Learn about the techniques used to create weights for the 2021 National Survey on Drug Use and Health (NSDUH) at the person level. The report reviews the generalized exponential model (GEM) used in weighting, discusses potential predictor variables, and details the practical steps used to implement GEM. The report also details the weight calibrations, and presents the evaluation measures of the calibrations, as well as a sensitivity analysis.Chapters:Introduces the survey and the remainder of the report.Reviews the impact of multimode data collection on weighting.Briefly describes of the generalized exponential model.Describes the predictor variables for the model calibration.Defines extreme weights.Discusses control totals for poststratification adjustments.Discusses weight calibration at the dwelling unit level.Discusses weight calibration at the person level.Presents the evaluation measures of calibrated weights and a sensitivity analysis of selected prevalence estimates.Explains the break-off analysis weights.Explains the alternative analysis weights.Appendices include technical details about the model and the evaluations that were performed.

Learn about the techniques used to create weights for the 2022 National Survey on Drug Use and Health (NSDUH) at the person level. The report reviews the generalized exponential model (GEM) used in weighting, discusses potential predictor variables, and details the practical steps used to implement GEM. The report also details the weight calibrations, and presents the evaluation measures of the calibrations, as well as a sensitivity analysis.Chapters:Introduces the survey and the remainder of the report.Reviews the impact of multimode data collection on weighting.Briefly describes of the generalized exponential model.Describes the predictor variables for the model calibration.Defines extreme weights.Discusses control totals for poststratification adjustments.Discusses weight calibration at the dwelling unit level.Discusses weight calibration at the person level.Presents the evaluation measures of calibrated weights and a sensitivity analysis of selected prevalence estimates.Explains the break-off analysis weights.Appendices include technical details about the model and the evaluations that were performed.

Stata module that implements Potter's (1990) weight distribution approach to trim extreme sampling weights. The basic idea is that the sampling weights are assumed to follow a beta distribution. The parameters of the distribution are estimated from the moments of the observed sampling weights and the resulting quantiles are used as cut-off points for extreme sampling weights. The process is repeated a specified number of times (10 by default) or until no sampling weights are more extreme than the specified quantiles.

1. File Information

This dataset is a synthetic dataset created based on sampling statistics from the Taiwan Ministry of Health and Welfare. It includes data on height, weight, BMI, and age of individuals, making it suitable for various health-related analyses.

2. Meta Data

3. Potential Analyses

Exploratory Data Analysis (EDA):

• Distribution analysis for height, weight, and BMI.
• Age and gender-based trends.

Regression Analysis:

• Linear Regression: Predict weight based on height and BMI.
• Logistic Regression: Classify individuals by BMI categories.

Clustering and Classification:

• Group individuals into categories (e.g., underweight, healthy, overweight) based on BMI.

Time-Series/Trend Analysis:

• Investigate how health metrics (BMI) evolve over age groups.

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🇺🇸 미국

Learn about the techniques used to create weights for the 2022 National Survey on Drug Use and Health (NSDUH) at the pair and questionnaire dwelling unit (QDU) levels. NSDUH is designed so that some of the sampled households have both an adult and a youth respondent who are paired. Because of this, NSDUH allows for estimating characteristics at the person level, pair level, or QDU level. This report describes pair selection probabilities, the generalized exponential model (including predictor variables used), and the multiple weight components that are used for pair or QDU levels of analysis. An evaluation of the calibration weights is also included.Chapters:Introduces the report.Discusses the probability of selection for pairs and QDUs.Briefly describes of the generalized exponential model.Describes the predictor variables for the model calibration.Defines extreme weights.Discusses weight calibrations.Evaluates the calibration weights.Appendices include technical details about the model and the evaluations that were performed.

This is the 2016 NSDUH report on QUESTIONNAIRE DWELLING UNIT-LEVEL AND PERSON PAIRLEVEL SAMPLING WEIGHT CALIBRATION.

