Mixed ANOVA - Domain Specific Means (DA01)
2026-02-03
Setup
library(tidyverse)
library(rstatix)
library(emmeans)
library(effectsize)
library(afex)
library(car)
options(scipen = 999)
afex::set_sum_contrasts()Data
# Data file is in parent of knit/ (eohi3/eohi3.csv)
df <- read.csv(
"/home/ladmin/Documents/DND/EOHI/eohi3/eohi3.csv",
stringsAsFactors = FALSE,
check.names = FALSE,
na.strings = "NA"
)
between_vars <- c("perspective", "temporalDO")
within_vars <- c(
"past_pref_MEAN", "past_pers_MEAN", "past_val_MEAN",
"fut_pref_MEAN", "fut_pers_MEAN", "fut_val_MEAN"
)
missing_vars <- setdiff(c(between_vars, within_vars, "pID"), names(df))
if (length(missing_vars) > 0) {
stop(paste("Missing required variables:", paste(missing_vars, collapse = ", ")))
}
anova_data <- df %>%
select(pID, all_of(between_vars), all_of(within_vars)) %>%
filter(
!is.na(perspective), perspective != "",
!is.na(temporalDO), temporalDO != ""
)Long format
long_data <- anova_data %>%
pivot_longer(
cols = all_of(within_vars),
names_to = "variable",
values_to = "MEAN_SCORE"
) %>%
mutate(
time = case_when(
grepl("^past_", variable) ~ "past",
grepl("^fut_", variable) ~ "fut",
TRUE ~ NA_character_
),
domain = case_when(
grepl("_pref_MEAN$", variable) ~ "pref",
grepl("_pers_MEAN$", variable) ~ "pers",
grepl("_val_MEAN$", variable) ~ "val",
TRUE ~ NA_character_
)
) %>%
mutate(
TIME = factor(time, levels = c("past", "fut")),
DOMAIN = factor(domain, levels = c("pref", "pers", "val")),
perspective = factor(perspective),
temporalDO = factor(temporalDO),
pID = factor(pID)
) %>%
select(pID, perspective, temporalDO, TIME, DOMAIN, MEAN_SCORE) %>%
filter(!is.na(MEAN_SCORE))Descriptive statistics
desc_stats <- long_data %>%
group_by(perspective, temporalDO, TIME, DOMAIN) %>%
summarise(
n = n(),
mean = round(mean(MEAN_SCORE), 5),
variance = round(var(MEAN_SCORE), 5),
sd = round(sd(MEAN_SCORE), 5),
median = round(median(MEAN_SCORE), 5),
q1 = round(quantile(MEAN_SCORE, 0.25), 5),
q3 = round(quantile(MEAN_SCORE, 0.75), 5),
min = round(min(MEAN_SCORE), 5),
max = round(max(MEAN_SCORE), 5),
.groups = "drop"
)
# Show all rows and columns (no truncation)
options(tibble.width = Inf)
print(desc_stats, n = Inf)## # A tibble: 24 × 13
## perspective temporalDO TIME DOMAIN n mean variance sd median q1
## <fct> <fct> <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 other future past pref 122 3.73 5.75 2.40 3.67 1.67
## 2 other future past pers 122 3.60 5.65 2.38 3.6 1.4
## 3 other future past val 122 3.60 6.27 2.50 3.5 1.33
## 4 other future fut pref 122 3.65 4.76 2.18 3.67 1.83
## 5 other future fut pers 122 3.50 4.74 2.18 3.4 1.65
## 6 other future fut val 122 3.38 5.17 2.27 3 1.5
## 7 other past past pref 139 3.95 5.86 2.42 4 2
## 8 other past past pers 139 3.82 6.58 2.56 3.6 1.6
## 9 other past past val 139 3.60 5.75 2.40 3.33 1.83
## 10 other past fut pref 139 3.67 6.24 2.50 3.33 1.75
## 11 other past fut pers 139 3.65 6.86 2.62 3.4 1.5
## 12 other past fut val 139 3.56 7.14 2.67 3.17 1.5
## 13 self future past pref 138 3.60 5.77 2.40 3.42 1.5
## 14 self future past pers 138 3.74 6.00 2.45 3.6 1.65
## 15 self future past val 138 3.79 6.52 2.55 3.67 1.67
## 16 self future fut pref 138 3.83 5.36 2.32 3.75 2
## 17 self future fut pers 138 3.83 6.24 2.50 3.9 1.4
## 18 self future fut val 138 3.86 6.15 2.48 4 1.83
## 19 self past past pref 123 3.84 5.82 2.41 3.83 1.83
## 20 self past past pers 123 4.19 6.60 2.57 4.2 1.9
## 21 self past past val 123 4.30 7.29 2.70 4.33 1.83
## 22 self past fut pref 123 3.65 6.76 2.60 3.33 1.33
## 23 self past fut pers 123 3.75 7.09 2.66 3.4 1.6
## 24 self past fut val 123 3.57 7.93 2.82 3.17 1
## q3 min max
## <dbl> <dbl> <dbl>
## 1 5.5 0 10
## 2 5 0 10
## 3 5.29 0 10
## 4 5.25 0 10
## 5 5.2 0 8
## 6 5.17 0 8.67
## 7 5.5 0 10
## 8 5.8 0 10
## 9 5.33 0 10
## 10 5.17 0 10
## 11 5.6 0 10
## 12 5.25 0 10
## 13 5.29 0 10
## 14 5.75 0 8.6
## 15 5.62 0 9
## 16 5.5 0 10
## 17 5.95 0 8.6
## 18 5.62 0 9.17
## 19 5.58 0 10
## 20 6.2 0 10
## 21 6.5 0 10
## 22 5.67 0 10
## 23 5.5 0 10
## 24 5.67 0 10Interpretations:
1. Mean and median values are similar, indicating distribution is
relatively symmetric and any skew is minimal. Any outliers are not
extreme.
