nobegosu0117
2023-01-17
1 定義
- 複雑さ：
- 1AS-unitあたりの従属節数 = jyuzokuclauseperasunit
- 1AS-unitあたりの節数 = clauseperasunit
- 語彙の多様さ：
- koto = 異なり語数
- nobe = 延べ語数
- wariai = 延べ語数に対する異なり語数の割合
- wariai2 = 延べ語数 x2の平方根あたりの異なり語数
- ld = 語彙密度(内容語数/総語数から算出)
- ld2 = 語彙密度(内容語数/節数から算出)
2 Boxplot ——————————————————————-
bxp <- ggboxplot(
dat, x = "task", y = "wariai", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Wariai")
bxp <- ggboxplot(
dat, x = "task", y = "wariai2", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Wariai2")
bxp <- ggboxplot(
dat, x = "task", y = "nobe", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Nobegosu")
bxp <- ggboxplot(
dat, x = "task", y = "koto", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Kotonarugosu")
bxp <- ggboxplot(
dat, x = "task", y = "ld", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Lexical density")
bxp <- ggboxplot(
dat, x = "task", y = "ld2", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Lexical density2")
bxp <- ggboxplot(
dat, x = "task", y = "jyuzokuclauseperasunit", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("jyuzokuclauseperasunit")
bxp <- ggboxplot(
dat, x = "task", y = "clauseperasunit", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("clauseperasunit")
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 Boxplot —–
bxp <- ggboxplot(
dat_MH, x = "task", y = "wariai", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Wariai MH")
bxp <- ggboxplot(
dat_MH, x = "task", y = "wariai2", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Wariai2 MH")
bxp <- ggboxplot(
dat_MH, x = "task", y = "nobe", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Nobegosu MH")
bxp <- ggboxplot(
dat_MH, x = "task", y = "koto", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Kotonarugosu MH")
bxp <- ggboxplot(
dat_MH, x = "task", y = "ld", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Lexical density MH")
bxp <- ggboxplot(
dat_MH, x = "task", y = "ld2", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Lexical density 2 MH")
bxp <- ggboxplot(
dat_MH, x = "task", y = "jyuzokuclauseperasunit", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("jyuzokuclauseperasunit MH")
bxp <- ggboxplot(
dat_MH, x = "task", y = "clauseperasunit", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("clauseperasunit MH")
3.2 Wariai MH ——-
3.2.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(wariai)## # A tibble: 7 × 17
## 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 19 19 45 4 43 80 8 9 1
## 2 M lower 20 20 45.7 5.25 45 92 7 8 1
## 3 M lower 21 21 43.3 3.62 49 67 6 8 2
## 4 M upper 28 29 51.9 4.67 38 54 4 6 2
## 5 H lower 32 33 51.2 5.5 34 43 4 4 0
## 6 H middle 3 3 45.2 5.18 56 126 9 11 2
## 7 H middle 24 24 50 7.12 54 114 6 8 2
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(wariai)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower wariai 0.939 0.510
## 2 M middle wariai 0.965 0.858
## 3 M upper wariai 0.848 0.0346
## 4 H lower wariai 0.805 0.0111
## 5 H middle wariai 0.945 0.571
## 6 H upper wariai 0.936 0.452ggqqplot(dat_MH, "wariai", 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(wariai ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 0.506 0.608
## 2 H 2 32 0.127 0.8813.2.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = wariai, 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 2.423 0.105 0.090000
## 2 task 1 32 0.072 0.790 0.000779
## 3 proficiency:task 2 32 0.974 0.389 0.021000interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$wariai, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
3.3 Wariai2 MH ——-
3.3.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(wariai2)## # A tibble: 1 × 17
## 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 18 18 48.7 4.46 74 119 8 13 5
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(wariai2)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower wariai2 0.921 0.325
