Analysis
Read in the data:
tdata <- read_csv("exp_data.csv")Remove participants who failed the timing check query:
tdata <- subset(tdata, timing_check_correct == "correct")1 Subject demographics
# demographics
min(tdata$age)## [1] 18max(tdata$age)## [1] 75mean(tdata$age)## [1] 39.97333sd(tdata$age)## [1] 14.21595# 1 = male, 2 = female, 3 = other
table(tdata$gender)##
## 1: male 2: female 3: non-binary
## 76 73 11 = male, 2 = female, 3 = non-binary, 4 = prefer not to say
2 Data preparation
Check n in each condition:
table(tdata$structure, tdata$test_queries)##
## novel original
## irreversible 50 25
## reversible 50 25Factorize:
tdata$structure <- factor(tdata$structure, levels = c("irreversible", "reversible"), labels = c("irreversible", "reversible"))
tdata$test_queries <- factor(tdata$test_queries, levels = c("original", "novel"), labels = c("original", "novel"))Make a subset containing only the columns relevant for analyses and turn into long format:
# Subset:
tdata_sub <- subset(tdata, select = c("subj_code", "condition", "structure", "test_queries", "first_caused_rating", "second_caused_rating",
"first_maintained_rating", "second_maintained_rating"))
tdata_original <- subset(tdata_sub, test_queries == "original", select = c(1:6))
tdata_novel <- subset(tdata_sub, test_queries == "novel")
# into long format:
library(tidyr)
tdata_long_original <- gather(tdata_original, entity, rating, 5:6)
tdata_long_novel <- gather(tdata_novel, entity, rating, 5:8)
# factorize entity
tdata_long_original$entity <- factor(tdata_long_original$entity, levels = c("first_caused_rating", "second_caused_rating"),
labels = c("first \ncaused", "second \ncaused"))
tdata_long_novel$entity <- factor(tdata_long_novel$entity, levels = c("first_caused_rating", "second_caused_rating",
"first_maintained_rating", "second_maintained_rating"),
labels = c("first \ncaused", "second \ncaused","first \nmaintained", "second \nmaintained"))
tdata_long_novel$rating <- as.numeric(tdata_long_novel$rating)3 Replication part of study
3.1 Graphs and summaries
Replication part of the study
myTheme <- theme(plot.title = element_text(face="bold", size = 22),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
axis.text.x = element_text(size = 14, angle = 0),
axis.text.y = element_text(size = 16, angle = 0),
legend.text = element_text(size = 18),
legend.title = element_text(face = "bold", size = 18),
strip.text.x = element_text(size = 18),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line.x = element_line(colour = "black"),
axis.line.y = element_line(colour = "black"),
axis.text = element_text(colour ="black"),
axis.ticks = element_line(colour ="black"))
tdata_sub <- tdata_long_original
library(see)
## first, turn sID into a factor
tdata_sub$subj_code <- factor(tdata_sub$subj_code)
pd <- position_dodge(width = 0.3)
tdata_sub$valueJitter <- jitter(tdata_sub$rating, factor = 0.01, amount = 0.004)
theme_set(theme_light(base_size = 20, base_family = "Poppins"))
g <- ggplot(tdata_sub, aes(x = entity, y = valueJitter)) +
guides(fill=FALSE)+
facet_grid( ~ structure, labeller = label_both)+
scale_y_continuous(limits = c(-0.3, 8.3), breaks=seq(0, 8, 1), expand = c(0,0)) +
geom_violinhalf(aes(y = rating, group = entity, fill = structure), color = NA,
position=position_dodge(1), alpha = 0.4)+
geom_jitter(aes(color = structure), alpha = 0.5, width = 0.15, height = 0.2) +
