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 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.

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 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.

Learn how to produce basic estimates with the 2022 National Survey on Drug Use and Health (NSDUH). The report describes the techniques that were used to make the 2022 NSDUH Detailed Tables and the 2022 NSDUH Annual National Report, but users may also find these techniques useful for their own research with NSDUH. The report describes the calculation of estimates and sampling errors, degrees of freedom, and the procedures for determining when low-precision estimates should be suppressed. It also includes sample code in several statistical languages that data users can modify to use in their own research.Chapters:Introduction to the report.Background on the survey design, including redesign and questionnaire changes.Prevalence estimates and how they were calculated, including specifics on various topics presented in the detailed tables.Discussion of how missing item responses of variables that are not imputed may lead to biased estimates.Discussion of sampling errors and how they were calculated.Description of degrees of freedom and how they were used to compare estimates.Discussion of how the statistical significance of differences between estimates was determined.Discussion of confidence interval estimation.Discussion of when estimates with low precision were suppressed.Appendix A contains code samples for various statistical procedures documented within the report.

This report describes in detail the editing and imputation task for the 2015 NSDUH.

This report focuses on the editing and statistical imputation procedures that were applied to respondent data for the 2013 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 the imputation procedures used in the 2011 National Survey on Drug Use and Health (NSDUH).