NATIONAL CENTER FOR HEALTH STATISTICS

Vital and Health Statistics

Series 2, Number 184 April 2020

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

NCHS reports can be downloaded from: https://www.cdc.gov/nchs/products/index.htm.

National Health and Nutrition 

Examination Survey, 2015−2018: 

Sample Design and Estimation 

Procedures

Data Evaluation and Methods Research
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Suggested citation

Chen TC, Clark J, Riddles MK, Mohadjer LK, Fakhouri THI. National Health and Nutrition 

Examination Survey, 2015−2018: Sample design and estimation procedures. National 

Center for Health Statistics. Vital Health Stat 2(184). 2020.

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U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

Hyattsville, Maryland

April 2020

DHHS Publication No. 2020–1384

National Health and Nutrition 

Examination Survey, 2015−2018: 

Sample Design and Estimation 

Procedures

Data Evaluation and Methods Research

NATIONAL CENTER FOR HEALTH STATISTICS

Vital and Health Statistics

Series 2, Number 184 April 2020
National Center for Health Statistics

Jennifer H. Madans, Ph.D., Acting Director

Amy M. Branum, Ph.D., Acting Associate Director for Science

Division of Health and Nutrition Examination Surveys

Ryne Paulose-Ram, M.A., Ph.D., Acting Director

Lara J. Akinbami, M.D., Acting Associate Director for Science
Series 2, Number 184 iii NATIONAL CENTER FOR HEALTH STATISTICS 

Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Sample Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Weighting the Sample Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Calculating Base Weights. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Nonresponse Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Trimming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

Computing Final Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Subsample Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Combining Survey Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

 Variance Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

Variance Units for Publicly Released Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

Restricted Data Access in the NCHS Research Data Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Appendix I. Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Appendix II. Supporting Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Text Tables

A. Sampling subdomains, by race and Hispanic origin, income, sex, and age: National Health and 

Nutrition Examination Survey, 2015–2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

B. Variables used to calibrate National Health and Nutrition Examination Survey, 2015–2016 and 

2017–2018 screening, interview, and mobile examination center examined weights . . . . . . . . . . . . . . . . . . . . 7

Appendix Figure

Primary sampling unit major strata: National Health and Nutrition Examination Survey, 2015–2018 . . . . . . . . . . . . . 15

Appendix Tables

I. Selected sample design parameters: National Health and Nutrition Examination Survey, 

2011–2014 and 2015–2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

II. Race and Hispanic origin and income group sampling fractions (Ak) used in calculating primary sampling 

unit measure of sizes: National Health and Nutrition Examination Survey, 2015–2018 . . . . . . . . . . . . . . . . . . 16

III. State grouping for primary sampling unit stratification: National Health and Nutrition Examination 

Survey, 2015–2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

IV. Characteristics of 14 major strata formed for selection of primary sampling units: National Health and 

Nutrition Examination Survey, 2015–2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

V. Final sampling rates and initial base weights: National Health and Nutrition Examination Survey, 

2015–2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
NATIONAL CENTER FOR HEALTH STATISTICS iv Series 2, Number 184

Contents—Con.

VI. Variables used in forming nonresponse adjustment cells for weighting interview samples: 

National Health and Nutrition Examination Survey, 2015–2016 and 2017–2018 . . . . . . . . . . . . . . . . . . . . . . 19

VII. Variables used in forming nonresponse adjustment cells for weighting mobile examination center 

examination samples: National Health and Nutrition Examination Survey, 2015–2016 and 2017–2018 . . . . . . . . . 22

VIII. Most common survey sample weights and their appropriate use: National Health and Nutrition 

Examination Survey, 2015–2018. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Series 2, Number 184 v NATIONAL CENTER FOR HEALTH STATISTICS 

The authors gratefully acknowledge the assistance of Lara 

Akinbami, Michele Chiappa, and Ryne Paulose-Ram in the 

preparation of this report.

Acknowledgments
Series 2, Number 184 1 NATIONAL CENTER FOR HEALTH STATISTICS

Introduction

The National Health and Nutrition Examination Survey 

(NHANES) is one of a series of health-related programs 

conducted by the National Center for Health Statistics 

(NCHS) to provide information on the health and nutritional 

status of the noninstitutionalized civilian resident population 

of the United States. This information is used to estimate the 

prevalence of various diseases and conditions and to provide 

information for use in planning health policy. 

NHANES is unique in collecting person-level demographic, 

health, and nutrition information from personal interviews 

and a standardized physical examination in a mobile 

examination center (MEC). The examination includes 

objective measures of health status, including height, 

weight, blood pressure, and the collection of blood and 

urine specimens for laboratory testing. 

The specific NHANES sample designs, including specifications 

for clustering, stratification, and oversampling population 

subgroups, have changed over time. Since 1999, the 

principal sample design has consisted of multiyear, 

stratified, clustered four-stage samples, with public-use 

data release in 2-year cycles. The NHANES 2015–2018 

sample design is summarized in Table I and differs from the 

NHANES 2011–2014 sample design in three key ways: (a) 

The threshold to be sampled in the “non-Hispanic white and 

other low income” sampling domain changed from less than 

or equal to 130% to less than or equal to 185% of the federal 

poverty level; (b) The NHANES 2011–2014 sample design 

had five state health-related groupings, including California 

as a separate group. For the NHANES 2015–2018 sample 

design, California was not separated, only four state healthrelated groupings were created, and the health groups were 

re-evaluated based on updated data, so some states were 

in different groups compared with 2011–2014 groups; and 

(c) The sample design had 14 major strata with 4 minor 

strata each, and 4 certainty primary sampling units (PSUs) 

compared with 13 major strata with 4 minor strata each, and 

8 certainty PSUs in the 2011–2014 sample design. Designs 

for earlier cycles from continuous NHANES are described in 

previous reports (1–3).

A previously published report, titled, “The National Health 

and Nutrition Examination Survey: Estimation Procedures, 

2011–2014” (4) describes the methods that were used to 

Abstract

Background

The purpose of the National Health and Nutrition 

Examination Survey (NHANES) is to produce national 

estimates representative of the total noninstitutionalized 

civilian U.S. population. The sample for NHANES is 

selected using a complex, four-stage sample design. 

NHANES sample weights are used by analysts to produce 

estimates of the health-related statistics that would 

have been obtained if the entire sampling frame (i.e., 

the noninstitutionalized civilian U.S. population) had 

been surveyed.

Sampling errors should be calculated for all survey 

estimates to aid in determining their statistical reliability. 

For complex sample surveys, exact mathematical 

formulas for variance estimates that fully incorporate 

the sample design are usually not available. Variance 

approximation procedures are required to provide 

reasonable, approximately unbiased, and designconsistent estimates of variance.

Objective

This report describes the NHANES 2015−2018 sample 

design and the methods used to create sample weights 

and variance units for the public-use data files, including 

sample weights for selected subsamples, such as the 

fasting subsample. The impacts of sample design 

changes on estimation for NHANES 2015−2018 are 

described. Approaches that data users can use to modify 

sample weights when combining survey cycles or when 

combining subsamples are also included.

Keywords: sampling • weighting • variance estimation • 

mobile examination center (MEC)

National Health and Nutrition Examination 

Survey, 2015−2018: Sample Design and 

Estimation Procedures

by Te-Ching Chen, Ph.D., National Center for Health Statistics; Jason Clark, M.S., Minsun K. Riddles, Ph.D., and 

Leyla K. Mohadjer, Ph.D., Westat; and Tala H. I. Fakhouri, Ph.D., M.P.H., National Center for Health Statistics
NATIONAL CENTER FOR HEALTH STATISTICS 2 Series 2, Number 184

construct sample weights and variance units for NHANES 

2011–2014. It also provides information for users on 

combining survey cycles, sample weights for subsamples, 

and using the strata and primary sampling unit information 

(variance units) provided in public-use files for variance 

estimation. Additional information for conducting data 

analysis can be found in the NHANES analytic guidelines 

(5,6).

This report describes the sample design and the construction 

of sample weights and variance units for NHANES 

2015–2018. The “Sample Design” section describes the 

sample design specifications for NHANES 2015–2018. The 

“Weighting the Sample Data” section describes the creation 

of sample weights for the NHANES 2015–2018 sample and 

subsamples, with a focus on how NHANES 2015–2018 sample 

design features affect the sample weights. The “Variance 

Estimation” section describes the variance units on the 

public-use data files and appropriate variance estimation 

methods for analysis. Appendix I provides a glossary of terms 

and Appendix II contains tables of supporting material. 

For additional information regarding the calculation of 

NHANES sample weights, please refer to “National Health 

and Nutrition Examination Survey: Estimation Procedures, 

2011–2014” (4).

Additional documentation of the survey content, data 

collection procedures, and methods for assessing 

nonsampling errors is provided elsewhere (https://www.

cdc.gov/nchs/nhanes.htm).

Sample Design 

The NHANES sample represents the noninstitutionalized 

civilian population residing in the 50 states and the District 

of Columbia. Since 1999, the sample design has consisted of 

multi-year, stratified, clustered four-stage samples, with data 

release in 2-year cycles. The NHANES sample is drawn in four 

stages: (a) PSUs (counties, groups of tracts within counties, 

or combinations of adjacent counties), (b) segments within 

PSUs (census blocks or combinations of blocks), (c) dwelling 

units (DUs) (households) within segments, and (d) individuals 

within households. PSUs are sampled from all U.S. counties. 

Screening is conducted at the DU level to identify sampled 

persons (SPs), based on oversampling criteria. 

NHANES 2015−2018 oversampled some subgroups to 

increase precision for subgroup estimates. The population 

subgroups chosen for oversampling directly determine the 

sampling domains used to select the sample at all stages. 

Specific NHANES sample designs, including specifications 

for clustering, stratification, and oversampling population 

subgroups, have changed over time. The set of domains 

for which specified reliability was desired in NHANES 

2015–2018 consisted of sex-age groups for Hispanic 

persons; non-Hispanic black persons; and non-Hispanic, 

non-black Asian (referred to as non-Hispanic Asian) persons, 

and income-sex-age groups for the remainder of the U.S. 

population. Specifically, the oversampled subgroups in the 

2015–2018 survey were:

● Hispanic persons;

● Non-Hispanic black persons;

● Non-Hispanic, non-black Asian persons;

● Non-Hispanic white persons and persons of other races

and ethnicities at or below 185% of the federal poverty

level; and

● Non-Hispanic white persons and persons of other races

and ethnicities aged 0–11 years or 80 years and over.