stat_summary(aes(y = rating, group=1), fun.data = mean_cl_boot,
geom = "errorbar", width = 0, size = 1) +
stat_summary(aes(y = rating, group=1, color = structure), fun.y=mean, geom="line",
shape = 22, size = 1.5, alpha = .7)+
stat_summary(aes(y = rating, group=1, fill = structure), fun.y=mean, geom="point",
color = "black", shape = 22, size = 3, group=1, alpha = 1)+
stat_summary(aes(y = rating,group=1), fun.y=median, geom="point", color = "black", shape = 3, size = 4,
group=1, alpha = 1, position = position_dodge(width = 0.5))+
labs(x = "Causal Statement", y = "Agreement rating") +
scale_color_manual(name = "Strength",values=c("#66c2a5", "#e78ac3", "#8da0cb", "#a6d854"))+
scale_fill_manual(name = "Strength",values=c("#66c2a5", "#e78ac3", "#8da0cb", "#a6d854"))+
theme(legend.position = "none")+
annotate("text", x = 0.5, y = 2, label = c("completely disagree"), angle = 90)+
annotate("text", x = 0.5, y = 8.2, label = c("completely agree"), angle = 90)+
myTheme+
theme(panel.grid.major = element_line(color = "lightgrey",
size = 0.5,
linetype = 'dotted'))+
stat_summary(aes(label=round(after_stat(y),2)), fun.y=mean, geom="text", size=5,
vjust = -2)
g
#ggsave("results_means_mainDV_original.svg",width=8,height=5)
#ggsave("results_means_mainDV_original.pdf",width=8,height=5)library(ggridges)
g2 <- ggplot(tdata_sub, aes(x = rating, y = entity, fill = entity)) +
facet_grid( ~ structure,labeller = label_both)+
scale_x_continuous(breaks = seq(0, 10, 1))+
geom_density_ridges(alpha = 0.5)+
#stat_summary(aes(x = rating_rec), fun.x=mean, geom="point",
# color = "black", shape = 22, size = 2, group=1, alpha = 1)+
scale_fill_manual(values=c("#66c2a5", "#e78ac3", "#8da0cb", "#a6d854"))+
#scale_fill_viridis_c(name = "Explanation \nRating", option = "C", breaks=c(-5,0,5), labels=c("narrow scope", "no preference", "broad scope"))+
labs(x = "Agreement rating", y = "Causal Statement") +
scale_y_discrete(limits=rev)+
myTheme+
theme_light(base_family = "Poppins", base_size = 20)+
theme(panel.grid = element_blank(), axis.text = element_text(colour ="black"))+
theme(legend.position="none",
legend.title=element_blank(),legend.key.width = unit(1.95, 'cm'))+
theme(axis.text.y = element_text(size = 14, angle = 0))
g2
#ggsave("results_dist_original.svg",width=8,height=5)
#ggsave("results_dist_original.pdf",width=8,height=5)3.2 Mixed ANOVA
library(afex)
library(emmeans)
a1 <- aov_car(rating ~ structure*entity + Error(subj_code/entity), tdata_long_original,
anova_table = list(es = "pes"))
a1## Anova Table (Type 3 tests)
##
## Response: rating
## Effect df MSE F pes p.value
## 1 structure 1, 48 4.24 5.90 * .109 .019
## 2 entity 1, 48 10.02 51.90 *** .520 <.001
## 3 structure:entity 1, 48 10.02 1.93 .039 .171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1No significant interaction between structure and entity (causal statement), but a clear effect of entity (causal statement). This replicates the original study.
3.3 Contrasts
library(lsmeans)
# means
ls2 <- lsmeans(a1, c("structure", "entity"))
ls2## structure entity lsmean SE df lower.CL upper.CL
## irreversible first..caused 6.80 0.464 48 5.868 7.73
## reversible first..caused 6.92 0.464 48 5.988 7.85
## irreversible second..caused 1.36 0.596 48 0.162 2.56
## reversible second..caused 3.24 0.596 48 2.042 4.44
##
## Confidence level used: 0.95contrasts <- emmeans(a1, ~ entity|structure)
s <- pairs(contrasts, adjust = "none")
s## structure = irreversible:
