機能語0124
2023-01-24
1 機能語の種類
Term Info1 Info2 Freq id
3 たい 助動詞 * 1 1
4 だ 助動詞 * 10 1
5 ない 助動詞 * 3 1
6 と 助詞 並立助詞 5 1
7 は 助詞 係助詞 8 1
8 も 助詞 係助詞 5 1
1.1 Hでは
1.1.1 Info 1
[1] “助動詞” “助詞” “動詞” “名詞” “接続詞” “その他” “接頭詞” “フィラー” [9] “形容詞”
1.1.2 Info 2
[1] “*” “並立助詞” “係助詞”
[4] “副助詞” “副助詞／並立助詞／終助詞” “接続助詞”
[7] “格助詞” “終助詞” “連体化”
[10] “接尾” “非自立” “間投”
[13] “副詞化” “名詞接続” “特殊”
1.2 Mでは
[1] “助動詞” “助詞” “動詞” “名詞” “感動詞” “接続詞” “接頭詞” “フィラー” [9] “形容詞”
[1] “*” “並立助詞” “係助詞”
[4] “副助詞” “副助詞／並立助詞／終助詞” “接続助詞”
[7] “格助詞” “終助詞” “連体化”
[10] “非自立” “接尾” “名詞接続”
[13] “副詞化”
1.3 Lでは
“助動詞” “助詞” “動詞” “名詞” “接続詞” “接頭詞” “フィラー”
[1] “*” “並立助詞” “係助詞”
[4] “副助詞” “副助詞／並立助詞／終助詞” “接続助詞”
[7] “格助詞” “終助詞” “連体化”
[10] “非自立” “接尾” “名詞接続”
[13] “副詞化”
1.4 例
1.4.1 助動詞
です、ます、たい
1.4.2 助詞
並立助詞 と
係助詞 は
副助詞 かも、じゃ
副助詞／並立助詞／終助詞 か
接続助詞 から、ので、けど
格助詞 が、で
終助詞 ね、よ
連体化 の
副詞化 に（極めて少ない）
1.4.3 名詞
接尾 的、さん
1.4.4 動詞
接尾 れる
1.4.5 形容詞
接尾 っぽい
1.4.6 動詞 非自立
1.4.7 名詞接続 接頭詞
1.4.8 特殊
ん（極めて少ない）
1.5 まとめ
ということで、
助動詞、
並立助詞、
係助詞、
副助詞、
副助詞／並立助詞／終助詞、
接続助詞、
格助詞、
終助詞、
連体化、
非自立動詞、
名詞接尾
を分析する。
2 Data import
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 1.0.0
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.5.0
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(ggpubr)
library(rstatix)##
## Attaching package: 'rstatix'
##
## The following object is masked from 'package:stats':
##
## filterlibrary(readxl)
dat <- read_excel("/Users/riku/Documents/goimitsudo_0123.xlsx")
dat$id <- as.factor(dat$id)
dat$trueid <- as.factor(dat$trueid)
dat$task <- ordered(dat$task, levels=c("L","M","H"))
dat$proficiency <- as.factor(dat$proficiency)
dat$proficiency <- ordered(dat$proficiency, levels=c("lower","middle","upper"))2.1 Boxplot
bxp <- ggboxplot(
dat, x = "task", y = "jyodoushi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("jyodoushi")
bxp <- ggboxplot(
dat, x = "task", y = "heiritsu", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("heiritsu")
bxp <- ggboxplot(
dat, x = "task", y = "kakari", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("kakari")
bxp <- ggboxplot(
dat, x = "task", y = "fukujyoshi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("fukujyoshi")
bxp <- ggboxplot(
dat, x = "task", y = "ka", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("ka")
bxp <- ggboxplot(
dat, x = "task", y = "setsuzokujyoshi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("setsuzokujyoshi")
bxp <- ggboxplot(
dat, x = "task", y = "kakujyoshi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("kakujyoshi")
bxp <- ggboxplot(
dat, x = "task", y = "syujyoshi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("syujyoshi")
bxp <- ggboxplot(
dat, x = "task", y = "rentai", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("rentai")
bxp <- ggboxplot(
dat, x = "task", y = "hijiritsu", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("hijiritsu")
bxp <- ggboxplot(
dat, x = "task", y = "setsubimeishi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("setsubimeishi")
bxp <- ggboxplot(
dat, x = "task", y = "setsubidoushi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("setsubidoushi")
bxp <- ggboxplot(
dat, x = "task", y = "setsubikeiyoushi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("setsubikeiyoushi")
3 Comparison between M and H ——-
dat_MH <- dat %>% filter(task == "M"| task == "H")
dat_MH$task <- ordered(dat_MH$task, levels=c("M","H"))3.1 Jyodoushi MH ——-
習熟度と認知負荷が有意であった。
3.1.1 Box plot ———–
bxp <- ggboxplot(
dat_MH, x = "task", y = "jyodoushi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("jyodoushi")
3.1.2 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(jyodoushi)## # A tibble: 1 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 H upper 10 10 38.3 4.9 64 128 8 10 2
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(jyodoushi)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower jyodoushi 0.974 0.926
## 2 M middle jyodoushi 0.889 0.116
## 3 M upper jyodoushi 0.879 0.0851
## 4 H lower jyodoushi 0.868 0.0738
## 5 H middle jyodoushi 0.967 0.878
## 6 H upper jyodoushi 0.954 0.696ggqqplot(dat_MH, "jyodoushi", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_MH %>%
group_by(task) %>%
