National Health and Nutrition Examination Survey

NHANES Tutorials (Under Construction)

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 6: 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 7: 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 8: 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 9: 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 10: 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.

Page last reviewed: 2/21/2020