | <0.001 | |||||
| Random Effects | ||||||||||
| σ2 | 0.01 | 0.01 | ||||||||
| τ00 | 0.02 participant | 0.02 participant | ||||||||
| 0.00 stimulus | 0.00 stimulus | |||||||||
| τ11 | 0.00 participant.indiv_1 | 0.00 participant.pract_1 | ||||||||
| 0.01 participant.charity_1 | 0.00 participant.charity_1 | |||||||||
| 0.00 participant.diff.c | 0.00 participant.diff.c | |||||||||
| 0.08 participant.money.c | 0.08 participant.money.c | |||||||||
| 0.00 participant.need.c | 0.00 participant.need.c | |||||||||
| ρ01 | 0.04 participant.indiv_1 | -0.51 participant.pract_1 | ||||||||
| -0.05 participant.charity_1 | -0.23 participant.charity_1 | |||||||||
| 0.15 participant.diff.c | 0.06 participant.diff.c | |||||||||
| 0.83 participant.money.c | 0.92 participant.money.c | |||||||||
| 0.71 participant.need.c | 0.67 participant.need.c | |||||||||
| ICC | 0.78 | 0.78 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| 21 stimulus | 21 stimulus | |||||||||
| Observations | 9827 | 9827 | ||||||||
| Marginal R2 / Conditional R2 | 0.256 / 0.836 | 0.256 / 0.836 | ||||||||
b. proportion donated
summary(m17 <- lmer(propDonate ~ (indiv_1 + charity_1) * money.c + need.c + diff.c +
(indiv_1 + charity_1 + money.c + need.c + diff.c | participant)+ (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (indiv_1 + charity_1) * money.c + need.c + diff.c +
## (indiv_1 + charity_1 + money.c + need.c + diff.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -8073.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4180 -0.3159 -0.0232 0.2506 6.1317
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 5.087e-02 0.225543
## indiv_1 1.635e-02 0.127878 -0.09
## charity_1 2.015e-02 0.141950 -0.18 0.80
## money.c 3.835e-03 0.061926 -0.25 0.09 0.10
## need.c 2.671e-03 0.051678 0.63 0.11 -0.07 -0.20
## diff.c 3.300e-03 0.057441 0.24 -0.12 -0.11 -0.48 0.02
## stimulus (Intercept) 1.897e-05 0.004356
## Residual 1.761e-02 0.132691
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.300419 0.013119 22.900
## indiv_1 0.012683 0.009127 1.390
## charity_1 -0.011947 0.009717 -1.230
## money.c -0.045109 0.011415 -3.952
## need.c 0.051274 0.003156 16.246
## diff.c 0.044544 0.003816 11.674
## indiv_1:money.c 0.038442 0.013679 2.810
## charity_1:money.c 0.053644 0.013665 3.926
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 mony.c need.c diff.c in_1:.
## indiv_1 -0.314
## charity_1 -0.363 0.808
## money.c -0.032 -0.026 -0.020
## need.c 0.511 -0.002 -0.116 -0.034
## diff.c 0.215 -0.076 -0.073 -0.158 -0.154
## indv_1:mny. -0.024 0.029 0.034 -0.770 -0.010 0.047
## chrty_1:mn. -0.023 0.035 0.026 -0.771 -0.009 0.050 0.645summary(m18 <- lmer(propDonate ~ (pract_1 + charity_1) * money.c + need.c + diff.c +
(pract_1 + charity_1 + money.c + need.c + diff.c | participant)+ (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (pract_1 + charity_1) * money.c + need.c + diff.c +
## (pract_1 + charity_1 + money.c + need.c + diff.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -8073.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4180 -0.3159 -0.0232 0.2506 6.1317
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 6.208e-02 0.249165
## pract_1 1.635e-02 0.127878 -0.43
## charity_1 7.311e-03 0.085504 -0.23 0.16
## money.c 3.835e-03 0.061926 -0.18 -0.09 0.04
## need.c 2.671e-03 0.051678 0.63 -0.11 -0.28 -0.20
## diff.c 3.300e-03 0.057441 0.16 0.12 0.00 -0.48 0.02
## stimulus (Intercept) 1.897e-05 0.004356
## Residual 1.761e-02 0.132691
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.313102 0.013424 23.324
## pract_1 -0.012683 0.009127 -1.390
## charity_1 -0.024630 0.005859 -4.204
## money.c -0.006667 0.008764 -0.761
## need.c 0.051274 0.003156 16.246
## diff.c 0.044544 0.003816 11.674
## pract_1:money.c -0.038442 0.013679 -2.810
## charity_1:money.c 0.015202 0.011513 1.320
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 mony.c need.c diff.c pr_1:.
