Section 2. Sample Weighting#
Sample weighting is a technique used to adjust datasets in order to make them representative of the population of interest. Weighting adjusts the responses of the sample so that the results reflect the true population more accurately.
Health Equity and Sample Weighting
|
What is Sample Weighting#
When trying to generalize data from a collected study sample to a wider population, researchers must consider and correct for sampling imbalances between collected samples and known population parameters. Sample weighting is used when a sample does not accurately reflect the population it is supposed to represent. For example, if a survey is conducted in an area where women are 40% of the population, but the sample only includes 30% women, the survey results can be weighted to reflect 40% of the population. This helps to reduce bias in the results.
Sample Weighting and Public Health#
Utilizing a dataset that is inappropriately weighted can result in misleading findings and recommendations, which can have detrimental effects. Inaccurate data weighting can result in decision-making biases and a general lack of confidence in data-driven processes. This may also result in inaccurate projections and data-driven solutions that don’t work in practice. Additionally, inappropriate weighting might produce unfair results since the biased nature of the data set may be used to unfairly criticize individuals and groups [Solon et al., 2015].
The basic steps to perform sample weighting:
Define the population of interest.
Determine the sample size needed to accurately represent the population.
Select a sampling method to use.
Collect the data needed to calculate the sample weights.
Calculate the sample weights.
Test the sample weights for accuracy.
Adjust the sample weights if necessary.
Use the sample weights to accurately represent the population in the sample.
Types of Sample Weighting#
Below is a table describing the three most common sample weighting methods. These methods can be used on their own or in combination with one or more other methods [Valliant et al., 2018].
If you are already familiar with methods for sample weighting, please continue to the next section
| Method | Description |
|---|---|
| Raking | Raking is a technique used in survey sampling to make sure that the sample accurately represents the population. It is a process of adjusting the weights of sample observations to make sure that the sample closely matches the population that it is intended to represent. Raking can be used to adjust for any potential biases in the sample, such as demographic characteristics, geographic location, and other attributes. |
| Matching | Matching is a technique used in survey sampling that is used to select a sample that closely resembles the population that it is intended to represent. It involves selecting a sample of individuals who have similar characteristics to the target population. |
| Propensity Weighting | Propensity weighting is a statistical technique used in survey sampling and observational studies to adjust for nonrandom selection or sampling bias. It assigns weights to individual observations based on their propensity or likelihood of being included in the sample, thus ensuring that the sample accurately represents the characteristics of the target population. |
Health Equity Considerations#
By giving some groups more weight and detracting from others, sample weighting is a technique for making a sample more representative of the population. If the weights are not applied properly or if they are biased due to the researcher’s personal biases, bias may be introduced. For instance, if a researcher wished to give a certain group in the sample greater weight to make it more representative of the population but applied more weight than was required, this could inject bias into the findings. Similarly, bias can be introduced if the researcher assigns weights based on their personal perception of what is significant or desirable [O'Donnell et al., 2008].
Below are some considerations to help avoid introducing bias when sample weighting:
Method |
Description |
|---|---|
Random Sample |
A random sample is one of the most effective ways of avoiding bias when sample weighting. By randomly selecting elements from your population, you can ensure that each and every element has an equal chance of being included in your sample. |
Stratified Sampling |
Stratified sampling is a method of sampling where you divide your population into distinct groups or strata based on certain characteristics. This ensures that the proportion of the different characteristics in the sample is representative of the population as a whole, thus reducing the possibility of introducing bias. |
Avoid Overweighting |
While weighting can help to reduce bias, it is important to be aware of the potential for introducing bias when applying overly large weights. Overweighting can lead to the sample becoming unrepresentative of the population as a whole, which may introduce bias. It is important to use appropriate weights when sample weighting to ensure the accuracy of your results. |
Case Study Example
Case study is for illustrative purposes and does not represent a specific study from the literature.
Data source: Researchers collected data from over 500,000 people, and a researcher wants to study the correlations of red meat and cancer.
Scenario: The research results indicated that those who ate the most red meat were more than twice as likely to get cancer as those who ate the least. However, when researchers looked more closely, they found that the data was skewed by a few extreme outliers who were eating large amounts of red meat, including more than a pound a day.
Analytic method: The analytic method used in this scenario involves studying the correlations between red meat consumption and cancer risk. The initial results showed a significant association, but upon closer inspection, the data was skewed by extreme outliers with exceptionally high red meat consumption, leading to misleading conclusions. To address this, data cleaning steps were performed, including outlier removal, to ensure the accuracy and reliability of the results.
Health Equity Considerations: To promote health equity, additional considerations need to be incorporated into the data cleaning process, specifically during outlier removal. By carefully evaluating outliers’ impact on different ethnic groups, potential biases can be mitigated to ensure fair representation and avoid disproportionate effects on certain populations. Furthermore, handling missing data with sensitivity to underrepresented groups can enhance the accuracy and inclusivity of the analysis, thus fostering equitable and reliable research outcomes.
Outliers: In order to merge disparate datasets, several data cleaning steps need to be performed.
Mitigation Approach:
Be sure to thoroughly understand your data
Eliminate duplicate data and redundant information.
Use standard naming conventions.
Use a consistent data format for each field.
Perform imputation and outlier removal where necessary
Once the data cleaning process is completed, apply the necessary data transformation step.
Several data cleaning steps may be necessary after the data transform as it may unify even more records
Case Discussion: After removing the outliers, the association between red meat and cancer was no longer statistically significant. This example shows how outliers in data can lead to false conclusions.
Considerations for Project Planning
|