levene_test(jyodoushi ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 0.775 0.469
## 2 H 2 32 0.371 0.6933.1.3 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = jyodoushi, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 7.932 0.002 * 0.242
## 2 task 1 32 6.988 0.013 * 0.072
## 3 proficiency:task 2 32 0.242 0.786 0.005interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$jyodoushi, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
3.2 Heiritsu
正規分布に従わなかった。
bxp <- ggboxplot(
dat_MH, x = "task", y = "heiritsu", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("heiritsu")
3.2.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(heiritsu)## # A tibble: 8 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 M middle 3 3 48.2 5 58 114 8 11 3
## 2 M middle 9 9 47.2 3.78 43 72 5 9 4
## 3 M middle 15 15 44.7 3.4 29 38 4 5 1
## 4 M middle 33 34 46.9 3.75 62 96 7 12 5
## 5 M upper 22 22 44.5 4.87 82 164 7 15 8
## 6 H lower 8 8 48.1 3.71 38 54 6 7 1
## 7 H lower 20 20 48.4 4.21 63 122 11 14 3
## 8 H middle 3 3 45.2 5.18 56 126 9 11 2
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(heiritsu)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower heiritsu 0.879 0.0998
## 2 M middle heiritsu 0.739 0.00205
## 3 M upper heiritsu 0.878 0.0826
## 4 H lower heiritsu 0.504 0.00000169
## 5 H middle heiritsu 0.731 0.00172
## 6 H upper heiritsu 0.839 0.0269ggqqplot(dat_MH, "heiritsu", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_MH %>%
group_by(task) %>%
levene_test(heiritsu ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 0.905 0.415
## 2 H 2 32 3.87 0.0312正規分布に従わない
3.3 kakari
習熟度も認知負荷も有意ではなかった。
bxp <- ggboxplot(
dat_MH, x = "task", y = "kakari", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("kakari")
3.3.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(kakari)## # A tibble: 3 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 M lower 20 20 45.7 5.25 45 92 7 8 1
## 2 M lower 30 31 46.1 3.5 46 76 8 10 2
## 3 H middle 3 3 45.2 5.18 56 126 9 11 2
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(kakari)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower kakari 0.948 0.619
## 2 M middle kakari 0.915 0.247
## 3 M upper kakari 0.938 0.470
## 4 H lower kakari 0.954 0.702
## 5 H middle kakari 0.903 0.172
## 6 H upper kakari 0.990 1.00ggqqplot(dat_MH, "kakari", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_MH %>%
group_by(task) %>%
levene_test(kakari ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 3.04 0.0618
## 2 H 2 32 0.163 0.8503.3.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = kakari, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 0.427 0.656 0.021
## 2 task 1 32 1.456 0.236 0.009
## 3 proficiency:task 2 32 0.316 0.731 0.004interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$kakari, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
3.4 setsuzokujyoshi
習熟度も認知負荷も有意であった。
bxp <- ggboxplot(
dat_MH, x = "task", y = "setsuzokujyoshi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("setsuzokujyoshi")
3.4.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(setsuzokujyoshi)## # A tibble: 1 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 H lower 30 31 42.7 3.89 44 82 7 9 2
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(setsuzokujyoshi)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower setsuzokujyoshi 0.905 0.210
## 2 M middle setsuzokujyoshi 0.965 0.849
## 3 M upper setsuzokujyoshi 0.932 0.397
## 4 H lower setsuzokujyoshi 0.786 0.00624
## 5 H middle setsuzokujyoshi 0.938 0.475
## 6 H upper setsuzokujyoshi 0.910 0.215ggqqplot(dat_MH, "setsuzokujyoshi", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_MH %>%
group_by(task) %>%
levene_test(setsuzokujyoshi ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 2.67 0.0849