## contrast estimate SE df t.ratio p.value
## first..caused - second..caused 5.44 0.895 48 6.077 <.0001
##
## structure = reversible:
## contrast estimate SE df t.ratio p.value
## first..caused - second..caused 3.68 0.895 48 4.111 0.0002confint(s, level = 0.95)## structure = irreversible:
## contrast estimate SE df lower.CL upper.CL
## first..caused - second..caused 5.44 0.895 48 3.64 7.24
##
## structure = reversible:
## contrast estimate SE df lower.CL upper.CL
## first..caused - second..caused 3.68 0.895 48 1.88 5.48
##
## Confidence level used: 0.95First cause receives higher ratings in both reversibility conditions.
Get standardized effect sizes for the contrasts
First get the SDs that need to be pooled (and the correlation between measures for the within-tests)
library(dplyr)
tdata_long_original %>%
group_by(structure, entity) %>%
summarise_at(vars(rating), list(name=sd))## # A tibble: 4 × 3
## # Groups: structure [2]
## structure entity name
## <fct> <fct> <dbl>
## 1 irreversible "first \ncaused" 2.25
## 2 irreversible "second \ncaused" 2.23
## 3 reversible "first \ncaused" 2.38
## 4 reversible "second \ncaused" 3.57tdata_original %>%
group_by(structure, test_queries) %>%
summarize(cor=cor(first_caused_rating, second_caused_rating))## # A tibble: 2 × 3
## # Groups: structure [2]
## structure test_queries cor
## <fct> <fct> <dbl>
## 1 irreversible original -0.506
## 2 reversible original -0.380# using the functions from the MOTE package (see https://matthewbjane.quarto.pub/guide-to-effect-sizes-and-confidence-intervals/Standardized-Mean-Differences.html#sec-repeated-measures-drm)
library(MOTE)
# irreversible condition
stats <- d.dep.t.rm(
m1 = 6.80,
m2 = 1.36,
sd1 = 2.254625,
sd2 = 2.23383,
n = 25,
a = 0.05,
r = -0.5063090
)
stats$estimate## [1] "$d_{rm}$ = 2.42, 95\\% CI [1.63, 3.20]"# reversible condition
stats <- d.dep.t.rm(
m1 = 6.92,
m2 = 3.24,
sd1 = 2.379075,
sd2 = 3.573980,
n = 25,
a = 0.05,
r = -0.3798763
)
stats$estimate## [1] "$d_{rm}$ = 1.23, 95\\% CI [0.70, 1.74]"4 Novel part of the study
tdata_sub <- tdata_long_novel
library(see)
## first, turn sID into a factor
tdata_sub$subj_code <- factor(tdata_sub$subj_code)
pd <- position_dodge(width = 0.3)
tdata_sub$valueJitter <- jitter(tdata_sub$rating, factor = 0.01, amount = 0.004)
theme_set(theme_light(base_size = 20, base_family = "Poppins"))
g <- ggplot(tdata_sub, aes(x = entity, y = valueJitter)) +
guides(fill=FALSE)+
facet_grid( ~ structure, labeller = label_both)+
scale_y_continuous(limits = c(-0.3, 8.3), breaks=seq(0, 8, 1), expand = c(0,0)) +
geom_jitter(aes(color = structure), alpha = 0.5, width = 0.15, height = 0.2) +
stat_summary(aes(y = rating, group=1), fun.data = mean_cl_boot,
geom = "errorbar", width = 0, size = 1) +
stat_summary(aes(y = rating, group=1, color = structure), fun.y=mean, geom="line",
shape = 22, size = 1.5, alpha = .7)+
stat_summary(aes(y = rating, group=1, fill = structure), fun.y=mean, geom="point",
color = "black", shape = 22, size = 3, group=1, alpha = 1)+
stat_summary(aes(y = rating,group=1), fun.y=median, geom="point", color = "black", shape = 3, size = 4,
group=1, alpha = 1, position = position_dodge(width = 0.5))+
labs(x = "Causal Statement", y = "Agreement Rating") +
scale_color_manual(name = "Strength",values=c("#66c2a5", "#e78ac3", "#8da0cb", "#a6d854"))+