## pract_1 -0.373
## charity_1 -0.257 0.217
## money.c -0.069 -0.013 0.026
## need.c 0.498 0.002 -0.189 -0.060
## diff.c 0.158 0.076 -0.003 -0.133 -0.154
## prct_1:mny. 0.004 0.029 -0.010 -0.557 0.010 -0.047
## chrty_1:mn. 0.007 -0.007 -0.027 -0.659 0.001 0.004 0.422tab_model(m17, m18,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| propDonate | propDonate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.30 | 0.01 | 0.27 – 0.33 | 22.90 | <0.001 | 0.31 | 0.01 | 0.29 – 0.34 | 23.32 | <0.001 |
| indiv_1 | 0.01 | 0.01 | -0.01 – 0.03 | 1.39 | 0.165 | |||||
| charity_1 | -0.01 | 0.01 | -0.03 – 0.01 | -1.23 | 0.219 | -0.02 | 0.01 | -0.04 – -0.01 | -4.20 | <0.001 |
| money.c | -0.05 | 0.01 | -0.07 – -0.02 | -3.95 | <0.001 | -0.01 | 0.01 | -0.02 – 0.01 | -0.76 | 0.447 |
| need.c | 0.05 | 0.00 | 0.05 – 0.06 | 16.25 | <0.001 | 0.05 | 0.00 | 0.05 – 0.06 | 16.25 | <0.001 |
| diff.c | 0.04 | 0.00 | 0.04 – 0.05 | 11.67 | <0.001 | 0.04 | 0.00 | 0.04 – 0.05 | 11.67 | <0.001 |
| indiv_1 * money.c | 0.04 | 0.01 | 0.01 – 0.07 | 2.81 | 0.005 | |||||
| charity_1 * money.c | 0.05 | 0.01 | 0.03 – 0.08 | 3.93 | <0.001 | 0.02 | 0.01 | -0.01 – 0.04 | 1.32 | 0.187 |
| pract_1 | -0.01 | 0.01 | -0.03 – 0.01 | -1.39 | 0.165 | |||||
| pract_1 * money.c | -0.04 | 0.01 | -0.07 – -0.01 | -2.81 | 0.005 | |||||
| Random Effects | ||||||||||
| σ2 | 0.02 | 0.02 | ||||||||
| τ00 | 0.05 participant | 0.06 participant | ||||||||
| 0.00 stimulus | 0.00 stimulus | |||||||||
| τ11 | 0.02 participant.indiv_1 | 0.02 participant.pract_1 | ||||||||
| 0.02 participant.charity_1 | 0.01 participant.charity_1 | |||||||||
| 0.00 participant.money.c | 0.00 participant.money.c | |||||||||
| 0.00 participant.need.c | 0.00 participant.need.c | |||||||||
| 0.00 participant.diff.c | 0.00 participant.diff.c | |||||||||
| ρ01 | -0.09 participant.indiv_1 | -0.43 participant.pract_1 | ||||||||
| -0.18 participant.charity_1 | -0.23 participant.charity_1 | |||||||||
| -0.25 participant.money.c | -0.18 participant.money.c | |||||||||
| 0.63 participant.need.c | 0.63 participant.need.c | |||||||||
| 0.24 participant.diff.c | 0.16 participant.diff.c | |||||||||
| ICC | 0.82 | 0.82 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| 21 stimulus | 21 stimulus | |||||||||
| Observations | 9827 | 9827 | ||||||||
| Marginal R2 / Conditional R2 | 0.196 / 0.853 | 0.196 / 0.853 | ||||||||
8. is there an interaction with condition and need while controlling money allocted and difference made
a. raw donated
summary(m15 <- lmer(donate ~ (indiv_1 + charity_1) * need.c + diff.c + money.c +
(indiv_1 + charity_1 + diff.c + money.c + need.c | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (indiv_1 + charity_1) * need.c + diff.c + money.c +
## (indiv_1 + charity_1 + diff.c + money.c + need.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -15690.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.5314 -0.3313 -0.0259 0.2600 7.8652
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 1.606e-02 0.126725
## indiv_1 4.128e-03 0.064249 -0.02
## charity_1 5.064e-03 0.071164 -0.11 0.73
## diff.c 7.480e-04 0.027350 0.09 -0.08 -0.06
## money.c 7.543e-02 0.274649 0.88 0.28 0.20 -0.17
## need.c 7.144e-04 0.026729 0.72 0.09 -0.14 -0.15 0.65
## stimulus (Intercept) 1.291e-05 0.003592
## Residual 8.419e-03 0.091753
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.154307 0.008113 19.019
## indiv_1 0.022433 0.006036 3.717
## charity_1 0.008832 0.006260 1.411
## need.c 0.014251 0.002419 5.890
## diff.c 0.017786 0.001942 9.161
## money.c 0.296126 0.014555 20.345
## indiv_1:need.c 0.017658 0.002164 8.160
## charity_1:need.c 0.017138 0.002215 7.737
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 need.c diff.c mony.c in_1:.
