Infographics: Education By Country from accreditedonlineschools.org

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Source: Accredited Online Schools

News Pick- International Higher Education

Below is my pick from related education/higheredu/international edu. Check it out.

Hand-Flip Quantitative Analysis? Mm...Maybe

Quantitative analysis looks intimating with those formulas, algebra, calculus....But after all, we still call it Applied Econometrics. That means it is still usable, doable and interpretable.


Recall one of my favourite professors said in class that suppose you get good-quality data( I mean it, really structured), running a model is just hand-flip thing. The crux is how you interpret the results, namely the beta coefficents, and how you are going to use it to change the status quo.

Notes on Feb. 2nd, 2013: Dabbleboard seems to shut down for good. So the flow chart below is no longer available.

Let's See Who Are Those International Students In The U.S.? Data From 2000-2009

Click on the bar and see the number of international students of that country. Enjoy~

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International Education

Data From IIE Open Doors. www.iie.org

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R: recode levels (missing value) to NA

R load dataset from spss and assign value to missing value. Also in my original dataset, they have coded missing value with a meaningful -8 -9. I hate it! After marathon search in online forum of R topic, I digged out my way to recode levels. Here are the solutions:


>attach(dataset)
> summary(variable)
>table(variable)
>table(as.numeric(variable))
>levels(variable)  ##if that s a numeric factor, it will have error message. command above just to have idea of how many NA and what value of level to recode. 


>levels(variable) [level value assigned] <- NA


#to check
>sum(is.na(variable))
# it tells you how many are missing, or
>table(as.numeric(variable)) 
#or
> levels(variable)
## NA level should appear then.

missing value for numeric variables:

> sum(is.na(BYSES2))

[1] 0

> BYSES2[BYSES2==-8] <- NA

> sum(is.na(BYSES2))

[1] 305

> BYSES2[BYSES2==-4] <- NA

> sum(is.na(BYSES2))

[1] 953

> describe(BYSES2)

Note: if type:
> variable[1:4] <- NA ## it means assign NA to the 1st to 4th row under this column(variable)
> variable[3] <- NA ## Likewise, assign NA to the 3rd row under the variable


It is little bit confusing but finally made it!