Race and Hispanic-origin domains used to create the sample 

design and weights differ from the categories used in 

variables in publicly released data files (e.g., RIDRETH3 and 

RIDRETH1 for NHANES 2015–2018). Race and Hispanic origin 

information used for sampling is based on census population 

estimates and obtained from the household screener to 

determine eligibility for inclusion in the survey. The race and 

Hispanic origin variables in publicly released data files are 

based on survey response. In addition, the variable RIDRETH3 

in the public-use files includes only single-race categories 

for non-Hispanic white, black, and Asian groups, with all 

participants reporting belonging to other or multi-race 

groups coded into the “other races, including multiracial” 

category. In contrast, when collecting race and Hispanic 

origin information at screening for NHANES 2015–2018, 

the black category included persons reporting non-Hispanic 

black as a single race or in combination with other races, 

including Asian; the Asian screening category included nonHispanic Asian persons as a single race and in combination 

with other races, except black. 

In previous NHANES cycles, the low income threshold for 

oversampling non-Hispanic white and other persons was set 

at or below 130% of the federal poverty level, which is the 

threshold used to determine income eligibility for 

participation in the Supplemental Nutrition Assistance 

Program (SNAP, formerly known as the Food Stamp Program). 

This oversampling threshold was changed to 185% of the 

federal poverty level in NHANES 2015–2018, which is the 

threshold used to determine income eligibility for the Special 

Supplemental Nutrition Program for Women, Infants, and 

Children (WIC). It was thought that increasing the sample of 

respondents with lower incomes may help reduce the 

amount of screening needed and increase response rates. 

The federal poverty levels are established 1 year before the 

year of data collection. Each annual federal poverty level 

referenced remained in place for the calendar year of data 

collection and was updated annually. 

Table A lists the set of sampling domains in NHANES 

2015–2018. Although data are released in 2-year cycles, 

at least 4 years of data must be combined to obtain an 

acceptable level of reliability for most of the sampling 

domains given in Table A. To create estimates for 2-year 

samples, domains must be collapsed to produce adequate 

sample sizes for analysis. 
Series 2, Number 184 3 NATIONAL CENTER FOR HEALTH STATISTICS

NHANES PSUs are selected with probabilities proportionate to size (PPS), where 

each PSU’s measure of size (MOS) is determined by criteria established in 

advance for obtaining health estimates for subgroups determined by age group, 

sex, race and Hispanic origin, and income. The MOS is a weighted average of 

population counts, where the weights, or Ak, are calculated to give relatively 

higher probabilities of selection to PSUs with higher proportions of individuals 

within the subgroups chosen for oversampling. For NHANES 2015−2018, the MOS 

weights were calculated to give relatively higher probabilities of selection to PSUs 

with higher proportions of black, Hispanic, Asian, or low-income white and other 

persons as was done for the NHANES 2011−2014 sample design (see reference 1 

in this report for full details). The weights used to assign the relative contribution 

from each race-Hispanic origin group in the computation of the MOS are listed in 

Table II.

Some counties have an MOS large enough that they are selected with certainty 

(certainty PSU, often referred to as self-representing or SR) (see reference 1 in this 

report for full details). The remaining counties are referred to as noncertainty PSUs 

(non-self-representing or NSR). The certainty PSUs are removed from the county 

frame before noncertainty PSU selection. The noncertainty PSUs are divided into 

strata before selection. There were 4 certainty PSUs for NHANES 2015–2018, one 

for each of the 4 years.

The stratification scheme for NHANES 2015–2018, as for NHANES 2011–2014, 

included state groups, major strata, and minor strata levels. As was done for 

NHANES 2011–2014, a state health 

index was calculated for each state 

based on the combination of several 

state-level health-related variables: 

death rate, infant mortality rate, 

percentage of adults with high blood 

pressure, percentage of adults who 

are overweight or obese, percentage 

of adults with poor nutrition, and 

percentage of adults who smoke. Statelevel health statistics were available 

from the National Vital Statistics System 

(7,8) and the Behavioral Risk Factor 

Surveillance System (9–11). States with 

similar health index values were put 

into the same group, so the healthiest 

states were in Group A and the least 

healthy states were in Group D. The 

NHANES 2011–2014 sample design 

had five state groups, with California as 

its own separate state group because 

of interest in multiyear estimates for 

California. For NHANES 2015–2018, 

California was not separated, but was 

included in State Group A. The states in 

each NHANES 2015–2018 state group 

are listed in Table III.

Each of the 4 state groups had 3 or 4 

major strata, for a total of 14 major 

strata, defined by census region and 

percentage of the population living in 

a rural area. Each major stratum was 

divided into 4 minor strata, for a total 

of 56 minor strata. Table IV shows the 

characteristics of major strata formed 

for selection of PSUs for NHANES 

2015–2018, and the Figure depicts the 

major strata and PSU information in a 

map with detailed PSU descriptions.

Over the 4-year sample, NHANES 

2015–2018 surveyed 60 PSUs, 

including 4 certainty PSUs and 1 

noncertainty PSU per minor stratum. 

Each year, the 15 sampled PSUs, 

including 1 PSU from each major 

stratum as well as 1 certainty PSU, 

formed a nationally representative 

sample. Within each PSU, the 

secondary sampling units (SSUs) 

were called segments, and comprised 

census blocks or combinations of 

blocks. As with PSUs, the SSUs were 

selected with PPS, where the MOS 

was a weighted average of population 

counts. The number of segments in the 

frame can vary widely by PSU; large 

Table A. Sampling subdomains, by race and Hispanic origin, income, 

sex, and age: National Health and Nutrition Examination Survey, 

2015–2018

Hispanic 

Non-Hispanic 

black

Non-Hispanic, 

non-black Asian

Non-Hispanic white and other

Low 

income1

Non-low 

income

All, age group (years)

Under 1 Under 1 Under 1 Under 1 Under 1

1–2 1–2 1–2 1–2 1–2

3–5 3–5 3–5 3–5 3–5

Male, age group (years)

6–11 6–11 6–11 6–11 6–11

12–19 12–19 12–19 12–19 12–19

20–39 20–39 20–39 20–29 20–29

... ... ... 30–39 30–39

40–49 40–49 40–49 40–49 40–49

50–59 50–59 50–59 50–59 50–59

60 and over 60 and over 60 and over 60–69 60–69

... ... ... 70–79 70–79

... ... ... 80 and over 80 and over

Female, age group (years)

6–11 6–11 6–11 6–11 6–11

12–19 12–19 12–19 12–19 12–19

20–39 20–39 20–39 20–29 20–29

... ... ... 30–39 30–39

40–49 40–49 40–49 40–49 40–49

50–59 50–59 50–59 50–59 50–59

60 and over 60 and over 60 and over 60–69 60–69

... ... ... 70–79 70–79

... ... ... 80 and over 80 and over

... Category not applicable. 1Persons living in household at or below 185% of the federal poverty level.

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2015–2018.
NATIONAL CENTER FOR HEALTH STATISTICS 4 Series 2, Number 184

PSUs could have more than 5,000 segments in the frame, 

while small PSUs could have fewer than 100 segments in 

the frame. For survey operational purposes, the sample was 

designed to produce approximately equal sample sizes per 

PSU, so 24 segments are sampled within each noncertainty 

PSU. Certainty PSUs may have any number of segments 

sampled to ensure appropriate representation in the sample. 

Weighting the Sample Data

Weighting of the NHANES data produces estimates 

representative of the civilian resident noninstitutionalized 

U.S. population. The weighting of sample data permits 

analysts to produce estimates of the statistics that would 

have been obtained if the entire eligible population had been 

surveyed. Sample weights can be considered measures of 

the number of persons in the target population represented 

by the particular participant. Weighting takes into account 

several features of the survey: the differential probabilities 

of selection for the sampling domains, survey nonresponse, 

and differences between the final sample distribution 

and the target population distribution. Each of the three 

levels of data collection for NHANES (screening, interview, 

examination) has a response rate. As a result, sample weights 

are calculated for each level of data collection.

The NHANES sample weighting is carried out in three steps. 

The first step involves the computation of stage base weights 

to compensate for unequal probabilities of selection for the 

sampling domains. The second step adjusts for nonresponse 

to reduce the potential bias. In the third step, the sample 

weights are calibrated to the reference population. 

Calibration is used to compensate for possible coverage 

differences from the eligible population and to reduce 

variances in the estimation procedure. The nonresponse 

and calibration steps are performed at each level of data 

collection: the screening, interview, and examination. The 

new weight for each step in the adjustment is the product of 

the weight before the adjustment and the adjustment factor 

of the step.

Adjustment factors are calculated within adjustment 

cells defined by characteristics available for both sample 

participants and nonrespondents at that step. Because 

available information differs for each step, adjustment cells 

differ for each step.

Calculating Base Weights

The initial base weight for each participant within a sampling 

domain is the same for all other participants in that domain 

and is equal to the inverse of the sampling rate (rk) within 

the sampling domain. The initial base weights by sampling 

domains are shown in Table V. For NHANES 2015–2018, the 

initial base weights were adjusted to account for: (a) the 

proportion of DUs released for screening in a PSU, fi (release),

(b) the increase in DU sample size needed in some PSUs, 

fi (inc) , and (c) the number of years in the sample, fi (year) . The 

three base weight adjustment factors are described below. 

The screening base weights are calculated as the product of 

the initial base weights and the three adjustment factors.

Adjustment for number of sampled DUs 

released to the field

At screening, not all DUs can be screened and not all of 

those screened contain SPs. As a result, a larger sample 

than needed is deliberately drawn in each study location, 

and subsamples are released for screening as needed, with 

the objective of obtaining a relatively fixed sample size of 

completed examinations. For NHANES 2015–2018, the 

selected sample in each study location was 80% more than 

was expected to be needed, but the actual sample released 

varied depending on the characteristics of the location. To 

adjust for this approach, a subsample factor is calculated 

for each study location as the inverse of the proportion of 

sampled DUs released for screening (Ri). 

Adjustment for increased DU sample size

Due to declining response rates and varying levels of 

growth and decline in different PSUs, the DU sample size 

was sometimes increased to ensure that enough SPs could 

be identified and examined in the PSU. To adjust for this 

approach, an increase factor is calculated for each study 

location as the inverse of the percentage increase in the DU 

sample (Ii). 

The increase in DU sample size could effectively be canceled 

out by the amount released. For example, if the DU sample 

size was increased to 125% (Ii = 1.25), and then 80% of the 

sample was released (Ri = 0.8), then the resulting combined 

factor (fi (release) × fi (inc)) = 1.

Adjustment for number of years in the sample

NHANES 2015–2018 is a 4-year sample design. Operationally, 

this means that the selected sample of 60 locations on 

which the sampling rates were based was implemented 

over 4 years. Fifteen of the locations were allocated to each 

single-year sample, with 30 locations included in each 2-year 

public-use data release cycle. Each single-year and 2-year 

sample is nationally representative; access to single-year 

data is restricted to use in the NCHS Research Data Center 

(RDC, described in “Restricted Data Access in the NCHS 

Research Data Center” below). 

