Measurement Error Diagnostics in Digital Data
Richard Mulenga
ABSTRACT
This study examines the use of the reverse regression (RR) model as a diagnostic instrument for detecting and correcting bias in the digital datasets by employing digitally simulated data from a hypothetical medium-sized online enterprise. Set against the backdrop of the high-frequency data generation by digital platforms, this study tackles the widespread issue of measurement error and endogeneity that arises from self-reported, algorithmically modified, and poorly validated datasets. The primary objective was to assess the effectiveness of RR in detecting bias when the assumptions of ordinary least squares (OLS) are not met, and to evaluate its potential for enhancing the reliability of empirical analysis in datasets derived from various platforms. The results indicate that RR enhances model robustness by identifying distortions in parameter estimates and facilitates more precise causal interpretations when used in conjunction with instrumental variables (IVs) and the generalised method of moments (GMM) framework. Empirical findings show that, ceteris paribus, a one-unit increase in actual digital advertising expenditure leads to a 5.04% rise in digital sales, whereas a similar increase in reported advertising expenditure results in a 4.78% increase in sales. In the reverse regressions, both actual and reported advertising expenditures remain positive and statistically significant, with coefficients of 3.06% and 2.72%, respectively. These consistent findings confirm that investments in advertising have a significant impact on online sales performance and consumer engagement. The study concludes by suggesting a policy-oriented framework aimed at enhancing data reliability
and informing the formulation of digital economy policies, particularly in calibrating tax incentives, innovation grants, and digital interventions based on empirically validated metrics.