## 2 M middle wariai2 0.912 0.225
## 3 M upper wariai2 0.910 0.214
## 4 H lower wariai2 0.932 0.432
## 5 H middle wariai2 0.931 0.393
## 6 H upper wariai2 0.926 0.343ggqqplot(dat_MH, "wariai2", 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(wariai2 ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 0.203 0.817
## 2 H 2 32 0.886 0.4223.3.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = wariai2, 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 2.975 0.065 0.12200
## 2 task 1 32 0.747 0.394 0.00600
## 3 proficiency:task 2 32 0.020 0.980 0.00032interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$wariai2, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
3.4 koto MH ——-
3.4.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(koto)## # A tibble: 1 × 17
## 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 20 20 48.4 4.21 63 122 11 14 3
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(koto)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower koto 0.979 0.963
## 2 M middle koto 0.937 0.459
## 3 M upper koto 0.922 0.299
## 4 H lower koto 0.922 0.332
## 5 H middle koto 0.922 0.303
## 6 H upper koto 0.913 0.234ggqqplot(dat_MH, "koto", 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(koto ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 1.49 0.240
## 2 H 2 32 0.891 0.4203.4.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = koto, 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 5.525 0.009 * 0.217
## 2 task 1 32 0.813 0.374 0.005
## 3 proficiency:task 2 32 0.741 0.484 0.009interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$koto, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
3.5 nobe MH ——-
3.5.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(nobe)## # A tibble: 1 × 17
## 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 20 20 48.4 4.21 63 122 11 14 3
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(nobe)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower nobe 0.926 0.367
## 2 M middle nobe 0.900 0.157
## 3 M upper nobe 0.965 0.851
## 4 H lower nobe 0.946 0.595
## 5 H middle nobe 0.960 0.781
## 6 H upper nobe 0.926 0.341ggqqplot(dat_MH, "nobe", 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(nobe ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 1.94 0.160
## 2 H 2 32 0.753 0.4793.5.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = nobe, 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 6.159 0.005 * 0.232
## 2 task 1 32 0.564 0.458 0.004
## 3 proficiency:task 2 32 1.976 0.155 0.026interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$nobe, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
3.6 ld MH ——-
3.6.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(ld)## # A tibble: 4 × 17
## 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 16 16 37.1 4.6 38 62 4 5 1
## 2 M upper 31 32 53.8 5.56 49 93 5 9 4
## 3 H upper 4 4 37.0 3.64 59 108 6 11 5
## 4 H upper 10 10 38.3 4.9 64 128 8 10 2
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(ld)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower ld 0.883 0.114
## 2 M middle ld 0.920 0.289
## 3 M upper ld 0.887 0.106
## 4 H lower ld 0.972 0.906
## 5 H middle ld 0.927 0.351
## 6 H upper ld 0.908 0.202ggqqplot(dat_MH, "ld", 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(ld ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 0.120 0.887
## 2 H 2 32 0.322 0.7273.6.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = ld, 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 1.156 0.327 0.050
## 2 task 1 32 9.666 0.004 * 0.078
## 3 proficiency:task 2 32 0.992 0.382 0.017interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$ld, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
3.7 ld2 MH ——-
3.7.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(ld2)## # A tibble: 4 × 17
## 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 27 28 45.1 6.83 56 91 5 6 1
## 2 M middle 24 24 49.6 5.7 60 115 9 10 1
## 3 H lower 32 33 51.2 5.5 34 43 4 4 0
## 4 H middle 24 24 50 7.12 54 114 6 8 2
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(ld2)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower ld2 0.845 0.0371