## indiv_1 -0.446
## charity_1 -0.471 0.808
## need.c 0.524 -0.234 -0.297
## diff.c 0.107 -0.034 -0.030 -0.209
## money.c 0.686 0.143 0.109 0.353 -0.124
## indv_1:nd.c -0.218 0.273 0.275 -0.675 0.015 0.008
## chrty_1:nd. -0.221 0.281 0.232 -0.656 0.002 0.011 0.760summary(m16 <- lmer(donate ~ (pract_1 + charity_1) * need.c + diff.c + money.c +
(pract_1 + charity_1 + diff.c + money.c + need.c | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (pract_1 + charity_1) * need.c + diff.c + money.c +
## (pract_1 + charity_1 + diff.c + money.c + need.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -15690.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.5314 -0.3313 -0.0259 0.2600 7.8652
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 1.988e-02 0.140988
## pract_1 4.128e-03 0.064249 -0.44
## charity_1 2.520e-03 0.050204 -0.23 0.25
## diff.c 7.480e-04 0.027350 0.05 0.08 0.02
## money.c 7.543e-02 0.274649 0.91 -0.28 -0.07 -0.17
## need.c 7.144e-04 0.026729 0.69 -0.09 -0.31 -0.15 0.65
## stimulus (Intercept) 1.291e-05 0.003592
## Residual 8.419e-03 0.091753
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.1767399 0.0076528 23.095
## pract_1 -0.0224332 0.0060357 -3.717
## charity_1 -0.0136014 0.0038123 -3.568
## need.c 0.0319091 0.0018613 17.143
## diff.c 0.0177860 0.0019415 9.161
## money.c 0.2961265 0.0145551 20.345
## pract_1:need.c -0.0176584 0.0021640 -8.160
## charity_1:need.c -0.0005205 0.0015189 -0.343
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 need.c diff.c mony.c pr_1:.
## pract_1 -0.316
## charity_1 -0.274 0.256
## need.c 0.464 -0.013 -0.131
## diff.c 0.087 0.034 0.005 -0.255
## money.c 0.840 -0.143 -0.047 0.468 -0.124
## prct_1:nd.c 0.016 0.273 -0.020 -0.285 -0.015 -0.008
## chrty_1:nd. 0.005 -0.021 -0.121 -0.361 -0.018 0.004 0.317tab_model(m15, m16,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| donate | donate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.15 | 0.01 | 0.14 – 0.17 | 19.02 | <0.001 | 0.18 | 0.01 | 0.16 – 0.19 | 23.09 | <0.001 |
| indiv_1 | 0.02 | 0.01 | 0.01 – 0.03 | 3.72 | <0.001 | |||||
| charity_1 | 0.01 | 0.01 | -0.00 – 0.02 | 1.41 | 0.158 | -0.01 | 0.00 | -0.02 – -0.01 | -3.57 | <0.001 |
| need.c | 0.01 | 0.00 | 0.01 – 0.02 | 5.89 | <0.001 | 0.03 | 0.00 | 0.03 – 0.04 | 17.14 | <0.001 |
| diff.c | 0.02 | 0.00 | 0.01 – 0.02 | 9.16 | <0.001 | 0.02 | 0.00 | 0.01 – 0.02 | 9.16 | <0.001 |
| money.c | 0.30 | 0.01 | 0.27 – 0.32 | 20.35 | <0.001 | 0.30 | 0.01 | 0.27 – 0.32 | 20.35 | <0.001 |
| indiv_1 * need.c | 0.02 | 0.00 | 0.01 – 0.02 | 8.16 | <0.001 | |||||
| charity_1 * need.c | 0.02 | 0.00 | 0.01 – 0.02 | 7.74 | <0.001 | -0.00 | 0.00 | -0.00 – 0.00 | -0.34 | 0.732 |
| pract_1 | -0.02 | 0.01 | -0.03 – -0.01 | -3.72 | <0.001 | |||||
| pract_1 * need.c | -0.02 | 0.00 | -0.02 – -0.01 | -8.16 | <0.001 | |||||
| Random Effects | ||||||||||
| σ2 | 0.01 | 0.01 | ||||||||
| τ00 | 0.02 participant | 0.02 participant | ||||||||
| 0.00 stimulus | 0.00 stimulus | |||||||||
| τ11 | 0.00 participant.indiv_1 | 0.00 participant.pract_1 | ||||||||
| 0.01 participant.charity_1 | 0.00 participant.charity_1 | |||||||||
| 0.00 participant.diff.c | 0.00 participant.diff.c | |||||||||
| 0.08 participant.money.c | 0.08 participant.money.c | |||||||||
| 0.00 participant.need.c | 0.00 participant.need.c | |||||||||