 

 

 
Series 2, Number 184 5 NATIONAL CENTER FOR HEALTH STATISTICS

As a result of the 4-year sample design, the initial base weights 

calculated from the original sampling rates correspond to 

4-year samples. These initial base weights are based on the 

sampling probabilities and, as a result, pertain to the 4-year 

sample before its implementation. This means that the initial 

base weights sum to a national population estimate. To 

produce weights that sum to national population totals for 

1- and 2-year samples, adjustment factors are needed. For 

example, to create the 2-year sample weights for NHANES 

2015–2016 and NHANES 2017–2018 data released in the 

public-use files, the 4-year base weights were multiplied by 

2 to account for the 2-year selection (the number of years in 

the design divided by the number of years in the sample, 4 

divided by 2 = 2). 

The interview base weights are set to the screening final 

weights, which are the product of the screening base weights 

and the three weighting adjustment factors described 

below. Similarly, the MEC examination base weights are set 

to the interview final weights, which are the product of the 

interview base weights and the three interview weighting 

adjustment factors described below.

Nonresponse Adjustment

If every selected household was screened, and every SP 

agreed to complete the interview and the examination, 

weighted estimates using the base weights described in the 

“Calculating Base Weights” section would be approximately 

unbiased estimates of characteristics for the civilian 

noninstitutionalized U.S. population. However, some of 

the selected households are not screened, some of the 

SPs who are screened refuse to be interviewed, and some 

of the interviewed participants refuse the examination. 

If nonresponse rates do not vary at any stage across the 

sampling domains, using the base weights would still be 

approximately unbiased. However, these assumptions do not 

hold for NHANES. To reduce the potential for nonresponse 

bias, base weights are adjusted for nonresponse at each 

stage of the survey (screening, interview, and examination). 

The amount of information that can be used for these 

adjustments increases at each progressive stage; only the 

sampling information is available at the screening stage, 

while person-specific information from the interview is 

available to adjust examination weights.

The nonresponse adjustment procedure consists of 

computing adjustment factors, 

and applying these to the survey weights as:

 

 

 

separately within nonresponse cells, where nonresponse 

cells are defined by categorical characteristics known for 

both participants and nonrespondents. 

Because little is known about households that do not 

complete the screener, the adjustment cells at the screening 

stage are just the segments, with the assumption that DUs 

in the same segment are similar. For the interview and 

examination nonresponse adjustments, a classification 

program is used to identify available variables most highly 

related to response propensity. Different variables are 

identified to form adjustment cells for the following age 

groups: 0–5 years, 6–19 years, 20–39 years, 40–59 years, and 

60 years and over. The use of these variables differs by survey 

collection period. Variables used to form the nonresponse 

adjustment cells for interview weights are listed in Table VI

and for examination weights in Table VII.

Nonresponse adjustment reduces bias if response rates and 

survey characteristics vary from cell to cell and if participants 

and nonrespondents sharing the same characteristics are in 

the same cell. 

An effect of nonresponse adjustment is that it increases 

the variability of the weights, which in turn increases the 

variance of estimates obtained from the data. When the 

nonresponse adjustment cells contain a sufficient number of 

cases and the adjustment factors are not too large, the effect 

on variances is modest. A large adjustment factor in a cell is 

usually the result of a small number of participants in that 

cell. To avoid having nonresponse adjustments based on very 

small sample sizes, or having large nonresponse adjustment 

factors, nonresponse adjustment cells can be collapsed to 

form larger cells. As with prior NHANES samples, cells with 

fewer than 30 participants were collapsed to create larger 

cells in NHANES 2015–2016 and NHANES 2017–2018. Due to 

increasing nonresponse to the interview, larger adjustment 

factors than in previous NHANES samples were allowed, 

which could result in higher variance for some survey 

estimates. The nonresponse adjustment factors for most 

cells were less than 2. The largest factor was 2.17 for NHANES 

2015–2016 and 3 for NHANES 2017–2018. The nonresponse 

adjustment factors’ range for NHANES 2015–2016 was 

similar to NHANES 2013–2014. 

Trimming

Weight-trimming procedures are used to reduce the impact 

of any extreme weights on estimation. Nonresponse 

adjustment can contribute to extreme weights. Even a few 

unexpectedly large weights can seriously inflate the variance 

of survey estimates. However, trimming sample weights may 

introduce estimation bias, so trimming is not automatically 

used for all sample weights. The impacts of trimming on both 

variance and bias are considered when deciding whether or 

not to trim weights. 
NATIONAL CENTER FOR HEALTH STATISTICS 6 Series 2, Number 184

Similar to the adjustment factors for earlier steps, calibration 

involves applying a ratio adjustment to the survey weights. 

In this step, the denominator of the adjustment factor 

for a particular demographic subgroup is the sum of the 

nonresponse-adjusted sample weights from the nonresponse 

adjustment step within the demographic subgroup, and the 

numerator Nc is the reference population control total for 

the demographic subgroup. 

The calibrated weights are then calculated as: 

If trimming was performed, the calibration adjustment 

factor fi(c) and calibration weights wi(c) were calculated from 

trimmed weights instead of nonresponse-adjusted weights. 

As in previous NHANES cycles, the NHANES 2015–2016 

weights were calibrated (using a poststratification process) 

to race-Hispanic origin-age-sex demographic subgroups at 

each stage of the survey. Known population totals for the 

calibration were obtained from the 2015 1-year American 

Community Survey (ACS), conducted by the Census Bureau. 

NHANES 2017–2018 weights were initially created following 

the same poststratification process as NHANES 2015–2016. 

However, a review of the NHANES 2017–2018 weights 

indicated a need for additional calibration to race-Hispanic 

origin-sex-education level subgroups for adults (aged 20 

and over) and household income. Education and income 

are known to be correlated with health (12,13), so these 

additional adjustments were applied to reduce the risk of 

bias in the survey estimates due to undercoverage. Income 

is known to have relatively high item nonresponse and 

falsification rates (14,15), so area-level mean household 

income was used for calibration instead of person-level 

income.

Race, Hispanic origin, age, and sex are collected from all 

SPs in the screener, and area-level mean household income 

can be obtained for the area (census tract) where each SP 

lives, so the 2017–2018 screening weights were calibrated 

(using 2-dimensional raking) to race-Hispanic origin-age-sex 

demographic subgroups and area-level household income. 

Highest education level for an SP is collected in the interview, 

so that value is only known for interview participants. The 

2017–2018 interview and MEC weights were calibrated 

(using 3-dimensional raking) to race-Hispanic origin-age-sex 

demographic subgroups, race-Hispanic origin-sex-education 

level subgroups, and area-level household income. 

For NHANES 2017–2018, the 2017 1-year ACS was used as 

the source for the reference totals for each race-Hispanic 

origin-age-sex subgroup and each race-Hispanic origin-sexeducation level subgroup. The 2017 5-year ACS was used to 

obtain the mean household income for each census tract, 

 

 

Trimming was not performed for any NHANES 2015–2016 

sample weights, but was for the NHANES 2017–2018 

interview weights and MEC weights. 

To determine whether or not to trim weights for samples 

(or subsets of samples), the distribution of weights within 

each sampling domain was inspected. The threshold for 

the NHANES 2015–2016 sample weights was defined as 

three times the sampling domain mean, which was used 

in earlier cycles. This threshold was increased to four 

times the sampling domain mean for the 2017–2018 cycle. 

Any extreme weights that exceeded this threshold were 

identified. The values of these extreme weights were reduced 

to the threshold, and the weights of all cases in the sampling 

domain were adjusted so that the sum of the weights in each 

sampling domain equaled the corresponding weighted sum 

before trimming. 

Trimmed sample weights are calculated as follows. Let ti be 

the weight after trimming for SPi, defined as:

Then the trimming factor, fi(TR), is calculated as:

where nb is the sample size of the bth race-Hispanic originincome-sex-age sampling domain and

Calibration

The final step in the weighting procedure for each survey 

stage is calibration to known population totals. Previous 

NHANES reports referred to this step as poststratification, 

which is a specific kind of calibration to a single set of 

population totals. As described below, calibration can 

also be done iteratively to multiple sets of population 

totals in a process called raking, which was done for the 

NHANES 2017–2018 weights. Calibration compensates for 

undercoverage or overcoverage of certain demographic 

groups and for any residual differential nonresponse among 

these groups. Like nonresponse adjustment, calibration is 

done at the screening, interview, and examination stages. 

As mentioned previously, a participant’s sample weight 

represents the number of persons with similar characteristics 

in the target population. The sum of all participants’ weights 

in a demographic subgroup could be considered as the total 

number of persons NHANES participants represented for 

this particular subgroup. Calibration adjusts the individual 

sample weights so that the sum of the sample weights 

within a demographic subgroup equals the population from 

an independent source for that subgroup. 

 

 

 
Series 2, Number 184 7 NATIONAL CENTER FOR HEALTH STATISTICS

and the tracts were then divided into deciles. The first decile 

contained the 10% of tracts with the lowest mean household 

incomes, and the tenth decile contained the 10% of tracts 

with the highest mean household incomes. The 2017 5-year 

ACS was then used as the source for the reference totals for 

each income decile. 

Table B shows the variables used to calibrate the 2015–2016 

and 2017–2018 weights, the sources of the data used for 

calibration, and the weighting stages where the calibration 

was applied.

The ACS population counts were adjusted to match the Census 

Bureau best estimates of the total noninstitutionalized 

civilian population of the United States, including people not 

counted in surveys or in the most recent decennial census. 

Calibration using ACS, therefore, brings the weighted totals 

up to the level of the presumed total noninstitutionalized 

civilian population in the United States. Detailed reports for 

ACS are available from the Census Bureau website (16).

A major effect of calibration is that it implicitly imputes survey 

characteristics for persons missed by the survey due to errors 

in the sampling frame and adjusts for residual nonresponse 

not previously corrected. The underlying assumption for 

calibration is that missed persons not covered by the survey 

have the same distribution of characteristics as surveyed 

persons within the calibration cells. This assumption is 

obviously an oversimplification; the missed persons are 

likely to be different. However, in the absence of information 

on the characteristics of the missed persons, calibration is a 

technique available for reducing bias due to undercoverage 

and residual nonresponse (17). 

Computing Final Weights 

The final sample weight for each participant at each 

stage is calculated as the product of the base weight, the 

nonresponse adjustment, the trimming adjustment (if 

needed), and the calibration adjustment. That is:

The final screening weight was calculated as:

 

 

The final weights from the screening stage are the base 

weights for the interview stage, and the final interview 

weight was calculated as:

The final weights from the interview stage are the base 

weights for the MEC examination stage, and the final MEC 

examination weight was calculated as:

 

The interview weight should be used for analyses of data 

from the household interview only when no variables from 

the examination are included. The examination weights 

should be used for analyses of data from the examination 

(including the MEC interview and some laboratory data) or in 

conjunction with the household interview data. In addition, 

there are special survey components and subsamples that 

require further adjustment of the examination weights due 

to specific inclusion criteria (e.g., morning fasting sample). 

Component-specific special weights, if needed, are released 

with the data for the component and described in the 

component’s documentation. 