2. Highest to lowest group n size ratio is 1.14 (139/122). Acceptable
ratio for ANOVA (under 1.5).
3. Highest to lowest overall group variance ratio is 1.67 (7.93/4.74).
Acceptable ratio for ANOVA (under 4).
For the sake of consistency w/ the other EHI studies, I also calculated
Hartley’s F-max ratio.
The conservative F-max critical value is 1.60, which is still higher
than the highest observed F-max ratio of 1.53.
Assumption checks
Missing values
missing_summary <- long_data %>%
group_by(perspective, temporalDO, TIME, DOMAIN) %>%
summarise(
n_total = n(),
n_missing = sum(is.na(MEAN_SCORE)),
pct_missing = round(100 * n_missing / n_total, 2),
.groups = "drop"
)
print(missing_summary, n = Inf)## # A tibble: 24 × 7
## perspective temporalDO TIME DOMAIN n_total n_missing pct_missing
## <fct> <fct> <fct> <fct> <int> <int> <dbl>
## 1 other future past pref 122 0 0
## 2 other future past pers 122 0 0
## 3 other future past val 122 0 0
## 4 other future fut pref 122 0 0
## 5 other future fut pers 122 0 0
## 6 other future fut val 122 0 0
## 7 other past past pref 139 0 0
## 8 other past past pers 139 0 0
## 9 other past past val 139 0 0
## 10 other past fut pref 139 0 0
## 11 other past fut pers 139 0 0
## 12 other past fut val 139 0 0
## 13 self future past pref 138 0 0
## 14 self future past pers 138 0 0
## 15 self future past val 138 0 0
## 16 self future fut pref 138 0 0
## 17 self future fut pers 138 0 0
## 18 self future fut val 138 0 0
## 19 self past past pref 123 0 0
## 20 self past past pers 123 0 0
## 21 self past past val 123 0 0
## 22 self past fut pref 123 0 0
## 23 self past fut pers 123 0 0
## 24 self past fut val 123 0 0No missing values. As expected.
Outliers
outlier_summary <- long_data %>%
group_by(perspective, temporalDO, TIME, DOMAIN) %>%
summarise(
n = n(),
n_outliers = sum(abs(scale(MEAN_SCORE)) > 3),
.groups = "drop"
)
print(outlier_summary, n = Inf)## # A tibble: 24 × 6
## perspective temporalDO TIME DOMAIN n n_outliers
## <fct> <fct> <fct> <fct> <int> <int>
## 1 other future past pref 122 0
## 2 other future past pers 122 0
## 3 other future past val 122 0
## 4 other future fut pref 122 0
## 5 other future fut pers 122 0
## 6 other future fut val 122 0
## 7 other past past pref 139 0
## 8 other past past pers 139 0
## 9 other past past val 139 0
## 10 other past fut pref 139 0
## 11 other past fut pers 139 0
## 12 other past fut val 139 0
## 13 self future past pref 138 0
## 14 self future past pers 138 0
## 15 self future past val 138 0
## 16 self future fut pref 138 0
## 17 self future fut pers 138 0
## 18 self future fut val 138 0
## 19 self past past pref 123 0
## 20 self past past pers 123 0
## 21 self past past val 123 0
## 22 self past fut pref 123 0
## 23 self past fut pers 123 0
## 24 self past fut val 123 0No outliers present. Good.
Homogeneity of variance
homogeneity_between <- long_data %>%
group_by(TIME, DOMAIN) %>%
rstatix::levene_test(MEAN_SCORE ~ perspective * temporalDO)
print(homogeneity_between, n = Inf)## # A tibble: 6 × 6
## TIME DOMAIN df1 df2 statistic p
## <fct> <fct> <int> <int> <dbl> <dbl>
## 1 past pref 3 518 0.0857 0.968
## 2 past pers 3 518 0.974 0.405
## 3 past val 3 518 1.81 0.144
## 4 fut pref 3 518 1.68 0.170
## 5 fut pers 3 518 2.82 0.0383
## 6 fut val 3 518 2.87 0.0360Levene’s test is sigifnicant for two cells: fut-pers and fut-val. However, variance ratios and F-max tests show that any heteroscedasticity is mild.