## 2 M middle ld2 0.939 0.486
## 3 M upper ld2 0.947 0.594
## 4 H lower ld2 0.948 0.622
## 5 H middle ld2 0.907 0.198
## 6 H upper ld2 0.901 0.164ggqqplot(dat_MH, "ld2", 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(ld2 ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 0.270 0.765
## 2 H 2 32 2.85 0.07273.7.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = ld2, 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 0.395 0.677 0.017
## 2 task 1 32 1.928 0.175 0.018
## 3 proficiency:task 2 32 0.671 0.518 0.013interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$ld2, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
3.8 jyuzokuclauseperasunit MH ——-
3.8.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(jyuzokuclauseperasunit)## # A tibble: 7 × 17
## 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 13 13 52.9 4.09 53 85 7 11 4
## 2 M lower 29 30 47.7 4.43 41 65 7 7 0
## 3 M middle 17 17 40.4 3.83 38 57 2 6 4
## 4 H lower 27 28 50.8 3.3 46 65 7 10 3
## 5 H lower 32 33 51.2 5.5 34 43 4 4 0
## 6 H lower 35 36 47.5 3.45 45 80 11 11 0
## 7 H middle 33 34 48.5 4.55 60 103 6 11 5
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(jyuzokuclauseperasunit)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower jyuzokuclauseperasunit 0.880 0.103
## 2 M middle jyuzokuclauseperasunit 0.718 0.00127
## 3 M upper jyuzokuclauseperasunit 0.989 1.00
## 4 H lower jyuzokuclauseperasunit 0.923 0.344
## 5 H middle jyuzokuclauseperasunit 0.898 0.149
## 6 H upper jyuzokuclauseperasunit 0.894 0.135ggqqplot(dat_MH, "jyuzokuclauseperasunit", 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(jyuzokuclauseperasunit ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 1.90 0.167
## 2 H 2 32 1.96 0.1583.8.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = jyuzokuclauseperasunit, 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 5.802 0.007 * 0.181
## 2 task 1 32 3.034 0.091 0.036
## 3 proficiency:task 2 32 0.868 0.429 0.021interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$jyuzokuclauseperasunit, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
3.9 clauseperasunit MH ——-
3.9.1 Check assumptions ——-
dat_MH %>%
group_by(task, proficiency) %>%
identify_outliers(clauseperasunit)## # A tibble: 7 × 17
## 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 13 13 52.9 4.09 53 85 7 11 4
## 2 M lower 29 30 47.7 4.43 41 65 7 7 0
## 3 M middle 17 17 40.4 3.83 38 57 2 6 4
## 4 H lower 27 28 50.8 3.3 46 65 7 10 3
## 5 H lower 32 33 51.2 5.5 34 43 4 4 0
## 6 H lower 35 36 47.5 3.45 45 80 11 11 0
## 7 H middle 33 34 48.5 4.55 60 103 6 11 5
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_MH %>%
group_by(task, proficiency) %>%
shapiro_test(clauseperasunit)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 M lower clauseperasunit 0.880 0.103
## 2 M middle clauseperasunit 0.718 0.00127
## 3 M upper clauseperasunit 0.989 1.00
## 4 H lower clauseperasunit 0.923 0.344
## 5 H middle clauseperasunit 0.898 0.149
## 6 H upper clauseperasunit 0.894 0.135ggqqplot(dat_MH, "clauseperasunit", 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(clauseperasunit ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 M 2 32 1.90 0.167
## 2 H 2 32 1.96 0.158dat_MH %>%
group_by(task, proficiency) %>%
get_summary_stats(clauseperasunit,type = "mean_sd")## # A tibble: 6 × 6
## task proficiency variable n mean sd
## <ord> <ord> <fct> <dbl> <dbl> <dbl>
## 1 M lower clauseperasunit 11 1.22 0.143
## 2 M middle clauseperasunit 12 1.52 0.521
## 3 M upper clauseperasunit 12 1.57 0.313
## 4 H lower clauseperasunit 11 1.20 0.123
## 5 H middle clauseperasunit 12 1.30 0.214
## 6 H upper clauseperasunit 12 1.49 0.2113.9.2 Computation ——-
res.aov <- anova_test(
data = dat_MH, dv = clauseperasunit, 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 5.802 0.007 * 0.181
## 2 task 1 32 3.034 0.091 0.036
## 3 proficiency:task 2 32 0.868 0.429 0.021interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency,
response = dat_MH$clauseperasunit, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4 Comparison between L and M ——-
