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This is potentially a more difficult question than you may realize. There are several different possibilities. You could perhaps construct a set of binary variables Yk, for k=0,1,2,3,4..... as follows. Yk=0 for women with k children; Yk=1 for women with more than k children; Yk="." for women with fewer than k children. You would then be describing the probability of going from parity k to parity k+1. This would be better for number of children ever born than for number of living children. Logit regression is not well suited for an outcome that is a count. Another possibility would be to use poisson regression or negative binomial regression. There is not a general agreement on a single way to analyze this outcome.

Sir i want to make the binary logistic analysis on fertility like the article of following link.

The Desire for Sons and Excess Fertility: A Household-Level Analysis of Parity Progression in India

Author(s): Sanjukta Chaudhuri

Source: International Perspectives on Sexual and Reproductive Health,Vol. 38, No. 4 (DECEMBER 2012), pp. 178-186

Published by: Guttmacher InstituteStable URL: http://www.jstor.org/stable/23343635Accessed: 20-04-2015 07:12 UTC

Sir, can you explain the dependent variable of this article (p-179)?

Also, Estimated odds ratios from binary logistic analysis (Table 6 and page-183)

What the author consider dependent variable in binary logistic analysis? What the author consider 0 and 1 in binary logistic analysis in each parity?

Sir, I am not clear the dependent variable consider at this article in fertility analysis. (((Page-183)))

Best Regards

Mohammad Nazmul Hoq

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I hope another user of the forum can help you with this question, or you can directly contact the author, Sanjukta Chaudhuri, whose contact information must be provided in the article.

Sir, Can you give me some suggestive analysis on the title given below:

"The Effects of Son Preference and Gender Composition of Surviving Children on Fertility in Bangladesh: Regional Differentials"

Data: BDHS-2011

I want to do binary logistic regression analysis on the above title. For the analysis of gender composition I construct different parity ( Parity-1 (those have one living children), parity-2 ( those have two living children) and so on ).

Sir, Is the binary logistic analysis appropriate for identifying different demographical factors in each parity ? If so, What should i consider 'o' and '1' in the dependent variable number of living children (as title is on surviving children)in each parity?

Sir, can you give me some article reference on the above title?

Sir, I will be grateful to you.

Regards

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