Examining Rater Errors in the Assessment of Written Composition With a Many-Faceted Rasch Model

This study describes several categories of rater errors (rater severity, halo effect, central tendency, and restriction of range). Criteria are presented for evaluating the quality of ratings based on a many-faceted Rasch measurement (FACETS) model for analyzing judgments. A random sample of 264 compositions rated by 15 raters and a validity committee from the 1990 administration of the Eighth Grade Writing Test in Georgia is used to illustrate the model. The data suggest that there are significant differences in rater severity. Evidence of a halo effect is found for two raters who appear to be rating the compositions holistically rather than analytically. Approximately 80% of the ratings are in the two middle categories of the rating scale, indicating that the error of central tendency is present. Restriction of range is evident when the unadjusted raw score distribution is examined, although this rater error is less evident when adjusted estimates of writing competence are used     

Rater Effects on Essay Scoring: A Multilevel Analysis of Severity Drift,Central Tendency,and Rater Experience

This study examined rater effects on essay scoring in an operational monitoring system from England's 2008 national curriculum English writing test for 14‐year‐olds. We fitted two multilevel models and analyzed: (1) drift in rater severity effects over time; (2) rater central tendency effects; and (3) differences in rater severity and central tendency effects by raters’ previous rating experience. We found no significant evidence of rater drift and, while raters with less experience appeared more severe than raters with more experience, this result also was not significant. However, we did find that there was a central tendency to raters’ scoring. We also found that rater severity was significantly unstable over time. We discuss the theoretical and practical questions that our findings raise.     

The Effects of Incomplete Rating Designs in Combination With Rater Effects

Researchers have explored a variety of topics related to identifying and distinguishing among specific types of rater effects, as well as the implications of different types of incomplete data collection designs for rater‐mediated assessments. In this study, we used simulated data to examine the sensitivity of latent trait model indicators of three rater effects (leniency, central tendency, and severity) in combination with different types of incomplete rating designs (systematic links, anchor performances, and spiral). We used the rating scale model and the partial credit model to calculate rater location estimates, standard errors of rater estimates, model–data fit statistics, and the standard deviation of rating scale category thresholds as indicators of rater effects and we explored the sensitivity of these indicators to rater effects under different conditions. Our results suggest that it is possible to detect rater effects when each of the three types of rating designs is used. However, there are differences in the sensitivity of each indicator related to type of rater effect, type of rating design, and the overall proportion of effect raters. We discuss implications for research and practice related to rater‐mediated assessments.     

Rater effects: Ego engagement in rater decision-making

The decision-making behaviors of 8 raters when scoring 39 persuasive and 39 narrative essays written by second language learners were examined, first using Rasch analysis and then, through think aloud protocols. Results based on Rasch analysis and think aloud protocols recorded by raters as they were scoring holistically and analytically suggested that rater background may have contributed to rater expectations that might explain individual differences in the application of the performance criteria of the rubrics when rating essays. The results further suggested that rater ego engagement with the text and/or author may have helped mitigate rater severity and that self-monitoring behaviors by raters may have had a similar mitigating effect.     

Latent trait modelling of rater accuracy in formative peer assessment of English-Chinese consecutive interpreting

Despite the increasing popularity of peer assessment in tertiary-level interpreter education, very little research has been conducted to examine the quality of peer ratings on language interpretation. While previous research on the quality of peer ratings, particularly rating accuracy, mainly relies on correlation and analysis of variance, latent trait modelling emerges as a useful approach to investigate rating accuracy in rater-mediated performance assessment. The present study demonstrates the use of multifaceted Rasch partial credit modelling to explore the accuracy of peer ratings on English-Chinese consecutive interpretation. The analysis shows that there was a relatively wide spread of rater accuracy estimates and that statistically significant differences were found between peer raters regarding rating accuracy. Additionally, it was easier for peer raters to assess some students accurately than others, to peer-assess target language quality accurately than the other rating domains, and to provide accurate ratings to English-to-Chinese interpretation than the other direction. Through these findings, latent trait modelling demonstrates its capability to produce individual-level indices, measure rater accuracy directly, and accommodate sparse data rating designs. It is therefore hoped that substantive inquiries into peer assessment of language interpretation could utilise latent trait modelling to move this line of research forward.     

