0322
2023-05-25
1 指標
1AS-unitあたりの誤用数 (e_asunit)、誤用を含まないAS-unitの割合 (e_nashi_as_count_wariai)、誤用を含む節数 (e_ari_count)、誤用を含まない節数 (e_nashi_count)を使って分析した。
2 Data import
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggpubr)
library(rstatix)##
## Attaching package: 'rstatix'
##
## The following object is masked from 'package:stats':
##
## filterlibrary(readxl)
dat <- read_excel("/Users/riku/Library/CloudStorage/Dropbox/zemizemi/paper/caf/data_accuracy/accuracy0525.xlsx", sheet = "Sheet1")
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"))3 Boxplot
3.0.1 Box plot ———–
bxp <- ggboxplot(
dat, x = "task", y = "e_asunit", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_asunit")
bxp <- ggboxplot(
dat, x = "task", y = "e_nashi_as_count_wariai", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_nashi_as_count_wariai")
bxp <- ggboxplot(
dat, x = "task", y = "e_ari_count", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_ari_count")
bxp <- ggboxplot(
dat, x = "task", y = "e_nashi_count", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_nashi_count")
bxp <- ggboxplot(
dat, x = "task", y = "e_nashi_count_wariai", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_nashi_count_wariai")
4 Comparison between M and H ——-
dat_MH <- dat %>% filter(task == "M"| task == "H")
dat_MH$task <- ordered(dat_MH$task, levels=c("M","H"))4.1 e_asunit MH ——-
4.1.1 Box plot ———–
bxp <- ggboxplot(
dat_MH, x = "task", y = "e_asunit", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_asunit")
4.1.2 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(e_asunit)## # A tibble: 3 × 11
## task proficiency id trueid e_asunit e_nashi_as_count_wariai e_ari_count
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl>
## 1 M upper 9 11 2.25 0.25 4
## 2 H middle 2 3 2.11 0 11
## 3 H middle 24 24 2.5 0 6
## # ℹ 4 more variables: e_nashi_count <dbl>, e_nashi_count_wariai <dbl>,
## # is.outlier <lgl>, is.extreme <lgl>dat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(e_asunit)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower e_asunit 0.899 0.181
## 2 M middle e_asunit 0.871 0.0678
## 3 M upper e_asunit 0.922 0.302
## 4 H lower e_asunit 0.927 0.378
## 5 H middle e_asunit 0.779 0.00542
## 6 H upper e_asunit 0.932 0.402ggqqplot(dat_MH, "e_asunit", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)
dat_MH %>%
group_by(task) %>%
levene_test(e_asunit ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 0.831 0.445
## 2 H 2 32 0.863 0.431#remove trueid 3 24 for extreme outlier
dat_MH_easunit <- dat_MH %>% filter(!(trueid %in% c("3", "24")))4.1.3 Computation ——-
res.aov <- anova_test(
data = dat_MH_easunit, dv = e_asunit, 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 0.320 0.728000 0.016
## 2 task 1 30 14.135 0.000736 * 0.107
## 3 proficiency:task 2 30 0.670 0.519000 0.011interaction.plot(x.factor = dat_MH_easunit$task, trace.factor = dat_MH_easunit$proficiency,
response = dat_MH_easunit$e_asunit, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
前回なかったが、今回 M > H
4.2 e_nashi_as_count_wariai MH ——-
4.2.1 Box plot ———–
bxp <- ggboxplot(
dat_MH, x = "task", y = "e_nashi_as_count_wariai", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_nashi_as_count_wariai")
4.2.2 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(e_nashi_as_count_wariai)## # A tibble: 2 × 11
## task proficiency id trueid e_asunit e_nashi_as_count_wariai e_ari_count
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl>
## 1 M middle 16 15 0 1 0
## 2 M upper 4 6 0 1 0
## # ℹ 4 more variables: e_nashi_count <dbl>, e_nashi_count_wariai <dbl>,
## # is.outlier <lgl>, is.extreme <lgl>dat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(e_nashi_as_count_wariai)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower e_nashi_as_count_wariai 0.885 0.119
## 2 M middle e_nashi_as_count_wariai 0.934 0.423
## 3 M upper e_nashi_as_count_wariai 0.946 0.584
## 4 H lower e_nashi_as_count_wariai 0.918 0.303
## 5 H middle e_nashi_as_count_wariai 0.940 0.498
## 6 H upper e_nashi_as_count_wariai 0.933 0.418ggqqplot(dat_MH, "e_nashi_as_count_wariai", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)
dat_MH %>%
