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WWC pretest effect size

<= 0.05   Satisfies the baseline equivalence requirement

0.05 <  and <= 0.25 Requires statistical adjustment to satisfy BA the requirement.

>0.25  Does not satisfy.

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Korean lesson 1

My teacher's notes:

ne -yes

aniyo -no

Seon saeng nim - teacher

Gae - dog

nae gae nun (my dog is)

nae - my

ne- yes

Mi guk e sarayeo? -do you live in America?

Nae yi utseun Han guk saram imnida

mal - language

shwayeo - easy

Nanun gal bi joahaeyo = I like gal bi

Jeo do = me too

Gatayo = the same

***

My notes:

My name is Kazu

nae ileum-eun kazibnida.

My car is Dodge.

nae chaneun Dodge.

 

 

I’m Japanese.

naneun ilbon salam-ibnida

I’m Korean.

jeoneun hangug salam-ibnida.

 

You

Tanshin

Sanzenim　(teacher) hangug

De= Yes

Anyo = No

 

San

 

KEE (Dog)

 

D

 

 

I live in America.

naneun migug-e sal-a.

 

I live in Japan.

naneun ilbon-e sanda.

 

 

My wife is American.

je anaeneun migug-in-ibnida.

 

 

We have one son.  He is 19 years old.

adeul-i hana issseubnida.geuneun 19 (shukko?) sal-ibnida.

 

 

My hobby is guitar.  I enjoy music.

 

 

Ne iyut

My neighbor

 

Nae yi utseun Han guk saram imnida

 

Oryowoyo

Hangul mae

 

Shiwayo easy

 

I like

Nanun

 

Nanun gal bi joahaeyo

 

Jeo do

 

Same

Nanun gal bi joahaeyo

 

 

Onu (which)

Chunun

TO = more

 

 

ILBONO Japanese

 

 

 

 

 

 

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Recoding in R

https://dplyr.tidyverse.org/reference/recode.html

# For character values, recode values with named arguments only. Unmatched # values are unchanged. char_vec <- sample(c("a", "b", "c"), 10, replace = TRUE) recode(char_vec, a = "Apple")
#> [1] "Apple" "b" "Apple" "b" "c" "Apple" "c" "c" "b" #> [10] "Apple"
recode(char_vec, a = "Apple", b = "Banana")
#> [1] "Apple" "Banana" "Apple" "Banana" "c" "Apple" "c" "c" #> [9] "Banana" "Apple"

 

My example:

court2qe$abc2<-recode(
court2qe$abc,"this is" = "X",
"That is" = "Y")