Normality (within-subjects residuals)
resid_within <- long_data %>%
group_by(pID) %>%
mutate(person_mean = mean(MEAN_SCORE, na.rm = TRUE)) %>%
ungroup() %>%
mutate(resid = MEAN_SCORE - person_mean) %>%
pull(resid)
resid_within <- resid_within[!is.na(resid_within)]
n_resid <- length(resid_within)
if (n_resid < 3L) {
message("Too few within-subjects residuals (n < 3); skipping Shapiro-Wilk.")
} else {
resid_for_shapiro <- if (n_resid > 5000L) {
set.seed(1L)
sample(resid_within, 5000L)
} else {
resid_within
}
print(shapiro.test(resid_for_shapiro))
}##
## Shapiro-Wilk normality test
##
## data: resid_for_shapiro
## W = 0.9431, p-value < 0.00000000000000022Q-Q plot
qqnorm(resid_within)
qqline(resid_within)
Shapiro-Wilk is significant but is sensitive to large sample size. QQ plot shows that centre residuals are normally distributed, with some tail heaviness. ANOVA is robust to violations of normality w/ large sample size.
Overall, ANOVA can proceed.
Mixed ANOVA
aov_afex <- aov_ez(
id = "pID",
dv = "MEAN_SCORE",
data = long_data,
between = c("perspective", "temporalDO"),
within = c("TIME", "DOMAIN"),
type = 3
)
cat("\n--- ANOVA Table (Type 3, uncorrected) ---\n")##
## --- ANOVA Table (Type 3, uncorrected) ---print(nice(aov_afex, correction = "none"))## Anova Table (Type 3 tests)
##
## Response: MEAN_SCORE
## Effect df MSE F ges p.value
## 1 perspective 1, 518 31.39 0.88 .001 .349
## 2 temporalDO 1, 518 31.39 0.36 <.001 .551
## 3 perspective:temporalDO 1, 518 31.39 0.00 <.001 .955
## 4 TIME 1, 518 1.86 10.05 ** <.001 .002
## 5 perspective:TIME 1, 518 1.86 0.02 <.001 .895
## 6 temporalDO:TIME 1, 518 1.86 9.81 ** <.001 .002
## 7 perspective:temporalDO:TIME 1, 518 1.86 7.91 ** <.001 .005
## 8 DOMAIN 2, 1036 1.17 0.63 <.001 .534
## 9 perspective:DOMAIN 2, 1036 1.17 7.79 *** <.001 <.001
## 10 temporalDO:DOMAIN 2, 1036 1.17 0.76 <.001 .470
## 11 perspective:temporalDO:DOMAIN 2, 1036 1.17 0.17 <.001 .843
## 12 TIME:DOMAIN 2, 1036 0.75 2.00 <.001 .136
## 13 perspective:TIME:DOMAIN 2, 1036 0.75 3.58 * <.001 .028
## 14 temporalDO:TIME:DOMAIN 2, 1036 0.75 0.01 <.001 .987
## 15 perspective:temporalDO:TIME:DOMAIN 2, 1036 0.75 3.11 * <.001 .045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1cat("\n--- ANOVA Table (Type 3, Greenhouse–Geisser correction) ---\n")##
## --- ANOVA Table (Type 3, Greenhouse–Geisser correction) ---print(nice(aov_afex, correction = "GG"))## Anova Table (Type 3 tests)
##
## Response: MEAN_SCORE
## Effect df MSE F ges
## 1 perspective 1, 518 31.39 0.88 .001
## 2 temporalDO 1, 518 31.39 0.36 <.001
## 3 perspective:temporalDO 1, 518 31.39 0.00 <.001
## 4 TIME 1, 518 1.86 10.05 ** <.001
## 5 perspective:TIME 1, 518 1.86 0.02 <.001
## 6 temporalDO:TIME 1, 518 1.86 9.81 ** <.001
## 7 perspective:temporalDO:TIME 1, 518 1.86 7.91 ** <.001
## 8 DOMAIN 1.94, 1004.66 1.21 0.63 <.001
## 9 perspective:DOMAIN 1.94, 1004.66 1.21 7.79 *** <.001
## 10 temporalDO:DOMAIN 1.94, 1004.66 1.21 0.76 <.001
## 11 perspective:temporalDO:DOMAIN 1.94, 1004.66 1.21 0.17 <.001
## 12 TIME:DOMAIN 2.00, 1034.51 0.75 2.00 <.001
## 13 perspective:TIME:DOMAIN 2.00, 1034.51 0.75 3.58 * <.001
## 14 temporalDO:TIME:DOMAIN 2.00, 1034.51 0.75 0.01 <.001
## 15 perspective:temporalDO:TIME:DOMAIN 2.00, 1034.51 0.75 3.11 * <.001
## p.value
## 1 .349
## 2 .551
## 3 .955
## 4 .002
## 5 .895
## 6 .002
## 7 .005
## 8 .529
## 9 <.001
## 10 .466
## 11 .837
## 12 .136
## 13 .028
## 14 .987
## 15 .045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GGMauchly’s test of sphericity is sig for DOMAIN main effect and
interaction (except w/ TIME). Use GG correction for these:
