Non-response problem is an issue of great concern to all the researchers because it pervades almost all survey research. This ultimately creates potential for bias. The higher the non-response rate the greater the bias if the characteristic under study in respondents differs markedly from non-respondents. This paper reviews and explores further the nature and properties of non-response weighting adjustment approach as applied to sample surveys as a means of handling non-coverage and unit non-response bias in survey estimates. Consequently, a non-response weighting scheme is herein developed. Not only the attitude of the interviewees, but also sample and survey design, fieldwork approach and characteristics of organization influence response results. This study encompasses the application of stratified random sampling because it is well known that stratification method minimizes the variance of the estimates through the process of dividing the population into groups (strata) which are more or less homogeneous. Thus, stratification increases the precision of the estimate. The emphasis of this paper is practical rather than theoretical. Hence the discussion is illustrated with real examples from surveys (in particular 2003 KDHS) conducted by Central Bureau of Statistics (CBS) - Kenya. Some suggestions are made for improving the quality of non-response weightin