scale_fill_manual(name = "Strength",values=c("#66c2a5", "#e78ac3", "#8da0cb", "#a6d854"))+
theme(legend.position = "none")+
annotate("text", x = 0.5, y = 1.7, label = c("completely disagree"), angle = 90)+
annotate("text", x = 0.5, y = 6.7, label = c("completely agree"), angle = 90)+
myTheme+
theme(panel.grid.major = element_line(color = "lightgrey",
size = 0.5,
linetype = 'dotted'))+
stat_summary(aes(label=round(after_stat(y),2)), fun.y=mean, geom="text", size=5,
vjust = -6)
g
#ggsave("results_means_mainDV_novel.svg",width=12,height=5)
#ggsave("results_means_mainDV_novel.pdf",width=12,height=5)library(ggridges)
g2 <- ggplot(tdata_sub, aes(x = rating, y = entity, fill = structure)) +
facet_grid( ~ structure,labeller = label_both)+
scale_x_continuous(breaks = seq(0, 10, 1))+
geom_density_ridges(alpha = 0.5)+
scale_fill_manual(values=c("#66c2a5", "#e78ac3", "#8da0cb", "#a6d854"))+
labs(x = "Agreement Rating", y = "Causal Statement") +
scale_y_discrete(limits=rev)+
myTheme+
theme_light(base_family = "Poppins", base_size = 20)+
theme(panel.grid = element_blank(), axis.text = element_text(colour ="black"))+
theme(legend.position="none",
legend.title=element_blank(),legend.key.width = unit(1.95, 'cm'))+
theme(axis.text.y = element_text(size = 14, angle = 0))+
annotate("text", y = 0.7, x = 0, label = c("completely disagree"), angle = 0)+
annotate("text", y = 0.7, x = 9, label = c("completely agree"), angle = 0)
g2
#ggsave("results_dist_novel.svg",width=12,height=5)
#ggsave("results_dist_novel.pdf",width=12,height=5)4.1 Mixed ANOVA
library(afex)
library(emmeans)
a2 <- aov_car(rating ~ structure*entity + Error(subj_code/entity), tdata_long_novel, anova_table = list(es = "pes"))
a2## Anova Table (Type 3 tests)
##
## Response: rating
## Effect df MSE F pes p.value
## 1 structure 1, 98 11.34 23.28 *** .192 <.001
## 2 entity 2.92, 286.36 6.84 98.83 *** .502 <.001
## 3 structure:entity 2.92, 286.36 6.84 8.11 *** .076 <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GGPredicted interaction effect was obtained, but also visualize the main effects:
Plot main effect of structure:
tdata_sub <- tdata_long_novel
library(see)
## first, turn sID into a factor
tdata_sub$subj_code <- factor(tdata_sub$subj_code)
pd <- position_dodge(width = 0.3)
tdata_sub$valueJitter <- jitter(tdata_sub$rating, factor = 0.01, amount = 0.004)
theme_set(theme_light(base_size = 20, base_family = "Poppins"))
g <- ggplot(tdata_sub, aes(x = structure, y = valueJitter)) +
guides(fill=FALSE)+
scale_y_continuous(limits = c(-0.3, 8.3), breaks=seq(0, 8, 1), expand = c(0,0)) +
geom_jitter(aes(color = structure), alpha = 0.5, width = 0.15, height = 0.2) +
stat_summary(aes(y = rating, group=1), fun.data = mean_cl_boot,
geom = "errorbar", width = 0, size = 1) +
stat_summary(aes(y = rating, group=1, fill = structure), fun.y=mean, geom="point",
color = "black", shape = 22, size = 3, group=1, alpha = 1)+
stat_summary(aes(y = rating,group=1), fun.y=median, geom="point", color = "black", shape = 3, size = 4,
group=1, alpha = 1, position = position_dodge(width = 0.5))+
labs(x = "Causal Statement", y = "Agreement Rating") +
scale_color_manual(name = "Strength",values=c("#66c2a5", "#e78ac3", "#8da0cb", "#a6d854"))+
scale_fill_manual(name = "Strength",values=c("#66c2a5", "#e78ac3", "#8da0cb", "#a6d854"))+