多面Rasch模型在主观题评分培训中的应用

主观题的评分受到很多因素的影响,如评分者的知识水平、综合能力和个人偏好等。这些评分者偏差不仅会导致不同评分者之间存在主观差异,也会到导致同一评分者在不同的时间也具有主观不稳定性,最终导致主观题评分信度的降低。本研究将多面Rasch模型运用到某国家级考试论述题的评分培训中。通过分析6名有经验评分者对58份试卷的试评数据,鉴别出四种评分者偏差,然后据此对每个评分者进行个别反馈,从而提高评分的客观性和精确性。     

The Consistency Between Raters Scoring in Different Test Years

The consistency between raters over 3 years of a high-stakes performance assessment was examined in 2 studies that involved students in Grades 3, 5, and 8. The students' performance was evaluated in reading, writing, language usage, mathematics, science, and social studies. The results showed that the groups of raters used in different years differed in severity. Their consistency tended to improve over years, but differences between the rater groups remained. It is shown that these differences could affect students' proficiency classifications, indicating the need to adjust for rater effects during the equating process. The Grade 8 raters generally were found to be more consistent than the Grade 3 and Grade 5 raters. Also, the raters in mathematics generally were the most consistent, those in the language arts areas were the least consistent, and the consistency of raters in science and social studies varied over grade levels.     

Real-Time Feedback on Rater Drift in Constructed-Response Items: An Example From the Golden State Examination

In this study, patterns of variation in severities of a group of raters over time or so-called "rater drift" was examined when raters scored an essay written under examination conditions. At the same time feedback was given to rater leaders (called "table leaders") who then interpreted the feedback and reported to the raters. Rater severities in five successive periods were estimated using a modified linear logistic test model (LLTM, Fischer, 1973) approach. It was found that the raters did indeed drift towards the mean, but a planned comparision of the feedback with a control condition was not successful; it was believed that this was due to contamination at the table leader level. A series of models was also estimated designed to detect other types of rater effects beyond severity: a tendency to use extreme scores, and tendency to prefer certain categories. The models for these effects were found to be showing significant improvement in fit, implying that these effects were indeed present, although they were difficult to detect in relatively short time periods.     

Monitoring Rater Performance Over Time: A Framework for Detecting Differential Accuracy and Differential Scale Category Use

In this study, we describe a framework for monitoring rater performance over time. We present several statistical indices to identify raters whose standards drift and explain how to use those indices operationally. To illustrate the use of the framework, we analyzed rating data from the 2002 Advanced Placement English Literature and Composition examination, employing a multifaceted Rasch approach to determine whether raters exhibited evidence of two types of differential rater functioning over time (i.e., changes in levels of accuracy or scale category use). Some raters showed statistically significant changes in their levels of accuracy as the scoring progressed, while other raters displayed evidence of differential scale category use over time.     

Exploring the Effects of Rater Linking Designs and Rater Fit on Achievement Estimates Within the Context of Music Performance Assessments

When good model-data fit is observed, the Many-Facet Rasch (MFR) model acts as a linking and equating model that can be used to estimate student achievement, item difficulties, and rater severity on the same linear continuum. Given sufficient connectivity among the facets, the MFR model provides estimates of student achievement that are equated to control for differences in rater severity. Although several different linking designs are used in practice to establish connectivity, the implications of design differences have not been fully explored. Research is also limited related to the impact of model-data fit on the quality of MFR model-based adjustments for rater severity. This study explores the effects of linking designs and model-data fit for raters on the interpretation of student achievement estimates within the context of performance assessments in music. Results indicate that performances cannot be effectively adjusted for rater effects when inadequate linking or model-data fit is present.     

A Method of Estimating Rater Reliability

A method for assessing rater reliability by means of a design of overlapping rater teams is presented. The products to be rated are split randomly into m disjoint subsamples, m equaling the number of raters. Each rater rates at least two subsamples according to a prefixed design. The covariances or correlations of the ratings can be analyzed with LISREL models, resulting in estimates of the rater reliabilities. Models in which the rater reliabilities are congeneric, tauequivalent, or parallel can be tested. We address problems concerning the identification and the degrees of freedom of the models and present two examples based on essay ratings.     

Evaluating Rater Accuracy in Performance Assessments

A new method for evaluating rater accuracy within the context of performance assessments is described. Accuracy is defined as the match between ratings obtained from operational raters and those obtained from an expert panel on a set of benchmark, exemplar, or anchor performances. An extended Rasch measurement model called the FACETS model is presented for examining rater accuracy. The FACETS model is illustrated with 373 benchmark papers rated by 20 operational raters and an expert panel. The data are from the 1993field test of the High School Graduation Writing Test in Georgia. The data suggest that there are statistically significant differences in rater accuracy; the data also suggest that it is easier to be accurate on some benchmark papers than on others. A small example is presented to illustrate how the accuracy ordering of raters may not be invariant over different subsets of benchmarks used to evaluate accuracy.     