## 2 H 2 32 0.287 0.7523.4.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = setsuzokujyoshi, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 8.463 0.001 * 0.288
## 2 task 1 32 6.541 0.015 * 0.046
## 3 proficiency:task 2 32 0.090 0.914 0.001interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$setsuzokujyoshi, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
3.5 kakujyoshi
bxp <- ggboxplot(
dat_MH, x = "task", y = "kakujyoshi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("kakujyoshi")
3.5.1 Check assumptions ——-
dat_MH_2 <- dat_MH %>% filter(!(trueid == "24"))
dat_MH_2 <- dat_MH_2 %>% filter(!(trueid == "3"))
dat_MH_2 %>%
group_by(task, proficiency) %>%
identify_outliers(kakujyoshi)## [1] task proficiency id
## [4] trueid ld ld2
## [7] koto nobe asunit
## [10] clause jyuzokuclause jyuzokuclauseperasunit
## [13] clauseperasunit wariai wariai2
## [16] jyodoushi heiritsu kakari
## [19] fukujyoshi ka setsuzokujyoshi
## [22] kakujyoshi syujyoshi rentai
## [25] hijiritsu setsubimeishi setsubidoushi
## [28] setsubikeiyoushi is.outlier is.extreme
## <0 rows> (or 0-length row.names)dat_MH_2 %>%
group_by(task, proficiency) %>%
shapiro_test(kakujyoshi)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower kakujyoshi 0.902 0.197
## 2 M middle kakujyoshi 0.942 0.573
## 3 M upper kakujyoshi 0.937 0.457
## 4 H lower kakujyoshi 0.948 0.619
## 5 H middle kakujyoshi 0.929 0.438
## 6 H upper kakujyoshi 0.935 0.435ggqqplot(dat_MH_2, "kakujyoshi", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_MH_2 %>%
group_by(task) %>%
levene_test(kakujyoshi ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 30 1.15 0.331
## 2 H 2 30 0.0615 0.9403.5.2 Computation ——-
res.aov <- anova_test(
data = dat_MH_2, dv = kakujyoshi, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 30 6.713 0.004 * 0.241
## 2 task 1 30 0.548 0.465 0.005
## 3 proficiency:task 2 30 2.929 0.069 0.054interaction.plot(x.factor = dat_MH_2$task, trace.factor = dat_MH_2$proficiency,
response = dat_MH_2$kakujyoshi, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
# Effect of group at each time point
one.way <- dat_MH_2 %>%
group_by(task) %>%
anova_test(dv = kakujyoshi, wid = trueid, between = proficiency) %>%
get_anova_table() %>%
adjust_pvalue(method = "bonferroni")
one.way## # A tibble: 2 × 9
## task Effect DFn DFd F p `p<.05` ges p.adj
## <ord> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 M proficiency 2 30 9.61 0.000596 "*" 0.39 0.00119
## 2 H proficiency 2 30 1.87 0.172 "" 0.111 0.344pwc <- dat_MH_2 %>%
group_by(task) %>%
pairwise_t_test(kakujyoshi ~ proficiency, p.adjust.method = "bonferroni")
pwc## # A tibble: 6 × 10
## task .y. group1 group2 n1 n2 p p.signif p.adj p.adj.s…¹
## * <ord> <chr> <chr> <chr> <int> <int> <dbl> <chr> <dbl> <chr>
## 1 M kakujyoshi lower middle 11 10 0.843 ns 1 ns
## 2 M kakujyoshi lower upper 11 12 0.000872 *** 0.00262 **
## 3 M kakujyoshi middle upper 10 12 0.000646 *** 0.00194 **
## 4 H kakujyoshi lower middle 11 10 0.421 ns 1 ns
## 5 H kakujyoshi lower upper 11 12 0.274 ns 0.823 ns
## 6 H kakujyoshi middle upper 10 12 0.0647 ns 0.194 ns
## # … with abbreviated variable name ¹p.adj.signif# Visualization: boxplots with p-values
pwc <- pwc %>% add_xy_position(x = "task")
pwc.filtered <- pwc %>% filter(task != "H")
bxp +
stat_pvalue_manual(pwc.filtered, tip.length = 0, hide.ns = TRUE) +
labs(
subtitle = get_test_label(res.aov, detailed = TRUE),
caption = get_pwc_label(pwc)
)
交互作用は有意傾向であった。
Mだけでは、上位群が中位群、下位群より高かった。
3.6 rentai
正規分布に従わなかった。
bxp <- ggboxplot(
dat_MH, x = "task", y = "rentai", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("rentai")
3.6.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(rentai)## # A tibble: 2 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 M upper 23 23 45.5 4.67 74 154 8 15 7
## 2 H upper 23 23 46.5 4.92 65 127 7 12 5
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(rentai)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower rentai 0.881 0.107
## 2 M middle rentai 0.920 0.288
## 3 M upper rentai 0.855 0.0428
## 4 H lower rentai 0.689 0.000334
## 5 H middle rentai 0.854 0.0412
## 6 H upper rentai 0.810 0.0123ggqqplot(dat_MH, "rentai", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_MH %>%
group_by(task) %>%
