This paper presents a multidimensional model of variation in writing quality, register, and genre in student essays, trained and tested via confirmatory factor analysis of 1.37 million essay submissions to ETS' digital writing service, Criterion®. The model was also validated with several other corpora, which indicated that it provides a reasonable fit for essay data from 4th grade to college. It includes an analysis of the test‐retest reliability of each trait, longitudinal trends by trait, both within the school year and from 4th to 12th grades, and analysis of genre differences by trait, using prompts from the Criterion topic library aligned with the major modes of writing (exposition, argumentation, narrative, description, process, comparison and contrast, and cause and effect). It demonstrates that many of the traits are about as reliable as overall e‐rater® scores, that the trait model can be used to build models somewhat more closely aligned with human scores than standard e‐rater models, and that there are large, significant trait differences by genre, consistent with genre differences in trait patterns described in the larger literature. Some of the traits demonstrated clear trends between successive revisions. Students using Criterion appear to have consistently improved grammar, usage, and spelling after getting Criterion feedback and to have marginally improved essay organization. Many of the traits also demonstrated clear grade level trends. These features indicate that the trait model could be used to support more detailed scoring and reporting for writing assessments and learning tools.

Grouping individuals according to a set of measured characteristics, or profiling, is frequently used in describing, understanding, and acting on a phenomenon. The advent of computer-based assessment offers new possibilities for profiling writing because aspects can be captured that were not heretofore observable. We explored whether writing processes could be profiled of over 30,000 adults taking a high-school equivalency examination. Process features were extracted from keystroke logs, aggregated into composite indicators, and used with essay score to assign individuals to profiles. Analyses included computing the percentages of individuals that could be classified, using MANOVA to examine differences among profiles on external variables, and examining if profiles could be distinguished from one another based on patterns derived from cluster analysis. Results showed that about 30% of examinees could be classified into profiles that were largely distinct. These results contribute toward a foundation for using such profiles in describing how individuals compose and in how their writing might be improved.