NHANES 2017–2018 weights have wider ranges than 

previous cycles with extended weights at both ends. Analysts 

should be aware of the potential influence of large weights, 

especially when extreme weights are associated with 

extreme data points. Trimming these weights can introduce 

bias into the estimates (18). 

Subsample Weights

Some laboratory or examination components are done on 

a subsample of NHANES participants. For example, some 

but not all NHANES 2015–2016 participants aged 12 years 

and over are selected to give blood samples for testing for 

lead and mercury in blood. The subsamples selected for 

 

Table B. Variables used to calibrate National Health and Nutrition Examination Survey, 2015–2016 and 

2017–2018 screening, interview, and mobile examination center examined weights

Characteristic

2015–2016 2017–2018

Adjusted weights Calibration source Adjusted weights Calibration source

Race-Hispanic origin-age-sex Screening, interview, MEC 2015 ACS 1 year Screening, interview, MEC 2017 ACS 1 year

Race-Hispanic origin-sex-education for 

those aged 20 years and over ... ... Interview, MEC 2017 ACS 1 year

Census tract mean household income ... ... Screening, interview, MEC 2017 ACS 5 year

... Category not applicable.

NOTES: MEC is mobile examination center. ACS is American Community Survey. 

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2015–2018.
NATIONAL CENTER FOR HEALTH STATISTICS 8 Series 2, Number 184

these components are chosen at random with a specified 

sampling fraction (e.g., one-half of this examined age 

group), according to the protocol for that component. Each 

subsample is selected to be a nationally representative 

sample of the target population and has its own designated 

sample weight that accounts for the additional probability 

of selection into the subsample component, as well as any 

additional nonresponse to the component. 

These subsample weights are included in the respective 

component data files. Because these weights differ from 

the examination weights, subsample weights must be used 

for statistical estimation of measures collected only in that 

sample and for analyses that include those measures (see 

Table VIII for a list of special component sample weights 

and information regarding their appropriate use.) See the 

respective survey protocol and documentation (https://

www.cdc.gov/nchs/nhanes.htm) for more detail on the 

laboratory tests and health measurements completed for a 

subsample of participants.

There are caveats when trying to combine data sets with 

subsample weights. Some subsamples, like the three 

environmental subsamples, are mutually exclusive (i.e., no 

overlapping participants) and cannot be combined in the 

same data release cycle or across cycles. On the other hand, 

certain combinations of subsamples have some overlap; 

for example, each of the three environmental subsamples 

has overlap with the fasting subsample. These could be 

combined within the same data release cycle. These could 

also be combined across cycles if the overlapping sample 

is adequate and there are no differences in the estimates 

across the cycles. However, NHANES does not provide 

sample weights for these combined data sets. 

To combine two or more subsamples for analysis, random 

overlap is needed between the subsamples, and appropriate 

weights need to be recalculated. While there are no specific 

recommendations regarding how to create combined 

sample weights for overlapping subsamples, there are 

calibration approaches to adjusting weights available within 

the SUDAAN software with the procedures WTADJUST and 

WTADJX (19,20). Adjustments to create sample weights 

for overlapping subsamples can be made by adjusting 

the sample weights to match population totals within 

adjustment cells defined by race and Hispanic origin, sex, 

and age group, demographic characteristics that are present 

in both subsamples. Additional adjustments specific to the 

analysis may also be made to the sample weights using 

characteristics common to both subsamples. The selection 

and categorization of the variables used for adjustment will 

depend on the size of the combined sample and the purpose 

of the analysis. Because the overlapping subsamples are 

typically small, coarser adjustment cells than those used for 

the original sample weight creation are often required. 

Note that sample weight adjustments using the public-use 

data files would use one of the released race and Hispanic 

origin variables (RIDRETH3 or RIDRETH1) available in the file 

for the calculations. As described earlier, sample weights 

created by NCHS use the sampling race and Hispanic origin 

information.

Because all NHANES participants are eligible for the MEC 

24-hour dietary recall interview, analysts should treat this 

component like any other full sample (e.g., interviewed 

or examined sample) when combining with a subsample 

and use the sample weights of the smallest subpopulation 

that includes all of the analytic variables, in this case, the 

subsample weights. However, the analyst should consider 

recalculating the subsample weight to account for the day 

of the week of reported consumption, which was originally 

accounted for in the 24-hour dietary weights but is not 

reflected in the subsample weight. 

Combining Survey Cycles

Two-year sample weights are provided in public-use files 

for NHANES 2015–2016 and 2017–2018. To combine these 

cycles to produce 4-year estimates, a new 4-year weight 

can be calculated by dividing the 2-year weights by two, 

the number of 2-year cycles. Six-year weights can similarly 

be created for estimates from any three combined survey 

cycles for 2001–2002 or later by dividing the 2-year weights 

for each cycle by three. Additional cycles can be added 

using these same methods (i.e., divide all the weights by 

the number of 2-year cycles). When 1999–2000 data are 

included, double the 4-year weights for 1999–2002 first, and 

then divide all the weights by the number of 2-year cycles. 

However, as a result of sample design changes initiated 

in 2011, it is important to consider changes in race and 

Hispanic origin sampling and the corresponding variables 

in the public-use files when calculating estimates based on 

data from combined cycles that overlap 2011, such as the 

6-year period 2009–2014. RIDRETH3 was created to account 

for the non-Hispanic Asian category starting in 2011 and 

is not available in public-use files for earlier cycles. Before 

2011, Asian participants were grouped in the “non-Hispanic 

white and other” category for sampling and the “other race 

—including multiracial” race category for race and Hispanic 

origin variables in public-use files. Therefore, for combined 

data sets that use cycles before and after 2011, non-Hispanic 

Asian cannot be analyzed as a separate race and Hispanic 

origin category, and this group should be added to the 

“other” category. Similarly, estimates from combined cycles 

should only be made for the total Hispanic subgroup from 

2007 onward. Additional information for race and Hispanic 

origin changes over time and combining sample weights can 

be found in the NHANES analytic guidelines (5,6).

When adjusting the sample weights to combine data for 

multiple survey cycles, the sum of combined weights will 

be reasonably close to an independent estimate of the U.S. 

civilian noninstitutionalized population at the midpoint of 

the combined interval. The estimate from combined years of 

data will be the average over the time period. Using multiple 
Series 2, Number 184 9 NATIONAL CENTER FOR HEALTH STATISTICS

cycles to create combined estimates is useful for increasing 

sample sizes, particularly for analysis of rare events and small 

subgroups. However, estimation using combined cycles is 

based on the assumption that there are no differences in the 

estimates across cycles, including increasing or decreasing 

trends, other than observed differences due to random 

variation.

Variance Estimation

This section introduces design-based methods of variance 

estimation for complex sample survey data and describes 

the creation of variables necessary for variance estimation 

on the public- and restricted-use data files for the NHANES 

2015–2018 samples.

Sampling errors should be calculated for all survey estimates 

to aid in determining the statistical reliability of those 

estimates. For complex sample surveys, exact mathematical 

formulas for variance estimates are not available. Variance 

approximation procedures are necessary to provide 

reasonable, approximately unbiased, and design-consistent 

estimates of variance. These routines require special software 

that accounts for the survey design. Standard statistical 

software routines that assume a simple random sample 

should not be used for computing variances for NHANES. 

Although each 2-year sample is nationally representative, it 

is selected from only 30 PSUs. This small number of PSUs 

sometimes poses challenges for variance estimation. First, 

the sample sizes for some specific race-Hispanic originincome-sex-age domains may be small. Second, with a small 

number of PSUs, direct design-based variance estimates 

may be unstable for some measures. 

Two variance approximation procedures that account for 

the complex sample design and allow for the computation 

of design effects are replication methods and Taylor series 

linearization. Replication methods provide a general means 

for estimating variances for the types of complex sample 

designs and weighting procedures usually encountered in 

practice. The basic idea behind the replication approach is 

to select subsamples (or “replicates”) repeatedly from the 

whole sample to calculate the statistic of interest for each 

of these subsamples, and then use the variability among 

these replicate statistics to estimate the variance of the fullsample statistic. The jackknife and Fay’s adjusted balanced 

repeated replication (Fay’s BRR) methods are two common 

procedures for deriving replicates from a full sample. The 

jackknife procedure drops a small part of the sample in 

each replicate and adjusts the weights for the sample that is 

retained. The Fay’s BRR approach retains the entire sample 

in each replicate, but adjusts the weights so that part of the 

sample has increased weights and part of the sample has 

decreased weights (21). 

For the linearization approach, nonlinear estimates are 

approximated by linear estimates for the purpose of variance 

estimation. The linear approximation is derived by taking 

the first-order Taylor series approximation for the estimator. 

Standard variance estimation methods for linear statistics 

are then used to estimate the variance of the linearized 

estimator. Currently, NCHS recommends using Taylor series 

linearization methods for variance estimation in analyses 

of NHANES data for 2-year cycles or combined cycles and 

replication methods for single-year data. SUDAAN, Stata, R, 

and SAS survey procedures can be used to obtain variance 

estimates by these methods.

Variance Units for Publicly Released Data

Noncertainty PSUs are grouped into major strata defined 

by state-level health-related variables as discussed in the 

“Sample Design” section. In any 2-year sample there are 

two noncertainty PSUs sampled from each major stratum. 

These strata are used as variance strata to estimate 

sampling error in the Taylor series linearization approach. 

Within each variance stratum, the two sampled PSUs are 

generally defined as variance units. Certainty PSUs are not 

selected within strata. Variance strata for these PSUs are 

formed based on the relative size of the PSU compared with 

the other PSUs. As a result, some of these variance strata 

may have one PSU split into multiple variance units, while 

other variance strata may comprise three PSUs for variance 

estimation, depending on the number and size of the 

certainty locations that year. 

Risks of data disclosure that may compromise participants’ 

confidentiality include the small number of PSUs in a 2-year 

NHANES sample, geographic data and other characteristics 

of the geographic area in the data files, and local publicity 

campaigns while the survey is in the field. As a result, masked 

variance units (MVUs) are provided for use with the publicuse data files to reduce the chance of matching PSUs in the 

sample to PSUs in the geographic areas, while minimizing 

the bias in the variance caused by altering the PSU structure. 

Collectively, the masked variance units (pseudo-PSUs) 

formed by the noncertainty and certainty locations can be 

used for variance estimations and to estimate sampling error. 

Though they are not the “true” design PSUs, the pseudoPSUs produce variance estimates that closely approximate 

the variances that would have been estimated using the true 

design PSUs. MVUs have been created for all 2-year survey 

cycles from NHANES 1999–2000 through 2017–2018 and can 

be used for analyzing any 2-year data set or any combined 4- 

to 20-year data set. Analysts can compute replicate weights 

for variance estimation based on MVUs.

