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Re: st: issue in data analysis


From   Abdel Rahmen El Lahga <rahmen.lahga@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: issue in data analysis
Date   Fri, 2 Apr 2010 22:17:09 +0200

I think that MCA analysis is appropriate to create a synthetic index
of "empowerment" because the binary nature of the data.
However Ama would run mca separately for each variable set. Also it is
her responsability to check that the predict score reflect the concept
of empowrement or any other axes created there is huge literature on
poverty analysis where we construct we composite index of household
well-being using binary data on household living condition and owning
durable good.
the free online book "ANALYSIS OF MULTIDIMENSIONAL POVERTY Theory and
Case Studies" of Louis-Marie Asselin,  available in
http://www.idrc.ca/en/ev-144551-201-1-DO_TOPIC.html    contain many
usefuel examples on constructing composite index

2010/4/2 Michael I. Lichter <mlichter@buffalo.edu>:
> Ama,
>
> My first suggestion is that you locate a model article or book in your field
> that does something like what you want to do and use the methods employed in
> that article/book as a starting point. I'm sure you would also find some
> data analysis reference books helpful; take a look at those available in the
> Stata bookstore.
>
> That said, the simplest and least sophisticated approach would be to sum all
> of the "yes" answers within each of your "axes", creating a summative scale
> or index of "empowerment". This gives each item equal weight, which might
> not be appropriate if you think that "access to credit" is a much better
> indicator of "empowerment" than "increase in revenue". If your variables are
> coded 0=no, 1=yes, you can create such a scale using -alpha, item
> generate(varname)-, which will also give you an indication of how well the
> items cohere, and whether it would be a good idea to drop one or more of the
> items. The resulting scale is ordinal and can be used as the dependent
> variable in an ordinal logistic regression (-ologit- or -gologit-) if
> assumptions are met; often, however, people treat these scales as
> continuous.
>
> A more sophisticated approach would involve a method like latent class
> analysis (not available in Stata) or factor analysis (-factor- and -rotate-,
> not entirely appropriate with dichotomous variables) to examine the
> structure underlying your measures of "empowerment". You can then create
> factor scores using -predict- or summative scales using -alpha- to create
> your outcome variables. You could also use some version of confirmatory
> factor analysis (not available in Stata) to determine whether your division
> of "empowerment" into three dimensions is supported by the data. If you
> search Statalist, I think you will come up with a number of recommended
> references on factor analysis.
>
> I believe that AbdelRahmen is wrong about -mca- unless your concern is with
> relationships between the "axes" rather than between these "axes" and the
> explanatory variables you mentioned.
>
> Michael
>
> amatoallah ouchen wrote:
>>
>> Thanks a lot Michael for your quick reply,
>>
>> In fact my survey is about " Measuring  Women's Empowerment through
>> microfinance, Evidence from Morocco" , in order to do so,  I have
>> three  principals axes that measure the effect of such program
>>
>> -Economical empowerment
>> -welfare empowerment
>> -the participation if the decision making
>>
>> of course under each axe there is a yes/no question for example
>> for the Economical empowerment I have for instance
>> -Do you have  access to credit? (yes/no)
>> -is there any significant increase in your revenue?(yes/no)
>> -etc..
>>
>> So I want to compute for each individual a dependant variable that
>> measure this "empowerment" from those yes/no questions , of course
>> this "empowerment" would be explained by other variables such as: the
>> level of education, the amount of mortgage, age, etc...
>>
>> thanks  a lot in advance.
>> *
>> *   For searches and help try:
>> *   http://www.stata.com/help.cgi?search
>> *   http://www.stata.com/support/statalist/faq
>> *   http://www.ats.ucla.edu/stat/stata/
>>
>
> --
> Michael I. Lichter, Ph.D. <mlichter@buffalo.edu>
> Research Assistant Professor & NRSA Fellow
> UB Department of Family Medicine / Primary Care Research Institute
> UB Clinical Center, 462 Grider Street, Buffalo, NY 14215
> Office: CC 126 / Phone: 716-898-4751 / FAX: 716-898-3536
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>



-- 
AbdelRahmen El Lahga

*
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