I am currently conducting a study about the involvement of male in family planning. It is of interest to create an index for the involvement of male in FP. My variables were only categorical variables and mainly dichotomous variables. Is there anyone who is knowledgeable on creating index using logistic approach? or any suggestions for the creation of this index. Thanks so much
How to create an index with only categorical variables (dichotomous, nominal and ordinal) only?
The general sequence for this kind of problem is:
- check your items actually scale together
- fit an IRT model to them
- confirm your model fits reasonably
- extract factor scores for cases
If you happen to be an R user then one can do 1. using the
aisp function in the
mokken package. This looks at your items and tries to tell you how many (if any) separate scales there are among your items. You can also compute Cronback alphas and other stats with this package.
If for 2. you want to fit an item response model -- I'm guessing this is what you mean by 'logistic approach' -- then you can use the
grm function from the
ltm package to fit a scale that has some mix of dichotomous or Likert items, and the
factor.scores function to get scale scores for whatever subset of items you chose.
More practical detail and pointers to the relevant theory is provided by the package vignette. As it happens the example code all uses the built in data set
WIRS which consists of 6 dichotomous items, so you can see how it works using that.
$\begingroup$ Thank you very much for your help. Is this approach available in Stata or SPSS? In 2, how can I fit a model if I don't have a dependent variable(s)? Is there any reference/source for examples of such creation of this kind of index? $\endgroup$ Mar 17, 2014 at 4:32
$\begingroup$ @paualdemita The answer to this related question may be helpful. In Stata it seems that one uses GLAMM here for binary, and here for ordinal items. $\endgroup$ Mar 17, 2014 at 8:45
1$\begingroup$ About the model: the point of measurement modelling is to build a model of how the unobserved thing to be measured affects the observed items, usually with the assumption it affects them each separately (local independence). In your case the model will be some set of logistic regressions where the 'independent' variable is the thing to be measured and the dependent variable is an item. It might be worth working through the first link in my first comment link to get the idea. $\endgroup$ Mar 17, 2014 at 8:52
$\begingroup$ To be detailed: My variables in the study are: 1. Male's fertility preference 2. Desire more children 3. Male currently use contraceptive method 4. Male heard about CM and/or FP 5. Male knows side effects of CM 6. No. of ideal children 7. total no. of living children 8. knows where to obtain CM 9. Male knows wife's menstruation cycle 10. who decides using CM 11.heard STDs and somesocio-demographic variables as well as female variables(same as male variables listed above) With these variables, I want to create an index measuring the involvement of male in Family Planning. $\endgroup$ Mar 17, 2014 at 11:23
$\begingroup$ With these variables, i can't fully grasp the idea of using logistic models to create the index since a dependent variable is needed to fit a regression model or IRT model.. Please help. $\endgroup$ Mar 17, 2014 at 11:27