theme(legend.position = "none")+
annotate("text", x = 0.5, y = 1.7, label = c("completely disagree"), angle = 90)+
annotate("text", x = 0.5, y = 6.7, label = c("completely agree"), angle = 90)+
myTheme+
theme(panel.grid.major = element_line(color = "lightgrey",
size = 0.5,
linetype = 'dotted'))+
stat_summary(aes(label=round(after_stat(y),2)), fun.y=mean, geom="text", size=5,
vjust = -6)
g
Plot main effect of entity (causal statement):
tdata_sub <- tdata_long_novel
library(see)
## first, turn sID into a factor
tdata_sub$subj_code <- factor(tdata_sub$subj_code)
pd <- position_dodge(width = 0.3)
tdata_sub$valueJitter <- jitter(tdata_sub$rating, factor = 0.01, amount = 0.004)
theme_set(theme_light(base_size = 20, base_family = "Poppins"))
g <- ggplot(tdata_sub, aes(x = entity, y = valueJitter)) +
guides(fill=FALSE)+
scale_y_continuous(limits = c(-0.3, 8.3), breaks=seq(0, 8, 1), expand = c(0,0)) +
geom_jitter(aes(color = entity), alpha = 0.5, width = 0.15, height = 0.2) +
stat_summary(aes(y = rating, group=1), fun.data = mean_cl_boot,
geom = "errorbar", width = 0, size = 1) +
stat_summary(aes(y = rating, group=1, fill = entity), fun.y=mean, geom="point",
color = "black", shape = 22, size = 3, group=1, alpha = 1)+
stat_summary(aes(y = rating,group=1), fun.y=median, geom="point", color = "black", shape = 3, size = 4,
group=1, alpha = 1, position = position_dodge(width = 0.5))+
labs(x = "Causal Statement", y = "Agreement Rating") +
scale_color_manual(name = "Strength",values=c("#66c2a5", "#e78ac3", "#8da0cb", "#a6d854"))+
scale_fill_manual(name = "Strength",values=c("#66c2a5", "#e78ac3", "#8da0cb", "#a6d854"))+
theme(legend.position = "none")+
annotate("text", x = 0.5, y = 1.7, label = c("completely disagree"), angle = 90)+
annotate("text", x = 0.5, y = 6.7, label = c("completely agree"), angle = 90)+
myTheme+
theme(panel.grid.major = element_line(color = "lightgrey",
size = 0.5,
linetype = 'dotted'))+
stat_summary(aes(label=round(after_stat(y),2)), fun.y=mean, geom="text", size=5,
vjust = -6)
g
4.2 Contrasts
library(lsmeans)
# means
ls2 <- lsmeans(a2, c("structure", "entity"))
ls2## structure entity lsmean SE df lower.CL upper.CL
## irreversible first..caused 7.70 0.213 98 7.27682 8.12
## reversible first..caused 7.44 0.213 98 7.01682 7.86
## irreversible second..caused 0.76 0.380 98 0.00677 1.51
## reversible second..caused 1.94 0.380 98 1.18677 2.69
## irreversible first..maintained 2.74 0.464 98 1.81965 3.66
## reversible first..maintained 5.44 0.464 98 4.51965 6.36
## irreversible second..maintained 2.24 0.471 98 1.30472 3.18
## reversible second..maintained 5.12 0.471 98 4.18472 6.06
##
## Confidence level used: 0.95contrasts <- emmeans(a2, ~ entity|structure)
s <- pairs(contrasts, adjust = "none")
s## structure = irreversible:
## contrast estimate SE df t.ratio p.value
## first..caused - second..caused 6.94 0.502 98 13.833 <.0001
## first..caused - first..maintained 4.96 0.525 98 9.443 <.0001
## first..caused - second..maintained 5.46 0.534 98 10.225 <.0001
## second..caused - first..maintained -1.98 0.546 98 -3.625 0.0005
## second..caused - second..maintained -1.48 0.471 98 -3.144 0.0022
## first..maintained - second..maintained 0.50 0.517 98 0.968 0.3354
##
## structure = reversible:
## contrast estimate SE df t.ratio