dat_LM <- dat %>% filter(task == "M"| task == "L")
dat_LM$task <- ordered(dat_LM$task, levels=c("L","M"))4.1 Boxplot —–
bxp <- ggboxplot(
dat_LM, x = "task", y = "wariai", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Wariai LM")
bxp <- ggboxplot(
dat_LM, x = "task", y = "wariai2", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Wariai2 LM")
bxp <- ggboxplot(
dat_LM, x = "task", y = "nobe", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Nobegosu LM")
bxp <- ggboxplot(
dat_LM, x = "task", y = "koto", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Kotonarugosu LM")
bxp <- ggboxplot(
dat_LM, x = "task", y = "ld", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Lexical density LM")
bxp <- ggboxplot(
dat_LM, x = "task", y = "ld", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Lexical density 2 LM")
bxp <- ggboxplot(
dat_LM, x = "task", y = "jyuzokuclauseperasunit", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("jyuzokuclauseperasunit LM")
bxp <- ggboxplot(
dat_LM, x = "task", y = "clauseperasunit", add = "jitter",
color = "proficiency", palette = "lancet"
)
bxp + ggtitle("clauseperasunit LM")
4.2 Wariai LM ——-
4.2.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(wariai)## # A tibble: 4 × 17
## 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 19 19 45 4 43 80 8 9 1
## 2 M lower 20 20 45.7 5.25 45 92 7 8 1
## 3 M lower 21 21 43.3 3.62 49 67 6 8 2
## 4 M upper 28 29 51.9 4.67 38 54 4 6 2
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(wariai)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower wariai 0.958 0.744
## 2 L middle wariai 0.948 0.609
## 3 L upper wariai 0.944 0.558
## 4 M lower wariai 0.939 0.510
## 5 M middle wariai 0.965 0.858
## 6 M upper wariai 0.848 0.0346ggqqplot(dat_LM, "wariai", 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(wariai ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 0.531 0.593
## 2 M 2 32 0.506 0.6084.2.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = wariai, 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 3.692 0.036 * 0.110
## 2 task 1 32 0.317 0.577 0.005
## 3 proficiency:task 2 32 0.293 0.748 0.008interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$wariai, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.3 Wariai2 LM ——-
4.3.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(wariai2)## # A tibble: 3 × 17
## 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 7 7 45.8 3.67 19 24 2 3 1
## 2 L middle 12 12 47.1 3.2 23 34 4 5 1
## 3 M middle 18 18 48.7 4.46 74 119 8 13 5
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(wariai2)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower wariai2 0.960 0.771
## 2 L middle wariai2 0.896 0.141
## 3 L upper wariai2 0.877 0.0812
## 4 M lower wariai2 0.921 0.325
## 5 M middle wariai2 0.912 0.225
## 6 M upper wariai2 0.910 0.214ggqqplot(dat_LM, "wariai2", 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(wariai2 ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 0.788 0.463
## 2 M 2 32 0.203 0.8174.3.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = wariai2, 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 2.562 0.093 0.110
## 2 task 1 32 3.108 0.087 0.022
## 3 proficiency:task 2 32 0.271 0.765 0.004interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$wariai2, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.4 koto LM ——-
4.4.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(koto)## # A tibble: 1 × 17
## 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 7 7 45.8 3.67 19 24 2 3 1
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(koto)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower koto 0.976 0.936