| ρ01 | -0.02 participant.indiv_1 | -0.44 participant.pract_1 | ||||||||
| -0.11 participant.charity_1 | -0.23 participant.charity_1 | |||||||||
| 0.09 participant.diff.c | 0.05 participant.diff.c | |||||||||
| 0.88 participant.money.c | 0.91 participant.money.c | |||||||||
| 0.72 participant.need.c | 0.69 participant.need.c | |||||||||
| ICC | 0.77 | 0.77 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| 21 stimulus | 21 stimulus | |||||||||
| Observations | 9827 | 9827 | ||||||||
| Marginal R2 / Conditional R2 | 0.251 / 0.831 | 0.251 / 0.831 | ||||||||
b. proportion donated
summary(m17 <- lmer(propDonate ~ (indiv_1 + charity_1) * need.c + diff.c + money.c +
(indiv_1 + charity_1 + money.c + need.c + diff.c | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (indiv_1 + charity_1) * need.c + diff.c + money.c +
## (indiv_1 + charity_1 + money.c + need.c + diff.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -8088.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4111 -0.3135 -0.0269 0.2482 6.1444
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 5.235e-02 0.228799
## indiv_1 1.507e-02 0.122749 -0.11
## charity_1 1.908e-02 0.138114 -0.20 0.79
## money.c 3.849e-03 0.062040 -0.24 0.08 0.09
## need.c 2.657e-03 0.051548 0.65 0.10 -0.08 -0.20
## diff.c 3.287e-03 0.057333 0.21 -0.08 -0.06 -0.48 0.01
## stimulus (Intercept) 2.053e-05 0.004531
## Residual 1.759e-02 0.132633
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.280417 0.013742 20.406
## indiv_1 0.031409 0.009591 3.275
## charity_1 0.006479 0.010159 0.638
## need.c 0.032006 0.004440 7.208
## diff.c 0.043992 0.003805 11.562
## money.c -0.007605 0.006020 -1.263
## indiv_1:need.c 0.021935 0.003685 5.953
## charity_1:need.c 0.022254 0.003789 5.873
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 need.c diff.c mony.c in_1:.
## indiv_1 -0.389
## charity_1 -0.428 0.821
## need.c 0.530 -0.248 -0.304
## diff.c 0.184 -0.046 -0.041 -0.119
## money.c -0.104 0.017 0.021 -0.085 -0.213
## indv_1:nd.c -0.234 0.319 0.311 -0.678 0.012 0.035
## chrty_1:nd. -0.236 0.327 0.277 -0.664 0.001 0.042 0.813summary(m18 <- lmer(propDonate ~ (pract_1 + charity_1) * need.c + diff.c + money.c +
(pract_1 + charity_1 + money.c + need.c + diff.c | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (pract_1 + charity_1) * need.c + diff.c + money.c +
## (pract_1 + charity_1 + money.c + need.c + diff.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -8088.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4111 -0.3135 -0.0269 0.2482 6.1444
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 6.132e-02 0.247625
## pract_1 1.507e-02 0.122749 -0.40
## charity_1 7.347e-03 0.085712 -0.23 0.16
## money.c 3.849e-03 0.062040 -0.18 -0.08 0.04
## need.c 2.657e-03 0.051548 0.65 -0.10 -0.27 -0.20
## diff.c 3.287e-03 0.057333 0.15 0.08 0.01 -0.48 0.01
## stimulus (Intercept) 2.053e-05 0.004531
## Residual 1.759e-02 0.132633
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.311826 0.013352 23.355
## pract_1 -0.031409 0.009591 -3.275
## charity_1 -0.024931 0.005929 -4.205
## need.c 0.053941 0.003331 16.194
## diff.c 0.043992 0.003805 11.562
## money.c -0.007605 0.006020 -1.263
## pract_1:need.c -0.021935 0.003685 -5.953
## charity_1:need.c 0.000319 0.002287 0.139
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 need.c diff.c mony.c pr_1:.