p.value
## first..caused - second..caused 5.50 0.502 98 10.963 <.0001
## first..caused - first..maintained 2.00 0.525 98 3.808 0.0002
## first..caused - second..maintained 2.32 0.534 98 4.345 <.0001
## second..caused - first..maintained -3.50 0.546 98 -6.409 <.0001
## second..caused - second..maintained -3.18 0.471 98 -6.755 <.0001
## first..maintained - second..maintained 0.32 0.517 98 0.620 0.5370confint(s, level = 0.95)## structure = irreversible:
## contrast estimate SE df lower.CL upper.CL
## first..caused - second..caused 6.94 0.502 98 5.944 7.936
## first..caused - first..maintained 4.96 0.525 98 3.918 6.002
## first..caused - second..maintained 5.46 0.534 98 4.400 6.520
## second..caused - first..maintained -1.98 0.546 98 -3.064 -0.896
## second..caused - second..maintained -1.48 0.471 98 -2.414 -0.546
## first..maintained - second..maintained 0.50 0.517 98 -0.525 1.525
##
## structure = reversible:
## contrast estimate SE df lower.CL upper.CL
## first..caused - second..caused 5.50 0.502 98 4.504 6.496
## first..caused - first..maintained 2.00 0.525 98 0.958 3.042
## first..caused - second..maintained 2.32 0.534 98 1.260 3.380
## second..caused - first..maintained -3.50 0.546 98 -4.584 -2.416
## second..caused - second..maintained -3.18 0.471 98 -4.114 -2.246
## first..maintained - second..maintained 0.32 0.517 98 -0.705 1.345
##
## Confidence level used: 0.95The planned contrasts from this list are “first caused - second caused” in each reversibility condition. They are both significant.
Get effect sizes for the relevant contrasts
First get the SDs that need to be pooled (and the correlation between the measures included in within-comparisons)
library(dplyr)
tdata_long_novel %>%
group_by(structure, entity) %>%
summarise_at(vars(rating), list(name=sd))## # A tibble: 8 × 3
## # Groups: structure [2]
## structure entity name
## <fct> <fct> <dbl>
## 1 irreversible "first \ncaused" 1.18
## 2 irreversible "second \ncaused" 2.04
## 3 irreversible "first \nmaintained" 3.43
## 4 irreversible "second \nmaintained" 3.37
## 5 reversible "first \ncaused" 1.77
## 6 reversible "second \ncaused" 3.20
## 7 reversible "first \nmaintained" 3.12
## 8 reversible "second \nmaintained" 3.30library(dplyr)
tdata_novel %>%
group_by(structure, test_queries) %>%
summarize(cor=cor(first_caused_rating, second_caused_rating))## # A tibble: 2 × 3
## # Groups: structure [2]
## structure test_queries cor
## <fct> <fct> <dbl>
## 1 irreversible novel -0.149
## 2 reversible novel -0.483# irreversible condition
stats <- d.dep.t.rm(
m1 = 7.70,
m2 = 0.76,
sd1 = 1.182353,
sd2 = 2.036002,
n = 50,
a = 0.05,
r = -0.1492076
)
stats$estimate## [1] "$d_{rm}$ = 4.20, 95\\% CI [3.33, 5.08]"# reversible condition
stats <- d.dep.t.rm(
m1 = 7.44,
m2 = 1.94,
sd1 = 0.213 * sqrt(50),
sd2 = 0.380 * sqrt(50),
n = 50,
a = 0.05,
r = -0.4834880
)
stats$estimate## [1] "$d_{rm}$ = 2.59, 95\\% CI [2.00, 3.17]"In the paper I also say that maintainer ratings for the first cause are higher in the reversible than in the irreversible condition. An effect size for this is given by:
# structure entity lsmean SE df lower.CL upper.CL
# irreversible first..maintained 2.74 0.464 98 1.81965 3.66
# reversible first..maintained 5.44 0.464 98 4.51965 6.36
stats <- d.ind.t(
m1 = 5.44,
m2 = 2.74,
sd1 = 1.774651,