## 2 L middle koto 0.917 0.259
## 3 L upper koto 0.948 0.607
## 4 M lower koto 0.979 0.963
## 5 M middle koto 0.937 0.459
## 6 M upper koto 0.922 0.299ggqqplot(dat_LM, "koto", 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(koto ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 1.60 0.218
## 2 M 2 32 1.49 0.2404.4.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = koto, 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 5.624 0.008 * 0.208
## 2 task 1 32 1.913 0.176 0.015
## 3 proficiency:task 2 32 0.851 0.436 0.013interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$koto, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.5 nobe MH ——-
4.5.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(nobe)## # A tibble: 2 × 17
## 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 7 7 45.8 3.67 19 24 2 3 1
## 2 L middle 16 16 41.9 4.5 79 172 9 16 7
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(nobe)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower nobe 0.968 0.867
## 2 L middle nobe 0.930 0.382
## 3 L upper nobe 0.933 0.419
## 4 M lower nobe 0.926 0.367
## 5 M middle nobe 0.900 0.157
## 6 M upper nobe 0.965 0.851ggqqplot(dat_LM, "nobe", 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(nobe ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 0.991 0.382
## 2 M 2 32 1.94 0.1604.5.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = nobe, 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 7.117 0.003 * 0.226
## 2 task 1 32 0.599 0.445 0.006
## 3 proficiency:task 2 32 0.896 0.418 0.019interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$nobe, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.6 ld MH ——-
4.6.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(ld)## # A tibble: 6 × 17
## 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 19 19 56.5 3.69 44 85 9 13 4
## 2 L lower 25 25 46.3 4.17 39 54 5 6 1
## 3 L middle 2 2 55.6 3.89 47 63 5 9 4
## 4 L middle 15 15 53.9 4.77 63 115 9 13 4
## 5 M middle 16 16 37.1 4.6 38 62 4 5 1
## 6 M upper 31 32 53.8 5.56 49 93 5 9 4
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(ld)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower ld 0.956 0.719
## 2 L middle ld 0.931 0.389
## 3 L upper ld 0.991 1.00
## 4 M lower ld 0.883 0.114
## 5 M middle ld 0.920 0.289
## 6 M upper ld 0.887 0.106ggqqplot(dat_LM, "ld", 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(ld ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 1.02 0.373
## 2 M 2 32 0.120 0.8874.6.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = ld, 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 2.826 0.074 0.102
## 2 task 1 32 11.109 0.002 * 0.111
## 3 proficiency:task 2 32 2.010 0.151 0.043interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$ld, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.7 ld2 MH ——-
4.7.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(ld2)## # A tibble: 4 × 17
## 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 24 24 47.3 6.62 66 112 4 8 4
## 2 L upper 23 23 49.5 8.17 65 99 5 6 1
## 3 M lower 27 28 45.1 6.83 56 91 5 6 1
## 4 M middle 24 24 49.6 5.7 60 115 9 10 1
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(ld2)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower ld2 0.876 0.0929
## 2 L middle ld2 0.933 0.409
## 3 L upper ld2 0.828 0.0198
## 4 M lower ld2 0.845 0.0371
## 5 M middle ld2 0.939 0.486
## 6 M upper ld2 0.947 0.594ggqqplot(dat_LM, "ld2", 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(ld2 ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 0.590 0.560
## 2 M 2 32 0.270 0.7654.7.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = ld2, 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 1.508 0.237 0.051
## 2 task 1 32 0.608 0.441 0.008
## 3 proficiency:task 2 32 0.324 0.725 0.009interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$ld2, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.8 jyuzokuclauseperasunit MH ——-
4.8.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(jyuzokuclauseperasunit)## # A tibble: 6 × 17
## 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 27 28 50.7 3.8 50 75 3 10 7
## 2 L upper 1 1 46.5 3.65 82 157 9 20 11
## 3 L upper 22 22 41.3 4.33 74 126 4 12 8
## 4 M lower 13 13 52.9 4.09 53 85 7 11 4
## 5 M lower 29 30 47.7 4.43 41 65 7 7 0
## 6 M middle 17 17 40.4 3.83 38 57 2 6 4
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(jyuzokuclauseperasunit)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower jyuzokuclauseperasunit 0.678 0.000243