Examining Differential Rater Functioning Using a Between‐Subgroup Outfit Approach

When practitioners use modern measurement models to evaluate rating quality, they commonly examine rater fit statistics that summarize how well each rater's ratings fit the expectations of the measurement model. Essentially, this approach involves examining the unexpected ratings that each misfitting rater assigned (i.e., carrying out analyses of standardized residuals). One can create plots of the standardized residuals, isolating those that resulted from raters’ ratings of particular subgroups. Practitioners can then examine the plots to identify raters who did not maintain a uniform level of severity when they assessed various subgroups (i.e., exhibited evidence of differential rater functioning). In this study, we analyzed simulated and real data to explore the utility of this between‐subgroup fit approach. We used standardized between‐subgroup outfit statistics to identify misfitting raters and the corresponding plots of their standardized residuals to determine whether there were any identifiable patterns in each rater's misfitting ratings related to subgroups.     

Components of Rater Error in a Complex Performance Assessment

Numerous studies have examined performance assessment data using generaliz-ability theory. Typically, these studies have treated raters as randomly sampled from a population, with each rater judging a given performance on a single occasion. This paper presents two studies that focus on aspects of the rating process that are not explicitly accounted for in this typical design. The first study makes explicit the "committee" facet, acknowledging that raters often work within groups. The second study makes explicit the "rating-occasion" facet by having each rater judge each performance on two separate occasions. The results of the first study highlight the importance of clearly specifying the relevant facets of the universe of interest. Failing to include the committee facet led to an overly optimistic estimate of the precision of the measurement procedure. By contrast, failing to include the rating-occasion facet, in the second study, had minimal impact on the estimated error variance.     

Predicting Operational Rater‐Type Classifications Using Rasch Measurement Theory and Random Forests: A Music Performance Assessment Perspective

The purpose of this study was to build a Random Forest supervised machine learning model in order to predict musical rater‐type classifications based upon a Rasch analysis of raters’ differential severity/leniency related to item use. Raw scores (N = 1,704) from 142 raters across nine high school solo and ensemble festivals (grades 9–12) were collected using a 29‐item Likert‐type rating scale embedded within five domains (tone/intonation, n = 6; balance, n = 5; interpretation, n = 6; rhythm, n = 6; and technical accuracy, n = 6). Data were analyzed using a Many Facets Rasch Partial Credit Model. An a priori k‐means cluster analysis of 29 differential rater functioning indices produced a discrete feature vector that classified raters into one of three distinct rater‐types: (a) syntactical rater‐type, (b) expressive rater‐type, or (c) mental representation rater‐type. Results of the initial Random Forest model resulted in an out‐of‐bag error rate of 5.05%, indicating that approximately 95% of the raters were correctly classified. After tuning a set of three hyperparameters (n_tree, m_try, and node size), the optimized model demonstrated an improved out‐of‐bag error rate of 2.02%. Implications for improvements in assessment, research, and rater training in the field of music education are discussed.     

Evaluation of construct-irrelevant variance yielded by machine and human scoring of a science teacher PCK constructed response assessment

Machine learning has been frequently employed to automatically score constructed response assessments. However, there is a lack of evidence of how this predictive scoring approach might be compromised by construct-irrelevant variance (CIV), which is a threat to test validity. In this study, we evaluated machine scores and human scores with regard to potential CIV. We developed two assessment tasks targeting science teacher pedagogical content knowledge (PCK); each task contains three video-based constructed response questions. 187 in-service science teachers watched the videos with each had a given classroom teaching scenario and then responded to the constructed-response items. Three human experts rated the responses and the human-consent scores were used to develop machine learning algorithms to predict ratings of the responses. Including the machine as another independent rater, along with the three human raters, we employed the many-facet Rasch measurement model to examine CIV due to three sources: variability of scenarios, rater severity, and rater sensitivity of the scenarios. Results indicate that variability of scenarios impacts teachers’ performance, but the impact significantly depends on the construct of interest; for each assessment task, the machine is always the most severe rater, compared to the three human raters. However, the machine is less sensitive than the human raters to the task scenarios. This means the machine scoring is more consistent and stable across scenarios within each of the two tasks.