Restricted Data Access in the NCHS 

Research Data Center

For the current sample design, NHANES data are released 

to the public in 2-year cycles. In addition to public-use data 

files, there are special data sets with restricted access that 

are available only through the NCHS RDC. These include 

data sets with (a) data items that were collected from a 
NATIONAL CENTER FOR HEALTH STATISTICS 10 Series 2, Number 184

single-year sample or collected for any time period other 

than a public release 2-year cycle (identified as such in the 

component description), (b) data merged geographically 

to some other contextual data files (often supplied by the 

data user), and (c) data items that are determined to be 

too sensitive or too detailed to be released to the public 

due to confidentiality restrictions. NHANES data linked with 

administrative data (e.g., Medicare, Medicaid or CHIP, and 

Social Security Administration) are only available in the RDC.

Some of these data files have special sample weights that 

should be used when these nonpublic data sets are analyzed 

within the confines of the RDC environment. For example, 

single-calendar-year data files have single-year interview 

and examination weights. These single-year sample weights 

can be combined with sample weights provided in the 

2-year public-use file to create 3-year sample weights. All 

1-year sample weights were calculated in the same manner 

as the public-use 2-year sample weights described above in 

“Weighting the Sample Data.” 

If a special data file involves subsampling, then special 

subsample weights that reflect the number of calendar years 

in the data file and the rate of subsampling were created for 

that file. For all special data files, appropriate documentation 

is provided in the RDC to describe the necessary sample 

weights.

Unmasked PSU and variance stratum codes (which differ 

from the MVU codes provided for the public-use files) can 

be provided for variance estimation for restricted data files 

in the RDC. These unmasked variance codes are necessary 

for studies that use geographically defined variables or for 

studies that geographically merge NHANES data with external 

data sets. In addition, Fay’s BRR weights for 2-year data 

and jackknife weights for single-year data with unmasked 

variance units are available in the RDC. If replication 

methods are to be used for combined survey years or cycles 

with unmasked variance units, replicate weights must be 

computed by the analyst in the RDC. Providing the unmasked 

PSU and stratum codes poses no disclosure risk because 

of the access restrictions in place at the RDC. Further, any 

subnational estimate that is generated from an RDC analysis 

must be reviewed and approved by NCHS staff to protect the 

confidentiality of participants.

More information on the RDC and lists of commonly used 

restricted NHANES data files and variables are available 

from the NCHS website at: https://www.cdc.gov/rdc/.

Other restricted variables not listed here may be available 

upon request. Information on proposals for use of stored 

specimens is available from: https://www.cdc.gov/nchs/

nhanes/biospecimens/biospecimens.htm.

References

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NATIONAL CENTER FOR HEALTH STATISTICS 12 Series 2, Number 184

Appendix I. Glossary 

Calibration—A statistical method that adjusts sample 

estimates of totals or percentages of target characteristics 

(e.g., sex, race and Hispanic origin, and age) to equal the 

target population totals or percentages. The adjustment cells 

for calibration are similar to the domains used for sample 

selection but can include variables that were not used for 

the original sampling frame.

Demographic subgroup—A demographic group defined by 

race and Hispanic origin, age, and sex, which might be a 

single sample domain or a combination of multiple sample 

domains. 

Domain—A demographic group of analytic interest (analytic 

domain). Analytic domains may also be sampling domains if 

a sample design is created to meet goals for those specific 

demographic groups. For the National Health and Nutrition 

Examination Survey (NHANES), sampling domains are 

defined by race and Hispanic origin, income, age, and sex. 

See Sampling domain. 

Dwelling unit (DU)—Also called a “housing unit.” A house, 

apartment, mobile home or trailer, group of rooms, or single 

room occupied as separate living quarters or, if vacant, 

intended for occupancy as separate living quarters. Separate 

living quarters are those in which the occupants live 

separately from other individuals in the building and which 

have direct access from outside the building or through a 

common hall. In this report, the term generally means 

those DUs that are eligible for the survey (i.e., excluding 

institutional group quarters), or that could become eligible 

(e.g., vacant at the time of sampling but could be occupied 

when screening begins). 

Household—All the persons who occupy a housing unit as 

their usual place of residence. 

Masked variance units (MVUs)—A collection of secondary 

sampling units aggregated into groups for the purpose of 

variance estimation and designed to prevent disclosure of 

the identity of the selected primary sampling units (PSUs). 

For NHANES, rather than using the units as sampled, some 

pseudo units are created. The resulting units produce 

variance estimates that closely approximate the “true” design 

variance estimates. MVUs have been created for all 2-year 

survey cycles from NHANES 1999–2000 through 2017–2018. 

They can also be used for analyzing any combined 4- to 20-

year data set.

Measure of size (MOS)—A value assigned to every sampling 

unit in a sample selection, usually a count of units associated 

with the elements to be selected. For NHANES, the MOS 

is actually a weighted average of estimates of population 

counts for the race-Hispanic origin-income groups of interest.

Noninstitutionalized civilian population—Includes all people 

living in households and noninstitutional group quarters 

who are not active members of the military. This is the target 

population for NHANES. 

Participant—A person selected into the NHANES sample 

during screening (sample person) who agrees to participate 

in the survey. In NHANES, persons agreeing to complete the 

in-home interview are considered “interview participants.” 

Persons agreeing to complete both the in-home interview 

and an examination in a mobile examination center (MEC) 

are considered “MEC participants.”

Primary sampling unit (PSU)—The first-stage selection unit 

in a multistage area probability sample. In NHANES, PSUs are 

counties or groups of counties in the United States. Some 

PSUs are so large that they are selected into the survey with 

a probability of one. These are referred to as PSUs selected 

with certainty (“certainty PSUs” or often referred to as selfrepresenting [SR] PSUs); all other PSUs are selected without 

certainty (“noncertainty PSUs” or non-self-representing 

PSUs). 

Probability proportionate to size (PPS) sampling—In this 

method, the probability of selecting any unit varies with 

the size of the unit, giving larger units a greater probability 

of selection and smaller units a lower probability. NHANES 

uses PPS sampling in the selection of PSUs and secondary 

sampling units.

Public-use data file—An electronic data set containing 

respondent records from a survey with a subset of variables 

collected in the survey that have been reviewed by analysts 

within the National Center for Health Statistics (NCHS) 

to ensure that participant identities are protected. NCHS 

disseminates these files to encourage public use of the 

survey data. 

Race and Hispanic origin—Unless otherwise specified, race 

and Hispanic origin in this report is used as it was used in 

NHANES sample selection and sample weight construction. 

It refers to Hispanic persons; non-Hispanic black persons; 

non-Hispanic, non-black Asian persons; and a fourth group 

that includes non-Hispanic white and all other persons. This 

is in contrast to the race and Hispanic origin categories in 

the public-use data files as reported by participants in the 

interview (non-Hispanic white, non-Hispanic black, nonHispanic Asian, Mexican American, other Hispanic, and 

other race including multiracial).

Replicates—Subsamples selected repeatedly from a sample 

used in some variance estimation approaches. With 

these approaches, the statistic of interest is calculated for 

each subsample, and the variability among the replicate 
Series 2, Number 184 13 NATIONAL CENTER FOR HEALTH STATISTICS

statistics is used to estimate the variance of the full sample 

statistic. The jackknife and Fay’s adjusted balanced repeated 

replication (Fay’s BRR) methods are two common procedures 

for the derivation of replicates from a full sample. The Fay’s 

BRR method was used in the creation of replicate weights 

for most of the NHANES 2015–2018 multiyear samples. 

Replicate weights are available through the Research Data 

Center (RDC).

Response rate—The number of survey respondents divided 

by the number of persons selected into the sample. 

Restricted-use data file—An electronic data set of survey 

respondent records, containing some information that may, 

if released to the public, risk disclosing individual survey 

respondents. These data are available only through the NCHS 

RDC. These include data sets with (a) data items collected 

from a single year); (b) data geographically linked to other 

contextual data files (often supplied by the data user); (c) 

data items determined to be too sensitive or too detailed to 

be released to the public due to confidentiality restrictions. 

Sample weight—The estimated number of persons in the 

target population that an NHANES respondent represents. 

For example, if a man in the sample represents 12,000 

men in his race-Hispanic origin-sex-age domain, then his 

sample weight is 12,000. The NHANES sample weights were 

adjusted for different sampling rates (of the race-Hispanic 

origin-income-sex-age groups), different response rates, and 

different coverage rates among persons in the sample, so 

that accurate national estimates could be made from the 

sample. The product of all of these adjustments is sometimes 

called the “final” sample weight.

Sampled person—A person selected for the NHANES sample 

based on oversampling targets for certain demographic 

subgroups. Sampled persons may become participants 

(agree to take part in the survey) or nonrespondents. 

Sampling domain—NHANES 2015–2018 included 87 

sampling domains, which were defined by race and Hispanic 

origin, income, age, and sex. Every person in the NHANES 

target population can be classified into exactly one of the 87 

sampling domains. See Domain. 

Sampling error—The portion of the difference between 

the sample estimate and true population value due to only 

observing a sample rather than the entire population. 

Sampling rate—The rate at which a unit is selected from a 

sampling frame. For NHANES, the rates required for sampling 

persons in the race-Hispanic origin-income-sex-age domains 

were designed to achieve the designated number of MEC 

examinations in each of those domains. The sampling rates 

are the driving force in all stages of sampling.

Screener—An interview (usually short) containing a set 

of questions asked of a household member to determine 

whether the household contains anyone who could be 

selected into the sample for the survey. In NHANES, the 

screener consisted of compiling a household roster and 

collecting the income level of the household and the race 

and Hispanic origin, age, and sex of all household members. 

For NHANES, only persons aged 18 and over can answer the 

screener. 

Screening—The process of conducting, or attempting 

to conduct, the screener interview in selected DUs 

(households). Occupied DUs are screened using the 

screener. Other units can also be screened; the process 

for these units is simply verification that they are either 

vacant or not DUs. See Screener. 

Secondary sampling unit—The second-stage selection unit 

in a multistage area probability sample. For NHANES, these 

are typically referred to as “segments.” See Segment.

Segment—A group of housing units located near each other, 

all of which were considered for selection into the sample. 

For NHANES, segments consist of a census block, or group 

of blocks, and their selection makes up the second stage 

of sampling. Within each segment, a sample of DUs was 

selected. 

Stratification; Strata—The partitioning of a population of 

sampling units into mutually exclusive categories (strata). 

Typically, stratification is used to increase the precision 

of survey estimates for subpopulations important to the 

survey’s objectives. To select the PSUs fielded in 2015–2018, 

PSUs were stratified based on health status, metropolitan 

statistical area status, and various population demographics. 

Study location—The set of segments within a PSU that were 

fielded together, with all MEC examinations conducted at the 

same physical location. The distinction between a PSU and 

a study location is necessary because some large certainty 

PSUs were divided into multiple study locations and fielded 

at different times.

Target population—The population to be described by 

estimates from the survey. In NHANES, the target population 

is the resident civilian noninstitutionalized population of the 

United States, which excludes all persons in supervised care 

or custody in institutional settings, all active-duty military 

personnel, active-duty family members living overseas, and 

any other persons residing outside the 50 states and the 

District of Columbia. 

