Statistics Feb 5, 2012

Cada:
First I combined all of the types of motion, all of the types of time, and all of the types of extent. But I had to remove extent because there wasn't enough data in that column to run the test. This gives the following matrix, where columns are 1=Motion, 2=Means, 3=Time and rows are 1=po and 2=pr

> twostatcada=matrix(c(78,40,37,38,0,56),ncol=3,byrow=TRUE)
> twostatcada
     [,1] [,2] [,3]
[1,]   78   40   37
[2,]   38    0   56
> chisq.test(twostatcada)

	Pearson's Chi-squared test

data:  twostatcada 
X-squared = 45.4593, df = 2, p-value = 1.345e-10

> sum(twostatcada)
[1] 249
> sqrt(45.4593/249)
[1] 0.4272791
Medium-to-large effect size

Cada without gaska:
> twostatcadanogaska=matrix(c(78,40,37,38,0,25),ncol=3,byrow=TRUE)
> chisq.test(twostatcadanogaska)

	Pearson's Chi-squared test

data:  twostatcadanogaska 
X-squared = 21.0366, df = 2, p-value = 2.704e-05

> sum(twostatcadanogaska)
[1] 218
> sqrt(21.0366/218)
[1] 0.3106415
Medium effect size

Miehta:
The data here has not changed. We collapse all types of extent and motion and get this matrix, where columns are 1=Time, 2=Extent, 3=Motion, and rows are 1= po and 2=pr
> twostatmiehta=matrix(c(95,5,0,13,105,16),ncol=3, byrow=TRUE)
> twostatmiehta
     [,1] [,2] [,3]
[1,]   95    5    0
[2,]   13  105   16
> chisq.test(twostatmiehta)

	Pearson's Chi-squared test

data:  twostatmiehta 
X-squared = 167.7701, df = 2, p-value < 2.2e-16
Very significant result

> sum(twostatmiehta)
[1] 234
> sqrt(167.7701/234)
[1] 0.8467386
Very large effect size.

Rasta:
Collapsed all motion and all extent and removed endpoint.
Columns are 1=Extent, 2=Motion, rows are 1=po, 20pr
> twostatrasta=matrix(c(44,47,23,77),ncol=2,byrow=TRUE)
> twostatrasta
     [,1] [,2]
[1,]   44   47
[2,]   23   77
> chisq.test(twostatrasta)

	Pearson's Chi-squared test with Yates' continuity correction

data:  twostatrasta 
X-squared = 12.3558, df = 1, p-value = 0.0004396

> sum(twostatrasta)
[1] 191
> sqrt(12.3558/191)
[1] 0.2543424
Small-to-medium effect size

Mannel:
The only thing that stands out is preverbal vs. postverbal. Here columns are 1=preverbal, 2=postverbal and rows are 1=po and 2=pr
> twostatmannel=matrix(c(56,43,35,72),ncol=2,byrow=TRUE)
> twostatmannel
     [,1] [,2]
[1,]   56   43
[2,]   35   72
> chisq.test(twostatmannel)

	Pearson's Chi-squared test with Yates' continuity correction

data:  twostatmannel 
X-squared = 10.9188, df = 1, p-value = 0.000952

> sum(twostatmannel)
[1] 206
> sqrt(10.9188/206)
[1] 0.2302257
Small-to-medium effect size

Mannil:
Here columns are 1=preverbal, 2=postverbal and rows are 1=po and 2=pr
> twostatmannil=matrix(c(68,35,21,78),ncol=2,byrow=TRUE)
> twostatmannil
     [,1] [,2]
[1,]   68   35
[2,]   21   78
> chisq.test(twostatmannil)

	Pearson's Chi-squared test with Yates' continuity correction

data:  twostatmannil 
X-squared = 39.3221, df = 1, p-value = 3.593e-10

> sum(twostatmannil)
[1] 202
> sqrt(39.3221/202)
[1] 0.4412073
Medium-to-large effect size