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 focuses on the editing and statistical imputation procedures that were applied to respondent data for the 2018 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 focuses on the editing and statistical imputation procedures that were applied to respondent data for the 2017 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 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.

The focus of this report is to describe the statistical inference procedures used to produce design-based estimates as presented in the 2013 detailed tables, the 2013 mental health detailed tables, the 2013 national findings report, and the 2013 mental health findings report. Thestatistical procedures and information found in this report can also be generally applied to analyses based on the public use file as well as the restricted-use file available through the data portal. This report is organized as follows: Section 2 provides background informationconcerning the 2013 NSDUH; Section 3 discusses the prevalence rates and how they were calculated, including specifics on topics such as mental illness, major depressive episode, and serious psychological distress; Section 4 briefly discusses how missing item responses of variables that are not imputed may lead to biased estimates; Section 5 discusses sampling errors and how they were calculated; Section 6 describes the degrees of freedom that were used when comparing estimates; and Section 7 discusses how the statistical significance of differences between estimates was determined. Section 8 discusses confidence interval estimation, and Section 9 describes how past year incidence of drug use was computed. Finally, Section 10 discusses the conditions under which estimates with low precision were suppressed. Appendix A contains examples that demonstrate how to conduct various statistical procedures documented within this report using SAS® and SUDAAN® Software for Statistical Analysis of Correlated Data (RTI International, 2012) along with separate examples using Stata® software.

The purpose of this report is to guide analysts interested in fitting regression models using data from the National Survey on Drug Use and Health (NSDUH) by providing them with methods for handling missing item values in regression analyses (MIVRA). The report includes a theoretical review of existing MIVRA methods, a simulation study that evaluates several of the more promising methods using existing NSDUH datasets, and a final chapter where the results of both the theoretical review and the simulation study are synthesized into guidance for analysts via decision trees.

This report addresses the following topics relating to data collection for the 2017 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 2018 detailed tables and the 2018 FFR, which are based on restricted-use data.