1 定義

1.1 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

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")

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")

3.2 Wariai MH ——-

3.2.1 Check assumptions ——-

dat_MH %>%
  group_by(task, proficiency) %>%
  identify_outliers(wariai)

## # A tibble: 7 × 37
##   task  proficiency id    name      trueid    ld   ld2  koto  nobe asunit clause
##   <ord> <ord>       <fct> <chr>     <fct>  <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>
## 1 M     lower       19    ZhouLupi… 19      45    4       43    80      8      9
## 2 M     lower       20    ZhangJia… 20      45.7  5.25    45    92      7      8
## 3 M     lower       21    YuanDan   21      43.3  3.62    49    67      6      8
## 4 M     upper       28    LiuXiaoj… 29      51.9  4.67    38    54      4      6
## 5 H     lower       32    WangBiyu… 33      51.2  5.5     34    43      4      4
## 6 H     middle      3     DuLanfei  3       45.2  5.18    56   126      9     11
## 7 H     middle      24    XuXingsh… 24      50    7.12    54   114      6      8
## # ℹ 26 more variables: jyuzokuclause <dbl>, jyuzokuclauseperasunit <dbl>,
## #   clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## #   heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## #   setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## #   hijiritsu <dbl>, setubimeishi <dbl>, setsubidoushi <dbl>,
## #   setsubikeiyoushi <dbl>, kotonarucontents <dbl>,
## #   kotonarucontents_asunit <dbl>, kotonarucontents_clause <dbl>, …

dat_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 0.452

ggqqplot(dat_MH, "wariai", ggtheme = theme_bw()) +
  facet_grid(task ~ proficiency)

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 0.881

3.2.2 Boxplot

bxp <- ggboxplot(
  dat_MH, x = "task", y = "wariai", add = "jitter",
  color = "proficiency", palette = "lancet"
)
bxp + ggtitle("Wariai MH")

3.2.3 Computation ——-

res.aov <- anova_test(
  data = dat_MH, dv = 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 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       0.021000

interaction.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 × 37
##   task  proficiency id    name      trueid    ld   ld2  koto  nobe asunit clause
##   <ord> <ord>       <fct> <chr>     <fct>  <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>
## 1 M     middle      18    HuangShi… 18      48.7  4.46    74   119      8     13
## # ℹ 26 more variables: jyuzokuclause <dbl>, jyuzokuclauseperasunit <dbl>,
## #   clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## #   heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## #   setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## #   hijiritsu <dbl>, setubimeishi <dbl>, setsubidoushi <dbl>,
## #   setsubikeiyoushi <dbl>, kotonarucontents <dbl>,
## #   kotonarucontents_asunit <dbl>, kotonarucontents_clause <dbl>, …

dat_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 0.343

ggqqplot(dat_MH, "wariai2", ggtheme = theme_bw()) +
  facet_grid(task ~ proficiency)

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 0.422

3.3.2 Computation ——-

res.aov <- anova_test(
  data = dat_MH, dv = wariai2, 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 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       0.00032

interaction.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 × 37
##   task  proficiency id    name      trueid    ld   ld2  koto  nobe asunit clause
##   <ord> <ord>       <fct> <chr>     <fct>  <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>
## 1 H     lower       20    ZhangJia… 20      48.4  4.21    63   122     11     14
## # ℹ 26 more variables: jyuzokuclause <dbl>, jyuzokuclauseperasunit <dbl>,
## #   clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## #   heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## #   setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## #   hijiritsu <dbl>, setubimeishi <dbl>, setsubidoushi <dbl>,
## #   setsubikeiyoushi <dbl>, kotonarucontents <dbl>,
## #   kotonarucontents_asunit <dbl>, kotonarucontents_clause <dbl>, …

dat_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 0.234

ggqqplot(dat_MH, "koto", ggtheme = theme_bw()) +
  facet_grid(task ~ proficiency)

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 0.420

3.4.2 Computation ——-

res.aov <- anova_test(
  data = dat_MH, dv = koto, wid = trueid,
  between = proficiency, within = task
)
get_anova_table(res.aov)

## ANOVA Table (type III tests)
## 
##             Effect DFn DFd     F     p p<.05   ges
## 1      proficiency   2  32 5.525 0.009     * 0.217
## 2             task   1  32 0.813 0.374       0.005
## 3 proficiency:task   2  32 0.741 0.484       0.009

interaction.plot(x.factor = dat_MH$task, trace.factor = dat_MH$proficiency, 
                 response = dat_MH$koto, fun = mean,
                 type = "b", legend = TRUE, trace.label = "TASK")

### main effects

dat_MH %>%
  pairwise_t_test(
    koto ~ 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 koto  lower  middle    22    24 0.344    ns       1        ns          
## 2 koto  lower  upper     22    24 0.000135 ***      0.000404 ***         
## 3 koto  middle upper     24    24 0.00231  **       0.00694  **

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")

4.2 Wariai LM ——-

4.2.1 Check assumptions ——-

dat_LM %>%
  group_by(task, proficiency) %>%
  identify_outliers(wariai)

## # A tibble: 4 × 37
##   task  proficiency id    name      trueid    ld   ld2  koto  nobe asunit clause
##   <ord> <ord>       <fct> <chr>     <fct>  <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>
## 1 M     lower       19    ZhouLupi… 19      45    4       43    80      8      9
## 2 M     lower       20    ZhangJia… 20      45.7  5.25    45    92      7      8
## 3 M     lower       21    YuanDan   21      43.3  3.62    49    67      6      8
## 4 M     upper       28    LiuXiaoj… 29      51.9  4.67    38    54      4      6
## # ℹ 26 more variables: jyuzokuclause <dbl>, jyuzokuclauseperasunit <dbl>,
## #   clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## #   heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## #   setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## #   hijiritsu <dbl>, setubimeishi <dbl>, setsubidoushi <dbl>,
## #   setsubikeiyoushi <dbl>, kotonarucontents <dbl>,
## #   kotonarucontents_asunit <dbl>, kotonarucontents_clause <dbl>, …

dat_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 0.0346

ggqqplot(dat_LM, "wariai", ggtheme = theme_bw()) +
  facet_grid(task ~ proficiency)

