require('ggpubr')
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require('emmeans')
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require('plyr')
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require('dplyr')
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require('Rmisc')
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require('effsize')
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require('reshape2')
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require('car')
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require('rstatix')
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require('readxl')
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require('stats')
require('multcomp')
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require('multcompView')
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require('ggplot2')
#preparing data
data <- read_excel("C:/Users/lab mazzoni 2/Downloads/Museum_Gulara/Museum_Gulara/Study 1_main data file.xlsx",
col_types = c("numeric", "numeric", "text",
"text", "text", "numeric", "text",
"text", "text", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric"))
data$Condition <- as.factor(data$Condition)
data$RT <- log(data$Recog_RT)
data <- na.omit(data)
############################EXPERIMENT 1#######################################
#one way anova for accuracy
accuracy <- aov(data = data, Recog_ACC ~ Condition)
summary(accuracy)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 130.8 65.42 6.535 0.00309 **
## Residuals 48 480.5 10.01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(accuracy)
## Condition
## 0.2140191
ggboxplot(data, x = "Condition", y = "Recog_ACC", color = "Condition",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
TUKEY <- TukeyHSD(accuracy, wich = 'data$Condition', conf.level = 0.95)
print(TUKEY)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Recog_ACC ~ Condition, data = data)
##
## $Condition
## diff lwr upr p adj
## 8-capture-1-capture -3.2647059 -5.8525707 -0.676841 0.0101841
## no-capture-1-capture 0.1727941 -2.4924734 2.838062 0.9865417
## no-capture-8-capture 3.4375000 0.8083734 6.066627 0.0075149
#anova for reaction times
rt <- aov(data = data, RT ~ Condition)
summary(rt)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 0.114 0.05683 0.584 0.562
## Residuals 48 4.671 0.09732
eta_squared(rt)
## Condition
## 0.02375085
ggboxplot(data, x = "Condition", y = "RT", color = "Condition",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
#anova for confidence ratings
conf <- aov(data = data, Conf_rate ~ Condition)
summary(conf)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 2.34 1.170 2.641 0.0816 .
## Residuals 48 21.26 0.443
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(conf)
## Condition
## 0.09913742
ggboxplot(data, x = "Condition", y = "Conf_rate", color = "Condition",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
phc <- TukeyHSD(conf, which = 'Condition', conf.level = 0.95)
print(phc)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Conf_rate ~ Condition, data = data)
##
## $Condition
## diff lwr upr p adj
## 8-capture-1-capture 0.01088235 -0.53351672 0.5552814 0.9987123
## no-capture-1-capture 0.46713235 -0.09354961 1.0278143 0.1195264
## no-capture-8-capture 0.45625000 -0.09682914 1.0093291 0.1243669
#confidence for correct trials
confcorr <- aov(data = data, Conf_aver_corr ~ Condition)
summary(confcorr)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 1.642 0.8212 1.825 0.172
## Residuals 48 21.603 0.4501
eta_squared(confcorr)
## Condition
## 0.07065596
ggboxplot(data, x = "Condition", y = "Conf_rate", color = "Condition",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
#confidence for incorrect trials
confincor <- aov(data = data, Conf_aver_incor ~ Condition)
summary(confincor)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 7.13 3.564 2.426 0.0991 .