## 2 L middle jyuzokuclauseperasunit 0.926 0.336
## 3 L upper jyuzokuclauseperasunit 0.761 0.00350
## 4 M lower jyuzokuclauseperasunit 0.880 0.103
## 5 M middle jyuzokuclauseperasunit 0.718 0.00127
## 6 M upper jyuzokuclauseperasunit 0.989 1.00ggqqplot(dat_LM, "jyuzokuclauseperasunit", 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(jyuzokuclauseperasunit ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 0.0444 0.957
## 2 M 2 32 1.90 0.1674.8.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = jyuzokuclauseperasunit, 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 1.892 0.167 0.064
## 2 task 1 32 2.749 0.107 0.035
## 3 proficiency:task 2 32 0.413 0.665 0.011interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$jyuzokuclauseperasunit, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
4.9 Clauseperasunit MH ——
4.9.1 Check assumptions ——-
dat_LM %>%
group_by(task, proficiency) %>%
identify_outliers(clauseperasunit)## # A tibble: 6 × 17
## 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 27 28 50.7 3.8 50 75 3 10 7
## 2 L upper 1 1 46.5 3.65 82 157 9 20 11
## 3 L upper 22 22 41.3 4.33 74 126 4 12 8
## 4 M lower 13 13 52.9 4.09 53 85 7 11 4
## 5 M lower 29 30 47.7 4.43 41 65 7 7 0
## 6 M middle 17 17 40.4 3.83 38 57 2 6 4
## # … with 6 more variables: jyuzokuclauseperasunit <dbl>, clauseperasunit <dbl>,
## # wariai <dbl>, wariai2 <dbl>, is.outlier <lgl>, is.extreme <lgl>, and
## # abbreviated variable name ¹jyuzokuclausedat_LM %>%
group_by(task, proficiency) %>%
shapiro_test(clauseperasunit)## # A tibble: 6 × 5
## task proficiency variable statistic p
## <ord> <ord> <chr> <dbl> <dbl>
## 1 L lower clauseperasunit 0.678 0.000243
## 2 L middle clauseperasunit 0.926 0.336
## 3 L upper clauseperasunit 0.761 0.00350
## 4 M lower clauseperasunit 0.880 0.103
## 5 M middle clauseperasunit 0.718 0.00127
## 6 M upper clauseperasunit 0.989 1.00ggqqplot(dat_LM, "clauseperasunit", 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(clauseperasunit ~ proficiency)## # A tibble: 2 × 5
## task df1 df2 statistic p
## <ord> <int> <int> <dbl> <dbl>
## 1 L 2 32 0.0444 0.957
## 2 M 2 32 1.90 0.167dat_LM %>%
group_by(task, proficiency) %>%
get_summary_stats(clauseperasunit,type = "mean_sd")## # A tibble: 6 × 6
## task proficiency variable n mean sd
## <ord> <ord> <fct> <dbl> <dbl> <dbl>
## 1 L lower clauseperasunit 11 1.49 0.654
## 2 L middle clauseperasunit 12 1.70 0.368
## 3 L upper clauseperasunit 12 1.62 0.536
## 4 M lower clauseperasunit 11 1.22 0.143
## 5 M middle clauseperasunit 12 1.52 0.521
## 6 M upper clauseperasunit 12 1.57 0.3134.9.2 Computation ——-
res.aov <- anova_test(
data = dat_LM, dv = clauseperasunit, 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 1.892 0.167 0.064
## 2 task 1 32 2.749 0.107 0.035
## 3 proficiency:task 2 32 0.413 0.665 0.011interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency,
response = dat_LM$clauseperasunit, fun = mean,
type = "b", legend = TRUE, trace.label = "TASK")
5 結果のまとめ
5.1 LとM（外在性負荷の有無）
5.1.1 発話の語彙の多様さの結果
- 延べ語数に対する異なり語数の割合では、習熟度の主効果は有意でした。
- 延べ語数 x2の平方根に対する異なり語数の割合では、認知負荷と習熟度の主効果は有意傾向(0.093, 0.087)でした。
- 異なる語数では、習熟度の主効果は有意でした。
- 延べ語数では、習熟度の主効果は有意でした。
- 語彙密度1(内容語数/総語数)では、認知負荷の主効果は有意でした。
5.1.2 語彙の複雑さの結果
- 1 AS UNITあたりの節数では、両方とも有意ではありませんでした(習熟度 0.167, 認知負荷 0.107)。
5.2 MとH（内在性負荷の有無）
5.2.1 発話の語彙の多様さの結果
延べ語数に対する異なり語数の割合では、両方とも有意ではありませんでした。 (習熟度, p = 0.105)
延べ語数 x2の平方根に対する異なり語数の割合では、習熟度は有意傾向(0.065)でした。
異なる語数では、習熟度の主効果は有意でした。
延べ語数では、習熟度の主効果は有意でした。
語彙密度1(内容語数/総語数)では、認知負荷の主効果は有意でした。
5.2.2 語彙の複雑さの結果
- 1 AS UNITあたりの節数では、習熟度は有意でした。認知負荷は有意傾向(0.091)でした。
5.2.3 LMHにおける複雑さ
- L lower clauseperasunit 11 1.492 0.654
- L middle clauseperasunit 12 1.695 0.368
- L upper clauseperasunit 12 1.620 0.536
- M lower clauseperasunit 11 1.222 0.143
- M middle clauseperasunit 12 1.516 0.521
- M upper clauseperasunit 12 1.573 0.313
- H lower clauseperasunit 11 1.199 0.123
- H middle clauseperasunit 12 1.299 0.214
- H upper clauseperasunit 12 1.490 0.211