- 8 DOMAIN 1.94, 1004.66 1.21 0.63 <.001, p = .529
## 9 perspective:DOMAIN 1.94, 1004.66 1.21 7.79 *** <.001, p
<.001
## 10 temporalDO:DOMAIN 1.94, 1004.66 1.21 0.76 <.001, p = .466
## 11 perspective:temporalDO:DOMAIN 1.94, 1004.66 1.21 0.17 <.001, p
= .837
The following are significant main effects and interactions:
## 15 perspective:temporalDO:TIME:DOMAIN 2, 1036 0.75 3.11 * <.001
.045
## 13 perspective:TIME:DOMAIN 2, 1036 0.75 3.58 * <.001 .028
## 9 perspective:DOMAIN 1.94, 1004.66 1.21 7.79 *** <.001, p <.001
(GG corrected)
## 6 temporalDO:TIME 1, 518 1.86 9.81 ** <.001 .002
## 7 perspective:temporalDO:TIME 1, 518 1.86 7.91 ** <.001 .005
## 4 TIME 1, 518 1.86 10.05 ** <.001 .002
Mauchly and epsilon
anova_wide <- anova_data %>%
select(pID, perspective, temporalDO, all_of(within_vars)) %>%
filter(if_all(all_of(within_vars), ~ !is.na(.)))
response_matrix <- as.matrix(anova_wide[, within_vars])
rm_model <- lm(response_matrix ~ perspective * temporalDO, data = anova_wide)
idata <- data.frame(
TIME = factor(rep(c("past", "fut"), each = 3), levels = c("past", "fut")),
DOMAIN = factor(rep(c("pref", "pers", "val"), 2), levels = c("pref", "pers", "val"))
)
rm_anova <- car::Anova(rm_model, idata = idata, idesign = ~ TIME * DOMAIN, type = 3)
rm_summary <- summary(rm_anova, multivariate = FALSE)## Warning in summary.Anova.mlm(rm_anova, multivariate = FALSE): HF eps > 1
## treated as 1if (!is.null(rm_summary$sphericity.tests)) {
cat("\nMauchly's Test of Sphericity:\n")
print(rm_summary$sphericity.tests)
}##
## Mauchly's Test of Sphericity:
## Test statistic p-value
## DOMAIN 0.96880 0.00028
## perspective:DOMAIN 0.96880 0.00028
## temporalDO:DOMAIN 0.96880 0.00028
## perspective:temporalDO:DOMAIN 0.96880 0.00028
## TIME:DOMAIN 0.99856 0.68858
## perspective:TIME:DOMAIN 0.99856 0.68858
## temporalDO:TIME:DOMAIN 0.99856 0.68858
## perspective:temporalDO:TIME:DOMAIN 0.99856 0.68858if (!is.null(rm_summary$epsilon)) {
cat("\nEpsilon (GG, HF):\n")
print(rm_summary$epsilon)
}Post-hoc comparisons
TIME (main effect)
emm_TIME <- emmeans(aov_afex, ~ TIME)
print(pairs(emm_TIME, adjust = "bonferroni"))## contrast estimate SE df t.ratio p.value
## past - fut 0.155 0.0488 518 3.170 0.0016
##
## Results are averaged over the levels of: perspective, temporalDO, DOMAINPairwise comparison provide supprot for EHI effect.
temporalDO:TIME
emm_temporalDO_TIME <- emmeans(aov_afex, ~ TIME | temporalDO)
print(pairs(emm_temporalDO_TIME, adjust = "bonferroni"))## temporalDO = future:
## contrast estimate SE df t.ratio p.value
## past - fut 0.00186 0.0692 518 0.027 0.9786
##
## temporalDO = past:
## contrast estimate SE df t.ratio p.value
## past - fut 0.30772 0.0689 518 4.465 <0.0001
##
## Results are averaged over the levels of: perspective, DOMAINContrast significant only for temporal display order of past first, then future. When grouped by time instead of temporalDO, no contrasts are significant.
