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NHANES Tutorials

The NHANES Tutorials are currently being reviewed and revised, and are subject to change. Specialized tutorials (e.g. Dietary, etc.) will be included in the future.
Page Description

Module 1: Datasets

The NHANES website is the most important data source and analytical resource for all data users. The website contains both historic and current datasets, and covers a wide range of critical topics. It is very important to learn to navigate this website and use these resources effectively. This module describes how Continuous NHANES data are structured and organized.

Module 2: Sample Design

NHANES uses a complex, multistage, probability sampling design. Researchers need to take this into account in their analyses by appropriately specifying the sampling design parameters. This can be done using any statistical software that can analyze complex survey designs. Specifying sampling design parameters using SUDAAN and SAS Survey procedures is presented in this module.

Module 3: Weighting

This module addresses why weights are created and how they are calculated, the importance of weights in making estimates that are representative of the U.S. civilian non-institutionalized population, how to select the appropriate weight to use in your analysis, when and how to construct weights when combining survey cycles, and how to correctly create subsets within your analysis population.

Module 4: Variance Estimation

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.

Module 5: Descriptive Statistics

NHANES data are often used to provide national estimates on important public health issues. This module introduces how to generate the descriptive statistics for NHANES data that are most often used to obtain these estimates. Topics covered in this module include checking frequency distribution and normality, generating percentiles, generating means, and generating proportions.

Module 6: Hypothesis Testing

The t-test and chi-square statistics are used to test statistical hypotheses about population parameters. This module will demonstrate the use of these statistics in NHANES data analysis.

Module 7: Age Standardization and Population Estimates

This module covers two issues that commonly arise when researchers analyze population data: age standardization and population counts (estimated numbers of persons in the U.S. with a particular characteristic). Addressing these issues in NHANES analyses requires the use of Census population data.

Module 8: Linear Regression

Linear Regression models, both simple and multiple, assess the association between independent variable(s) (Xi) — sometimes called exposure or predictor variables — and a continuous dependent variable (Y) — sometimes called the outcome or response variable. In cross-sectional surveys such as NHANES, linear regression analyses can be used to examine associations between covariates and health outcomes.

Module 9: Logistic Regression

Logistic Regression is a statistical method used to assess the likelihood of a disease or health condition as a function of a risk factor (and covariates). There are two kinds of logistic regression, simple and multiple. Both simple and multiple logistic regression, assess the association between independent variable(s) (Xi) — sometimes called exposure or predictor variables — and a dichotomous dependent variable (Y) — sometimes called the outcome or response variable.

Sample Code

Listing of all the sample code and datasets used in the Continuous NHANES tutorial. They are organized by module and then task. Programs are available as SAS programs (i.e. SAS Survey Procedures and SAS-callable SUDAAN) and Stata programs. The datasets are SAS or Stata datasets for Windows.

Analytic Guidelines

Survey Methods
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