levene_test(rentai ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 0.486 0.619
## 2 H 2 32 5.93 0.006443.6.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = rentai, wid = id,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 0.849 0.437 0.038000
## 2 task 1 32 0.048 0.828 0.000387
## 3 proficiency:task 2 32 1.544 0.229 0.024000interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$rentai, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
3.7 setsubimeishi
認知負荷が有意であった。
bxp <- ggboxplot(
dat_MH, x = "task", y = "setsubimeishi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("setsubimeishi")
3.7.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(setsubimeishi)## # A tibble: 4 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 M upper 6 6 43.8 3.83 53 105 8 12 4
## 2 M upper 23 23 45.5 4.67 74 154 8 15 7
## 3 H lower 19 19 57.3 4.27 43 82 9 11 2
## 4 H upper 10 10 38.3 4.9 64 128 8 10 2
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(setsubimeishi)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower setsubimeishi 0.913 0.263
## 2 M middle setsubimeishi 0.860 0.0494
## 3 M upper setsubimeishi 0.800 0.00949
## 4 H lower setsubimeishi 0.866 0.0680
## 5 H middle setsubimeishi 0.891 0.120
## 6 H upper setsubimeishi 0.809 0.0119ggqqplot(dat_MH, "setsubimeishi", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_MH %>%
group_by(task) %>%
levene_test(setsubimeishi ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 0.180 0.836
## 2 H 2 32 1.26 0.2993.7.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = setsubimeishi, wid = id,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 0.137 0.873 0.007
## 2 task 1 32 4.829 0.035 * 0.029
## 3 proficiency:task 2 32 0.835 0.443 0.010interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$setsubimeishi, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4 Comparison between L and M ——-
dat_LM <- dat %>% filter(task == "L"| task == "M")
dat_LM$task <- ordered(dat_LM$task, levels=c("L","M"))4.1 Jyodoushi LM ——-
習熟度は有意であった。
4.1.1 Box plot ———–
bxp <- ggboxplot(
dat_LM, x = "task", y = "jyodoushi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("jyodoushi")
4.1.2 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(jyodoushi)## # A tibble: 3 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 L middle 12 12 47.1 3.2 23 34 4 5 1
## 2 L middle 16 16 41.9 4.5 79 172 9 16 7
## 3 L upper 10 10 39.0 5.5 68 141 7 10 3
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(jyodoushi)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower jyodoushi 0.944 0.567
## 2 L middle jyodoushi 0.903 0.172
## 3 L upper jyodoushi 0.891 0.122
## 4 M lower jyodoushi 0.974 0.926
## 5 M middle jyodoushi 0.889 0.116
## 6 M upper jyodoushi 0.879 0.0851ggqqplot(dat_LM, "jyodoushi", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_LM %>%
group_by(task) %>%
levene_test(jyodoushi ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 1.23 0.305
## 2 M 2 32 0.775 0.4694.1.3 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = jyodoushi, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 6.571 0.004 * 2.34e-01
## 2 task 1 32 0.009 0.923 7.54e-05
## 3 proficiency:task 2 32 0.469 0.630 7.00e-03interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$jyodoushi, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.2 Heiritsu
正規分布に従わなかった。
bxp <- ggboxplot(
dat_LM, x = "task", y = "heiritsu", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("heiritsu")
4.2.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(heiritsu)## # A tibble: 7 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 L lower 8 8 50 5 60 90 5 9 4
## 2 L lower 19 19 56.5 3.69 44 85 9 13 4
## 3 M middle 3 3 48.2 5 58 114 8 11 3
## 4 M middle 9 9 47.2 3.78 43 72 5 9 4
## 5 M middle 15 15 44.7 3.4 29 38 4 5 1
## 6 M middle 33 34 46.9 3.75 62 96 7 12 5
## 7 M upper 22 22 44.5 4.87 82 164 7 15 8
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(heiritsu)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower heiritsu 0.822 0.0184
