Data for "Explaining the Score Concentrates Judgment: How an Explanation of Algorithmic Advice Reshapes Candidate Evaluation"

This dataset contains the profile-level rating data underlying the study "Explaining the Score Concentrates Judgment: How an Explanation of Algorithmic Advice Reshapes Candidate Evaluation." Five hundred twenty-nine participants each evaluated 32 candidate profiles in a simulated AI-assisted résumé-screening task, yielding 16,928 profile-level observations. Each profile crossed the algorithmic employability score (low vs. high) with the presence of a required qualification (a relevant degree), two non-required attributes (age and hobby), and candidate gender, in a fully crossed 2×2×2×2×2 within-subjects design. Between the two rating phases, participants were assigned to an explanation condition (a plain-language description of the score) or a control condition (re-shown job posting). The file contains two sheets: "data" (one row per participant × profile; 16,928 rows × 17 variables, including the three rating outcomes at each of the two phases) and "codebook" (variable names, descriptions, and coding). The dataset is fully anonymised and contains no direct or indirect personal identifiers.