Statistics

Infertility is considered as critical health problem among all societies all over the world. Infertility has many consequences for men and especially for women. Couples experience stigma, sense of loss, and diminished self-esteem in the setting of their infertility. Childless couples are stigmatized and discriminated against society. The blame for infertility is unquestioningly placed on the women. In this study an attempt has been made to estimate infertility. In this study an attempt has been made to estimate infertility from the direct questions regarding infertility and health seeking behaviour in Uttar Pradesh using the DLHS-III survey data collected in 2007-08. Here the study has been done for rural and urban regions separately. Chi-square techniques have been used to dependency of infertility on the various socio-demographic characteristics. Prevalence of lifetime infertility as well as lifetime primary infertility is higher in rural than urban areas. Treatment seeking is slightly higher among urban than rural women but there is not very big difference. Infertility is perceived as a problem across virtually all cultures and societies. The number of couples seeking treatment for infertility is increasing now-a-days due to more awareness of available services and latest and more successful techniques.

One of the main problems in sampling survey is the optimal allocation of resources. The solution of this problem is rather arbitrary due to the fact that no best allocation is defined. In this model, the allocation problem was to find the allocation of a sample to strata which minimizes cost of investigation. The idea of optimal allocation under a multivariate stratified sampling based on an alternative approach earlier worked on by Diaz- Garcia and Ranos– Quiroga was applied. The matrix of the variance-covariances of the vector of the stratified variables was obtained. An emperical data from a household survey conducted in Ogun state was used. The frame consisted 880,970 households in the twenty local government areas (LGA) of Ogun state. Each of the 20 LGAs that made up the state was seen as a stratified cluster. Post stratification in this study ensured that some variables that are suitable for stratification was achieved after selection of the sample. The four characteristics of interest were occupation, income, household size and educational level. The study transformed all variables in the stratified sampling plan by using dummy variables ranging from 1-3. The estimates used in the computation were calculated using statistical software Splus. The post stratification estimator ?st does not have the same variance as the stratified sample mean. Stratification produced a gain in precision in the estimation of characteristics of the household survey. The results of the estimates in the study revealed that proportionate allocation can lead to smaller sampling variances than for simple random samples of the same size.