## Residuals 48 70.51 1.469
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(confincor)
## Condition
## 0.09181125
ggboxplot(data, x = "Condition", y = "Conf_rate", color = "Condition",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
########################EXPERIMENT 2############################################
data2 <- read_excel("C:/Users/lab mazzoni 2/Downloads/Museum_Gulara/Museum_Gulara/Study 2_main data file.xlsx")
data2 <- na.omit(data2)
data2$Condition <- as.factor(data2$Condition)
data2$RT <- log(data2$Recog_RT)
#one way anova for accuracy
accuracy2 <- aov(data = data2, Recog_ACC ~ Condition)
summary(accuracy2)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 90 44.89 0.533 0.59
## Residuals 49 4126 84.20
eta_squared(accuracy2)
## Condition
## 0.02129541
TUKEY2 <- TukeyHSD(accuracy2, wich = 'data2$Condition', conf.level = 0.95)
print(TUKEY2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Recog_ACC ~ Condition, data = data2)
##
## $Condition
## diff lwr upr p adj
## 8-capture-1-capture -2.79084967 -10.291538 4.709839 0.6433265
## no-capture-1-capture -0.05882353 -7.665910 7.548263 0.9998074
## no-capture-8-capture 2.73202614 -4.768663 10.232715 0.6551496
#anova for reaction times
rt2 <- aov(data = data2, RT ~ Condition)
summary(rt2)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 0.696 0.348 3.283 0.0459 *
## Residuals 49 5.194 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(rt2)
## Condition
## 0.1181543
ggboxplot(data2, x = "Condition", y = "RT", color = "Condition",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
TUKEYRT <- TukeyHSD(rt2, wich = 'data2$Condition', conf.level = 0.95)
print(TUKEYRT)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = RT ~ Condition, data = data2)
##
## $Condition
## diff lwr upr p adj
## 8-capture-1-capture 0.27020100 0.004069802 0.53633220 0.0458808
## no-capture-1-capture 0.06798671 -0.201919584 0.33789301 0.8160075
## no-capture-8-capture -0.20221429 -0.468345484 0.06391691 0.1685053
#anova for reaction times correct and incorrect
data2$RT_aver_corr <- log(data2$RT_aver_corr)
data2$logRT_aver_incor <- log(data2$RT_aver_incor)
#correct rt
rtcorr <- aov(data = data2, RT_aver_corr ~ Condition)
summary(rtcorr)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 0.587 0.2936 2.765 0.0728 .
## Residuals 49 5.202 0.1062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(rtcorr)
## Condition
## 0.1014179
corrposthoc <- TukeyHSD(rtcorr, wich = 'data2$Condition', conf.level = 0.95)
print(corrposthoc)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = RT_aver_corr ~ Condition, data = data2)
##
## $Condition
## diff lwr upr p adj
## 8-capture-1-capture 0.24870474 -0.01762747 0.5150369 0.0717198
## no-capture-1-capture 0.06426593 -0.20584423 0.3343761 0.8340123
## no-capture-8-capture -0.18443881 -0.45077101 0.0818934 0.2254482
#incorrect rt
incorrt <- aov(data = data2, logRT_aver_incor ~ Condition)
summary(incorrt)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 0.703 0.3515 2.594 0.0849 .
## Residuals 49 6.639 0.1355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(incorrt)
## Condition
## 0.09574576
incorrposthoc <- TukeyHSD(incorrt, wich = 'data2$Condition', conf.level = 0.95)
print(incorrposthoc)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = logRT_aver_incor ~ Condition, data = data2)
##
## $Condition
## diff lwr upr p adj
## 8-capture-1-capture 0.242986283 -0.05789967 0.54387223 0.1352070
## no-capture-1-capture -0.002807798 -0.30796185 0.30234625 0.9997274
## no-capture-8-capture -0.245794082 -0.54668003 0.05509187 0.1293446
#anova for confidence ratings