Learn about the techniques used to create weights for the 2020 National Survey on Drug Use and Health (NSDUH) at the person level. The report reviews the generalized exponential model (GEM) used in weighting, discusses potential predictor variables, and details the practical steps used to implement GEM. The report also details the weight calibrations, and presents the evaluation measures of the calibrations, as well as a sensitivity analysis.Chapters:Introduces the survey design, weight components, and the remainder of the report.Discusses the impact of the survey interruption on the weighting for 2020.Reviews GEM and outlines how GEM provides a unified approach to adjustments for nonresponse, poststratification, and extreme weights.Discusses potential predictor variables and the strategy for dealing with many predictors.Discusses extreme weights in the GEM.Discusses the control totals for poststratification adjustments.Details the weight calibrations at the dwelling unit level.Discusses the design weight components and weight calibration at the person level.Presents the evaluation measures of calibrated weights and a sensitivity analysis of selected prevalence estimates.Discusses the break-off analysis weights.Appendices include technical details about the model and the evaluations that were performed.

Learn about the techniques used to create weights for the 2021 National Survey on Drug Use and Health (NSDUH) at the pair and questionnaire dwelling unit (QDU) levels. NSDUH is designed so that some of the sampled households have both an adult and a youth respondent who are paired. Because of this, NSDUH allows for estimating characteristics at the person level, pair level, or QDU level. This report describes pair selection probabilities, the generalized exponential model (including predictor variables used), and the multiple weight components that are used for pair or QDU levels of analysis. An evaluation of the calibration weights is also included.Chapters:Introduces the report.Discusses the probability of selection for pairs and QDUs.Briefly describes of the generalized exponential model.Describes the predictor variables for the model calibration.Defines extreme weights.Discusses weight calibrations.Evaluates the calibration weights.Appendices include technical details about the model and the evaluations that were performed.

1: This value is the absolute value of the ratio of the estimated to the known (i.e. Column 2/Column 1) which is transformed with a logarithm (base 2). Successive columns (5, 7, 9, 11, 13, 15, 17) use the preceding estimation value.Recursive Back Estimation Process to Identify and Eliminate Poor Predictors Using the Original Estimator Without Weights.

This report contains a brief review of the sampling weight calibration methodology used for the 2018 National Survey on Drug Use and Health (NSDUH). This report also lists detailed documentation on the implementation steps and evaluation results from the weight calibration application. The constrained exponential modeling (CEM) method used in the surveys before 1999 (referred to in this report as the generalized exponential model [GEM]) was modified to provide more flexibility in dealing internally with the extreme weights and for setting bounds directly on the weight adjustment factors so they can become suitable for nonresponse (nr) and poststratification (ps) adjustments.

This is the 2016 NSDUH Sampling Weight Report.

Change in Three Population Estimates and Personal Network Size without Recursive Trimming over the Original and MoS Estimator, with Weights and Without.

This dataset tracks the updates made on the dataset "2016 Sampling Weight Report" as a repository for previous versions of the data and metadata.

Supplementary Material 1. Analysis Results.

This report contains a brief review of the sampling weight calibration methodology used for the 2017 National Survey on Drug Use and Health (NSDUH). This report also lists detailed documentation on the implementation steps and evaluation results from the weight calibration application. The constrained exponential modeling (CEM) method used in the surveys before 1999 (referred to in this report as the generalized exponential model [GEM]) was modified to provide more flexibility in dealing internally with the extreme weights and for setting bounds directly on the weight adjustment factors so they can become suitable for nonresponse (nr) and poststratification (ps) adjustments.

2015 Person-Level Sample Weight Calibration

Performance of unweighted versus weighted gradient boosting model implementations using data from the National Health and Nutrition Examination Survey III.

This dataset tracks the updates made on the dataset "NSDUH 2022 Pair Level Sampling Weight Report" as a repository for previous versions of the data and metadata.

NSDUH allows for estimating characteristics at the person level, pair level, and household or QDU level. This report describes the weight calibration methods used for the pair- and QDU-level respondents. As described in the person-level report, NSDUH is an annual survey of about 67,500 people selected from the civilian, noninstitutionalized population aged 12 or older from all 50 states and the District of Columbia. A coordinated sample design was developed for the 2014 through 2017 NSDUHs. The coordinated design facilitated 50 percent overlap in third-stage units (area segments) within each successive 2-year period from 2014 through 2017.