Survey Weighting: Definition, Techniques, Examples

Survey weighting is a statistical process used to adjust the results of a survey to ensure they accurately reflect the target population. This technique is vital because raw survey data often does not perfectly represent the larger group due to sampling biases and varying response rates. By applying survey weights, each respondent’s answers are scaled to account for the proportion of the population they represent, leading to more accurate and reliable survey results. Other statistical methods such as Key Drivers Analysis and Latent Class Analysis can incorporate survey weights when estimating models.

In any survey, the weighted sample is a subset of the target population, ideally chosen to mirror the broader group’s characteristics. However, discrepancies often arise due to non-random sampling, non-response, and other biases. Survey weighting bridges this gap by adjusting the sample to better align with the known attributes of the target population, such as age, gender, income, and other demographic factors. The application of appropriate data weighting ensures other statistical techniques such as Segmentation Analysis give an accurate distribution (percentage in each segment) for the population being studied.

Adjusting survey weights is crucial for achieving representativeness. Without weighting, certain groups within the population might be under or overrepresented in the survey results, leading to skewed insights and potential misinformed decisions. Weighting data corrects these imbalances, ensuring that the survey results are reflective of the entire population. This adjustment allows for more accurate weighted analysis, helping researchers and organisations make informed decisions based on a true representation of the population’s views and behaviours. A correctly weighted sample is essential for other statistical techniques such as Conjoint Analysis, to ensure that the inferences we make align with the population we are interested in.

Why weight the surveys?

Survey weighting is essential to ensure that survey results are accurate, representative, and credible. Here are the key reasons why weighting survey data is crucial:

1. Correcting Biases: Adjusts for sampling biases, ensuring the data accurately reflects the target population.
   
2. Improved Representativeness: Ensures diverse population segments are proportionately represented, leading to more accurate results.
   
3. Enhanced Credibility: Increases confidence in the findings by producing data that mirrors the population.
   
4. Actionable Insights: Provides relevant insights for informed decision-making by accurately representing the population’s characteristics and behaviours.
   
5. Accurate Trend Analysis: Supports reliable tracking of changes over time.

Considerations When Weighting a Survey

When conducting a survey, applying weights to the data is crucial for ensuring accurate and representative results. Properly weighted data can correct for biases and reflect the true characteristics of the target population. At the Stats People, these are key considerations we keep in mind:

1. Identify Key Demographic Variables

Consider which demographic factors are most relevant to your survey objectives, such as age, gender, income, and education. These variables will form the basis of your weighting adjustments.

2. Source Reliable Population Benchmarks

Use reliable sources like census data or large-scale national surveys to establish accurate population benchmarks. These benchmarks are crucial for aligning your survey sample with the target population.

3. Calculate Initial Sampling Weights

Assign initial weights based on the inverse probability of selection for each respondent. This step corrects for overrepresented or underrepresented groups within your sample.

4. Calculate Non-Response Weights

Account for potential non-response bias by adjusting weights to reflect the likelihood of different demographic groups responding to your survey.

5. Calibrate to population estimates

Apply rake weighting for other sample characteristics not covered in earlier stages of weighting.

6. Adjust Weights for Extremes

Trim and smooth weights as necessary to prevent extreme values from skewing your results. This ensures a more balanced and accurate representation of the population.

7. Test Weighting Adjustments

Before finalising, test your weighting adjustments to ensure they produce the desired representativeness and accuracy. This might involve running simulations or comparing weighted results against known benchmarks.

8. Document the Weighting Process

Thoroughly document your weighting process, including the variables used, sources of population benchmarks, and any adjustments made. Transparency in this process enhances the credibility of your survey results.

9. Monitor and Update Weights

Regularly review and update your weights to ensure they remain accurate over time, especially if the target population characteristics change or new benchmarks become available.

By considering these factors, you can enhance the accuracy and credibility of your survey results, ensuring that they provide meaningful and actionable insights.

