New information technologies and social networks make a wide variety of opinions and advice easily accessible across different contexts. Therefore, assessing how much people are affected by informational influences is gaining importance in the social sciences. Traditionally, ratio-of-differences-based indices specify how strongly people move their judgment in the direction of advice compared to judgments made before receiving external information. The advantages of deterministic weighting indices are their intuitive interpretability and ease of calculation. Intermixing endogenous and exogenous components, however, is costly because it can lead to measurement problems and limits research to an overly restrictive set of questions and hypotheses. As a solution, we propose process-consistent mixed-effects regression for advice taking and related paradigms such as anchoring. Mixed-effects regression coefficients of various exogenous sources of information also measure individual weighting but are based on a conceptually consistent representation of the endogenous judgment process. Additionally, this statistically more adequate multilevel modeling approach enables the estimation of individual weights for nonlinear utilization strategies, sequentially sampled information, and multidimensional belief updating. The practical relevance of the proposed modeling framework becomes manifest in multiple reanalyses of existing empirical findings such as the functional form of the relationship between advice weighting and distance, or the quantification of informational influences without independent initial judgments in sequential collaboration chains. By process-consistent modeling of information sampling and utilization, mixed-effects regression weights (of advice) have the potential to improve research practices and can be applied to develop new substantive areas.