Two-way relation between interethnic social capital and drunk driving, risky drinking, binge drinking

 

 

Interethnic social capital is measured by frequency of interethnic drinking (at least monthly)

 

This is the model for drunk driving:

 

This is the model for binge drinking (expert definition, drinking 6 cups or more at a time)

 

This is the model for risky drinking (expert definition, AUDIT >7 for men)

This is the model for binge drinking (an ‘agreed’ version between ‘expert knowledge’ and ‘lay knowledge’)

This is the model for risky drinking (an ‘agreed’ version between ‘expert knowledge’ and ‘lay knowledge’)

Drinking a cup of tea, performing an identity

Take them home. Dry them up – until they become a bit dark, black. Serve with hot water, using a tea pot, or a coffee cup. Add sugar if you like. In rural area, you may want to call a friend over and share it with him, her.

Green tea is very much a piece of social interaction. It is not simply a drink. The smell, the colour, and the taste of your tea tell others who you are. Tea makes an distinction, shows an identity, and mark a status. Tea functions to glue seemingly strangers, maintain communal harmony, cement solidarity, provide a sense of being healthy. You can have a good network of friends just because you like to drink tea with them. You may use a cup of tea to show other that you belong to some class, some ethnic group, some gender, some region, and some education level. When you drink your tea, you make a performance of your identities. You negate the similarities between you and other. You play a role, like in a theatre so that you yourself and other will feel happy. You may talk about your tea drinking in a certain way, in order to convey certain meanings, conceal some information, or manage other impression of you. You drink your tea, therefore you are who you are.
So, have you had a cup of tea today?
Interested in a tea/alcohol/coffee research? Consider using identity performance framework. We drink to express, symbolically, our gender, ethnic, class identities. However, somebody may always there, who have the power to interpret, label, stigmatise our performance. We need to negotiate by repeating the performance, talking about it, explaining it. We use ‘techniques of information control’, ‘techniques of neutralization’, and perhaps strategy of mitigation as well, if we feel like what we ‘ve just said is ‘politically incorrect’! (I am not racist, but his practice is very backward!)
Look at Goffman (1959) for a definition of ‘performance’
Look at Butler (1989) for a definition of gender performance
Look at Connell (1987) for a definition of negation (reduce similarities)
Look at Room (2005) for a piece on ethnic performance

Guide for Chapter Formation

A standard chapter in a thesis, or a article should satisfy the following :

1) what is the aim of this chapter?

2) what is going to happen (what will be presented here)?

3) Heading 1: say about it, but keep mentioning in passing other key ideas in the chapter

4) Heading 2: say about it, and referring back to what have been said so far

5) Provide a conclusion:

- a summary of what have been mentioned

- highlight the idea/concept (among many things you mentioned) that is important at most in your work

-tell what is going to happen /be presented in the next chapter

Other requirements:

-Should be 7-8 thousand words in length

-Should cover (the number of thousand words x 10) = 8 x 10 = 80 references

-References should be the key studies in the mentioned area. Do Google Scholar search to determine roughly which studies get cited the most. The more cited, the more influential. References will not be counted in word counting.

- Map, table, formula all need caption. Insert them, right click, choose caption, choose among options (table, figure, equation), give a name

-Margins: Left: 3cm, Right, free, Top/Bottom: 2,5

-Page number: top, right

-Font: 12, double spaced, Times New Roman

-Appendixes are at the end of thesis

How to spend the last month of your PhD crazily?

It is not easy. You have to do the following:

1) Write only when all the ideas are clear to you

2) Disagree with your academic adviser’s style of commenting

3) Follow your working routine: wakeup at 5, leave home at 7, stop for lunch at 12, and come back to work at 2, leave for dinner at 8, and sleep at 11

4) Read more, since you keep finding new articles

5) Stop being nice with people because you ‘are busy’

6) Watch some movies, nice pictures, face booking,

7) Forget about visa expiration

8) Forget about financial stuff

9) For get all the ‘hygienic stuff’

10) Eat biscuits everyday – or ask somebody to buy it for you

RESEARCH AREA

HOW TO deal with ENDOGENEITY of binary variable

I-SHORT ANSWER

This is a common problem in social sciences,

y= x1……..xn, but

x1=x2…..xn

How to deal with it? In Stata, use ivprobit if the suspected endogenous variable is continuous, if it is binary/discrete, use treatreg.

II-LONG ANSWER

look for lnsigma and athrho 

1) meaning of lnsigma

lnsigma = log of sigma

sigma could be understood as standard error of….what?

of ….correlation….between what?

2) meaning of athrho

athrho= transformation of rho, and rho is basically a correlation measure

In this context, rho is the correlation between the errors (residuals) of the probit equation (full equation) and the reduced equation for the suspected endogenous variable (now let us call this correlation between two residuals ‘A’)

full model: y=x1+……………….xn ---> error 1

reduced model: x1=x2….xn --->error 2

Does error1 correlate significantly with error2?

The task here is to test a Ho : there is no endogeneity problem in the set of predictors in relation with the dependent variable. In other words, we test that A is not significant.

if athrho >0.05, that means the possibility that A is not significant is larger than 5%

if athrho <0.05, that means the chance for A to be insignificant is very tiny.

IN SHORT, IF athrho is larger than 5%, we can say that: we cannot reject Ho, or, it is less likely that there is an endogeneity issue in here.

Then WHAT ---> RUN a normal model using probit or logit, depending on what you like, then use it for final results.

IF athrho <0.05, you should keep the output result of ivprobit, which include estimate for both models

NOTE THAT: ivprobit should be used for continuous (suspected) endogenous variable. Consider transform your variable if it is discrete. Nevertheless, the test idea is the same: Does residual in FULL MODEL significantly correlate with residual in REDUCED MODEL?

Consider using –treatreg FOR BINARY endogenous variable:

svy, subpop(sex): treatreg [Y] [ALL_X] , treat(BINARYVARGOESHERE = ALL_X)

est store m1

estout m1, cells(b(star fmt(3)) ) l starlevels(+ 0.1 * 0.05 ** 0.01 *** 0.001) stats(N F,star(r2 p))

Buffalo and pig raising in Tan Lac from 1966 to 2008