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Elucidating the neural substrates of Pavlovian reward learning requires reliable behavioral readouts. In conditioned magazine approach studies, rodents express reward expectancy by approaching the food magazine during cues that predict reward. This behavior is typically quantified using one of three measures: number of head entries, percentage of time in the magazine, or latency to respond. Yet these measures often diverge within the same discrimination task, making reliance on a single metric problematic. At the individual level, some animals express discrimination learning most clearly in one measure while showing little or no learning in the others, and animals may even switch their preferred measure across training. Reporting only one measure therefore risks underestimating the ability of a subset of animals. At the group level, sampling error can produce apparent differences across replications of the same design, limiting replicability. Moreover, brain manipulations can alter response topography, such that choosing one measure over another may lead to conflicting interpretations of neural function. To address this issue, we recommend reporting all raw behavioral measures and supplementing them with a dimensionality-reduction approach such as principal component analysis (PCA). Across multiple discrimination tasks in rats from both sexes, we show that subject-specific first principal component (PC1) scores provide a composite index that more consistently reflects discrimination learning than any single raw measure. This approach enhances statistical power, improves reproducibility, and helps distinguish true learning deficits from changes in response topography. However, its broader application will require continued validation and careful consideration of its inherent methodological trade-offs. Significance Statement Accurately characterizing Pavlovian reward learning requires reliable measurement of individual behavioral responses. In conditioned magazine approach studies, behavior is typically quantified by a single measure—such as head entries, time at the magazine, or response latency—but these measures often diverge. Reliance on one metric can underestimate discrimination ability, compromise reproducibility, and distort interpretations of neural manipulations. We show that applying principal component analysis (PCA) to integrate multiple response measures yields a robust discrimination index that better reflects individual performance. This approach increases effect sizes, strengthens replicability, and reduces misinterpretation, providing scientific, economic, and ethical benefits for research on cue–reward learning.