Weighting National Survey Data: why, how and which weight?

Abstract Background Weighting of national data is a procedure that enables the sample to be more representative of the target population. Weighting procedure is a thorough exercise and yields several types of weights. However, considerable variation exists among authors on which weight to use leaving the researchers baffled. In this article we share our experience on weighting for a few recent national surveys in Bangladesh. Methods We generated four weights: the base weight calculated from probabilities of selection, and non-response adjustments, population calibration, and trimmed weights. Finally we checked weighted means, medians, ranges, standard errors, confidence intervals, variances, multiplicative effects, design effects and prevalence of a key variable of the survey to decide on which weight to use. Results Compared to unweighted distribution, weighting makes the sample distribution to conform to the population. Among the four calculated weights, the trimmed weight had narrow standard error and variance, and smallest design and multiplicative effects. It yielded an acceptable prevalence and distribution of a core variable. Conclusion Though weighting is a time intensive exercise, it had a favorable effect on the sample distribution to comply with the Bangladeshi population. Among the four weights, we show that the trimmed weight met all parameters of good quality and precision. Therefore, we recommend to use this weight for national level surveys in Bangladesh.