Complex Sample Design Effects and Health Insurance Variance Estimation

Abstract

Fifty‐one articles using complex sample data published between 2000 and 2007 in three journals are reviewed. Of these, three articles indicate whether the analyses account for sampling design when calculating standard errors. To demonstrate how neglecting to properly calculate variances increases the probability of Type I errors, data from the Survey of Income and Program Participation (SIPP) are used to estimate health insurance coverage using three methods: simple random sample (SRS), generalized variance functions (GVFs), and direct estimation via replicate weights. The analysis shows that researchers using complex sample data are likely to draw improper inferences if they do not use replicate weights to estimate standard errors.