shapiro.test(data2$Conf_rate)
##
## Shapiro-Wilk normality test
##
## data: data2$Conf_rate
## W = 0.94736, p-value = 0.02254
conf2 <- aov(data = data2, Conf_rate ~ Condition)
summary(conf2)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 1.202 0.6009 1.426 0.25
## Residuals 49 20.648 0.4214
eta_squared(conf2)
## Condition
## 0.05499815
phc <- TukeyHSD(conf, which = 'Condition', conf.level = 0.95)
print(phc)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Conf_rate ~ Condition, data = data)
##
## $Condition
## diff lwr upr p adj
## 8-capture-1-capture 0.01088235 -0.53351672 0.5552814 0.9987123
## no-capture-1-capture 0.46713235 -0.09354961 1.0278143 0.1195264
## no-capture-8-capture 0.45625000 -0.09682914 1.0093291 0.1243669
#confidence for correct trials
confcorr2 <- aov(data = data2, Conf_aver_corr ~ Condition)
summary(confcorr2)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 1.45 0.7248 1.495 0.234
## Residuals 49 23.75 0.4847
eta_squared(confcorr)
## Condition
## 0.07065596
#confidence for incorrect trials
confincor2 <- aov(data = data2, Conf_aver_incor ~ Condition)
summary(confincor2)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 1.51 0.7531 0.479 0.623
## Residuals 49 77.10 1.5735
eta_squared(confincor2)
## Condition
## 0.01916038
################CROSS-EXPERIMENTAL RESULTS######################################
sdat <- data[,c('Study no', 'Condition', 'Recog_ACC', 'RT', 'Conf_rate', 'Conf_aver_incor',
'Conf_aver_corr')]
names(sdat) <- c('Study','Condition', 'Recog_ACC', 'RT', 'Conf_rate', 'Conf_aver_incor',
'Conf_aver_corr')
sdata <- data2[,c('Study', 'Condition', 'Recog_ACC', 'RT', 'Conf_rate', 'Conf_aver_incor',
'Conf_aver_corr')]
as.data.frame(sdat)
## Study Condition Recog_ACC RT Conf_rate Conf_aver_incor Conf_aver_corr
## 1 1 1-capture 34 6.914979 3.00 3.50 2.98
## 2 1 1-capture 35 7.305766 4.00 2.00 4.05
## 3 1 1-capture 28 6.813291 4.17 3.25 4.42
## 4 1 1-capture 31 7.195330 4.31 3.40 4.45
## 5 1 1-capture 30 7.171319 3.00 1.67 3.27
## 6 1 1-capture 30 7.466702 3.64 3.00 3.77
## 7 1 1-capture 35 6.856841 3.56 6.00 3.48
## 8 1 1-capture 33 7.182162 3.39 4.33 3.30
## 9 1 1-capture 34 7.148283 3.92 3.50 3.94
## 10 1 1-capture 34 6.999094 3.47 3.50 3.47
## 11 1 1-capture 32 7.209385 3.94 5.50 3.75
## 12 1 1-capture 30 6.933501 3.83 4.67 3.67
## 13 1 1-capture 35 6.778591 3.78 1.00 3.85
## 14 1 1-capture 32 7.781844 3.03 3.75 2.93
## 15 1 1-capture 33 7.065451 5.36 5.00 5.39
## 16 1 1-capture 24 7.116305 2.08 1.42 2.42
## 17 1 1-capture 30 7.087925 3.64 3.00 3.77
## 18 1 8-capture 27 7.372709 2.78 1.78 3.11
## 19 1 8-capture 31 7.190005 3.78 4.20 3.71
## 20 1 8-capture 29 7.316661 3.78 4.28 3.65
## 21 1 8-capture 20 7.955572 4.50 4.44 4.55
## 22 1 8-capture 33 7.063142 3.50 2.00 3.63
## 23 1 8-capture 32 6.635185 3.22 3.50 3.19
## 24 1 8-capture 29 6.794777 3.39 4.00 3.24
## 25 1 8-capture 30 7.179773 3.94 4.50 3.83
## 26 1 8-capture 30 6.995610 3.50 3.17 3.57
## 27 1 8-capture 29 7.894866 3.89 4.00 3.86
## 28 1 8-capture 26 7.167184 2.69 2.10 2.92
## 29 1 8-capture 32 7.029699 3.64 2.50 3.78
## 30 1 8-capture 24 7.390749 4.42 3.92 4.67
## 31 1 8-capture 27 6.981442 3.89 3.78 3.92
## 32 1 8-capture 26 7.000945 3.00 2.50 3.19
## 33 1 8-capture 24 7.778944 4.58 4.17 4.79
## 34 1 8-capture 34 7.014518 3.19 1.00 3.32
## 35 1 8-capture 30 7.022645 4.28 3.67 4.40
## 36 1 no-capture 29 6.932126 3.92 3.57 4.00
## 37 1 no-capture 28 7.745085 3.33 3.12 3.39
## 38 1 no-capture 32 7.224877 3.50 3.50 3.50
## 39 1 no-capture 24 7.341017 3.81 4.67 3.37
## 40 1 no-capture 33 6.821020 4.06 4.33 4.03
## 41 1 no-capture 34 6.940039 3.81 3.50 3.82
## 42 1 no-capture 34 7.092964 5.89 5.00 5.94
## 43 1 no-capture 34 6.848886 3.75 4.50 3.70
## 44 1 no-capture 33 7.868798 3.78 2.00 3.93
## 45 1 no-capture 32 6.875056 3.64 3.50 3.65
## 46 1 no-capture 35 6.946120 3.75 6.00 3.68
## 47 1 no-capture 33 6.792872 4.31 3.33 4.39
## 48 1 no-capture 32 7.231229 4.92 5.50 4.84
## 49 1 no-capture 34 6.996023 3.47 6.00 3.32
## 50 1 no-capture 34 7.005689 4.53 3.00 4.61
## 51 1 no-capture 30 7.014518 5.47 5.17 5.53
as.data.frame(sdata)