## pract_1 -0.318
## charity_1 -0.258 0.210
## need.c 0.478 -0.022 -0.142
## diff.c 0.156 0.046 0.005 -0.145
## money.c -0.095 -0.017 0.010 -0.076 -0.213
## prct_1:nd.c 0.011 0.319 -0.016 -0.202 -0.012 -0.035
## chrty_1:nd. 0.005 -0.027 -0.115 -0.301 -0.017 0.014 0.264tab_model(m17, m18,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| propDonate | propDonate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.28 | 0.01 | 0.25 – 0.31 | 20.41 | <0.001 | 0.31 | 0.01 | 0.29 – 0.34 | 23.35 | <0.001 |
| indiv_1 | 0.03 | 0.01 | 0.01 – 0.05 | 3.28 | 0.001 | |||||
| charity_1 | 0.01 | 0.01 | -0.01 – 0.03 | 0.64 | 0.524 | -0.02 | 0.01 | -0.04 – -0.01 | -4.20 | <0.001 |
| need.c | 0.03 | 0.00 | 0.02 – 0.04 | 7.21 | <0.001 | 0.05 | 0.00 | 0.05 – 0.06 | 16.19 | <0.001 |
| diff.c | 0.04 | 0.00 | 0.04 – 0.05 | 11.56 | <0.001 | 0.04 | 0.00 | 0.04 – 0.05 | 11.56 | <0.001 |
| money.c | -0.01 | 0.01 | -0.02 – 0.00 | -1.26 | 0.206 | -0.01 | 0.01 | -0.02 – 0.00 | -1.26 | 0.206 |
| indiv_1 * need.c | 0.02 | 0.00 | 0.01 – 0.03 | 5.95 | <0.001 | |||||
| charity_1 * need.c | 0.02 | 0.00 | 0.01 – 0.03 | 5.87 | <0.001 | 0.00 | 0.00 | -0.00 – 0.00 | 0.14 | 0.889 |
| pract_1 | -0.03 | 0.01 | -0.05 – -0.01 | -3.28 | 0.001 | |||||
| pract_1 * need.c | -0.02 | 0.00 | -0.03 – -0.01 | -5.95 | <0.001 | |||||
| Random Effects | ||||||||||
| σ2 | 0.02 | 0.02 | ||||||||
| τ00 | 0.05 participant | 0.06 participant | ||||||||
| 0.00 stimulus | 0.00 stimulus | |||||||||
| τ11 | 0.02 participant.indiv_1 | 0.02 participant.pract_1 | ||||||||
| 0.02 participant.charity_1 | 0.01 participant.charity_1 | |||||||||
| 0.00 participant.money.c | 0.00 participant.money.c | |||||||||
| 0.00 participant.need.c | 0.00 participant.need.c | |||||||||
| 0.00 participant.diff.c | 0.00 participant.diff.c | |||||||||
| ρ01 | -0.11 participant.indiv_1 | -0.40 participant.pract_1 | ||||||||
| -0.20 participant.charity_1 | -0.23 participant.charity_1 | |||||||||
| -0.24 participant.money.c | -0.18 participant.money.c | |||||||||
| 0.65 participant.need.c | 0.65 participant.need.c | |||||||||
| 0.21 participant.diff.c | 0.15 participant.diff.c | |||||||||
| ICC | 0.82 | 0.82 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| 21 stimulus | 21 stimulus | |||||||||
| Observations | 9827 | 9827 | ||||||||
| Marginal R2 / Conditional R2 | 0.193 / 0.852 | 0.193 / 0.852 | ||||||||
9. is there an interaction with condition and difference made while controlling money allocted and need
a. raw donated
summary(m15 <- lmer(donate ~ (indiv_1 + charity_1) * diff.c + money.c + need.c +
(indiv_1 + charity_1 + diff.c + money.c + need.c | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (indiv_1 + charity_1) * diff.c + money.c + need.c +
## (indiv_1 + charity_1 + diff.c + money.c + need.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -15669.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.5320 -0.3290 -0.0278 0.2537 7.8585
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 1.599e-02 0.126464
## indiv_1 4.673e-03 0.068358 -0.03
## charity_1 5.284e-03 0.072690 -0.11 0.75
## diff.c 7.318e-04 0.027051 0.11 -0.08 -0.07
## money.c 7.569e-02 0.275120 0.86 0.30 0.21 -0.16
## need.c 7.191e-04 0.026816 0.72 0.08 -0.14 -0.13 0.63
## stimulus (Intercept) 1.224e-05 0.003499
## Residual 8.402e-03 0.091662
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.1633766 0.0079026 20.674
## indiv_1 0.0139507 0.0059077 2.361
## charity_1 0.0002977 0.0060750 0.049
## diff.c 0.0059418 0.0025638 2.318
## money.c 0.2970217 0.0145802 20.372
## need.c 0.0289219 0.0017109 16.904
## indiv_1:diff.c 0.0142994 0.0022600 6.327
## charity_1:diff.c 0.0152599 0.0022566 6.762
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 diff.c mony.c need.c in_1:.