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 0.608

4.2.2 Computation ——-

res.aov <- anova_test(
  data = dat_LM, dv = 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 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       0.008

interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency, 
                 response = dat_LM$wariai, fun = mean,
                 type = "b", legend = TRUE, trace.label = "TASK")

4.2.3 main effects

dat_LM %>%
  pairwise_t_test(
    wariai ~ 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 wariai lower  middle    22    24 0.596   ns       1      ns          
## 2 wariai lower  upper     22    24 0.00939 **       0.0282 *           
## 3 wariai middle upper     24    24 0.032   *        0.096  ns

4.3 Wariai2 LM ——-

4.3.1 Check assumptions ——-

dat_LM %>%
  group_by(task, proficiency) %>%
  identify_outliers(wariai2)

## # A tibble: 3 × 37
##   task  proficiency id    name      trueid    ld   ld2  koto  nobe asunit clause
##   <ord> <ord>       <fct> <chr>     <fct>  <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>
## 1 L     middle      7     NiuXuerui 7       45.8  3.67    19    24      2      3
## 2 L     middle      12    LiangShi… 12      47.1  3.2     23    34      4      5
## 3 M     middle      18    HuangShi… 18      48.7  4.46    74   119      8     13
## # ℹ 26 more variables: jyuzokuclause <dbl>, jyuzokuclauseperasunit <dbl>,
## #   clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## #   heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## #   setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## #   hijiritsu <dbl>, setubimeishi <dbl>, setsubidoushi <dbl>,
## #   setsubikeiyoushi <dbl>, kotonarucontents <dbl>,
## #   kotonarucontents_asunit <dbl>, kotonarucontents_clause <dbl>, …

dat_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 0.214

ggqqplot(dat_LM, "wariai2", ggtheme = theme_bw()) +
  facet_grid(task ~ proficiency)

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 0.817

4.3.2 Computation ——-

res.aov <- anova_test(
  data = dat_LM, dv = wariai2, 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 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       0.004

interaction.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 × 37
##   task  proficiency id    name      trueid    ld   ld2  koto  nobe asunit clause
##   <ord> <ord>       <fct> <chr>     <fct>  <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>
## 1 L     middle      7     NiuXuerui 7       45.8  3.67    19    24      2      3
## # ℹ 26 more variables: jyuzokuclause <dbl>, jyuzokuclauseperasunit <dbl>,
## #   clauseperasunit <dbl>, wariai <dbl>, wariai2 <dbl>, jyodoushi <dbl>,
## #   heiritsu <dbl>, kakari <dbl>, fukujyoshi <dbl>, ka <dbl>,
## #   setsuzokujyoshi <dbl>, kakujyoshi <dbl>, syujyoshi <dbl>, rentai <dbl>,
## #   hijiritsu <dbl>, setubimeishi <dbl>, setsubidoushi <dbl>,
## #   setsubikeiyoushi <dbl>, kotonarucontents <dbl>,
## #   kotonarucontents_asunit <dbl>, kotonarucontents_clause <dbl>, …

dat_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 0.299

ggqqplot(dat_LM, "koto", ggtheme = theme_bw()) +
  facet_grid(task ~ proficiency)

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 0.240

4.4.2 Computation ——-

res.aov <- anova_test(
  data = dat_LM, dv = koto, wid = trueid,
  between = proficiency, within = task
)
get_anova_table(res.aov)

## ANOVA Table (type III tests)
## 
##             Effect DFn DFd     F     p p<.05   ges
## 1      proficiency   2  32 5.624 0.008     * 0.208
## 2             task   1  32 1.913 0.176       0.015
## 3 proficiency:task   2  32 0.851 0.436       0.013

interaction.plot(x.factor = dat_LM$task, trace.factor = dat_LM$proficiency, 
                 response = dat_LM$koto, fun = mean,
                 type = "b", legend = TRUE, trace.label = "TASK")

### main effects

dat_LM %>%
  pairwise_t_test(
    koto ~ 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 koto  lower  middle    22    24 0.126    ns       0.378    ns          
## 2 koto  lower  upper     22    24 0.000121 ***      0.000362 ***         
## 3 koto  middle upper     24    24 0.0117   *        0.0351   *

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。上位群が下位群より低かった。
延べ語数 x2の平方根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。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。上位群は中位群、下位群より高かった。

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。
延べ語数 x2の平方根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。上位群は中位群、下位群より高かった。

多様さ0611

2023-06-11

1 定義

1.1 語彙の多様さ：

2 Boxplot ——————————————————————-

3 Comparison between M and H ——-

3.1 Boxplot —–

3.2 Wariai MH ——-

3.2.1 Check assumptions ——-

3.2.2 Boxplot

3.2.3 Computation ——-

3.3 Wariai2 MH ——-

3.3.1 Check assumptions ——-

3.3.2 Computation ——-

3.4 koto MH ——-

3.4.1 Check assumptions ——-

3.4.2 Computation ——-

4 Comparison between L and M ——-

4.1 Boxplot —–

4.2 Wariai LM ——-

4.2.1 Check assumptions ——-

4.2.2 Computation ——-

4.2.3 main effects

4.3 Wariai2 LM ——-

4.3.1 Check assumptions ——-

4.3.2 Computation ——-

4.4 koto LM ——-

4.4.1 Check assumptions ——-

4.4.2 Computation ——-

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

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