## Study Condition Recog_ACC RT Conf_rate Conf_aver_incor Conf_aver_corr
## 1 2 1-capture 32 7.046264 4.28 4.50 4.25
## 2 2 1-capture 28 7.993593 3.94 3.00 4.21
## 3 2 1-capture 31 7.181067 4.11 4.00 4.13
## 4 2 1-capture 34 6.802606 4.56 3.00 4.65
## 5 2 1-capture 34 6.786649 3.97 2.00 4.09
## 6 2 1-capture 33 7.016457 3.67 4.33 3.61
## 7 2 1-capture 34 6.922072 4.83 3.50 4.92
## 8 2 1-capture 29 6.672071 3.42 3.43 3.42
## 9 2 1-capture 33 6.921934 4.06 4.33 4.03
## 10 2 1-capture 33 6.923412 3.92 3.00 4.00
## 11 2 1-capture 32 6.867131 3.61 4.00 3.56
## 12 2 1-capture 27 6.967598 3.92 4.89 3.59
## 13 2 1-capture 8 7.068649 3.64 4.75 3.32
## 14 2 1-capture 25 6.811828 3.22 3.00 3.32
## 15 2 1-capture 6 7.274674 3.97 5.00 3.77
## 16 2 1-capture 35 7.257059 3.58 1.00 3.66
## 17 2 1-capture 9 6.827813 3.00 4.22 2.59
## 18 2 8-capture 29 7.194670 5.06 4.86 5.10
## 19 2 8-capture 24 7.413687 3.22 2.67 3.50
## 20 2 8-capture 27 6.900449 4.11 3.67 4.26
## 21 2 8-capture 19 7.964514 3.33 3.53 3.16
## 22 2 8-capture 30 7.501789 3.31 3.67 3.23
## 23 2 8-capture 22 7.319514 4.14 4.50 3.91
## 24 2 8-capture 33 6.973000 3.78 3.00 3.85
## 25 2 8-capture 27 7.072235 3.03 2.44 3.22
## 26 2 8-capture 26 7.964639 4.19 3.70 4.38
## 27 2 8-capture 30 7.176996 3.33 4.67 3.07
## 28 2 8-capture 27 7.451143 3.36 4.22 3.07
## 29 2 8-capture 31 7.030221 3.67 3.40 3.71
## 30 2 8-capture 26 6.863605 3.11 4.20 2.69
## 31 2 8-capture 3 7.461962 3.31 3.33 3.30
## 32 2 8-capture 34 7.083925 3.67 4.50 3.62
## 33 2 8-capture 13 7.564010 2.64 2.69 2.61
## 34 2 8-capture 26 7.092524 3.83 4.30 3.65
## 35 2 8-capture 13 7.195667 2.28 2.46 2.17
## 36 2 no-capture 33 7.023064 3.69 3.00 3.76
## 37 2 no-capture 35 7.196829 3.47 6.00 3.40
## 38 2 no-capture 34 6.699094 3.33 5.00 3.23
## 39 2 no-capture 30 7.701526 5.83 6.00 5.80
## 40 2 no-capture 34 6.652682 4.47 6.00 4.38
## 41 2 no-capture 32 6.788972 3.36 1.00 3.66
## 42 2 no-capture 34 6.859552 3.39 6.00 3.23
## 43 2 no-capture 25 7.250160 3.31 3.45 3.24
## 44 2 no-capture 26 6.869970 2.78 1.70 3.19
## 45 2 no-capture 33 6.928459 3.69 3.00 3.76
## 46 2 no-capture 34 6.933852 3.50 1.00 3.65
## 47 2 no-capture 26 7.920105 4.53 4.30 4.61
## 48 2 no-capture 9 6.996581 3.28 3.33 3.26
## 49 2 no-capture 27 7.244349 3.97 3.44 4.15
## 50 2 no-capture 10 7.545902 3.06 4.00 2.69
## 51 2 no-capture 35 6.961002 5.39 6.00 5.37
## 52 2 no-capture 5 6.924553 3.69 5.00 3.48
cer <- bind_rows(sdat, sdata)
cer$Study <- as.factor(cer$Study)
#one way anova for accuracy
acc_enc <- aov(data = cer, Recog_ACC ~ Condition + Study)
summary(acc_enc)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 214 106.8 2.294 0.10615
## Study 1 507 507.0 10.891 0.00134 **
## Residuals 99 4609 46.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(acc_enc)
## Condition Study
## 0.04008473 0.09513317
ggboxplot(cer, x = "Condition", y = "Recog_ACC", color = "Study",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
TUKEYCER <- TukeyHSD(acc_enc, wich = 'Condition', conf.level = 0.95)
print(TUKEYCER)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Recog_ACC ~ Condition + Study, data = cer)