Undercoverage—The result of failing to include all of the 

target population within the sampling frame.

Variance—A measure of the dispersion of a set of numbers. 

In this report, the variance is specifically the sample 

variance, which is a measure of the variation of a statistic, 

such as a proportion or a mean, calculated as a function of 

the sampling design and the population parameter being 

estimated. Many common statistical software packages 

compute “population variances” by default; these may 

underestimate the sampling variance because they do not 

incorporate any effects of having taken a sample instead 
NATIONAL CENTER FOR HEALTH STATISTICS 14 Series 2, Number 184

of collecting data from every person in the full population. 

Estimating the variance in NHANES requires special statistical 

software, as discussed in this report. 

Variance stratum—The cluster of variance units used when 

forming a replicate for variance estimation. For NHANES, 

the PSU sampling strata usually correspond to the variance 

strata. 

Variance unit—A collection of secondary sampling units 

aggregated into groups and excluded when forming a 

replicate for variance estimation. For NHANES, an entire PSU 

usually corresponds to a variance unit. 

Weight—See Sample weight.
Series 2, Number 184 15 NATIONAL CENTER FOR HEALTH STATISTICS

 

Figure. Primary sampling unit major strata: National Health and Nutrition Examination Survey, 2015–2018

Major stratum Primary sampling unit description

A1 State Group A with less than 8.6% of the 

population living in rural areas in the Northeast

A2 State Group A with 8.6% or more of the 

population living in rural areas in the Northeast, 

or with 10.5% or more of the population living in 

rural areas in the West

A3 State Group A with 3.1% to less than 10.5% of 

the population living in rural areas in the West

A4 State Group A with less than 3.1% of the 

population living in rural areas in the West

B1 State Group B with less than 24.6% of the 

population living in rural areas in the Northeast

B2 State Group B with 24.6% or more of the 

population living in rural areas in the Northeast 

or the Midwest, or with less than 3.4% of the 

population living in rural areas in the South

B3 State Group B with 3.4% or more of the 

population living in rural areas in the South or 

the West

Major stratum Primary sampling unit description

C1 State Group C with less than 17.5% of the 

population living in rural areas in the Midwest

C2 State Group C with 17.5% or more of the 

population living in rural areas in the Midwest, 

or with 29.5% or more of the population living in 

rural areas in the South

C3 State Group C with 4.5% to less than 29.5% of 

the population living in rural areas in the South

C4 State Group C with less than 4.5% of the 

population living in rural areas in the South or 

the West

D1 State Group D with less than 69.2% of the 

population living in rural areas in the Midwest

D2 State Group D with 69.2% or more of the 

population living in rural areas in the Midwest, 

or with 28.5% or more of the population living in 

rural areas in the South

D3 State Group D with less than 28.5% of the 

population living in rural areas in the South
NATIONAL CENTER FOR HEALTH STATISTICS 16 Series 2, Number 184

Appendix II. Supporting Tables

Table I. Selected sample design parameters: National Health and Nutrition Examination Survey, 2011–2014 and 

2015–2018

Characteristic 2011–2014 2015–2018

Age of noninstitutionalized civilian target population All ages All ages

Geographic area United States1 United States1

Average number of sampled persons per 

eligible household 2 2

Number of study locations 60 60

Domains for oversampling Predesignated: 87 domains of sex-age 

groups for non-Hispanic black persons; 

non-Hispanic, non-black Asian persons; 

and Hispanic persons, and income-sex-age 

groups for other persons

Oversampled: Hispanic persons; non-Hispanic 

black persons; non-Hispanic, non-black 

Asian persons; low-income white and other 

persons (at or below 130% of federal poverty 

level); and adults aged 80 and over

Predesignated: 87 domains of sex-age 

groups for non-Hispanic black persons; 

non-Hispanic, non-black Asian persons; 

and Hispanic persons, and income-sex-age 

groups for other persons

Oversampled: Hispanic persons; non-Hispanic 

black persons; non-Hispanic, non-black 

Asian persons; low-income white and other 

persons (at or below 185% of federal poverty 

level); children aged 0–11 years; and adults 

aged 80 and over

Number of selected persons 27,763 31,538

Number of interviewed persons 19,931 19,225

Number of examined persons 19,151 18,248

1Includes 50 states and the District of Columbia. U.S. territories are not included in the National Health and Nutrition Examination Survey sample.

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2011–2018.

Table II. Race and Hispanic origin and income group 

sampling fractions (Ak) used in calculating primary 

sampling unit measure of sizes: National Health and 

Nutrition Examination Survey, 2015–2018

Race and Hispanic origin Ak value

Hispanic 0.000206

Non-Hispanic black 0.000287

Non-Hispanic, non-black Asian 0.000453

Non-Hispanic, low income, white and other 0.000184

Non-Hispanic, non-low income, white and other 0.000086

NOTE: For in-depth information on the calculation of Ak, see reference 1 in 

this report.

SOURCE: NCHS, National Health and Nutrition Examination Survey, 

2015–2018.

Table III. State grouping for primary sampling 

unit stratification: National Health and Nutrition 

Examination Survey, 2015–2018

State group State abbreviation

A AZ, CA, HI, MA, NH, NY, OR, UT, VT, WA

B CO, CT, DE, FL, ID, ME, MN, MT, NJ, NM, NV, PA, RI

C AK, DC, GA, IA, IL, MD, MI, ND, NE, OK, SD, TX, 

VA, WI, WY

D AL, AR, IN, KS, KY, LA, MO, MS, NC, OH, SC, TN, WV

SOURCE: NCHS, National Health and Nutrition Examination Survey, 

2015–2018.
Series 2, Number 184 17 NATIONAL CENTER FOR HEALTH STATISTICS

Table IV. Characteristics of 14 major strata formed for selection of primary sampling units: National Health and 

Nutrition Examination Survey, 2015–2018

State group Major stratum

Number of 

minor strata

Number of primary 

sampling units per 

major stratum1 Census region

Mean percent 

rural population2

A A1 4 17 Northeast 1.6

A2 4 220 Northeast, West 28.4

A3 4 20 West 4.1

A4 4 12 West 2.1

B B1 4 41 Northeast 6.0

B2 4 170 Northeast, Midwest, South 21.6

B3 4 232 South, West 16.8

C C1 4 45 Midwest 5.3

C2 4 897 Midwest, South 53.2

C3 4 103 South 11.4

C4 4 66 South, West 3.7

D D1 4 246 Midwest 20.2

D2 4 738 Midwest, South 60.9

D3 4 90 South 13.5

1For a 4-year sample, 4 primary sampling units are chosen for each major stratum, 1 for each of its 4 minor strata, for a total of 56 primary sampling units plus 

the additional 4 primary sampling units chosen with certainty, for a grand total of 60 primary sampling units.

2Weighted by 2012 civilian noninstitutional population estimates of noncertainty primary sampling units.

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2015–2018.
NATIONAL CENTER FOR HEALTH STATISTICS 18 Series 2, Number 184

Race and Hispanic origin-incomesex-age sampling domain1Numerator of 

sampling rate2

Initial base 

weight

Non-Hispanic black

All:

Under 1 1.00 1,814.82

1–2 0.84 2,158.28

3–5 0.60 3,005.41

Male:

6–11 0.62 2,915.84

12–19 0.50 3,638.12

20–39 0.39 4,623.63

40–49 0.50 3,644.75

50–59 0.48 3,762.67

60 and over 1.00 1,814.82

Female:

6–11 0.64 2,826.73

12–19 0.51 3,524.72

20–39 0.33 5,434.63

40–49 0.40 4,506.67

50–59 0.37 4,842.46

60 and over 0.67 2,703.47

Hispanic

All:

Under 1 0.93 1,950.64

1–2 0.47 3,865.98

3–5 0.34 5,286.79

Male:

6–11 0.34 5,299.42

12–19 0.29 6,155.91

20–39 0.25 7,347.30

40–49 0.28 6,398.99

50–59 0.42 4,317.43

60 and over 1.00 1,814.82

Female:

6–11 0.38 4,806.88

12–19 0.32 5,660.18

20–39 0.23 7,960.54

40–49 0.28 6,374.51

50–59 0.39 4,696.32

60 and over 0.88 2,055.93

Non-Hispanic, non-black Asian

All:

Under 1 0.95 1,917.73

1–2 1.00 1,814.82

3–5 1.00 1,814.82

Male:

6–11 1.00 1,814.82

12–19 1.00 1,814.82

20–39 0.63 2,874.44

40–49 0.80 2,260.34

50–59 1.00 1,814.82

60 and over 1.00 1,814.82

Female:

6–11 1.00 1,814.82

12–19 1.00 1,814.82

20–39 0.60 3,004.83

40–49 0.68 2,687.58

50–59 0.77 2,370.10

60 and over 1.00 1,814.82

Race and Hispanic origin-incomesex-age sampling domain1Numerator of 

sampling rate2

Initial base 

weight

Non-Hispanic white or other, 

low income

All:

Under 1 1.00 1,814.82

1–2 0.60 3,033.38

3–5 0.37 4,849.75

Male:

6–11 0.40 4,502.70

12–19 0.33 5,419.49

20–29 0.22 8,324.09

30–39 0.30 6,059.67

40–49 0.33 5,431.33

50–59 0.33 5,552.45

60–69 0.38 4,776.97

70–79 0.84 2,153.25

80 and over 1.00 1,814.82

Female:

6–11 0.45 4,022.33

12–19 0.38 4,761.14

20–29 0.13 13,588.21

30–39 0.19 9,309.00

40–49 0.26 7,019.96

50–59 0.22 8,247.26

60–69 0.28 6,469.73

70–79 0.31 5,780.92

80 and over 0.52 3,479.10

Non-Hispanic white or other, 

non-low income

All:

Under 1 0.78 2,332.29

1–2 0.50 3,597.49

3–5 0.29 6,214.22

Male:

6–11 0.26 7,012.11

12–19 0.17 10,528.23

20–29 0.11 16,229.23

30–39 0.11 16,493.76

40–49 0.10 17,502.40

50–59 0.09 20,224.32

60–69 0.10 18,446.86

70–79 0.19 9,634.63

80 and over 0.45 4,067.35

Female:

6–11 0.26 7,043.13

12–19 0.19 9,501.80

20–29 0.14 13,400.71

30–39 0.11 17,060.86

40–49 0.09 19,706.19

50–59 0.08 21,360.31

60–69 0.09 19,617.21

70–79 0.18 10,014.45

80 and over 0.39 4,655.36

1Age in years.

2Corresponds to a 180% sample; sampling rates may be calculated by 

dividing the numerator by 1,815.

SOURCE: NCHS, National Health and Nutrition Examination Survey, 

2015–2018.

