Abstract
Abstract Previous research that has explored potential antecedents of rater effects in essay scoring has focused on a range of contextual variables, such as rater background, rating context, and prompt demand. This study predicts the difficulty of accurately scoring an essay based on that essay's content by utilizing linear regression modeling to measure the association between essay features (e.g., length, lexical diversity, sentence complexity) and raters’ ability to assign scores to essays that match those assigned by expert raters. We found that two essay features – essay length and lexical diversity – account for 25% of the variance in ease of scoring measures, and these variables are selected in the predictive modeling whether the essay's true score is included in the equation or not. We suggest potential applications for these results to rater training and monitoring in direct writing assessment scoring projects.
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