##
## $Condition
## diff lwr upr p adj
## 8-capture-1-capture -3.02777778 -6.9102801 0.8547246 0.1571686
## no-capture-1-capture -0.01515152 -3.9824456 3.9521425 0.9999545
## no-capture-8-capture 3.01262626 -0.9000124 6.9252649 0.1644790
##
## $Study
## diff lwr upr p adj
## 2-1 -4.437017 -7.105064 -1.76897 0.0013457
plot(TUKEYCER, col="#00AFBB")
#anova for reaction times
rt_enc <- aov(data = cer, RT ~ Condition + Study)
summary(rt_enc)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 0.669 0.3346 3.310 0.0406 *
## Study 1 0.003 0.0026 0.026 0.8719
## Residuals 99 10.007 0.1011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(rt_enc)
## Condition Study
## 0.062668688 0.000247421
ggboxplot(cer, x = "Condition", y = "RT", color = "Study",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
rtcer <- TukeyHSD(rt_enc, wich = 'cer$Condition', conf.level = 0.95)
print(rtcer)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = RT ~ Condition + Study, data = cer)
##
## $Condition
## diff lwr upr p adj
## 8-capture-1-capture 0.18061267 -0.0002976329 0.36152297 0.0504757
## no-capture-1-capture 0.02651398 -0.1583472959 0.21137526 0.9378582
## no-capture-8-capture -0.15409868 -0.3364132236 0.02821586 0.1148147
##
## $Study
## diff lwr upr p adj
## 2-1 -0.01012882 -0.13445 0.1141923 0.8719029
#anova for confidence ratings
conf_enc <- aov(data = cer, Conf_rate ~ Condition + Study)
summary(conf_enc)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 2.32 1.1618 2.668 0.0744 .
## Study 1 0.17 0.1697 0.390 0.5339
## Residuals 99 43.11 0.4355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(conf_enc)
## Condition Study
## 0.05094824 0.00372051
ggboxplot(cer, x = "Condition", y = "Conf_rate", color = "Condition",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
postcer <- TukeyHSD(conf_enc, which = 'Condition', conf.level = 0.95)
print(postcer)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Conf_rate ~ Condition + Study, data = cer)
##
## $Condition
## diff lwr upr p adj
## 8-capture-1-capture -0.1666340 -0.54215478 0.2088868 0.5436498
## no-capture-1-capture 0.2005882 -0.18313372 0.5843102 0.4303998
## no-capture-8-capture 0.3672222 -0.01121339 0.7456578 0.0591759
#confidence for correct trials
confcorr <- aov(data = data, Conf_aver_corr ~ Condition)
summary(confcorr)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 1.642 0.8212 1.825 0.172
## Residuals 48 21.603 0.4501
eta_squared(confcorr)
## Condition
## 0.07065596
ggboxplot(data, x = "Condition", y = "Conf_rate", color = "Condition",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
#confidence for incorrect trials
confincor <- aov(data = data, Conf_aver_incor ~ Condition)
summary(confincor)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 7.13 3.564 2.426 0.0991 .
## Residuals 48 70.51 1.469
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(confincor)
## Condition
## 0.09181125
ggboxplot(data, x = "Condition", y = "Conf_rate", color = "Condition",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
#########################EXPERIMENT 3##########################################
study3 <- read_excel("C:/Users/lab mazzoni 2/Downloads/Museum_Gulara/Museum_Gulara/main data file study 3.xlsx")
study3$detail <- as.factor(study3$detail)
study3$Condition <- as.factor(study3$Condition)
study3$`Timing` <- as.factor(study3$`Recog Timing`)
study3$`Part no` <- as.factor(study3$`Part no`)
study3 <- na.omit(study3)
acc3 <- aov(acc ~ (detail*Condition*Timing) +
Error(`Part no`/detail) + (Condition*Timing), study3)
## Warning in aov(acc ~ (detail * Condition * Timing) + Error(`Part no`/detail) +
## : Error() model is singular
summary(acc3)