Table V. Final sampling rates and initial base weights: National Health and Nutrition Examination Survey, 

2015–2018
Series 2, Number 184 19 NATIONAL CENTER FOR HEALTH STATISTICS

Table VI. Variables used in forming nonresponse adjustment cells for weighting interview samples: 

National Health and Nutrition Examination Survey, 2015–2016 and 2017–2018

Variables considered for 

nonresponse, by age group

Categories of variables cross-classified to form nonresponse adjustment cells

2015–2016 2017–2018

0–5 years

Census region ... Northeast, Midwest, South, West

Health-based state groups ... Healthiest states, 2nd healthiest, 3rd healthiest, 

least healthy

Household composition 1 SP in HH under age 16 years, more than 1 

SP in HH all under 16, more than 1 SP in HH 

mixed ages

...

Household size ... 1–2, 3–4, 5–6, 7 or more

Race and Hispanic origin of household 

reference person

... Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

Race and Hispanic origin of sampled person Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

...

Race and Hispanic origin and income level of 

sampled person

... Hispanic; non-Hispanic black; non-Hispanic 

Asian; non-Hispanic, other low income; 

non-Hispanic other, non-low income

Sex of household reference person Male, female Male, female

Tract-level median income (by quartile) First quartile, second, third, fourth ...

Tract-level median income (by quintile) ... First quintile, second, third, fourth, fifth

Tract-level percentage of the population aged 25 years 

and over with a college education or higher

First quartile, second, third, fourth ...

Tract-level percentage of the population born in 

the United States

... First quartile, second, third, fourth

Tract-level percentage of the population that is Asian ... First quartile, second, third, fourth

Tract-level percentage of the population with 

no health insurance

... First quartile, second, third, fourth

Urbanicity of the PSU Urban, suburban, rural ...

6–19 years

Census region Northeast, Midwest, South, West Northeast, Midwest, South, West

Health-based state groups Healthiest states, 2nd healthiest, 3rd healthiest, 

least healthy

Healthiest states, 2nd healthiest, 3rd healthiest, 

least healthy

Household size 1–2, 3–4, 5–6, 7 or more 1–2, 3–4, 5–6, 7 or more

Number of sampled persons in household 1–2, 3, 4, 5 or more 1, 2, 3, 4, 5, 6, 7, 8, 9 or more

Population size of the PSU ... Less than 100,000, greater than or equal to 

100,000 to less than 250,000, greater than 

or equal to 250,000 to less than 1,000,000, 

greater than or equal to 1,000,000

Race and Hispanic origin of sampled person Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

...

Race and Hispanic origin and income level of 

sampled person

... Hispanic; non-Hispanic black; non-Hispanic 

Asian; non-Hispanic, other low income; 

non-Hispanic other, non-low income

Sex of household reference person Male, female Male, female

Tract-level median age ... First quartile, second, third, fourth

Tract-level median income (by quintile) ... First quintile, second, third, fourth, fifth

Tract-level percentage of the population aged 18 years 

and over with a disability

... First quartile, second, third, fourth

Tract-level percentage of the population aged 25 years 

and over with a college education or higher

First quartile, second, third, fourth First quartile, second, third, fourth

Tract-level percentage of the population born in 

the United States

... First quartile, second, third, fourth

Tract-level percentage of the population that is Asian ... First quartile, second, third, fourth

See footnotes at end of table.
NATIONAL CENTER FOR HEALTH STATISTICS 20 Series 2, Number 184

Table VI. Variables used in forming nonresponse adjustment cells for weighting interview samples: 

National Health and Nutrition Examination Survey, 2015–2016 and 2017–2018—Con.

Variables considered for 

nonresponse, by age group

Categories of variables cross-classified to form nonresponse adjustment cells

2015–2016 2017–2018

20–39 years

Census region Northeast, Midwest, South, West ...

Health-based state groups ... Healthiest states, 2nd healthiest, 3rd healthiest, 

least healthy

Household composition 1 SP in HH at least aged 16 years, more than 1 

SP in HH all at least 16, more than 1 SP in HH 

mixed ages

...

Household size 1–2, 3–4, 5–6, 7 or more ...

Number of sampled persons in household ... 1, 2, 3, 4, 5, 6, 7, 8, 9 or more

Population size of the PSU ... Less than 100,000, greater than or equal to 

100,000 to less than 250,000, greater than 

or equal to 250,000 to less than 1,000,000, 

greater than or equal to 1,000,000

Race and Hispanic origin of household 

reference person

... Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

Sex of household reference person Male, female Male, female

Sex of sampled person Male, female Male, female

Tract-level median income (by quintile) ... First quintile, second, third, fourth, fifth

Tract-level percentage of the population aged 25 years 

and over with a college education or higher

First quartile, second, third, fourth First quartile, second, third, fourth

Tract-level percentage of the population born in 

the United States

... First quartile, second, third, fourth

Tract-level percentage of the population with 

no health insurance

... First quartile, second, third, fourth

Whether any children aged 0–17 years live in 

dwelling unit

... Yes, no

40–59 years

Health-based state groups Healthiest states, 2nd healthiest, 3rd healthiest, 

least healthy

Healthiest states, 2nd healthiest, 3rd healthiest, 

least healthy

Household composition 1 SP in HH at least aged 16 years, more than 1 

SP in HH all at least 16, more than 1 SP in HH 

mixed ages

...

Household size 1–2, 3–4, 5–6, 7 or more 1–2, 3–4, 5–6, 7 or more

Number of sampled persons in household ... 1, 2, 3, 4, 5, 6, 7, 8, 9 or more

Race and Hispanic origin of household 

reference person

... Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

Race and Hispanic origin of sampled person Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

...

Race and Hispanic origin and income level of 

sampled person

... Hispanic; non-Hispanic black; non-Hispanic 

Asian; non-Hispanic, other low income; 

non-Hispanic other, non-low income

Sex of household reference person ... Male, female

Sex of sampled person ... Male, female

See footnotes at end of table.
Series 2, Number 184 21 NATIONAL CENTER FOR HEALTH STATISTICS

Table VI. Variables used in forming nonresponse adjustment cells for weighting interview samples: 

National Health and Nutrition Examination Survey, 2015–2016 and 2017–2018—Con.

Variables considered for 

nonresponse, by age group

Categories of variables cross-classified to form nonresponse adjustment cells

2015–2016 2017–2018

40–59 years—Con.

Tract-level median age ... First quartile, second, third, fourth

Tract-level median income (by quartile) First quartile, second, third, fourth ...

Tract-level median income (by quintile) ... First quintile, second, third, fourth, fifth

Tract-level percentage of the population aged 18 years 

and over with a disability

... First quartile, second, third, fourth

Tract-level percentage of the population with a disability ... First quartile, second, third, fourth

Tract-level percentage of the population born in the 

United States

... First quartile, second, third, fourth

Urbanicity of the PSU Urban, suburban, rural ...

60 years and over

Census region Northeast, Midwest, South, West Northeast, Midwest, South, West

Household composition 1 SP in HH at least aged 16 years, more than 1 

SP in HH all at least 16, more than 1 SP in HH 

mixed ages

...

Household size ... 1–2, 3–4, 5–6, 7 or more

Number of sampled persons in household ... 1, 2, 3, 4, 5, 6, 7, 8, 9 or more

Population size of the PSU ... Less than 100,000, greater than or equal to 

100,000 to less than 250,000, greater than 

or equal to 250,000 to less than 1,000,000, 

greater than or equal to 1,000,000

Race and Hispanic origin of household 

reference person

... Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

Race and Hispanic origin of sampled person Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

...

Race and Hispanic origin and income level of 

sampled person

... Hispanic; non-Hispanic black; non-Hispanic 

Asian; non-Hispanic, other low income; 

non-Hispanic other, non-low income

Sex of household reference person Male, female ...

Tract-level median age ... First quartile, second, third, fourth

Tract-level median income (by quintile) ... First quintile, second, third, fourth, fifth

Tract-level percentage of the population aged 18 years 

and over with a disability

... First quartile, second, third, fourth

Tract-level percentage of the population with a disability ... First quartile, second, third, fourth

Tract-level percentage of the population with 

no health insurance

... First quartile, second, third, fourth

Urbanicity Very large, urban, suburban, rural ...

Whether any children aged 0–17 years live in 

dwelling unit

... Yes, no

... Category not applicable.

NOTES: SP is sampled person. HH is household. PSU is primary sampling unit.

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2015–2018.
NATIONAL CENTER FOR HEALTH STATISTICS 22 Series 2, Number 184

Table VII. Variables used in forming nonresponse adjustment cells for weighting mobile examination center 

examination samples: National Health and Nutrition Examination Survey, 2015–2016 and 2017–2018

Variables considered for 

nonresponse, by age group

Categories of variables cross-classified to form nonresponse adjustment cells

2015–2016 2017–2018

0–5 years

Home ownership Own, rent, some other arrangement ...

Household composition 1 SP in household under age 16 years, more than 

1 SP in household all under 16, more than 1 SP 

in household mixed ages

...

Household size 1–2, 3–4, 5–6, 7 or more 1–2, 3–4, 5–6, 7 or more

Number of sampled persons in household ... 1, 2, 3, 4, 5, 6, 7, 8, 9 or more

Population size of the PSU ... Less than 100,000, greater than or equal to 

100,000 to less than 250,000, greater than or 

equal to 250,000 to less than 1,000,000, greater 

than or equal to 1,000,000

Race and Hispanic origin of sampled person Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

...

Race-Hispanic origin and income level of 

sampled person

... Hispanic; non-Hispanic black; non-Hispanic 

Asian; non-Hispanic, other low income; 

non-Hispanic other, non-low income

Sex of sampled person ... Male, female

Tract-level median age ... First quartile, second, third, fourth

Tract-level percentage of the population 

aged 18 years and over with a disability

... First quartile, second, third, fourth

Tract-level percentage of the population that is 

Asian alone or in combination

First quartile, second, third, fourth First quartile, second, third, fourth

Tract-level percentage of the population with 

no health insurance

... First quartile, second, third, fourth

6–19 years

Health-based state groups ... Healthiest states, 2nd healthiest, 3rd healthiest, 

least healthy

Highest education level of any person in 

the household

... Less than high school, high school or GED, more 

than high school

Home ownership Own, rent, some other arrangement ...

Household composition 1 SP in household under age 16 years, 1 SP 

in household over 16, more than 1 SP in 

household all under 16, more than 1 SP in 

household all over 16, more than 1 SP in 

household mixed ages

1 SP in household under age 16 years, 1 SP 

in household over 16, more than 1 SP in 

household all under 16, more than 1 SP in 

household all over 16, more than 1 SP in 

household mixed ages

Household size 1–4, 5–6, 7 or more ...

Race and Hispanic origin of household 

reference person

... Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

Race and Hispanic origin of sampled person Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

...

Sex of household reference person Male, female ...

Tract-level median income (by quartile) First quartile, second, third, fourth ...

Tract-level median income (by quintile) ... First quintile, second, third, fourth, fifth

Tract-level percentage of the population 

born in the United States

First quartile, second, third, fourth ...

