Evaluating the Role of Statistical Weighting in Correcting Sampling Bias and Enhancing Survey Data Representativeness

Posted: Oct 06, 2018

• Mateo Rivera
• James Perez
• Zoey Green

Abstract

Survey research represents a cornerstone of empirical investigation across numerous disciplines, providing critical insights into human behavior, attitudes, and characteristics. However, the validity and reliability of survey findings are fundamentally contingent upon the representativeness of the sample relative to the target population. In contemporary research environments, achieving representative samples has become increasingly challenging due to declining response rates, the proliferation of non-probability sampling methods, and growing population heterogeneity. Sampling bias, which occurs when certain segments of the population are systematically overrepresented or underrepresented in the sample, poses a significant threat to the external validity of survey findings and can lead to erroneous conclusions and misguided policy decisions. Statistical weighting has emerged as a primary methodological approach for addressing sampling bias and enhancing the representativeness of survey data. Traditional weighting techniques, such as post-stratification and raking, typically rely on demographic variables like age, gender, race, and education.