## indiv_1 -0.409
## charity_1 -0.441 0.801
## diff.c 0.175 -0.136 -0.127
## money.c 0.693 0.166 0.123 -0.102
## need.c 0.516 -0.040 -0.157 -0.213 0.497
## indv_1:dff. -0.119 0.148 0.150 -0.608 0.019 -0.008
## chrty_1:df. -0.124 0.155 0.129 -0.622 0.020 0.011 0.758summary(m16 <- lmer(donate ~ (pract_1 + charity_1) * diff.c + money.c + need.c +
(pract_1 + charity_1 + diff.c + money.c + need.c | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: donate ~ (pract_1 + charity_1) * diff.c + money.c + need.c +
## (pract_1 + charity_1 + diff.c + money.c + need.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -15669.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.5320 -0.3290 -0.0278 0.2537 7.8585
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 2.012e-02 0.141852
## pract_1 4.673e-03 0.068358 -0.45
## charity_1 2.538e-03 0.050382 -0.24 0.28
## diff.c 7.318e-04 0.027051 0.06 0.08 0.01
## money.c 7.569e-02 0.275120 0.91 -0.30 -0.09 -0.16
## need.c 7.191e-04 0.026816 0.68 -0.08 -0.31 -0.13 0.63
## stimulus (Intercept) 1.224e-05 0.003499
## Residual 8.402e-03 0.091662
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.1773272 0.0076904 23.058
## pract_1 -0.0139507 0.0059077 -2.361
## charity_1 -0.0136530 0.0037800 -3.612
## diff.c 0.0202412 0.0021526 9.403
## money.c 0.2970217 0.0145802 20.372
## need.c 0.0289219 0.0017109 16.904
## pract_1:diff.c -0.0142995 0.0022600 -6.327
## charity_1:diff.c 0.0009605 0.0015708 0.611
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 diff.c mony.c need.c pr_1:.
## pract_1 -0.348
## charity_1 -0.282 0.275
## diff.c 0.081 0.006 0.020
## money.c 0.840 -0.166 -0.063 -0.102
## need.c 0.499 0.040 -0.190 -0.262 0.497
## prct_1:dff. 0.009 0.148 -0.010 -0.326 -0.019 0.008
## chrty_1:df. 0.001 -0.009 -0.063 -0.389 0.002 0.027 0.350tab_model(m15, m16,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| donate | donate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.16 | 0.01 | 0.15 – 0.18 | 20.67 | <0.001 | 0.18 | 0.01 | 0.16 – 0.19 | 23.06 | <0.001 |
| indiv_1 | 0.01 | 0.01 | 0.00 – 0.03 | 2.36 | 0.018 | |||||
| charity_1 | 0.00 | 0.01 | -0.01 – 0.01 | 0.05 | 0.961 | -0.01 | 0.00 | -0.02 – -0.01 | -3.61 | <0.001 |
| diff.c | 0.01 | 0.00 | 0.00 – 0.01 | 2.32 | 0.020 | 0.02 | 0.00 | 0.02 – 0.02 | 9.40 | <0.001 |
| money.c | 0.30 | 0.01 | 0.27 – 0.33 | 20.37 | <0.001 | 0.30 | 0.01 | 0.27 – 0.33 | 20.37 | <0.001 |
| need.c | 0.03 | 0.00 | 0.03 – 0.03 | 16.90 | <0.001 | 0.03 | 0.00 | 0.03 – 0.03 | 16.90 | <0.001 |
| indiv_1 * diff.c | 0.01 | 0.00 | 0.01 – 0.02 | 6.33 | <0.001 | |||||
| charity_1 * diff.c | 0.02 | 0.00 | 0.01 – 0.02 | 6.76 | <0.001 | 0.00 | 0.00 | -0.00 – 0.00 | 0.61 | 0.541 |
| pract_1 | -0.01 | 0.01 | -0.03 – -0.00 | -2.36 | 0.018 | |||||
| pract_1 * diff.c | -0.01 | 0.00 | -0.02 – -0.01 | -6.33 | <0.001 | |||||
| Random Effects | ||||||||||
| σ2 | 0.01 | 0.01 | ||||||||
| τ00 | 0.02 participant | 0.02 participant | ||||||||
| 0.00 stimulus | 0.00 stimulus | |||||||||
| τ11 | 0.00 participant.indiv_1 | 0.00 participant.pract_1 | ||||||||
