Tips - Stata: How to test significance of interactions of categorical variables

Tips - Stata: How to test significance of interactions of categorical variables

We have the model

   . webuse fvex

   . regress y i.sex##i.group age

We can test the overall significance of the sex#group interaction by typing

   . contrast sex#group

Thoughts: Education is The Key to Reduce Health Disparities

Thoughts: Education is The Key to Reduce Health Disparities

This is great. Even more, I would like to see teachers can team up to reduce disparities. My Mom’s experience tells me that the key to reduce the poverty is education, education, and still education. My Mom was a teach of elementary school in China. Sixty years ago, she went a remote poor countryside teaching not just kids but also the parents how to read. Now this area is one of the richest counties in China. My Mom is so proud of what she had done. By the way, my 80-year old Mom is still going to a senior college as a student to have fun and meet friends.

Subject: Lawyers, Doctors Team Up To Reduce Health Disparities

California Watch: Lawyers, Doctors Team Up To Reduce Health Disparities
On Kate Marr's first day practicing law at The Children's Clinic in Long Beach last week, she met with the mother of an asthmatic 7-year-old. ... The Long Beach program is the latest effort by community clinics and hospitals across the country to add lawyers to their medical teams as a way to resolve issues associated with the "social determinants of health," such as housing, domestic violence and poverty (Yeung, 11/10).

Tips - Stata: How to generate composite categorical variables and indicate/dummy variables

Tips - Stata: How to generate composite categorical variables and indicate/dummy variables, convert a continuous variable into a categorical variable

1. to generate composite categorical variables:

   .egen compvar=group(var1 var2 var3), label

   Now, the dataset has a categorical variable with the different combinations of three variables: var1, var2, and var3.

2. to generate indicate variables:

   .tabulate compvar, generate(compvar)

   Now, the dataset has multiple indicate variables with a prefix of ‘compvar’


3. to convert a continuous variable into a categorical variable
   .gen agecat = recode(age, 24,29,34,39,44,49,54,59,64, ///
        69, 74, 79, 90) if !missing(age)
   or
   .gen age13grp = 1+irecode(age,24,29,34,39,44,49,54,59,64, ///
        69, 74, 79, 90) if !missing(age)

Tips - Stata: how to do 'lincom' after a three-way 'margins'?

Tips - Stata: how to do 'lincom' after a three-way 'margins'?
The easiest way to determine how to refer to the margins is to type
   .margins, coeflegend
after the -margins- command. This will list the estimated margins as well as the _b[] notation that can be used to refer to them. Here is an example:
   .webuse nhanes2, clear
   .svy, subpop(female): logit highbp i.race##i.diabetes##c.age weight
   .margins race, over(diabetes) at(age = (30(5)50)) subpop(female) ///
            vce(svy) post
  .margins, coeflegend
This tells us that if we want to compute the difference in the margins for age=30, diabetes=0, race=1 and age=35, diabetes=0, race=1, we could type
   .lincom _b[1bn._at#0bn.diabetes#1bn.race] - ///
           _b[2._at#0bn.diabetes#1bn.race]
-- by a senior statistician of Stata

Nice gadgets (freeware) from Microsoft

Nice gadgets (freeware) from Microsoft

Image Composite Editor (ICE)

ICE is an simple advanced panoramic image stitcher.
Mathematics 

This is an amazing powerful calculator has many functions with graphing options. It also has unit conversion tool.

You may also download and install Mathematics Add-In for Word and OneNote here; you can easily to plot graphs in 2D and 3D, calculate numerical results, solve equations or inequalities, and simplify algebraic expressions in your Word documents and OneNote notebooks.

You may find more information for teachers/education here.
Sysinternals Suite

Sysinternals Suite, a troubleshooting utility package, was bought by Microsoft a few years ago.

Final Data Collection Standards for Race, Ethnicity, Primary Language, Sex, and Disability Status Required by Section 4302 of the Affordable Care Act

Final Data Collection Standards for Race, Ethnicity, Primary Language, Sex, and Disability Status Required by Section 4302 of the Affordable Care Act

Source: HHS – the Office of Minority Health

HHS on Oct. 31, 2011, published final standards for data collection on race, ethnicity, sex, primary language and disability status, as required by Section 4302 of the Affordable Care Act [PDF | 1.6 MB].

The law requires that data collection standards for these measures be used, to the extent practicable, in all national population health surveys. They will apply to self-reported information only. The law also requires any data standards published by HHS comply with standards created by the Office of Management and Budget (OMB).

Proposed standards were published on June 29, 2011, and public comments were accepted until August 1, 2011.

The standards, effective upon publication today, apply to population health surveys sponsored by HHS, where respondents either self-report information or a knowledgeable person responds for all members of a household. HHS will begin implementation of these new data standards in all new surveys and at the time of major revisions to current surveys.