group_by(task) %>%
levene_test(e_nashi_as_count_wariai ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 0.153 0.859
## 2 H 2 32 0.777 0.4684.2.3 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = e_nashi_as_count_wariai, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 0.224 0.801 0.009
## 2 task 1 32 2.842 0.102 0.027
## 3 proficiency:task 2 32 1.183 0.320 0.023interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$e_nashi_as_count_wariai, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.3 e_ari_count MH ——-
4.3.1 Box plot ———–
bxp <- ggboxplot(
dat_MH, x = "task", y = "e_ari_count", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_ari_count")
4.3.2 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(e_ari_count)## # A tibble: 3 × 11
## task proficiency id trueid e_asunit e_nashi_as_count_wariai e_ari_count
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl>
## 1 M upper 4 6 0 1 0
## 2 H lower 21 20 1 0.364 10
## 3 H middle 2 3 2.11 0 11
## # ℹ 4 more variables: e_nashi_count <dbl>, e_nashi_count_wariai <dbl>,
## # is.outlier <lgl>, is.extreme <lgl>dat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(e_ari_count)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower e_ari_count 0.865 0.0674
## 2 M middle e_ari_count 0.956 0.728
## 3 M upper e_ari_count 0.861 0.0505
## 4 H lower e_ari_count 0.828 0.0218
## 5 H middle e_ari_count 0.860 0.0485
## 6 H upper e_ari_count 0.910 0.212ggqqplot(dat_MH, "e_ari_count", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)
dat_MH %>%
group_by(task) %>%
levene_test(e_ari_count ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 1.98 0.154
## 2 H 2 32 0.124 0.8844.3.3 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = e_ari_count, 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 0.100 0.905 0.004
## 2 task 1 32 1.158 0.290 0.010
## 3 proficiency:task 2 32 0.062 0.940 0.001interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$e_ari_count, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.4 e_nashi_count MH ——-
4.4.1 Box plot ———–
bxp <- ggboxplot(
dat_MH, x = "task", y = "e_nashi_count", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_nashi_count")
4.4.2 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(e_nashi_count)## # A tibble: 4 × 11
## task proficiency id trueid e_asunit e_nashi_as_count_wariai e_ari_count
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl>
## 1 M middle 3 2 0.333 0.667 3
## 2 M middle 10 12 1 0.4 4
## 3 M middle 11 9 0.2 0.8 1
## 4 M middle 15 16 1.25 0.25 4
## # ℹ 4 more variables: e_nashi_count <dbl>, e_nashi_count_wariai <dbl>,
## # is.outlier <lgl>, is.extreme <lgl>dat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(e_nashi_count)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower e_nashi_count 0.868 0.0722
## 2 M middle e_nashi_count 0.886 0.106
## 3 M upper e_nashi_count 0.851 0.0378
## 4 H lower e_nashi_count 0.907 0.223
## 5 H middle e_nashi_count 0.927 0.351
## 6 H upper e_nashi_count 0.929 0.366ggqqplot(dat_MH, "e_nashi_count", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)
dat_MH %>%
group_by(task) %>%
levene_test(e_nashi_count ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 4.39 0.0207
## 2 H 2 32 0.832 0.4454.4.3 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = e_nashi_count, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 3.838 0.032 * 0.150
## 2 task 1 32 3.062 0.090 0.024
## 3 proficiency:task 2 32 1.136 0.334 0.018interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$e_nashi_count, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
dat_MH %>%
pairwise_t_test(
e_nashi_count ~ task,
p.adjust.method = "bonferroni"
)## # A tibble: 1 × 9
## .y. group1 group2 n1 n2 p p.signif p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <chr> <dbl> <chr>
## 1 e_nashi_count M H 35 35 0.253 ns 0.253 nsdat_MH %>%
pairwise_t_test(
e_nashi_count ~ proficiency,
p.adjust.method = "bonferroni"
)## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 p p.signif p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <chr> <dbl> <chr>
