Correcting Bias in Marketing Surveys: A Practical Approach Using Inverse Propensity Score Weighting

Survey research often faces the challenge of demographic imbalances, where certain groups are overrepresented while others are underrepresented, leading to biased results.This paper presents the use of Inverse Propensity Score Weighting (IPSW) as a method to correct such imbalances and produce more representative findings.Synthetic survey data was created, with demographic variables including age, gender, and region, and simulated to reflect typical nonresponse patterns.The study compares unweighted and weighted means across seven survey questions to assess the impact of weighting on the outcomes.The findings show that while the overall effect of weighting is small for most questions, certain areas, such as Private space, saw significant changes in the mean responses when the data was weighted.This shift reflects the influence of underrepresented groups who had different opinions from the overrepresented groups in the unweighted analysis.By applying IPSW, the results are adjusted to provide a more accurate reflection of the entire population, reducing bias and improving the quality of the insights.The study emphasizes the importance of weighting in survey analysis, particularly in marketing research and other fields where representativeness is crucial.The application of IPSW offers a practical approach to ensure that survey results are reliable and applicable across diverse demographic groups.