| 0.01 participant.charity_1 | 0.00 participant.charity_1 | |||||||||
| 0.00 participant.diff.c | 0.00 participant.diff.c | |||||||||
| 0.08 participant.money.c | 0.08 participant.money.c | |||||||||
| 0.00 participant.need.c | 0.00 participant.need.c | |||||||||
| ρ01 | -0.03 participant.indiv_1 | -0.45 participant.pract_1 | ||||||||
| -0.11 participant.charity_1 | -0.24 participant.charity_1 | |||||||||
| 0.11 participant.diff.c | 0.06 participant.diff.c | |||||||||
| 0.86 participant.money.c | 0.91 participant.money.c | |||||||||
| 0.72 participant.need.c | 0.68 participant.need.c | |||||||||
| ICC | 0.78 | 0.78 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| 21 stimulus | 21 stimulus | |||||||||
| Observations | 9827 | 9827 | ||||||||
| Marginal R2 / Conditional R2 | 0.252 / 0.832 | 0.252 / 0.832 | ||||||||
b. proportion donated
summary(m17 <- lmer(propDonate ~ (indiv_1 + charity_1) * diff.c + money.c + need.c +
(indiv_1 + charity_1 + money.c + need.c + diff.c | participant) + (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (indiv_1 + charity_1) * diff.c + money.c + need.c +
## (indiv_1 + charity_1 + money.c + need.c + diff.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -8085.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4167 -0.3141 -0.0255 0.2495 6.1221
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 5.200e-02 0.228036
## indiv_1 1.630e-02 0.127668 -0.11
## charity_1 1.956e-02 0.139846 -0.19 0.80
## money.c 3.894e-03 0.062406 -0.22 0.04 0.06
## need.c 2.656e-03 0.051535 0.64 0.09 -0.08 -0.19
## diff.c 3.243e-03 0.056951 0.22 -0.08 -0.07 -0.48 0.02
## stimulus (Intercept) 1.956e-05 0.004423
## Residual 1.756e-02 0.132529
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.291368 0.013359 21.810
## indiv_1 0.020994 0.009293 2.259
## charity_1 -0.003732 0.009792 -0.381
## diff.c 0.027050 0.004737 5.711
## money.c -0.006208 0.006042 -1.028
## need.c 0.051206 0.003150 16.256
## indiv_1:diff.c 0.020554 0.003715 5.533
## charity_1:diff.c 0.021124 0.003780 5.588
##
## Correlation of Fixed Effects:
## (Intr) indv_1 chrt_1 diff.c mony.c need.c in_1:.
## indiv_1 -0.343
## charity_1 -0.386 0.811
## diff.c 0.237 -0.148 -0.137
## money.c -0.100 0.006 0.012 -0.221
## need.c 0.512 -0.017 -0.126 -0.118 -0.079
## indv_1:dff. -0.121 0.163 0.161 -0.560 0.075 -0.019
## chrty_1:df. -0.125 0.172 0.145 -0.575 0.078 -0.005 0.792summary(m18 <- lmer(propDonate ~ (pract_1 + charity_1) * diff.c + money.c + need.c +
(pract_1 + charity_1 + money.c + need.c + diff.c | participant)+ (1 | stimulus),
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000)), data = d))## Linear mixed model fit by REML ['lmerMod']
## Formula: propDonate ~ (pract_1 + charity_1) * diff.c + money.c + need.c +
## (pract_1 + charity_1 + money.c + need.c + diff.c | participant) +
## (1 | stimulus)
## Data: d
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: -8085.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4167 -0.3141 -0.0255 0.2495 6.1221
