This report focuses on the editing and statistical imputation procedures that were applied to respondent data for the 2019 NSDUH. Logical editing uses data from elsewhere within the same respondent's record to reduce the occurrence of missing or ambiguous data or to resolve inconsistencies between related variables. Imputation is defined as the replacement of missing values with valid, nonmissing values. Statistical imputation usually involves some randomness to preserve the natural variability in the data.

This report describes editing and imputation for the 2016 NSDUH.

Learn about the editing and statistical imputation procedures that were applied to respondent data for the 2022 National Survey on Drug Use and Health (NSDUH). Logical editing resolves inconsistencies or ambiguous data based on a respondent’s answers to other questions in the survey. Statistical imputation uses mathematical techniques to assign values when they are missing in the data.Introductory Chapters:An introduction, including a discussion of changes from the 2021 to 2022 survey.A description of the procedures and general principles for editing the NSDUH data.A description of the general imputation procedures used in NSDUH.Remaining chapters are descriptions of the editing and imputation for the following types of variables:Front-end demographics.Back-end demographics.Substance use.Special drugs and substance use disorder.Additional substance use, including treatment and emerging issues.Substance use risk and protective factors.Physical and mental health.Roster variables.Income.Health insurance.Pair variables.

Learn about the editing and statistical imputation procedures that were applied to respondent data for the 2021 National Survey on Drug Use and Health (NSDUH). Logical editing resolves inconsistencies or ambiguous data based on a respondent’s answers to other questions in the survey. Statistical imputation uses mathematical techniques to assign values when they are missing in the data.Introductory Chapters:An introduction, including a discussion of changes from the 2020 to 2021 survey.A description of the procedures and general principles for editing the NSDUH data.A description of the general imputation procedures used in NSDUH.Remaining chapters are descriptions of the editing and imputation for the following types of variables:Front-end demographics.Back-end demographics.Substance use.Special drugs and substance use disorder.Additional substance use, including treatment and emerging issues.Substance use risk and protective factors.Physical and mental health.Roster variables.Income.Health insurance.Pair variables.

This report describes the general principles and procedures for editing and imputation for the variables in the 2012 National Survey on Drug Use and Health (NSDUH). The report also describes imputation and the predictive mean neighborhood methodology.

This report describes tasks relating to data collection for the 2018 NSDUH: Sampling and Counting and Listing Operations, Data Collection Staffing, Preparation of Survey Materials, Field Staff Training, Data Collection, Data Collection Results, and Quality Control.

This report describes tasks relating to data collection for the 2019 NSDUH: Sampling and Counting and Listing Operations, Data Collection Staffing, Preparation of Survey Materials, Field Staff Training, Data Collection, Data Collection Results, and Quality Control.

The focus of this report is to describe the statistical inference procedures used to produce design-based estimates as presented in the 2017 detailed tables and the 2017 FFR, which are based on restricted-use data.

2014 NSDUH MRB Editing and Imputation Report

The focus of this report is to describe the statistical inference procedures used to produce design-based estimates as presented in the 2019 detailed tables and the 2019 FFR, which are based on restricted-use data.