perspective:temporalDO:TIME
emm_pt_TIME <- emmeans(aov_afex, ~ TIME | perspective + temporalDO)
print(pairs(emm_pt_TIME, adjust = "bonferroni"))## perspective = other, temporalDO = future:
## contrast estimate SE df t.ratio p.value
## past - fut 0.133 0.1010 518 1.317 0.1883
##
## perspective = self, temporalDO = future:
## contrast estimate SE df t.ratio p.value
## past - fut -0.129 0.0948 518 -1.362 0.1739
##
## perspective = other, temporalDO = past:
## contrast estimate SE df t.ratio p.value
## past - fut 0.164 0.0944 518 1.736 0.0831
##
## perspective = self, temporalDO = past:
## contrast estimate SE df t.ratio p.value
## past - fut 0.451 0.1000 518 4.497 <0.0001
##
## Results are averaged over the levels of: DOMAINEHI is significant only for self perspective and past first temporal display order.
When grouped by perspective or temporalDO instead of TIME, no contrasts are significant.
perspective:DOMAIN
emm_perspective_DOMAIN <- emmeans(aov_afex, ~ perspective | DOMAIN)
print(pairs(emm_perspective_DOMAIN, adjust = "bonferroni"))## DOMAIN = pref:
## contrast estimate SE df t.ratio p.value
## other - self 0.0191 0.202 518 0.094 0.9248
##
## DOMAIN = pers:
## contrast estimate SE df t.ratio p.value
## other - self -0.2379 0.208 518 -1.141 0.2543
##
## DOMAIN = val:
## contrast estimate SE df t.ratio p.value
## other - self -0.3459 0.213 518 -1.622 0.1053
##
## Results are averaged over the levels of: temporalDO, TIMEemm_perspective_DOMAIN_domain <- emmeans(aov_afex, ~ DOMAIN | perspective)
print(pairs(emm_perspective_DOMAIN_domain, adjust = "bonferroni"))## perspective = other:
## contrast estimate SE df t.ratio p.value
## pref - pers 0.11064 0.0696 518 1.591 0.3369
## pref - val 0.21702 0.0706 518 3.074 0.0067
## pers - val 0.10638 0.0610 518 1.744 0.2454
##
## perspective = self:
## contrast estimate SE df t.ratio p.value
## pref - pers -0.14631 0.0695 518 -2.104 0.1075
## pref - val -0.14800 0.0706 518 -2.097 0.1093
## pers - val -0.00169 0.0610 518 -0.028 1.0000
##
## Results are averaged over the levels of: temporalDO, TIME
## P value adjustment: bonferroni method for 3 testssignificance is driven by the change from preferences to values in the “other” perspective.
perspective:TIME:DOMAIN
emm_pt_TIME_domain <- emmeans(aov_afex, ~ TIME | perspective + DOMAIN)
print(pairs(emm_pt_TIME_domain, adjust = "bonferroni"))## perspective = other, DOMAIN = pref:
## contrast estimate SE df t.ratio p.value
## past - fut 0.1799 0.0862 518 2.087 0.0374
##
## perspective = self, DOMAIN = pref:
## contrast estimate SE df t.ratio p.value
## past - fut -0.0228 0.0862 518 -0.265 0.7912
##
## perspective = other, DOMAIN = pers:
## contrast estimate SE df t.ratio p.value
## past - fut 0.1351 0.0950 518 1.422 0.1555
##
## perspective = self, DOMAIN = pers:
## contrast estimate SE df t.ratio p.value
## past - fut 0.1759 0.0949 518 1.852 0.0645
##
## perspective = other, DOMAIN = val:
## contrast estimate SE df t.ratio p.value
## past - fut 0.1301 0.0968 518 1.344 0.1797
##
## perspective = self, DOMAIN = val:
## contrast estimate SE df t.ratio p.value
## past - fut 0.3306 0.0968 518 3.416 0.0007
##
## Results are averaged over the levels of: temporalDOEHI effects present for other-perspective in the preferences domain
and for self-perspective in the values domain.