sd2 = 1.182353,
n1 = 50,
n2 = 50,
a = 0.05
)
stats$estimate## [1] "$d_s$ = 1.79, 95\\% CI [1.32, 2.25]"The other planned contrast is the one comparing the maintainer ratings between the two reversibility conditions. The following code will produce a long list of comparisons, from which the relevant one will be “second..maintained irreversible - second..maintained reversible”
contrasts <- emmeans(a2, ~ entity*structure)
s <- pairs(contrasts, adjust = "none")
s## contrast estimate
## first..caused irreversible - second..caused irreversible 6.94
## first..caused irreversible - first..maintained irreversible 4.96
## first..caused irreversible - second..maintained irreversible 5.46
## first..caused irreversible - first..caused reversible 0.26
## first..caused irreversible - second..caused reversible 5.76
## first..caused irreversible - first..maintained reversible 2.26
## first..caused irreversible - second..maintained reversible 2.58
## second..caused irreversible - first..maintained irreversible -1.98
## second..caused irreversible - second..maintained irreversible -1.48
## second..caused irreversible - first..caused reversible -6.68
## second..caused irreversible - second..caused reversible -1.18
## second..caused irreversible - first..maintained reversible -4.68
## second..caused irreversible - second..maintained reversible -4.36
## first..maintained irreversible - second..maintained irreversible 0.50
## first..maintained irreversible - first..caused reversible -4.70
## first..maintained irreversible - second..caused reversible 0.80
## first..maintained irreversible - first..maintained reversible -2.70
## first..maintained irreversible - second..maintained reversible -2.38
## second..maintained irreversible - first..caused reversible -5.20
## second..maintained irreversible - second..caused reversible 0.30
## second..maintained irreversible - first..maintained reversible -3.20
## second..maintained irreversible - second..maintained reversible -2.88
## first..caused reversible - second..caused reversible 5.50
## first..caused reversible - first..maintained reversible 2.00
## first..caused reversible - second..maintained reversible 2.32
## second..caused reversible - first..maintained reversible -3.50
## second..caused reversible - second..maintained reversible -3.18
## first..maintained reversible - second..maintained reversible 0.32
## SE df t.ratio p.value
## 0.502 98 13.833 <.0001
## 0.525 98 9.443 <.0001
## 0.534 98 10.225 <.0001
## 0.302 98 0.862 0.3907
## 0.435 98 13.230 <.0001
## 0.510 98 4.427 <.0001
## 0.517 98 4.987 <.0001
## 0.546 98 -3.625 0.0005
## 0.471 98 -3.144 0.0022
## 0.435 98 -15.343 <.0001
## 0.537 98 -2.198 0.0303
## 0.599 98 -7.809 <.0001
## 0.605 98 -7.205 <.0001
## 0.517 98 0.968 0.3354
## 0.510 98 -9.207 <.0001
## 0.599 98 1.335 0.1850
## 0.656 98 -4.117 0.0001
## 0.661 98 -3.599 0.0005
## 0.517 98 -10.052 <.0001
## 0.605 98 0.496 0.6212
## 0.661 98 -4.840 <.0001
## 0.667 98 -4.321 <.0001
## 0.502 98 10.963 <.0001
## 0.525 98 3.808 0.0002
## 0.534 98 4.345 <.0001
## 0.546 98 -6.409 <.0001
## 0.471 98 -6.755 <.0001
## 0.517 98 0.620 0.5370confint(s, level = 0.95)## contrast estimate
## first..caused irreversible - second..caused irreversible 6.94
## first..caused irreversible - first..maintained irreversible 4.96
