Article,
An Evaluation of Alternative PC-Based Software Packages Developed for the Analysis of Complex Survey Data
The American Statistician, (1997)
Abstract
INTRODUCTION National surveys conducted by government organizations, industry, political organizations, and market research firms often share the same survey design objective to minimize the variance in survey estimates, subject to fixed cost and time constraints. As a consequence, most large-scale national health care surveys are characterized by sample designs with varying degrees of complexity, with design features that include clustering, stratification, disproportionate sampling, and multiple stages of sample selection. Most of the standard statistical software packages such as SAS, SPSS, SYSTAT, and BMDP assume that the data were obtained from a simple random sample in which the observations are independent and identically distributed, and selected with equal probability. When the data have been collected from a survey with a complex sample design, variance estimates of survey statistics derived under simple random sampling assumptions generally underestimate the true va