Estimate is higher in the self-perspective than in the
other-perspective.
emm_pt_domain_domain <- emmeans(aov_afex, ~ DOMAIN | perspective + TIME)
print(pairs(emm_pt_domain_domain, adjust = "bonferroni"))## perspective = other, TIME = past:
## contrast estimate SE df t.ratio p.value
## pref - pers 0.1330 0.0894 518 1.488 0.4122
## pref - val 0.2419 0.0936 518 2.584 0.0301
## pers - val 0.1089 0.0791 518 1.377 0.5072
##
## perspective = self, TIME = past:
## contrast estimate SE df t.ratio p.value
## pref - pers -0.2457 0.0894 518 -2.749 0.0186
## pref - val -0.3247 0.0936 518 -3.470 0.0017
## pers - val -0.0791 0.0790 518 -1.001 0.9526
##
## perspective = other, TIME = fut:
## contrast estimate SE df t.ratio p.value
## pref - pers 0.0882 0.0850 518 1.038 0.8993
## pref - val 0.1921 0.0843 518 2.280 0.0690
## pers - val 0.1039 0.0839 518 1.239 0.6480
##
## perspective = self, TIME = fut:
## contrast estimate SE df t.ratio p.value
## pref - pers -0.0470 0.0850 518 -0.553 1.0000
## pref - val 0.0287 0.0842 518 0.341 1.0000
## pers - val 0.0757 0.0838 518 0.903 1.0000
##
## Results are averaged over the levels of: temporalDO
## P value adjustment: bonferroni method for 3 testsSignificant contrasts are driven by domain changes from preferences
to values in the self vs other perspectives, in the past-oriented
questions.
Trends reverse depending on perspective, where values have higher
estimates than preferences in the self-perspective, but lower estimates
than preferences in the other-perspective.
perspective:temporalDO:TIME:DOMAIN
emm_ptt_TIME <- emmeans(aov_afex, ~ TIME | perspective + temporalDO + DOMAIN)
print(pairs(emm_ptt_TIME, adjust = "bonferroni"))## perspective = other, temporalDO = future, DOMAIN = pref:
## contrast estimate SE df t.ratio p.value
## past - fut 0.0792 0.126 518 0.630 0.5292
##
## perspective = self, temporalDO = future, DOMAIN = pref:
## contrast estimate SE df t.ratio p.value
## past - fut -0.2367 0.118 518 -2.001 0.0459
##
## perspective = other, temporalDO = past, DOMAIN = pref:
## contrast estimate SE df t.ratio p.value
## past - fut 0.2806 0.118 518 2.380 0.0177
##
## perspective = self, temporalDO = past, DOMAIN = pref:
## contrast estimate SE df t.ratio p.value
## past - fut 0.1911 0.125 518 1.525 0.1280
##
## perspective = other, temporalDO = future, DOMAIN = pers:
## contrast estimate SE df t.ratio p.value
## past - fut 0.1033 0.139 518 0.745 0.4566
##
## perspective = self, temporalDO = future, DOMAIN = pers:
## contrast estimate SE df t.ratio p.value
## past - fut -0.0841 0.130 518 -0.645 0.5193
##
## perspective = other, temporalDO = past, DOMAIN = pers:
## contrast estimate SE df t.ratio p.value
## past - fut 0.1669 0.130 518 1.285 0.1993
##
## perspective = self, temporalDO = past, DOMAIN = pers:
## contrast estimate SE df t.ratio p.value
## past - fut 0.4358 0.138 518 3.156 0.0017
##
## perspective = other, temporalDO = future, DOMAIN = val:
## contrast estimate SE df t.ratio p.value
## past - fut 0.2158 0.141 518 1.527 0.1273
##
## perspective = self, temporalDO = future, DOMAIN = val:
## contrast estimate SE df t.ratio p.value
## past - fut -0.0664 0.133 518 -0.500 0.6174
##
## perspective = other, temporalDO = past, DOMAIN = val:
## contrast estimate SE df t.ratio p.value
## past - fut 0.0444 0.132 518 0.335 0.7377
##
## perspective = self, temporalDO = past, DOMAIN = val:
## contrast estimate SE df t.ratio p.value
## past - fut 0.7276 0.141 518 5.169 <0.0001EHI effects are present for three contrasts:
- past - fut 0.2806 0.118 518 2.380 0.0177 (other-perspective,
preferences domain, past-first temporal display order)
- past - fut 0.4358 0.138 518 3.156 0.0017 (self-perspective,
personality domain, past-first temporal display order)
- past - fut 0.7276 0.141 518 5.169 <0.0001 (self-perspective, values
domain, past-first temporal display order)
A reverse EHI effect is present for 1 contrast:
- past - fut -0.2367 0.118 518 -2.001 0.0459 (self-personality,
preferences domain, future-first temporal display order)
emm_ptt_perspective <- emmeans(aov_afex, ~ perspective | temporalDO + TIME + DOMAIN)