## first..caused irreversible - second..maintained irreversible 5.46
## first..caused irreversible - first..caused reversible 0.26
## first..caused irreversible - second..caused reversible 5.76
## first..caused irreversible - first..maintained reversible 2.26
## first..caused irreversible - second..maintained reversible 2.58
## second..caused irreversible - first..maintained irreversible -1.98
## second..caused irreversible - second..maintained irreversible -1.48
## second..caused irreversible - first..caused reversible -6.68
## second..caused irreversible - second..caused reversible -1.18
## second..caused irreversible - first..maintained reversible -4.68
## second..caused irreversible - second..maintained reversible -4.36
## first..maintained irreversible - second..maintained irreversible 0.50
## first..maintained irreversible - first..caused reversible -4.70
## first..maintained irreversible - second..caused reversible 0.80
## first..maintained irreversible - first..maintained reversible -2.70
## first..maintained irreversible - second..maintained reversible -2.38
## second..maintained irreversible - first..caused reversible -5.20
## second..maintained irreversible - second..caused reversible 0.30
## second..maintained irreversible - first..maintained reversible -3.20
## second..maintained irreversible - second..maintained reversible -2.88
## first..caused reversible - second..caused reversible 5.50
## first..caused reversible - first..maintained reversible 2.00
## first..caused reversible - second..maintained reversible 2.32
## second..caused reversible - first..maintained reversible -3.50
## second..caused reversible - second..maintained reversible -3.18
## first..maintained reversible - second..maintained reversible 0.32
## SE df lower.CL upper.CL
## 0.502 98 5.944 7.936
## 0.525 98 3.918 6.002
## 0.534 98 4.400 6.520
## 0.302 98 -0.338 0.858
## 0.435 98 4.896 6.624
## 0.510 98 1.247 3.273
## 0.517 98 1.553 3.607
## 0.546 98 -3.064 -0.896
## 0.471 98 -2.414 -0.546
## 0.435 98 -7.544 -5.816
## 0.537 98 -2.245 -0.115
## 0.599 98 -5.869 -3.491
## 0.605 98 -5.561 -3.159
## 0.517 98 -0.525 1.525
## 0.510 98 -5.713 -3.687
## 0.599 98 -0.389 1.989
## 0.656 98 -4.002 -1.398
## 0.661 98 -3.692 -1.068
## 0.517 98 -6.227 -4.173
## 0.605 98 -0.901 1.501
## 0.661 98 -4.512 -1.888
## 0.667 98 -4.203 -1.557
## 0.502 98 4.504 6.496
## 0.525 98 0.958 3.042
## 0.534 98 1.260 3.380
## 0.546 98 -4.584 -2.416
## 0.471 98 -4.114 -2.246
## 0.517 98 -0.705 1.345
##
## Confidence level used: 0.95The relevant contrast “second..maintained irreversible - second..maintained reversible” is significant
Get a d value for this one too:
# structure entity lsmean SE df lower.CL upper.CL
# irreversible second..maintained 2.24 0.471 98 1.30472 3.18
# reversible second..maintained 5.12 0.471 98 4.18472 6.06
#SDs
#irreversible first \ncaused 1.182353
#irreversible second \ncaused 2.036002
#irreversible first \nmaintained 3.427440
#irreversible second \nmaintained 3.365855
#reversible first \ncaused 1.774651
#reversible second \ncaused 3.203378
#reversible first \nmaintained 3.124361
#reversible second \nmaintained 3.298979
stats <- d.ind.t(
m1 = 5.12,
m2 = 2.24,
sd1 = 3.298979,
sd2 = 3.365855,
n1 = 50,
n2 = 50,
a = 0.05
)
stats$estimate## [1] "$d_s$ = 0.86, 95\\% CI [0.45, 1.27]"