##
## Error: Part no
## Df Sum Sq Mean Sq F value Pr(>F)
## detail 1 5.9 5.92 1.272 0.2620
## Condition 1 225.2 225.19 48.423 3.51e-10 ***
## Timing 1 15.3 15.34 3.298 0.0723 .
## Condition:Timing 1 1.5 1.48 0.318 0.5739
## Residuals 101 469.7 4.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: `Part no`:detail
## Df Sum Sq Mean Sq F value Pr(>F)
## detail 1 137.62 137.62 55.685 3.06e-11 ***
## detail:Condition 1 0.40 0.40 0.161 0.689
## detail:Timing 1 0.20 0.20 0.081 0.777
## detail:Condition:Timing 1 2.17 2.17 0.879 0.351
## Residuals 101 249.61 2.47
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
partial_eta_squared(acc3$`Part no`)
## detail Condition Timing Condition:Timing
## 0.012439066 0.324064400 0.031619166 0.003141853
partial_eta_squared(acc3$`\`Part no\`:detail`)
## detail detail:Condition detail:Timing
## 0.3553928375 0.0015934618 0.0007982474
## detail:Condition:Timing
## 0.0086241741
ggboxplot(study3, x = c("Condition"), y = "acc", color = "detail",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
posthoc <- emmeans(acc3, pairwise ~ detail | Condition)
## Note: re-fitting model with sum-to-zero contrasts
## Warning in aov(formula = acc ~ (detail * Condition * Timing) + Error(`Part
## no`/detail) + : Error() model is singular
## NOTE: Results are based on intra-block estimates and are biased.
## NOTE: Results may be misleading due to involvement in interactions
emmip(posthoc, detail ~ Condition, CIs = TRUE, type ="response") + theme_light() + labs(x = "Group", y = "Accuracy")
## NOTE: Results may be misleading due to involvement in interactions
emmeans(acc3, pairwise ~ Condition)
## Note: re-fitting model with sum-to-zero contrasts
## Warning in aov(formula = acc ~ (detail * Condition * Timing) + Error(`Part
## no`/detail) + : Error() model is singular
## NOTE: Results are based on intra-block estimates and are biased.
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## 8-photo 13.5 0.21 101 13.1 14
## no-photo 15.6 0.21 101 15.2 16
##
## Results are averaged over the levels of: detail, Timing
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## (8-photo) - (no-photo) -2.07 0.298 101 -6.946 <.0001
##
## Results are averaged over the levels of: detail, Timing
emmeans(acc3, pairwise ~ detail)
## Note: re-fitting model with sum-to-zero contrasts
## Warning in aov(formula = acc ~ (detail * Condition * Timing) + Error(`Part
## no`/detail) + : Error() model is singular
## NOTE: Results are based on intra-block estimates and are biased.
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
## detail emmean SE df lower.CL upper.CL
## nozoom 15.4 0.184 185 15.0 15.7
## zoom 13.8 0.184 185 13.4 14.1
##
## Results are averaged over the levels of: Condition, Timing
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## nozoom - zoom 1.62 0.217 101 7.454 <.0001
##
## Results are averaged over the levels of: Condition, Timing
##rt analysis
study3$LogRT <- log(study3$Recog_RT)
RT3 <- aov(LogRT ~ (detail*Condition*Timing) +
Error(`Part no`/detail) + (Condition*Timing), study3)
## Warning in aov(LogRT ~ (detail * Condition * Timing) + Error(`Part no`/detail)
## + : Error() model is singular
summary(RT3)
##
## Error: Part no
## Df Sum Sq Mean Sq F value Pr(>F)
## detail 1 0.007 0.0070 0.053 0.819
## Condition 1 0.217 0.2165 1.635 0.204
## Timing 1 2.662 2.6623 20.101 1.94e-05 ***
## Condition:Timing 1 0.037 0.0372 0.281 0.597
## Residuals 101 13.377 0.1324
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: `Part no`:detail
## Df Sum Sq Mean Sq F value Pr(>F)
## detail 1 7.100e-30 7.144e-30 0.722 0.398
## detail:Condition 1 6.100e-30 6.118e-30 0.618 0.434
## detail:Timing 1 5.200e-30 5.164e-30 0.522 0.472
## detail:Condition:Timing 1 3.400e-30 3.415e-30 0.345 0.558
## Residuals 101 9.995e-28 9.896e-30
partial_eta_squared(RT3$`Part no`)
## detail Condition Timing Condition:Timing
## 0.0005210538 0.0159274713 0.1659870658 0.0027765468
partial_eta_squared(RT3$`\`Part no\`:detail`)
## detail detail:Condition detail:Timing
## 0.007097032 0.006083905 0.005139805
## detail:Condition:Timing
## 0.003405289
emmeans(RT3, pairwise ~ Timing)
