Estimating Variance, Analyzing Subgroups and Calculating Degrees of Freedom for Performing Statistical Tests and Calculating Confidence Intervals


This module introduces the basic concepts of variance (sampling error) estimation for NHANES data. You will learn how the complex survey design of NHANES and clustering of the data affect variance estimation, which methods are appropriate to use when calculating variance for NHANES data, and how to calculate degrees of freedom and construct confidence limits for NHANES estimates. In addition, there will be specific examples using the NHANES-CMS linked data.

Task 1: Explain Variance Estimation within NHANES

This first task describes the importance of accounting for the complex sampling structure of NHANES when estimating variances. In general, using statistical weights that reflect the probability of selection and propensity of response for sampled individuals will affect parameter estimates. Incorporating the attributes of the complex sample design (i.e., differential weighting, clustering and stratification) will affect variance estimates (estimated standard errors and thereby test statistics and confidence intervals).

Task 2: Describe Methods for Variance Estimation Used in NHANES

The second task briefly describes the method used to calculate variance estimates with NHANES data and the sample design parameters required.

Task 3: Calculate Degrees of Freedom for Performing Statistical Tests and Calculating Confidence Limits

This third task will discuss calculation of degrees of freedom using SUDAAN and SAS Survey procedures. The accurate determination of degrees of freedom is important for performing statistical tests and calculating confidence limits.

Page last reviewed: June 19, 2018
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