Required sample size

The EEG and behavioural results of Chemin et al. (2014) are used as parameters.

EEG-response amplitude

H1a & H3a: Main effect of the session (pre vs. post)

pwr.t.test(n = NULL,
           d = 1.53,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")

     Paired t test power calculation 

              n = 7.132031
              d = 1.53
      sig.level = 0.02
          power = 0.9
    alternative = greater

NOTE: n is number of *pairs*

H1b & H3b: Movement Condition x Session interaction

# Movement Condition x Session interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 learning type
           nm = 2, # 2 sessions
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect
Repeated-measures ANOVA analysis

          n    f ng nm nscor alpha power
    19.4089 0.89  2  2     1  0.02   0.9

NOTE: Power analysis for interaction-effect test
URL: http://psychstat.org/rmanova
# Simple effect of the movement condition
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")

     Paired t test power calculation 

              n = 5.970644
              d = 1.77
      sig.level = 0.02
          power = 0.9
    alternative = greater

NOTE: n is number of *pairs*

H5a: Group x Metre Frequency interaction

# Group x Metre Frequency interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 groups
           nm = 2, # 2 metre frequencies
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect
Repeated-measures ANOVA analysis

          n    f ng nm nscor alpha power
    19.4089 0.89  2  2     1  0.02   0.9

NOTE: Power analysis for interaction-effect test
URL: http://psychstat.org/rmanova
# Simple effect of the metre frequency
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")

     Paired t test power calculation 

              n = 5.970644
              d = 1.77
      sig.level = 0.02
          power = 0.9
    alternative = greater

NOTE: n is number of *pairs*

H6a: Group x Metre Frequency interaction

# Group x Metre Frequency interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 groups
           nm = 2, # 2 metre frequencies
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect
Repeated-measures ANOVA analysis

          n    f ng nm nscor alpha power
    19.4089 0.89  2  2     1  0.02   0.9

NOTE: Power analysis for interaction-effect test
URL: http://psychstat.org/rmanova
# Simple effect of the group
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "two.sample",
           alternative = "greater")

     Two-sample t test power calculation 

              n = 8.293783
              d = 1.77
      sig.level = 0.02
          power = 0.9
    alternative = greater

NOTE: n is number in *each* group

H7a: Group x Movement Condition interaction

# Group x Movement Condition interaction effect
wp.kanova(n = NULL,
          ng = 4, # 2 groups x 2 movement conditions
          ndf = 1,
          f = 0.89,
          alpha = 0.02,
          power = 0.90)
Multiple way ANOVA analysis

           n ndf      ddf    f ng alpha power
    19.77242   1 15.77242 0.89  4  0.02   0.9

NOTE: Sample size is the total sample size
URL: http://psychstat.org/kanova
# Simple effect of the group
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "two.sample",
           alternative = "greater")

     Two-sample t test power calculation 

              n = 8.293783
              d = 1.77
      sig.level = 0.02
          power = 0.9
    alternative = greater

NOTE: n is number in *each* group

Clapping-response amplitude

H2a & H4a: Main effect of the session (pre vs. post)

pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")

     Paired t test power calculation 

              n = 5.970644
              d = 1.77
      sig.level = 0.02
          power = 0.9
    alternative = greater

NOTE: n is number of *pairs*

H2b & H4b: Movement Condition x Session interaction

# Movement Condition x Session interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 learning type
           nm = 2, # 2 sessions
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect
Repeated-measures ANOVA analysis

          n    f ng nm nscor alpha power
    19.4089 0.89  2  2     1  0.02   0.9

NOTE: Power analysis for interaction-effect test
URL: http://psychstat.org/rmanova
# Simple effect of the movement condition
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")

     Paired t test power calculation 

              n = 5.970644
              d = 1.77
      sig.level = 0.02
          power = 0.9
    alternative = greater

NOTE: n is number of *pairs*

H5a: Group x Metre Frequency interaction

# Group x Metre Frequency interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 groups
           nm = 2, # 2 metre frequencies
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect
Repeated-measures ANOVA analysis

          n    f ng nm nscor alpha power
    19.4089 0.89  2  2     1  0.02   0.9

NOTE: Power analysis for interaction-effect test
URL: http://psychstat.org/rmanova
# Simple effect of the metre frequency
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")

     Paired t test power calculation 

              n = 5.970644
              d = 1.77
      sig.level = 0.02
          power = 0.9
    alternative = greater

NOTE: n is number of *pairs*

H6b: Group x Metre Frequency interaction

# Group x Metre Frequency interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 groups
           nm = 2, # 2 metre frequencies
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect
Repeated-measures ANOVA analysis

          n    f ng nm nscor alpha power
    19.4089 0.89  2  2     1  0.02   0.9

NOTE: Power analysis for interaction-effect test
URL: http://psychstat.org/rmanova
# Simple effect of the group
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "two.sample",
           alternative = "greater")