## 2 L middle heiritsu 0.787 0.00663
## 3 L upper heiritsu 0.894 0.133
## 4 M lower heiritsu 0.879 0.0998
## 5 M middle heiritsu 0.739 0.00205
## 6 M upper heiritsu 0.878 0.0826ggqqplot(dat_LM, "heiritsu", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_LM %>%
group_by(task) %>%
levene_test(heiritsu ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 2.51 0.0975
## 2 M 2 32 0.905 0.4154.3 Kakari
bxp <- ggboxplot(
dat_LM, x = "task", y = "kakari", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("kakari")
習熟度も認知負荷も有意ではなかった。
4.3.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(kakari)## # A tibble: 2 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 M lower 20 20 45.7 5.25 45 92 7 8 1
## 2 M lower 30 31 46.1 3.5 46 76 8 10 2
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(kakari)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower kakari 0.905 0.215
## 2 L middle kakari 0.965 0.848
## 3 L upper kakari 0.972 0.928
## 4 M lower kakari 0.948 0.619
## 5 M middle kakari 0.915 0.247
## 6 M upper kakari 0.938 0.470ggqqplot(dat_LM, "kakari", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_LM %>%
group_by(task) %>%
levene_test(kakari ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 1.10 0.345
## 2 M 2 32 3.04 0.06184.3.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = kakari, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 0.057 0.945 0.003
## 2 task 1 32 0.628 0.434 0.005
## 3 proficiency:task 2 32 1.857 0.173 0.028interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$kakari, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.4 setsuzokujyoshi
習熟度が有意であった。
bxp <- ggboxplot(
dat_LM, x = "task", y = "setsuzokujyoshi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("setsuzokujyoshi")
4.4.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(setsuzokujyoshi)## # A tibble: 1 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 L middle 16 16 41.9 4.5 79 172 9 16 7
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(setsuzokujyoshi)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower setsuzokujyoshi 0.912 0.255
## 2 L middle setsuzokujyoshi 0.844 0.0312
## 3 L upper setsuzokujyoshi 0.952 0.666
## 4 M lower setsuzokujyoshi 0.905 0.210
## 5 M middle setsuzokujyoshi 0.965 0.849
## 6 M upper setsuzokujyoshi 0.932 0.397ggqqplot(dat_LM, "setsuzokujyoshi", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_LM %>%
group_by(task) %>%
levene_test(setsuzokujyoshi ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 3.67 0.0368
## 2 M 2 32 2.67 0.08494.4.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = setsuzokujyoshi, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 7.385 0.002 * 0.248
## 2 task 1 32 0.130 0.721 0.001
## 3 proficiency:task 2 32 1.676 0.203 0.029interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$setsuzokujyoshi, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.5 kakujyoshi
習熟度も認知負荷も有意であった。
bxp <- ggboxplot(
dat_LM, x = "task", y = "kakujyoshi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("kakujyoshi")
4.5.1 Check assumptions ——-
dat_LM_2 <- dat_LM
#dat_LM_2 <- dat_LM %>% filter(!(trueid == "24"))
#dat_LM_2 <- dat_LM_2 %>% filter(!(trueid == "3"))
dat_LM_2 %>%
group_by(task, proficiency) %>%
identify_outliers(kakujyoshi)## # A tibble: 2 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 L lower 21 21 52.7 5.9 56 112 9 10 1
## 2 M middle 24 24 49.6 5.7 60 115 9 10 1
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_LM_2 %>%
group_by(task, proficiency) %>%
shapiro_test(kakujyoshi)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower kakujyoshi 0.894 0.154
## 2 L middle kakujyoshi 0.962 0.813
## 3 L upper kakujyoshi 0.946 0.583
## 4 M lower kakujyoshi 0.902 0.197
## 5 M middle kakujyoshi 0.874 0.0743
## 6 M upper kakujyoshi 0.937 0.457ggqqplot(dat_LM_2, "kakujyoshi", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_LM_2 %>%
group_by(task) %>%
levene_test(kakujyoshi ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 0.734 0.488