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 6.188e-02 0.248767
## pract_1 1.630e-02 0.127668 -0.41
## charity_1 7.379e-03 0.085899 -0.24 0.19
## money.c 3.894e-03 0.062406 -0.18 -0.04 0.03
## need.c 2.656e-03 0.051535 0.63 -0.09 -0.27 -0.19
## diff.c 3.243e-03 0.056951 0.16 0.08 0.00 -0.48 0.02
## stimulus (Intercept) 1.956e-05 0.004423
## Residual 1.756e-02 0.132529
## Number of obs: 9827, groups: participant, 378; stimulus, 21
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.3123616 0.0134034 23.305
## pract_1 -0.0209936 0.0092925 -2.259
## charity_1 -0.0247260 0.0058913 -4.197
## diff.c 0.0476040 0.0040637 11.714
## money.c -0.0062084 0.0060416 -1.028
## need.c 0.0512057 0.0031499 16.256
## pract_1:diff.c -0.0205538 0.0037148 -5.533
## charity_1:diff.c 0.0005697 0.0024193 0.235
##
## Correlation of Fixed Effects:
## (Intr) prct_1 chrt_1 diff.c mony.c need.c pr_1:.
## pract_1 -0.351
## charity_1 -0.260 0.230
## diff.c 0.149 0.023 0.017
## money.c -0.095 -0.006 0.009 -0.189
## need.c 0.498 0.017 -0.182 -0.155 -0.079
## prct_1:dff. 0.007 0.163 -0.011 -0.261 -0.075 0.019
## chrty_1:df. 0.003 -0.018 -0.063 -0.317 0.008 0.021 0.298tab_model(m17, m18,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "B",
digits = 2)| propDonate | propDonate | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | B | SE | CI | t | p | B | SE | CI | t | p |
| (Intercept) | 0.29 | 0.01 | 0.27 – 0.32 | 21.81 | <0.001 | 0.31 | 0.01 | 0.29 – 0.34 | 23.30 | <0.001 |
| indiv_1 | 0.02 | 0.01 | 0.00 – 0.04 | 2.26 | 0.024 | |||||
| charity_1 | -0.00 | 0.01 | -0.02 – 0.02 | -0.38 | 0.703 | -0.02 | 0.01 | -0.04 – -0.01 | -4.20 | <0.001 |
| diff.c | 0.03 | 0.00 | 0.02 – 0.04 | 5.71 | <0.001 | 0.05 | 0.00 | 0.04 – 0.06 | 11.71 | <0.001 |
| money.c | -0.01 | 0.01 | -0.02 – 0.01 | -1.03 | 0.304 | -0.01 | 0.01 | -0.02 – 0.01 | -1.03 | 0.304 |
| need.c | 0.05 | 0.00 | 0.05 – 0.06 | 16.26 | <0.001 | 0.05 | 0.00 | 0.05 – 0.06 | 16.26 | <0.001 |
| indiv_1 * diff.c | 0.02 | 0.00 | 0.01 – 0.03 | 5.53 | <0.001 | |||||
| charity_1 * diff.c | 0.02 | 0.00 | 0.01 – 0.03 | 5.59 | <0.001 | 0.00 | 0.00 | -0.00 – 0.01 | 0.24 | 0.814 |
| pract_1 | -0.02 | 0.01 | -0.04 – -0.00 | -2.26 | 0.024 | |||||
| pract_1 * diff.c | -0.02 | 0.00 | -0.03 – -0.01 | -5.53 | <0.001 | |||||
| Random Effects | ||||||||||
| σ2 | 0.02 | 0.02 | ||||||||
| τ00 | 0.05 participant | 0.06 participant | ||||||||
| 0.00 stimulus | 0.00 stimulus | |||||||||
| τ11 | 0.02 participant.indiv_1 | 0.02 participant.pract_1 | ||||||||
| 0.02 participant.charity_1 | 0.01 participant.charity_1 | |||||||||
| 0.00 participant.money.c | 0.00 participant.money.c | |||||||||
| 0.00 participant.need.c | 0.00 participant.need.c | |||||||||
| 0.00 participant.diff.c | 0.00 participant.diff.c | |||||||||
| ρ01 | -0.11 participant.indiv_1 | -0.41 participant.pract_1 | ||||||||
| -0.19 participant.charity_1 | -0.24 participant.charity_1 | |||||||||
| -0.22 participant.money.c | -0.18 participant.money.c | |||||||||
| 0.64 participant.need.c | 0.63 participant.need.c | |||||||||
| 0.22 participant.diff.c | 0.16 participant.diff.c | |||||||||
| ICC | 0.82 | 0.82 | ||||||||
| N | 378 participant | 378 participant | ||||||||
| 21 stimulus | 21 stimulus | |||||||||
| Observations | 9827 | 9827 | ||||||||
| Marginal R2 / Conditional R2 | 0.197 / 0.853 | 0.197 / 0.853 | ||||||||