## 1 e_nashi_count lower middle 22 24 0.494 ns 1 ns
## 2 e_nashi_count lower upper 22 24 0.00225 ** 0.00676 **
## 3 e_nashi_count middle upper 24 24 0.0132 * 0.0397 *Upper > Lower
Upper > Middle
5 Comparison between L and M ——-
dat_LM <- dat %>% filter(task == "L"| task == "M")
dat_LM$task <- ordered(dat_LM$task, levels=c("L","M"))5.1 e_asunit LM ——-
5.1.1 Box plot ———–
bxp <- ggboxplot(
dat_LM, x = "task", y = "e_asunit", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_asunit")
5.1.2 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(e_asunit)## # A tibble: 2 × 11
## task proficiency id trueid e_asunit e_nashi_as_count_wariai e_ari_count
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl>
## 1 L middle 24 24 3.25 0 8
## 2 M upper 9 11 2.25 0.25 4
## # ℹ 4 more variables: e_nashi_count <dbl>, e_nashi_count_wariai <dbl>,
## # is.outlier <lgl>, is.extreme <lgl>dat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(e_asunit)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower e_asunit 0.972 0.906
## 2 L middle e_asunit 0.816 0.0143
## 3 L upper e_asunit 0.897 0.144
## 4 M lower e_asunit 0.899 0.181
## 5 M middle e_asunit 0.871 0.0678
## 6 M upper e_asunit 0.922 0.302ggqqplot(dat_LM, "e_asunit", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)
dat_LM %>%
group_by(task) %>%
levene_test(e_asunit ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 0.942 0.400
## 2 M 2 32 0.831 0.445# REMOVE outlier
dat_LM_easunit <- dat_LM %>% filter(!(trueid %in% c("11","24")))5.1.3 Computation ——-
res.aov <- anova_test(
data = dat_LM_easunit, dv = e_asunit, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 30 0.336 0.717 0.017000
## 2 task 1 30 0.045 0.833 0.000328
## 3 proficiency:task 2 30 1.324 0.281 0.019000interaction.plot(x.factor = dat_LM_easunit$task, trace.factor = dat_LM_easunit$proficiency,
response = dat_LM_easunit$e_asunit, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
5.2 e_nashi_as_count_wariai LM ——-
5.2.1 Box plot ———–
bxp <- ggboxplot(
dat_LM, x = "task", y = "e_nashi_as_count_wariai", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_nashi_as_count_wariai")
5.2.2 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(e_nashi_as_count_wariai)## # A tibble: 2 × 11
## task proficiency id trueid e_asunit e_nashi_as_count_wariai e_ari_count
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl>
## 1 M middle 16 15 0 1 0
## 2 M upper 4 6 0 1 0
## # ℹ 4 more variables: e_nashi_count <dbl>, e_nashi_count_wariai <dbl>,
## # is.outlier <lgl>, is.extreme <lgl>dat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(e_nashi_as_count_wariai)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower e_nashi_as_count_wariai 0.922 0.335
## 2 L middle e_nashi_as_count_wariai 0.986 0.998
## 3 L upper e_nashi_as_count_wariai 0.928 0.362
## 4 M lower e_nashi_as_count_wariai 0.885 0.119
## 5 M middle e_nashi_as_count_wariai 0.934 0.423
## 6 M upper e_nashi_as_count_wariai 0.946 0.584ggqqplot(dat_LM, "e_nashi_as_count_wariai", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)
dat_LM %>%
group_by(task) %>%
levene_test(e_nashi_as_count_wariai ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 1.22 0.308
## 2 M 2 32 0.153 0.8595.2.3 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = e_nashi_as_count_wariai, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 0.206 0.815 0.009
## 2 task 1 32 3.372 0.076 0.028
## 3 proficiency:task 2 32 0.111 0.895 0.002interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$e_nashi_as_count_wariai, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
TASKは有意傾向
5.3 e_ari_count LM ——-
5.3.1 Box plot ———–
bxp <- ggboxplot(
dat_LM, x = "task", y = "e_ari_count", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_ari_count")
5.3.2 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(e_ari_count)## # A tibble: 4 × 11
## task proficiency id trueid e_asunit e_nashi_as_count_wariai e_ari_count
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl>
