Nothing
Code
df_1
Output
# A tibble: 4 x 15
mu statistic df.error p.value method alternative effectsize
<dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
1 0.25 0.242 55 0.810 One Sample t-test two.sided Cohen's d
2 0.25 0.242 55 0.595 One Sample t-test less Hedges' g
3 0.25 0.242 55 0.405 One Sample t-test greater Cohen's d
4 0.25 0.242 55 0.810 One Sample t-test two.sided Hedges' g
estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
<dbl> <dbl> <dbl> <dbl> <chr> <chr> <int>
1 0.0323 0.89 -0.181 0.246 ncp t 56
2 0.0319 0.99 -0.308 0.371 ncp t 56
3 0.0323 0.9 -0.188 0.252 ncp t 56
4 0.0319 0.5 -0.0572 0.121 ncp t 56
expression
<list>
1 <language>
2 <language>
3 <language>
4 <language>
Code
df_2_between
Output
# A tibble: 4 x 18
parameter1 parameter2 mean.parameter1 mean.parameter2 statistic df.error
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 wt am 3.77 2.41 5.26 30
2 wt am 3.77 2.41 5.49 29.2
3 wt am 3.77 2.41 5.26 30
4 wt am 3.77 2.41 5.49 29.2
p.value method alternative effectsize estimate conf.level
<dbl> <chr> <chr> <chr> <dbl> <dbl>
1 0.0000113 Two Sample t-test two.sided Cohen's d 1.93 0.89
2 1.00 Welch Two Sample t-test less Hedges' g 1.88 0.99
3 0.00000563 Two Sample t-test greater Cohen's d 1.93 0.9
4 0.00000627 Welch Two Sample t-test two.sided Hedges' g 1.88 0.5
conf.low conf.high conf.method conf.distribution n.obs expression
<dbl> <dbl> <chr> <chr> <int> <list>
1 1.23 2.61 ncp t 32 <language>
2 -Inf 2.86 ncp t 32 <language>
3 1.36 Inf ncp t 32 <language>
4 1.58 2.15 ncp t 32 <language>
Code
df_2_within
Output
# A tibble: 4 x 16
term group statistic df.error p.value method alternative
<chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 desire condition 3.61 89 0.000500 Paired t-test two.sided
2 desire condition 3.61 89 0.000500 Paired t-test two.sided
3 desire condition 3.61 89 0.000500 Paired t-test two.sided
4 desire condition 3.61 89 0.000500 Paired t-test two.sided
effectsize estimate conf.level conf.low conf.high conf.method
<chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 Cohen's d 0.381 0.89 0.205 0.554 ncp
2 Hedges' g 0.378 0.99 0.0978 0.656 ncp
3 Cohen's d 0.381 0.9 0.200 0.559 ncp
4 Hedges' g 0.378 0.5 0.304 0.450 ncp
conf.distribution n.obs expression
<chr> <int> <list>
1 t 90 <language>
2 t 90 <language>
3 t 90 <language>
4 t 90 <language>
Code
df_3_between
Output
# A tibble: 4 x 14
statistic df df.error p.value
<dbl> <dbl> <dbl> <dbl>
1 4.14 3 52 0.0105
2 2.63 3 11.1 0.102
3 4.14 3 52 0.0105
4 2.63 3 11.1 0.102
method effectsize estimate
<chr> <chr> <dbl>
1 One-way analysis of means Eta2 0.193
2 One-way analysis of means (not assuming equal variances) Omega2 0.245
3 One-way analysis of means Eta2 0.193
4 One-way analysis of means (not assuming equal variances) Omega2 0.245
conf.level conf.low conf.high conf.method conf.distribution n.obs expression
<dbl> <dbl> <dbl> <chr> <chr> <int> <list>
1 0.89 0.0585 1 ncp F 56 <language>
2 0.8 0 1 ncp F 56 <language>
3 0.9 0.0545 1 ncp F 56 <language>
4 0.5 0.0974 1 ncp F 56 <language>
Code
df_3_within
Output
# A tibble: 2 x 18
term sumsq sum.squares.error df df.error meansq statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 condition 233. 984. 2.63 229. 4.30 20.6 8.27e-11
2 condition 233. 984. 2.63 229. 4.30 20.6 8.27e-11
method effectsize estimate
<chr> <chr> <dbl>
1 ANOVA estimation for factorial designs using 'afex' Eta2 (partial) 0.191
2 ANOVA estimation for factorial designs using 'afex' Omega2 (partial) 0.0783
conf.level conf.low conf.high conf.method conf.distribution n.obs expression
<dbl> <dbl> <dbl> <chr> <chr> <int> <list>
1 0.89 0.136 1 ncp F 88 <language>
2 0.9 0.0362 1 ncp F 88 <language>
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