## Note: re-fitting model with sum-to-zero contrasts
## Warning in aov(formula = LogRT ~ (detail * Condition * Timing) + Error(`Part
## no`/detail) + : Error() model is singular
## NOTE: Results are based on intra-block estimates and are biased.
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
## Timing emmean SE df lower.CL upper.CL
## 3 sec 7.35 0.0355 101 7.28 7.43
## infinite 7.58 0.0355 101 7.51 7.65
##
## Results are averaged over the levels of: detail, Condition
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 3 sec - infinite -0.226 0.0503 101 -4.497 <.0001
##
## Results are averaged over the levels of: detail, Condition
posthocRT <- emmeans(RT3, pairwise ~ Timing)
## Note: re-fitting model with sum-to-zero contrasts
## Warning in aov(formula = LogRT ~ (detail * Condition * Timing) + Error(`Part
## no`/detail) + : Error() model is singular
## NOTE: Results are based on intra-block estimates and are biased.
## NOTE: Results may be misleading due to involvement in interactions
plot(posthocRT)
ggboxplot(study3, x = "Timing", y = "LogRT", color = "Timing",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
##confidence
conf3 <- aov(Conf_rate ~ (detail*Condition*Timing) +
Error(`Part no`/detail) + (Condition*Timing), study3)
## Warning in aov(Conf_rate ~ (detail * Condition * Timing) + Error(`Part
## no`/detail) + : Error() model is singular
summary(conf3)
##
## Error: Part no
## Df Sum Sq Mean Sq F value Pr(>F)
## detail 1 0.17 0.167 0.232 0.631
## Condition 1 18.88 18.879 26.128 1.52e-06 ***
## Timing 1 0.56 0.560 0.775 0.381
## Condition:Timing 1 0.31 0.314 0.434 0.511
## Residuals 101 72.98 0.723
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: `Part no`:detail
## Df Sum Sq Mean Sq F value Pr(>F)
## detail 1 1.900e-30 1.938e-30 0.592 0.444
## detail:Condition 1 5.700e-30 5.679e-30 1.734 0.191
## detail:Timing 1 5.200e-30 5.223e-30 1.594 0.210
## detail:Condition:Timing 1 9.000e-31 9.150e-31 0.279 0.598
## Residuals 101 3.308e-28 3.276e-30
partial_eta_squared(conf3$`Part no`)
## detail Condition Timing Condition:Timing
## 0.002288295 0.205523982 0.007614700 0.004282897
partial_eta_squared(conf3$`\`Part no\`:detail`)
## detail detail:Condition detail:Timing
## 0.005823094 0.016877269 0.015540801
## detail:Condition:Timing
## 0.002756780
ggboxplot(study3, x = "Condition", y = "Conf_rate", color = "Condition",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
phc3 <- emmeans(conf3, pairwise ~ Condition)
