Complex sampling designs and statistical issues in secondary analysis
Related Experiment Video
Updated: Sep 30, 2026

Sampling Soils in a Heterogeneous Research Plot
Published on: January 7, 2019
Shawn M Kneipp1, Hossein N Yarandi
1Department of Healthcare Environments & Systems, University of Florida, Health Science Center, College of Nursing, Gainesville, USA.
Abstract:
Conducting secondary analysis using large national survey data sets to answer pressing research questions is gaining acceptance in the nursing science community. There are, however, challenges confronted by researchers who wish to apply secondary analysis to large data sets due to the incorporation of complex sampling designs. This article presents sampling design issues inherent in many large national surveys and explains the rationale for applying sample and variance estimation weights when conducting statistical analyses. In addition, the rationale for using statistical software packages capable of analyzing data derived from complex sampling designs is described. Examples of differences in statistical outcomes with and without weights using Stata and SPSS are provided using data from the Medical Expenditure Panel Survey (MEPS). Based on the example analyses, the implications of the statistical outcome differences for study findings are discussed.
Related Concept Videos
Sampling Plans
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Study Design in Statistics
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Sampling Methods: Overview
In analytical chemistry, the choice of sampling...
Cluster Sampling Method
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Random Sampling Method
Stratified Sampling Method
To choose a stratified sample, divide the population into groups called strata and then take a...