     Two-sample t test power calculation 

              n = 8.293783
              d = 1.77
      sig.level = 0.02
          power = 0.9
    alternative = greater

NOTE: n is number in *each* group

H7b: Group x Movement Condition interaction

# Group x Movement Condition interaction effect
wp.kanova(n = NULL,
          ng = 4, # 2 groups x 2 movement conditions
          ndf = 1,
          f = 0.89,
          alpha = 0.02,
          power = 0.90)
Multiple way ANOVA analysis

           n ndf      ddf    f ng alpha power
    19.77242   1 15.77242 0.89  4  0.02   0.9

NOTE: Sample size is the total sample size
URL: http://psychstat.org/kanova
# Simple effect of the group
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "two.sample",
           alternative = "greater")

     Two-sample t test power calculation 

              n = 8.293783
              d = 1.77
      sig.level = 0.02
          power = 0.9
    alternative = greater

NOTE: n is number in *each* group

SESOI

The smallest effect size of interest (SESOI) is computed using the small-telescopes approach based on Chemin et al. (2014).

pwr.t.test(n = 14,
           d = NULL,
           power = 0.33,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater") # "greater" for t test SESOI; "two.sided" for TOST SESOI

     Paired t test power calculation 

              n = 14
              d = 0.4690511
      sig.level = 0.02
          power = 0.33
    alternative = greater

NOTE: n is number of *pairs*
---
title: "Power Analysis"
output: 
  html_notebook:
    theme: united
    toc: yes
---

```{r setup, include=FALSE}
# ------ CLEANING R SESSION ####
rm(list=ls()) # clean environment window
graphics.off() # clean plot window

# ------ PACKAGE LOADING ####
library(pwr) # for t test and correlation
library(WebPower) # for ANOVA
```

# Required sample size

The EEG and behavioural results of [Chemin et al. (2014)](https://doi.org/10.1177/0956797614551161) are used as parameters.

## EEG-response amplitude

### H1a & H3a: Main effect of the session (pre vs. post)

```{r session effect}
pwr.t.test(n = NULL,
           d = 1.53,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")
```

### H1b & H3b: Movement Condition x Session interaction

```{r}
# Movement Condition x Session interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 learning type
           nm = 2, # 2 sessions
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect

# Simple effect of the movement condition
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")
```

### H5a: Group x Metre Frequency interaction

```{r}
# Group x Metre Frequency interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 groups
           nm = 2, # 2 metre frequencies
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect

# Simple effect of the metre frequency
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")
```

### H6a: Group x Metre Frequency interaction

```{r}
# Group x Metre Frequency interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 groups
           nm = 2, # 2 metre frequencies
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect

# Simple effect of the group
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "two.sample",
           alternative = "greater")
```

### H7a: Group x Movement Condition interaction

```{r}
# Group x Movement Condition interaction effect
wp.kanova(n = NULL,
          ng = 4, # 2 groups x 2 movement conditions
          ndf = 1,
          f = 0.89,
          alpha = 0.02,
          power = 0.90)

# Simple effect of the group
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "two.sample",
           alternative = "greater")
```

## Clapping-response amplitude

### H2a & H4a: Main effect of the session (pre vs. post)

```{r}
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")
```

### H2b & H4b: Movement Condition x Session interaction

```{r}
# Movement Condition x Session interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 learning type
           nm = 2, # 2 sessions
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect

# Simple effect of the movement condition
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")
```

### H5a: Group x Metre Frequency interaction

```{r}
# Group x Metre Frequency interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 groups
           nm = 2, # 2 metre frequencies
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect

# Simple effect of the metre frequency
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater")
```

### H6b: Group x Metre Frequency interaction

```{r}
# Group x Metre Frequency interaction effect
wp.rmanova(n = NULL,
           ng = 2, # 2 groups
           nm = 2, # 2 metre frequencies
           f = 0.89,
           alpha = 0.02,
           power = 0.90,
           nscor = 1, # non-sphericity correction coefficient
           type = 2)  # "0" for between-effect; "1" for within-effect; and "2" for interaction effect

# Simple effect of the group
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "two.sample",
           alternative = "greater")
```

### H7b: Group x Movement Condition interaction

```{r}
# Group x Movement Condition interaction effect
wp.kanova(n = NULL,
          ng = 4, # 2 groups x 2 movement conditions
          ndf = 1,
          f = 0.89,
          alpha = 0.02,
          power = 0.90)

# Simple effect of the group
pwr.t.test(n = NULL,
           d = 1.77,
           power = 0.90,
           sig.level = 0.02,
           type = "two.sample",
           alternative = "greater")
```

# SESOI

The smallest effect size of interest (SESOI) is computed using the small-telescopes approach based on [Chemin et al. (2014)](https://doi.org/10.1177/0956797614551161).

```{r}
pwr.t.test(n = 14,
           d = NULL,
           power = 0.33,
           sig.level = 0.02,
           type = "paired",
           alternative = "greater") # "greater" for t test SESOI; "two.sided" for TOST SESOI
```
