Dear Steve and others
It is a DHS survey from Measuredhs. I will be using svy: commands in
due course - I am trying to get my ideas straight first.
The survey variables are usually common
svy: v001 [weight=v005], strata(v024) // usually, though weights vary
with statistics
svy: reg ....... // as before
?
Richard
On Thu, Feb 12, 2009 at 2:45 PM, Steven Samuels
<[email protected]> wrote:
> 0.503 looks like the right number Richard. But I'd like to know more: What
> was the study design which gave 2,698 observations in 1999? If it is sample
> survey, you should be using Stata's survey commands.
>
>
> -Steve
> On Feb 12, 2009, at 6:27 AM, Richard Palmer-Jones wrote:
>
>> Thanks for the replies, and sorry not to have been clearer and the
>> delay in responding (student strike!)
>>
>> the variables are:
>> label yearsedn "years of education" // cross section in 1999
>> gen old = born >= 1956 & born <= 1961
>> gen young = born >- 1970 & born <= 1975
>> gen highintensity = state >= 5 & state <= 19 // states 1 -4 = low
>> intensity
>> label highintensity "high primary school funding in 1976-81" // (i.e.
>> treated)
>> gen young_high = highintensity * young
>>
>> * Maarten suggests
>> . reg yearsedn young highintensity young_high if young | old
>>
>> Source | SS df MS Number of obs =
>> 2698
>> -------------+------------------------------ F( 3, 2694) =
>> 115.81
>> Model | 8584.36711 3 2861.4557 Prob > F =
>> 0.0000
>> Residual | 66562.2808 2694 24.7076024 R-squared =
>> 0.1142
>> -------------+------------------------------ Adj R-squared =
>> 0.1132
>> Total | 75146.6479 2697 27.8630508 Root MSE =
>> 4.9707
>>
>>
>> ------------------------------------------------------------------------------
>> yearsedn | Coef. Std. Err. t P>|t| [95% Conf.
>> Interval]
>>
>> -------------+----------------------------------------------------------------
>> young | 2.286417 .4506203 5.07 0.000 1.40282
>> 3.170013
>> highintens~y | -3.533677 .3945928 -8.96 0.000 -4.307412
>> -2.759941
>> young_high | .7222411 .503475 1.43 0.152 -.2649952
>> 1.709477
>> _cons | 6.118812 .3497354 17.50 0.000 5.433035
>> 6.804589
>>
>> ------------------------------------------------------------------------------
>>
>> I want a table with mean and standard errors of years of education
>> where the Xs are in the following table, and the marginal differences
>>
>> young
>> 0 1 difference
>> _______________________________________
>> lo | X | X | X
>> high | X | X | X
>> _______________________________________
>> difference | X | X | X
>>
>> The row and column Xs can be filled in from calcuations of means (e.g
>> in ttest), except for the bottom right X
>>
>> Maarten's suggestion is that the appropriate se is 0.503
>>
>> Thanks for further help
>>
>> RIchard
>>
>>
>>
>>
>>
>> On Tue, Feb 10, 2009 at 5:18 PM, Steven Samuels
>> <[email protected]> wrote:
>>>
>>> While I agree with Maarten about the analysis procedure, I am unclear
>>> about
>>> the design- does "one of which" in your description refer to region or
>>> to
>>> cohort? Was there a treated cohort and a non-treated cohort within each
>>> region? Or, were there two cohorts within each region, but with treatment
>>> differing between region? Do you have a before/after differences , or
>>> are
>>> all comparisons cross-sectional?
>>>
>>> Please lay out in more detail the design of the study
>>>
>>> -Steve
>>>
>>>>
>>>> --- On Tue, 10/2/09, Richard Palmer-Jones wrote:
>>>>>
>>>>> I want to calculate the standard error of the mean
>>>>> "diffference in difference". I have samples from two
>>>>> cohorts in each of two regions, one of which received
>>>>> a treatment, and their respective years of education;
>>>>> how to I calculate the standard error of the
>>>>> difference in difference of the years of education?
>>>>
>>>> Looks to me like an interaction effect:
>>>> The dummy for cohort tells you how much the average
>>>> education differs over cohorts, and you think that
>>>> this difference differs across regions, so the
>>>> interaction effect between cohort and region gives
>>>> you this difference in difference, and -regress- will
>
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