print(pairs(emm_ptt_perspective, adjust = "bonferroni"))## temporalDO = future, TIME = past, DOMAIN = pref:
## contrast estimate SE df t.ratio p.value
## other - self 0.1343 0.299 518 0.449 0.6540
##
## temporalDO = past, TIME = past, DOMAIN = pref:
## contrast estimate SE df t.ratio p.value
## other - self 0.1067 0.298 518 0.358 0.7207
##
## temporalDO = future, TIME = fut, DOMAIN = pref:
## contrast estimate SE df t.ratio p.value
## other - self -0.1817 0.299 518 -0.608 0.5436
##
## temporalDO = past, TIME = fut, DOMAIN = pref:
## contrast estimate SE df t.ratio p.value
## other - self 0.0171 0.298 518 0.058 0.9541
##
## temporalDO = future, TIME = past, DOMAIN = pers:
## contrast estimate SE df t.ratio p.value
## other - self -0.1451 0.310 518 -0.469 0.6396
##
## temporalDO = past, TIME = past, DOMAIN = pers:
## contrast estimate SE df t.ratio p.value
## other - self -0.3714 0.309 518 -1.204 0.2292
##
## temporalDO = future, TIME = fut, DOMAIN = pers:
## contrast estimate SE df t.ratio p.value
## other - self -0.3325 0.311 518 -1.070 0.2852
##
## temporalDO = past, TIME = fut, DOMAIN = pers:
## contrast estimate SE df t.ratio p.value
## other - self -0.1025 0.310 518 -0.331 0.7407
##
## temporalDO = future, TIME = past, DOMAIN = val:
## contrast estimate SE df t.ratio p.value
## other - self -0.1951 0.315 518 -0.619 0.5362
##
## temporalDO = past, TIME = past, DOMAIN = val:
## contrast estimate SE df t.ratio p.value
## other - self -0.6972 0.314 518 -2.220 0.0268
##
## temporalDO = future, TIME = fut, DOMAIN = val:
## contrast estimate SE df t.ratio p.value
## other - self -0.4774 0.319 518 -1.495 0.1355
##
## temporalDO = past, TIME = fut, DOMAIN = val:
## contrast estimate SE df t.ratio p.value
## other - self -0.0139 0.318 518 -0.044 0.96501 significant contrast:
- other - self -0.6972 0.314 518 -2.220 0.0268 (values domain,
past-oriented questions, past-first temporal display order)
not really of theoretical interest but speaks to the perspective:TIME:DOMAIN interaction.
no significant contrasts when grouped by temporalDO instead of TIME or perspective.
Cohen’s d (significant contrasts only)
d_data <- anova_data %>%
mutate(
past_mean = (past_pref_MEAN + past_pers_MEAN + past_val_MEAN) / 3,
fut_mean = (fut_pref_MEAN + fut_pers_MEAN + fut_val_MEAN) / 3,
pref_mean = (past_pref_MEAN + fut_pref_MEAN) / 2,
pers_mean = (past_pers_MEAN + fut_pers_MEAN) / 2,
val_mean = (past_val_MEAN + fut_val_MEAN) / 2
)
cohens_d_results <- tibble(
contrast = character(),
condition = character(),
d = double()
)
# TIME main: past vs fut
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "TIME (past - fut)",
condition = "overall",
d = suppressMessages(effectsize::cohens_d(d_data$past_mean, d_data$fut_mean, paired = TRUE)$Cohens_d)
)
# temporalDO:TIME: past vs fut for temporalDO = past
d_past_tdo <- d_data %>% filter(temporalDO == "past")
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "TIME (past - fut)",
condition = "temporalDO = past",
d = suppressMessages(effectsize::cohens_d(d_past_tdo$past_mean, d_past_tdo$fut_mean, paired = TRUE)$Cohens_d)
)
# perspective:temporalDO:TIME: past vs fut for self, past temporalDO
d_self_past <- d_data %>% filter(perspective == "self", temporalDO == "past")
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "TIME (past - fut)",
condition = "self, temporalDO = past",
d = suppressMessages(effectsize::cohens_d(d_self_past$past_mean, d_self_past$fut_mean, paired = TRUE)$Cohens_d)
)
# perspective:DOMAIN: pref vs val for perspective = other
d_other <- d_data %>% filter(perspective == "other")
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "DOMAIN (pref - val)",
condition = "perspective = other",
d = suppressMessages(effectsize::cohens_d(d_other$pref_mean, d_other$val_mean, paired = TRUE)$Cohens_d)
)
# perspective:TIME:DOMAIN - TIME: other, pref
d_other_pref <- d_data %>% filter(perspective == "other")
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "TIME (past - fut)",
condition = "other, pref domain",
d = suppressMessages(effectsize::cohens_d(d_other_pref$past_pref_MEAN, d_other_pref$fut_pref_MEAN, paired = TRUE)$Cohens_d)
)
# perspective:TIME:DOMAIN - TIME: self, val
d_self_val <- d_data %>% filter(perspective == "self")