Tract-level percentage of the population with 

no health insurance

... First quartile, second, third, fourth

Urbanicity Very large, urban, suburban, rural ...

See footnotes at end of table.
Series 2, Number 184 23 NATIONAL CENTER FOR HEALTH STATISTICS

Table VII. Variables used in forming nonresponse adjustment cells for weighting mobile examination center 

examination samples: National Health and Nutrition Examination Survey, 2015–2016 and 2017–2018—Con.

Variables considered for 

nonresponse, by age group

Categories of variables cross-classified to form nonresponse adjustment cells

2015–2016 2017–2018

20–39 years

Census region ... Northeast, Midwest, South, West

Education level of the sampled person ... Less than high school, high school or GED, more 

than high school

Highest education level of any person in 

the household

... Less than high school, high school or GED, more 

than high school

Home ownership Own, rent, some other arrangement ...

Household composition 1 SP in household at least aged 16 years, more 

than 1 SP in household all at least 16, more than 

1 SP in household mixed ages

...

Household size 1–2, 3–4, 5–6, 7 or more ...

Number of sampled persons in household ... 1, 2, 3, 4, 5, 6, 7, 8, 9 or more

Population size of the PSU ... Less than 100,000, greater than or equal to 

100,000 to less than 250,000, greater than or 

equal to 250,000 to less than 1,000,000, greater 

than or equal to 1,000,000

Race and Hispanic origin of household 

reference person

... Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

Sampled person perceived health status Good, not good ...

Self-reported height and weight indicated 

sampled person was overweight

... Yes, no

Sex of household reference person Male, female ...

Sex of sampled person ... Male, female

Tract-level median age First quartile, second, third, fourth ...

Tract-level median income (by quintile) ... First quintile, second, third, fourth, fifth

Tract-level percentage of the population 

aged 18 years and over with a disability

... First quartile, second, third, fourth

Tract-level percentage of the population 

born in the United States

First quartile, second, third, fourth ...

40–59 years

Census region ... Northeast, Midwest, South, West

Education level of the sampled person ... Less than high school, high school or GED, more 

than high school

Health-based state groups Healthiest states, 2nd healthiest, 3rd healthiest, 

least healthy

Healthiest states, 2nd healthiest, 3rd healthiest, 

least healthy

Highest education level of any person in 

the household

High school or less, more than high school ...

Home ownership Own, rent, some other arrangement ...

Household size ... 1–2, 3–4, 5–6, 7 or more

Whether any children aged 0–17 years live 

in household

Yes, no ...

Race and Hispanic origin of sampled person Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

...

Sampled person perceived health status ... Excellent, very good or good, fair or poor

Sex of sampled person ... Male, female

Tract-level median age First quartile, second, third, fourth ...

Tract-level percentage of the population 

born in the United States

First quartile, second, third, fourth ...

Tract-level percentage of the population with 

no health insurance

... First quartile, second, third, fourth

See footnotes at end of table.
NATIONAL CENTER FOR HEALTH STATISTICS 24 Series 2, Number 184

Table VII. Variables used in forming nonresponse adjustment cells for weighting mobile examination center 

examination samples: National Health and Nutrition Examination Survey, 2015–2016 and 2017–2018—Con.

Variables considered for 

nonresponse, by age group

Categories of variables cross-classified to form nonresponse adjustment cells

2015–2016 2017–2018

60 years and over

Activity status of the respondent Yes, no ...

Census region ... Northeast, Midwest, South, West

Health-based state groups ... Healthiest states, 2nd healthiest, 3rd healthiest, 

least healthy

Highest education level of any person in 

the household

Less than high school, high school, more than 

high school

Less than high school, high school or GED, more 

than high school

Household composition 1 SP in household at least aged 16 years, more 

than 1 SP in household all at least 16, more than 

1 SP in household mixed ages

1 SP in household at least aged 16 years, more 

than 1 SP in household all at least 16, more than 

1 SP in household mixed ages

Race and Hispanic origin of sampled person Hispanic, non-Hispanic black, non-Hispanic 

Asian, non-Hispanic other

...

Sampled person perceived health status Good, not good Excellent, very good or good, fair or poor

Self-reported height and weight indicated 

sampled person was overweight

... Yes, no

Tract-level median age First quartile, second, third, fourth ...

Tract-level median income (by quartile) First quartile, second, third, fourth ...

Tract-level percentage of the population 

aged 25 years and over with a college 

education or higher

First quartile, second, third, fourth ...

Tract-level percentage of the population 

aged 18 years and over with a disability

... First quartile, second, third, fourth

Tract-level percentage of the population 

that is Asian

... First quartile, second, third, fourth

Urbanicity Very large, urban, suburban, rural ...

... Category not applicable.

NOTES: SP is sample person. PSU is primary sampling unit.

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2015–2018.
Series 2, Number 184 25 NATIONAL CENTER FOR HEALTH STATISTICS

Table VIII. Most common survey sample weights and their appropriate use: National Health and Nutrition 

Examination Survey, 2015–2018

Sample weight Application Notes

Full sample

Interview weights (WTINT2YR) Use when analyzing data from the home interview 

only. Do not use if the analysis includes variables 

that were also collected on examined persons in 

the MEC.

...

Examination weights (WTMEC2YR) Use when analyzing data from the MEC examination. 

Do not use if the analysis includes variables 

collected as part of one of the dietary interviews 

or part of one of the subsamples (e.g., fasting or 

environmental).

...

Dietary Day 1 sample weights (WTDRD1) Use when analyzing data from sample with 

completed Day 1 24-hour dietary recall or the 

Flexible Consumer Behavior Survey telephone 

follow-up module.

Instead of adjusting for race and Hispanic originage-day of the week (weekdays, Fridays, Saturdays, 

and Sundays) where totals are aligned for each 

of the 48 cells defined by the full crosstabulation 

of these factors, as done in NHANES 2013–2014, 

NHANES 2015–2016, and NHANES 2017–2018, 

Day 1 dietary weights were marginally calibrated 

to race and Hispanic origin, age group, sex, and 

four categories of day of the week, where the best 

adjustment was determined by aligning population 

totals for each variable separately using an iterative 

method.

Dietary Day 1 and Day 2 completed sample 

(WTDR2D)

Use when analyzing data from the smaller sample 

with 2 days of completed 24-hour dietary recall.

Dietary 2-day sample weights were marginally 

calibrated to race and Hispanic origin, age group, 

sex, and Day 1 and 2 day weekday-weekend 

categories.

One-half subsample

Blood metals subsample weights 

(WTSH2YR)

Use when analyzing blood lead, cadmium, total 

mercury, selenium, and manganese, etc.

Blood metals one-half subsample weights are only 

for 12 years and over in 2015–2016. MEC weights 

should be used for children aged 1–11 years in 

2015–2016 and all ages in 2017–2018. 

Fasting subsample weights (WTSAF2YR) Use when analyzing the plasma fasting glucose, 

insulin levels, triglycerides, and low-density 

lipoprotein cholesterol (lipids) only for examined 

persons assigned to and meeting the criteria for 

the fasting subsample.

Fasting subsample weights are for participants 

aged 12 years and over who were examined 

in a morning session and fasted 8–23 hours. 

Diagnosed diabetes is a separate poststratification 

category (to the interview weights).

Folates subsample weights (WTSF2YR) Use when analyzing whole blood folate and serum 

total folate.

Folates subsample weights are for participants 

aged 1 year and over who were MEC-examined in 

2017–2018. MEC weights should be used for all 

ages in 2015–2016.

OGTT subsample weights (WTSOG2YR) Use when analyzing only OGTT glucose levels or 

OGTT glucose levels with other data such as insulin 

or fasting levels for examined persons assigned to 

and meeting the criteria for the OGTT.

OGTT subsample weights are for participants 

aged 12 years and over who were examined in a 

morning session, fasted 9–23 hours, and had valid 

glucose test results, except for women stating 

that they were pregnant. Women stating that they 

were not pregnant, but who were later determined 

to be pregnant from the laboratory results, were 

eligible and are included. Diagnosed diabetics were 

not given the OGTT, but nonpregnant diagnosed 

diabetics are included in the OGTT file with a 

weight equal to their fasting weight. OGTT was 

discontinued in 2017–2018.

VOC subsample weights (WTSVOC2Y) Use when analyzing data from VOC one-half 

laboratory subsample for examined persons 

assigned to and meeting the criteria for this 

subsample.

VOC subsample weights are for participants aged 12 

years and over. For 2015–2016, WTSVOC2Y equals 

to WTSH2YR for participants 12 years and over.

VOC smoking weights (WTSVS2YR) Use when analyzing the VOC smoking special 

sample.

VOC smoking weights are for participants in the 

VOC subsample plus current smokers. Current 

smokers were identified based on the smoking 

questions asked of all participants aged 18 years 

and over for 2015–2016. The VOC smoking special 

sample was discontinued in 2017–2018.

See footnotes at end of table.
NATIONAL CENTER FOR HEALTH STATISTICS 26 Series 2, Number 184

Table VIII. Most common survey sample weights and their appropriate use: National Health and Nutrition 

Examination Survey, 2015–2018—Con.

Sample weight Application Notes

One-third subsample

Environmental chemicals A weights 

(WTSA2YR)

Use when analyzing data from the one-third 

laboratory environmental subsample A for 

examined persons assigned to and meeting the 

criteria for this subsample.

Environmental subsample A, B, and C weights are 

each for a one-third subsample of examined 

participants aged 6 years and over. Participants are 

assigned to one of the three mutually exclusive 

one-third environmental subsamples based on the 

sampling scheme. The analytes in each of the three 

subsamples are varied depending on what is 

included in the cycle. The names Subsample A, 

Subsample B, and Subsample C are for convenience 

and are not representative of any ordering. The 

proper subsample weights attached in the data set 

should be used to analyze the subsample. As the 

same analytes might be in different subsamples, it 

is important to check the weight.

Environmental chemicals B weights 

(WTSB2YR)

Use when analyzing data from the one-third 

laboratory environmental subsample B for 

examined persons assigned to and meeting the 

criteria for this subsample.

See Environmental chemicals A weights.

Environmental chemicals C weights 

(WTSC2YR)

Use when analyzing data from the one-third 

laboratory environmental subsample C for 

examined persons assigned to and meeting the 

criteria for this subsample.

See Environmental chemicals A weights.

Smoking weights (WTSFM) Use when analyzing the smoking special sample. These weights apply to the participants in 

environmental chemicals subsample A plus all 

examined adults who were current smokers. 

Current smokers were identified based on the 

smoking questions asked of all participants aged 

18 years and over for 2015–2016. The smoking 

special sample was discontinued in 2017–2018.

... Category not applicable.

NOTES: MEC is mobile examination center. NHANES is National Health and Nutrition Examination Survey. OGTT is oral glucose tolerance test. VOC is 

volatile organic compound.

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2015–2018.
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