## 2 M 2 32 0.162 0.8514.5.2 Computation ——-
res.aov <- anova_test(
data = dat_LM_2, dv = kakujyoshi, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 5.301 0.010 * 0.188
## 2 task 1 32 5.494 0.025 * 0.049
## 3 proficiency:task 2 32 0.191 0.827 0.004interaction.plot(x.factor = dat_LM_2$task, trace.factor = dat_LM_2$proficiency,
response = dat_LM_2$kakujyoshi, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.6 rentai
習熟度も認知負荷も有意ではなかった。
bxp <- ggboxplot(
dat_LM, x = "task", y = "rentai", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("rentai")
4.6.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(rentai)## # A tibble: 3 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 L lower 30 31 51.8 4.4 47 85 10 10 0
## 2 L middle 3 3 46.0 5.2 62 113 8 10 2
## 3 M upper 23 23 45.5 4.67 74 154 8 15 7
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(rentai)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower rentai 0.814 0.0144
## 2 L middle rentai 0.901 0.164
## 3 L upper rentai 0.829 0.0204
## 4 M lower rentai 0.881 0.107
## 5 M middle rentai 0.920 0.288
## 6 M upper rentai 0.855 0.0428ggqqplot(dat_LM, "rentai", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_LM %>%
group_by(task) %>%
levene_test(rentai ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 0.0112 0.989
## 2 M 2 32 0.486 0.6194.6.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = rentai, wid = id,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 0.695 0.506 0.024
## 2 task 1 32 0.325 0.573 0.004
## 3 proficiency:task 2 32 0.378 0.688 0.010interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$rentai, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.7 setsubimeishi
まあまあ正規分布に従わなかったが、無理やりやると、認知負荷だけが有意であった。
bxp <- ggboxplot(
dat_LM, x = "task", y = "setsubimeishi", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("setsubimeishi")
4.7.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(setsubimeishi)## # A tibble: 3 × 30
## task proficiency id trueid ld ld2 koto nobe asunit clause jyuzoku…¹
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 L middle 33 34 44.6 5 72 112 5 10 5
## 2 M upper 6 6 43.8 3.83 53 105 8 12 4
## 3 M upper 23 23 45.5 4.67 74 154 8 15 7
## # … with 19 more variables: jyuzokuclauseperasunit <dbl>,
## # clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## # heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## # setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## # hijiritsu <dbl>, setsubimeishi <dbl>, setsubidoushi <dbl>,
## # setsubikeiyoushi <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(setsubimeishi)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower setsubimeishi 0.886 0.124
## 2 L middle setsubimeishi 0.836 0.0247
## 3 L upper setsubimeishi 0.881 0.0895
## 4 M lower setsubimeishi 0.913 0.263
## 5 M middle setsubimeishi 0.860 0.0494
## 6 M upper setsubimeishi 0.800 0.00949ggqqplot(dat_LM, "setsubimeishi", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
dat_LM %>%
group_by(task) %>%
levene_test(setsubimeishi ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 0.330 0.721
## 2 M 2 32 0.180 0.8364.7.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = setsubimeishi, wid = id,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 0.166 0.848 8.00e-03
## 2 task 1 32 8.985 0.005 * 6.60e-02
## 3 proficiency:task 2 32 0.002 0.998 3.22e-05interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$setsubimeishi, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
5 まとめ
5.1 助動詞
不一致
内在性負荷だけが影響を及ぼす。
5.1.1 MH
習熟度と認知負荷が有意であった。
5.1.2 LM
習熟度は有意であった。
5.2 係助詞
一致
5.2.1 MH
習熟度も認知負荷も有意ではなかった。
5.2.2 LM
習熟度も認知負荷も有意ではなかった。
5.3 接続助詞
不一致
内在性負荷だけが影響を及ぼす。
5.3.1 MH
習熟度も認知負荷も有意であった。
5.3.2 LM
習熟度が有意であった。
5.4 格助詞
不一致
上位群は外在性認知負荷があっても、ある程度に維持でき、中位群と下位群を上回っていたが（M条件での話）、内在性負荷が上がると、中位群と下位群と同じレベルになってしまう。
5.4.1 MH
習熟度も認知負交互作用は有意傾向であった (0.069)。 Mだけでは、上位群が中位群、下位群より高かった。
5.4.2 LM
習熟度も認知負荷も有意であった。
5.5 名詞接尾 (的)
ほぼ一致
5.5.1 MH
認知負荷が有意であった。
5.5.2 LM
まあまあ正規分布に従わなかったが、無理やりやると、認知負荷だけが有意であった。