Calibration Weights in NSDUH Overview

The percentage of individuals or units who participate in a survey or study.

The share of data points that fall in the highest or lowest ranges of a dataset.

Observations that have unusually high or low values, often indicating outliers.

The rate at which cases are lost or dropped out of a study over time.

Measures that summarize the impact of weighting adjustments on data, often including mean, standard deviation, and range.

Analyzing how changes in input values or assumptions affect the results of a study.

Statistical estimates of the prevalence of drug use in a population.

A statistical model used for weighting survey data, adjusting for non-response and other sampling biases.

A factor used to adjust selection probabilities in a household sampling scheme, ensuring the sum of probabilities is close to 2. It's calculated as the minimum of T(λ) / S and the maximum of the adjusted individual selection probabilities.

A function used to calculate the scaling factor Fs. It's defined as S + λ (2 – S), where S represents the total selection probability in a household and λ is a constant typically set to 0.5.

A constant value used in the T(λ) function, typically set to 0.5. If λ is 0, then the scaling factor becomes 1, resulting in no adjustment of probabilities.

The revised selection probability of the ith person in a household after adjusting by the scaling factor Fs.

The sum of the adjusted selection probabilities after applying the Fs scaling factor. It represents the total probability of selecting someone from a household.

A constant used in Brewer's pairwise selection method, calculated based on the adjusted selection probabilities. It ensures the probabilities of selecting different pairs of individuals are balanced.

A method for calculating the pairwise selection probability within a household based on the adjusted individual selection probabilities. It helps to select pairs of individuals from a household in a balanced way.

Fictional individuals added to households to ensure the total probability of selecting someone from a household is close to 2. They are used in the situation when sum of the adjusted selection probabilities is less than 2.

Groups of units (e.g., individuals) with similar selection probabilities in a sample, used to identify and handle extreme weights that can arise from different sampling designs.

A statistical technique used to treat extreme values in a dataset by replacing very high or low values with less extreme values, usually the next highest or lowest value within a defined range.

Techniques used to adjust weights in a sample to improve the accuracy of estimates. Calibration controls ensure that the weighted sample matches known population characteristics.

A statistical estimator used to calculate the total value of a population characteristic based on a sample. The HT estimator accounts for unequal selection probabilities.

A non-random sampling technique where units (e.g., individuals) are selected for a sample based on specific criteria or characteristics. This can lead to biased estimates.

A systematic error in an estimate that consistently deviates from the true population value. Bias can arise from factors such as non-random sampling or extreme outlier values.

A measure of how close repeated estimates are to each other. Higher precision means estimates are more consistent and less likely to vary drastically.

Methods used to address extreme weights in a sample, such as winsorization or trimming, to improve the accuracy and stability of estimates.

These weights are used to adjust the sample of dwelling units in the National Survey on Drug Use and Health (NSDUH) to better represent the population of the United States. This means that each sampled dwelling unit is assigned a weight based on its characteristics, such as location and size, to ensure that the survey results accurately reflect the overall population.

In the context of NSDUH weighting, a model group refers to a geographic region or a combination of regions where the same statistical model is used to calculate the dwelling unit weights. This ensures that weights are consistently applied within a specific geographic area to maintain accuracy and consistency in the survey data.

Covariates are variables that are included in the statistical model used to calculate dwelling unit weights. These variables are chosen because they are likely to be associated with the characteristics of the dwelling unit, including location, size, and household composition. They help ensure that the weights accurately reflect the population.

This refers to a specific type of weight used in the NSDUH survey for dwelling units. The 'sel' stands for 'selection', indicating that the weight is calculated based on the selection process used to sample the dwelling units. The 'ps' stands for 'probability sample', implying that the weights are designed to adjust for the probability of a dwelling unit being selected for the survey.

This type of weight in the NSDUH survey is used for dwelling units and relates to the 'residence' of the unit. 'res' indicates that the weight is based on the characteristics of the dwelling unit, specifically its residence status. 'nr' refers to the non-response adjustment, signifying that the weight is adjusted to account for dwelling units that did not participate in the survey.

This NSDUH dwelling unit weight is related to both 'residence' and 'probability sample'. The 'res' signifies that the weight is based on the characteristics of the dwelling unit, particularly its residence status. 'ps' stands for 'probability sample', meaning the weights are adjusted to account for the likelihood of a dwelling unit being selected into the survey.

These weights are used in the NSDUH survey to adjust the sample of pairs (consisting of an individual and their household) to accurately represent the population of the United States. Similar to dwelling unit weights, they ensure that the survey results are representative by accounting for factors like location, household composition, and individual characteristics.

This type of pair weight in the NSDUH survey is related to 'residence', 'probability sample', and 'event'. It is calculated based on the characteristics of the dwelling unit, taking into account both the residence status and the probability of being selected for the survey. The 'ev' indicates that the weight is further adjusted to account for the occurrence of specific demographic events within the household.

Categorization of individuals based on their age ranges, commonly used in market research and demographic analysis.

Classification of individuals into distinct gender groups, typically male and female, used for demographic analysis and understanding gender-related trends.

Living situations where individuals share housing and facilities, often for specific purposes, such as college dorms, nursing homes, or military barracks.

The number of individuals residing within a dwelling unit, used to understand family structures and population densities.

Classification of households based on the age ranges of its members, reflecting family composition and life stages.

The proportion of homes in a particular area that are owned by the residents, indicating the level of homeownership and housing affordability.

The number of people living per unit area, indicating the concentration of population and its impact on resources and infrastructure.

Classification of individuals based on their racial and ethnic backgrounds, used for demographic analysis and understanding cultural diversity.

Designation of the individual's relationship to the head of a household, indicating family ties and living arrangements.

Categorization of housing units into five groups based on their median rental or market value, showing the distribution of housing costs within a region.

The group in an experiment that does not receive the treatment or intervention being studied. It serves as a baseline for comparison with the experimental group.

A composite measure that combines information about rent, housing value, and homeownership rates to provide a broader understanding of housing costs and affordability in a particular area.

Sampling weights that are significantly higher or lower than the typical weights in a sample, often indicating outliers or unusual sample characteristics.

A measure of the spread of data that represents the difference between the 75th percentile (upper quartile) and the 25th percentile (lower quartile) of a dataset.

A technique used in statistical modeling to adjust or limit the influence of extreme weights, ensuring more accurate and reliable results.

A variable used in GEM to adjust weights based on their extremity. For high extreme weights, mk is smaller, and for low extreme weights, mk is bigger.

Parameters used in GEM to define the upper and lower bounds for weight adjustment factors, allowing for different levels of control for high, non-, and low extreme weights.