Weighting Techniques

General weighting techniques include basic adjustments like cell and rim weighting, which account for major population characteristics to improve representativeness. Applying advanced weighting techniques is essential in more complex surveys for reducing sampling bias and ensuring accuracy. These techniques align survey results with known population characteristics. These include:

Selection weighting

Selection weighting involves estimating the probability of selection for different cohorts in the sample and then weighting the cohort by one divided by this probability. Appropriate models and assumptions are used, depending on the sampling strategy. In the simple case this can be achieved by comparing the sample size with the population size. For complex sampling strategies, multivariate statistical models are needed to estimate these probabilities.

Regression weighting

Regression weighting uses logistic regression, multinomial regression or linear regression to estimate the probability of case that is sampled completing an interview, conditional on its responses on a series of predictor variables, usually demographics. The weight is calculated as one divided by this estimated probability. Its purpose is to correct for differences in ‘conversion rate’ for different cases in the sample.

Rim Weighting

Rim Weighting, or as it is known in academic literature Iterative Proportional Fitting (IPF) or Raking, uses an iterative algorithm to calibrate survey data to fit the marginal (non-interlocking) profiles on key population parameters, using known population estimates. It preserves the odds ratios in the data whilst adjusting the relative proportions in the categories of each variable to fit the population.  It is often used to adjust for additional population estimates that were not part of the original sampling frame.

By implementing these advanced weighting techniques, researchers can enhance the accuracy, reliability, and representativeness of their survey data, leading to more valid and actionable insights.

Case Studies: Examples of Successful Survey Weighting

Survey weighting has proven to be a crucial tool in enhancing the accuracy and representativeness of survey data. Here are three specific examples from our case studies demonstrating the effectiveness of survey weighting:

1. English Longitudinal Survey of Aging (ELSA). 

The English Longitudinal Study of Ageing (ELSA) is a unique and rich longitudinal resource of information on the dynamics of health, social, wellbeing and economic circumstances in the English population aged 50 and older.

We have overseen, executed and documented the complex cross-sectional and longitudinal weighting for the last two waves of this survey, working on behalf of and in collaboration with NatCen, University College London (UCL), The Institute for Fiscal Studies (IFS) and the University of East Anglia.

The weighting is extremely complex as the survey has run for over 20 years and involved seven different cohorts of respondents drawn over that period from various waves of the Health Survey for England (HSE) to ensure the sample is representative of over 50s living in private households in England. The most recent wave was further complicated by the introduction of Non-White-British Ethnicity boosts which needed to be accounted for in the weighting plan.

2. Employer Skills Survey (ESS)

Since 2018 we have worked as principal statistician and methodologist for IFF Research on behalf of DfE. Our principal role over the past 3-4 years has been to manage methodological change on the Employer Skills Survey (ESS), one of the UKs largest CATI surveys of business establishments with between 2 and 250+ employees and the Apprenticeship Evaluation Surveys (AeS), which incorporates surveys from two independent samples across a business audience and three across current and former learners.

Both projects are large scale and methodologically demanding. In both cases we designed and implemented new, more robust random probability-compliant weighting processes for both surveys. This was extremely challenging given the complexity of sampling processes, competing analysis requirements and need to account for overlapping membership of various sample cohorts of interest. In both cases we documented the weighting in detailed chapters for survey technical reports

3.  FCA’s Financial Lives and associated follow-up surveys

We have been the Financial Conduct Authority’s (FCA’s) Statistical Consultant on the Financial Lives Survey (FLS) since October 2018. The FLS is one of the largest and most complex consumer surveys in the UK, focusing on consumer experience and engagement with Financial Services.

Our role has been to manage and quality control over the past two waves the sampling and weighting work undertaken by NatCen, which included the development of a complex set of 47 different weights over the past two survey waves. We have also developed and undertaken complex weighting on multiple follow-up and longitudinal studies using respondents from the main survey waves.