Sequential effects in essay ratings: Evidence of assimilation effects using cross-classified models

Abstract

Writing assessments are an indispensable part of most language competency tests. In our research, we used cross-classified models to study rater effects in the real essay rating process of a large-scale, high-stakes educational examination administered in China in 2011. Generally, four cross-classified models are suggested for investigation of rater effects: (1) the existence of sequential effects, (2) the direction of the sequential effects, and (3) differences in raters by their individual characteristics. We applied these models to the data to account for possible cluster effects caused by the application of multiple rating strategies. The results of our research showed that raters demonstrated sequential effects during the rating process. In contrast to many other studies on rater effects, our study found that raters exhibited assimilation effects. The more experienced, lenient, and qualified raters were less susceptible to assimilation effects. In addition, our research demonstrated the feasibility and appropriateness of using cross-classified models in assessing rater effects for such data structures. This paper also discusses the implications for educators and practitioners who are interested in reducing sequential effects in the rating process, and suggests directions for future research.