## Note: re-fitting model with sum-to-zero contrasts
## Warning in aov(formula = Conf_rate ~ (detail * Condition * Timing) +
## Error(`Part no`/detail) + : Error() model is singular
## NOTE: Results are based on intra-block estimates and are biased.
## NOTE: Results may be misleading due to involvement in interactions
phc3
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## 8-photo 4.54 0.0829 101 4.38 4.71
## no-photo 5.14 0.0829 101 4.98 5.31
##
## Results are averaged over the levels of: detail, Timing
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## (8-photo) - (no-photo) -0.598 0.117 101 -5.096 <.0001
##
## Results are averaged over the levels of: detail, Timing
###confidence for correct and incorrect recognitions
#correct recognitions, confidence
conf3corr <- aov(Conf_aver_corr ~ (detail*Condition*Timing) +
Error(`Part no`/detail) + (Condition*Timing), study3)
## Warning in aov(Conf_aver_corr ~ (detail * Condition * Timing) + Error(`Part
## no`/detail) + : Error() model is singular
summary(conf3corr)
##
## Error: Part no
## Df Sum Sq Mean Sq F value Pr(>F)
## detail 1 0.05 0.045 0.070 0.791
## Condition 1 16.06 16.064 24.856 2.57e-06 ***
## Timing 1 0.17 0.174 0.269 0.605
## Condition:Timing 1 0.23 0.229 0.354 0.553
## Residuals 101 65.27 0.646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: `Part no`:detail
## Df Sum Sq Mean Sq F value Pr(>F)
## detail 1 1.650e-30 1.650e-30 0.560 0.456
## detail:Condition 1 3.950e-30 3.952e-30 1.341 0.250
## detail:Timing 1 5.760e-30 5.764e-30 1.955 0.165
## detail:Condition:Timing 1 4.300e-31 4.350e-31 0.147 0.702
## Residuals 101 2.977e-28 2.948e-30
partial_eta_squared(conf3corr$`Part no`)
## detail Condition Timing Condition:Timing
## 0.0006965424 0.1974941033 0.0026534772 0.0034969627
partial_eta_squared(conf3corr$`\`Part no\`:detail`)
## detail detail:Condition detail:Timing
## 0.005510702 0.013101720 0.018991902
## detail:Condition:Timing
## 0.001457697
phc3corr <- emmeans(conf3corr, pairwise ~ Condition)
## Note: re-fitting model with sum-to-zero contrasts
## Warning in aov(formula = Conf_aver_corr ~ (detail * Condition * Timing) + :
## Error() model is singular
## NOTE: Results are based on intra-block estimates and are biased.
## NOTE: Results may be misleading due to involvement in interactions
phc3corr
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## 8-photo 4.73 0.0784 101 4.58 4.89
## no-photo 5.28 0.0784 101 5.13 5.44
##
## Results are averaged over the levels of: detail, Timing
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## (8-photo) - (no-photo) -0.552 0.111 101 -4.974 <.0001
##
## Results are averaged over the levels of: detail, Timing
#incorrect recognitions, confidence
conf3incor <- aov(Conf_aver_incor ~ (detail*Condition*Timing) +
Error(`Part no`/detail) + (Condition*Timing), study3)
## Warning in aov(Conf_aver_incor ~ (detail * Condition * Timing) + Error(`Part
## no`/detail) + : Error() model is singular
summary(conf3incor)
##
## Error: Part no
## Df Sum Sq Mean Sq F value Pr(>F)
## detail 1 3.92 3.924 2.251 0.137
## Condition 1 3.38 3.384 1.941 0.167
## Timing 1 1.30 1.298 0.745 0.390
## Condition:Timing 1 0.16 0.160 0.092 0.763
## Residuals 101 176.06 1.743
##
## Error: `Part no`:detail
## Df Sum Sq Mean Sq F value Pr(>F)
## detail 1 2.300e-30 2.314e-30 0.286 0.594
## detail:Condition 1 2.000e-31 1.920e-31 0.024 0.878
## detail:Timing 1 2.140e-29 2.135e-29 2.636 0.108
## detail:Condition:Timing 1 4.500e-30 4.488e-30 0.554 0.458
## Residuals 101 8.183e-28 8.102e-30
partial_eta_squared(conf3incor$`Part no`)
## detail Condition Timing Condition:Timing
## 0.0218000354 0.0188582606 0.0073197717 0.0009051693
partial_eta_squared(conf3incor$`\`Part no\`:detail`)
## detail detail:Condition detail:Timing
## 0.0028197321 0.0002346517 0.0254315543
## detail:Condition:Timing
## 0.0054545517
phc3incor <- emmeans(conf3incor, pairwise ~ Condition)
## Note: re-fitting model with sum-to-zero contrasts
## Warning in aov(formula = Conf_aver_incor ~ (detail * Condition * Timing) + :
## Error() model is singular
## NOTE: Results are based on intra-block estimates and are biased.
## NOTE: Results may be misleading due to involvement in interactions
phc3incor
## $emmeans
## Condition emmean SE df lower.CL upper.CL
## 8-photo 3.90 0.129 101 3.64 4.15
## no-photo 4.15 0.129 101 3.89 4.41
##
## Results are averaged over the levels of: detail, Timing
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## (8-photo) - (no-photo) -0.252 0.182 101 -1.382 0.1701
##
## Results are averaged over the levels of: detail, Timing