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "TIME (past - fut)",
condition = "self, val domain",
d = suppressMessages(effectsize::cohens_d(d_self_val$past_val_MEAN, d_self_val$fut_val_MEAN, paired = TRUE)$Cohens_d)
)
# perspective:TIME:DOMAIN - DOMAIN: other, past TIME
d_other_past <- d_data %>% filter(perspective == "other")
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "DOMAIN (pref - val)",
condition = "other, past TIME",
d = suppressMessages(effectsize::cohens_d(d_other_past$past_pref_MEAN, d_other_past$past_val_MEAN, paired = TRUE)$Cohens_d)
)
# perspective:TIME:DOMAIN - DOMAIN: self, past TIME: pref - pers
d_self_past_t <- d_data %>% filter(perspective == "self")
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "DOMAIN (pref - pers)",
condition = "self, past TIME",
d = suppressMessages(effectsize::cohens_d(d_self_past_t$past_pref_MEAN, d_self_past_t$past_pers_MEAN, paired = TRUE)$Cohens_d)
)
# perspective:TIME:DOMAIN - DOMAIN: self, past TIME: pref - val
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "DOMAIN (pref - val)",
condition = "self, past TIME",
d = suppressMessages(effectsize::cohens_d(d_self_past_t$past_pref_MEAN, d_self_past_t$past_val_MEAN, paired = TRUE)$Cohens_d)
)
# 4-way TIME: self, future temporalDO, pref (reverse EHI)
d_self_fut_pref <- d_data %>% filter(perspective == "self", temporalDO == "future")
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "TIME (past - fut) [reverse]",
condition = "self, future temporalDO, pref domain",
d = suppressMessages(effectsize::cohens_d(d_self_fut_pref$past_pref_MEAN, d_self_fut_pref$fut_pref_MEAN, paired = TRUE)$Cohens_d)
)
# 4-way TIME: other, past temporalDO, pref
d_other_past_pref <- d_data %>% filter(perspective == "other", temporalDO == "past")
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "TIME (past - fut)",
condition = "other, past temporalDO, pref domain",
d = suppressMessages(effectsize::cohens_d(d_other_past_pref$past_pref_MEAN, d_other_past_pref$fut_pref_MEAN, paired = TRUE)$Cohens_d)
)
# 4-way TIME: self, past temporalDO, pers
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "TIME (past - fut)",
condition = "self, past temporalDO, pers domain",
d = suppressMessages(effectsize::cohens_d(d_self_past$past_pers_MEAN, d_self_past$fut_pers_MEAN, paired = TRUE)$Cohens_d)
)
# 4-way TIME: self, past temporalDO, val
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "TIME (past - fut)",
condition = "self, past temporalDO, val domain",
d = suppressMessages(effectsize::cohens_d(d_self_past$past_val_MEAN, d_self_past$fut_val_MEAN, paired = TRUE)$Cohens_d)
)
# 4-way perspective: past temporalDO, past TIME, val domain (other - self, between-subjects)
d_ptt_val <- d_data %>%
filter(temporalDO == "past") %>%
select(perspective, past_val_MEAN)
d_other_ptt <- d_ptt_val %>% filter(perspective == "other") %>% pull(past_val_MEAN)
d_self_ptt <- d_ptt_val %>% filter(perspective == "self") %>% pull(past_val_MEAN)
cohens_d_results <- cohens_d_results %>%
add_row(
contrast = "perspective (other - self)",
condition = "past temporalDO, past TIME, val domain",
d = suppressMessages(effectsize::cohens_d(d_other_ptt, d_self_ptt, paired = FALSE)$Cohens_d)
)
cohens_d_results %>%
mutate(d = round(d, 3)) %>%
print(n = Inf)## # A tibble: 14 × 3
## contrast condition d
## <chr> <chr> <dbl>
## 1 TIME (past - fut) overall 0.13
## 2 TIME (past - fut) temporalDO = past 0.249
## 3 TIME (past - fut) self, temporalDO = past 0.413
## 4 DOMAIN (pref - val) perspective = other 0.185
## 5 TIME (past - fut) other, pref domain 0.126
## 6 TIME (past - fut) self, val domain 0.189
## 7 DOMAIN (pref - val) other, past TIME 0.159
## 8 DOMAIN (pref - pers) self, past TIME -0.171
## 9 DOMAIN (pref - val) self, past TIME -0.218
## 10 TIME (past - fut) [reverse] self, future temporalDO, pref domain -0.174
## 11 TIME (past - fut) other, past temporalDO, pref domain 0.172
## 12 TIME (past - fut) self, past temporalDO, pers domain 0.309
## 13 TIME (past - fut) self, past temporalDO, val domain 0.435
## 14 perspective (other - self) past temporalDO, past TIME, val domain -0.274