## 1 L lower 14 13 0.286 0.714 2
## 2 L lower 30 31 1 0.2 8
## 3 L lower 32 33 0.333 0.667 1
## 4 M upper 4 6 0 1 0
## # ℹ 4 more variables: e_nashi_count <dbl>, e_nashi_count_wariai <dbl>,
## # is.outlier <lgl>, is.extreme <lgl>dat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(e_ari_count)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower e_ari_count 0.923 0.340
## 2 L middle e_ari_count 0.923 0.308
## 3 L upper e_ari_count 0.955 0.708
## 4 M lower e_ari_count 0.865 0.0674
## 5 M middle e_ari_count 0.956 0.728
## 6 M upper e_ari_count 0.861 0.0505ggqqplot(dat_LM, "e_ari_count", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)
dat_LM %>%
group_by(task) %>%
levene_test(e_ari_count ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 2.90 0.0695
## 2 M 2 32 1.98 0.1545.3.3 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = e_ari_count, 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 0.098 0.907 0.004
## 2 task 1 32 1.130 0.296 0.011
## 3 proficiency:task 2 32 0.302 0.742 0.006interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$e_ari_count, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
5.4 e_nashi_count LM ——-
5.4.1 Box plot ———–
bxp <- ggboxplot(
dat_LM, x = "task", y = "e_nashi_count", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("e_nashi_count")
5.4.2 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(e_nashi_count)## # A tibble: 5 × 11
## task proficiency id trueid e_asunit e_nashi_as_count_wariai e_ari_count
## <ord> <ord> <fct> <fct> <dbl> <dbl> <dbl>
## 1 L upper 1 1 0.333 0.667 3
## 2 M middle 3 2 0.333 0.667 3
## 3 M middle 10 12 1 0.4 4
## 4 M middle 11 9 0.2 0.8 1
## 5 M middle 15 16 1.25 0.25 4
## # ℹ 4 more variables: e_nashi_count <dbl>, e_nashi_count_wariai <dbl>,
## # is.outlier <lgl>, is.extreme <lgl>dat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(e_nashi_count)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower e_nashi_count 0.916 0.289
## 2 L middle e_nashi_count 0.942 0.529
## 3 L upper e_nashi_count 0.864 0.0553
## 4 M lower e_nashi_count 0.868 0.0722
## 5 M middle e_nashi_count 0.886 0.106
## 6 M upper e_nashi_count 0.851 0.0378ggqqplot(dat_LM, "e_nashi_count", ggtheme = theme_bw()) +
facet_grid(task ~ proficiency)
dat_LM %>%
group_by(task) %>%
levene_test(e_nashi_count ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 1.20 0.313
## 2 M 2 32 4.39 0.02075.4.3 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = e_nashi_count, wid = trueid,
between = proficiency, within = task
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 proficiency 2 32 3.089 0.059 1.21e-01
## 2 task 1 32 0.003 0.953 3.13e-05
## 3 proficiency:task 2 32 1.729 0.194 3.00e-02interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$e_nashi_count, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
dat_LM %>%
pairwise_t_test(
e_nashi_count ~ task,
p.adjust.method = "bonferroni"
)## # A tibble: 1 × 9
## .y. group1 group2 n1 n2 p p.signif p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <chr> <dbl> <chr>
## 1 e_nashi_count L M 35 35 1 ns 1 nsdat_LM %>%
pairwise_t_test(
e_nashi_count ~ proficiency,
p.adjust.method = "bonferroni"
)## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 p p.signif p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <chr> <dbl> <chr>
## 1 e_nashi_count lower middle 22 24 0.309 ns 0.926 ns
## 2 e_nashi_count lower upper 22 24 0.00464 ** 0.0139 *
## 3 e_nashi_count middle upper 24 24 0.0558 ns 0.167 ns習熟度は有意傾向 p = .059, Upper > Lower
6 まとめ
6.1 1AS-unitあたりの誤用数 (e_asunit)
MH, M > H,
6.2 誤用を含まないAS-unitの割合 (e_nashi_as_count_wariai)
MH タスクは有意傾向(p = .102), M < H
LM タスクは有意傾向(p = .076), L < M
6.3 誤用を含まない節数 (e_nashi_count)
MHでは、習熟度が有意、Lower < Upper; Middle < Upper,
LMでは、習熟度が有意傾向(p = .059), Lower < Upper。
6.4 まとめのまとめ
6.4.1 1AS-unitあたりの誤用数
内在性負荷が上がると、習熟度に関わらず、1AS-unitあたりの誤用数が少なくなる。
外在性負荷ではそういう変化がない。
内在性負荷が高いタスクがより複雑であるため、参加者がそのタスクに集中し、その結果として誤用数が減少した可能性がある。 外在性負荷と誤用数との間に直接的な関連性はないかも？
6.4.2 誤用を含まないAS-unitの割合
外在性・内在性負荷を問わず、習熟度に関わらず、誤用を含まないAS-unitの割合は負荷の高い方が高かくなる。 高負荷タスクでは参加者がより注意深くなり、その結果誤用を含まないAS-unitが増えた。
？？シンプルな文を言ってしまう？？他の指標との照合が必要。
6.4.3 誤用を含まない節数
MHでは、Middle < Upperですが、LMではなかった。
中位群 (middle) は、外在性負荷が上がっても誤用を含まない節数は減らないが、内在性負荷が上がったら、上位群 (upper) に比べて誤用を含まない節数が少なくなる。
習熟度が高い参加者が内在性負荷の高いタスクに対してより適応しているかも。