I knew the answer involved separating reviewer tendencies from actual ad quality, but articulating why mixed effects specifically took me longer than it should have.
Start by framing the problem as a hierarchical data structure where ratings are nested within reviewers and ads, then propose a mixed-effects linear regression with random intercepts for reviewers and ads to account for bias and ad quality. Explain how this model separates reviewer bias from ad effects and why it's more appropriate than fixed effects or simple averaging.
Pro tip: Mention that you would validate the model by checking residual plots and comparing variance components, and discuss how to handle missing data or unbalanced designs—showing you think about practical implementation, not just theory.
Recognize that the data is hierarchical: 100 ratings per reviewer and 100 ratings per ad, creating crossed random effects. This violates independence assumptions of standard linear regression.
Specify a model: rating = intercept + ad effect + reviewer effect + error, where ad and reviewer effects are random. This accounts for reviewer bias (e.g., some reviewers rate harshly) and ad quality simultaneously.
Highlight that mixed-effects models handle repeated measures, estimate variance components, and provide shrinkage (partial pooling) which improves estimates for ads with few ratings. Fixed effects would be inefficient with many parameters.
Mention using software like lme4 in R or statsmodels in Python, and validating with residual diagnostics, checking for convergence, and comparing to simpler models via AIC/BIC or cross-validation.
Consider adding random slopes if reviewer bias varies by ad characteristics, or including ad features as fixed effects. Discuss computational complexity and interpretability trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.