Impact of Non-Weighting in the Analysis of Data Obtained from Complex Samples

Abstract

Objective: To compare the estimates obtained, considering or not the weighting data. Material and Methods: Secondary data from the Oral Health Survey of the State of São Paulo (SBSP2015) was used for calculation of mean estimates, standard errors of the mean and confidence intervals (CI) for the DMFT index and components (decayed, lost and filled), in the age group of 35-44 years. Multiple logistic regression models were estimated, considering or not the weighting from the sampling plan (p<0.05). Results: It was observed that the estimates of the DMFT index and the carious component did not vary much when the design was considered or not (1.1% and 2.0%, respectively). However, the data referring to the lost and filled component showed greater differences between the values of the means. The averages fluctuated up and down by up to 6.7% for weighted versus unweighted analyses. The standard error was underestimated in the unweighted analysis and the confidence interval showed variations. Differences between the regression models obtained by the weighted and unweighted analysis of the data were detected. Conclusion:Although weighted and unweighted models presented differences of less than 10% in estimates of the mean, confidence intervals, as well as statistical inferences, were different. Thus, weighting should be applied in the population base data analysis collected by sampling with complex designs.

Demographic and economic correlates of health security in West Sumatra province - Indonesia

“…Nevertheless, a notable strength of this research lies in the weighted analysis, which enables the findings to be generalized to the entire West Sumatran population. Although the discrepancy of our weighted and un-weighted analysis was less than 10 %, showing an un-biased result, previous study suggested that weighting should be taken as a priority in the population base data analysis collected by sampling with complex designs [ 30 ]. Moreover, the substantial sample size of this study, with a total of 32,366 data subjects, contributes to the precision of the estimates.…”