Using Multigroup Confirmatory Factor Analysis to Test Measurement Invariance in Raters: A Clinical Skills Examination Application

Psychometric models based on structural equation modeling framework are commonly used in many multiple-choice test settings to assess measurement invariance of test items across examinee subpopulations. The premise of the current article is that they may also be useful in the context of performance assessment tests to test measurement invariance of raters. The modeling approach and how it can be used for performance tests with less than optimal rater designs are illustrated using a data set from a performance test designed to measure medical students’ patient management skills. The results suggest that group-specific rater statistics can help spot differences in rater performance that might be due to rater bias, identify specific weaknesses and strengths of individual raters, and enhance decisions related to future task development, rater training, and test scoring processes.     

PETS口试评分培训效果的多面Rasch分析

本研究以PETS-1级拟聘口试教师为研究对象,对口试教师评分的培训效果进行了研究。采用多面Rasch分析对比口试教师接受培训前后的评分效果。结果发现：培训后,提升了口试教师与专家评分完全一致的比率,评分偏于严格的口试教师在评分标准上做了恰当的调整,所有口试教师评分拟合值都在可接受范围内,总体上,口试教师评分的培训比较有效,培训后提升了评分的准确性。多面Rasch分析有助于发现评分过于宽松、过于严格、评分拟合差的口试教师以及评分异常情况,为开展有针对性地培训提供了可靠的依据。     

Examiner scoring of ambiguous WISC-R responses

Fabricated test protocols were used to study how effectively examiners agree in scoring ambiguous WISC-R responses. Clinical and school psychologists (N = 62) and graduate students (N = 48) scored WISC-R Similarities, Comprehension, and Vocabulary responses obtained in a group administration procedure. From over 15,000 responses obtained, only unusual, atypical, or ambiguous responses were selected; 726 responses were scored, with 11 groups of 10 raters each scoring 66 responses. Considerable scoring disagreement occurred. Unanimous agreement (within each group of 10 raters) was found on only 13% of the responses, while 80% of the raters agreed on 44% of the responses. Rates of scoring agreement were not related to level of rater experience, but were related to specific subtests. Higher rates of agreement were found on the Similarities and Comprehension subtests than on the Vocabulary subtest for both experienced and inexperienced raters. The results suggest that even with the improved WISC-R manual, scoring remains a difficult and challenging task.     

Investigating rater/prompt interactions in writing assessment: Quantitative and qualitative approaches

This study investigates how experienced and inexperienced raters score essays written by ESL students on two different prompts. The quantitative analysis using multi-faceted Rasch measurement, which provides measurements of rater severity and consistency, showed that the inexperienced raters were more severe than the experienced raters on one prompt but not on the other prompt, and that differences between the two groups of raters were eliminated following rater training. The qualitative analysis, which consisted of analysis of raters' think-aloud protocols while scoring essays, provided insights into reasons for these differences. Differences were related to the ease with which the scoring rubric could be applied to the two prompts and to differences in how the two groups of raters perceived the appropriateness of the prompts.     

Interrater reliability of the original and a revised scoring system for the developmental test of visual-motor integration

This study investigated the interrater reliability of teachers' and school psychology ex-terns' scoring of protocols for the Developmental Test of Visual-Motor Integration (VMI). Previous studies suggest that the scoring criteria of the VMI are ambiguous, which when coupled with raters' lack of scoring experience, as well as limited knowledge of testing issues, contributes to low rater reliability. The original manual scoring system was used by four trained teachers with no VMI experience and by four experienced raters. A VMI scoring system, revised to eliminate ambiguous scoring criteria, was used by an additional four teachers inexperienced with the VMI and by four experienced raters. High reliability coefficients (>.90) were found for all raters, regardless of the scoring system employed. The influence on interrater reliability of factors such as training, nature of the training setting, characteristics of the raters, and ambiguity of scoring criteria is discussed.     

An Investigation of Rater Cognition in the Assessment of Projects

In the United Kingdom, the majority of national assessments involve human raters. The processes by which raters determine the scores to award are central to the assessment process and affect the extent to which valid inferences can be made from assessment outcomes. Thus, understanding rater cognition has become a growing area of research in the United Kingdom. This study investigated rater cognition in the context of the assessment of school‐based project work for high‐stakes purposes. Thirteen teachers across three subjects were asked to “think aloud” whilst scoring example projects. Teachers also completed an internal standardization exercise. Nine professional raters across the same three subjects standardized a set of project scores whilst thinking aloud. The behaviors and features attended to were coded. The data provided insights into aspects of rater cognition such as reading strategies, emotional and social influences, evaluations of features of student work (which aligned with scoring criteria), and how overall judgments are reached. The findings can be related to existing theories of judgment. Based on the evidence collected, the cognition of teacher raters did not appear to be substantially different from that of professional raters.     

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.     

Rater accuracy and training group effects in Expert- and Supervisor-based monitoring systems

This study evaluated rater accuracy with rater-monitoring data from high stakes examinations in England. Rater accuracy was estimated with cross-classified multilevel modelling. The data included face-to-face training and monitoring of 567 raters in 110 teams, across 22 examinations, giving a total of 5500 data points. Two rater-monitoring systems (Expert consensus scores and Supervisor judgement of correct scores) were utilised for all raters. Results showed significant group training (table leader) effects upon rater accuracy and these were greater in the expert consensus score monitoring system. When supervisor judgement methods of monitoring were used, differences between training teams (table leader effects) were underestimated. Supervisor-based judgements of raters’ accuracies were more widely dispersed than in the Expert consensus monitoring system. Supervisors not only influenced their teams’ scoring accuracies, they overestimated differences between raters’ accuracies, compared with the Expert consensus system. Systems using supervisor judgements of correct scores and face-to-face rater training are, therefore, likely to underestimate table leader effects and overestimate rater effects.     

Rater calibration when observational assessment occurs at large scale: Degree of calibration and characteristics of raters associated with calibration

Observational assessment is used to study program and teacher effectiveness across large numbers of classrooms, but training a workforce of raters that can assign reliable scores when observations are used in large-scale contexts can be challenging and expensive. Limited data are available to speak to the feasibility of training large numbers of raters to calibrate to an observation tool, or the characteristics of raters associated with calibration. This study reports on the success of rater calibration across 2093 raters trained by the Office of Head Start (OHS) in 2008–2009 on the Classroom Assessment Scoring System (CLASS), and for a subsample of 704 raters, characteristics that predict their calibration. Findings indicate that it is possible to train large numbers of raters to calibrate to an observation tool, and rater beliefs about teachers and children predicted the degree of calibration. Implications for large-scale observational assessments are discussed.     

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 impact of the scoring system of a large-scale standardized EFL writing assessment on its score variability and reliability: Implications for assessment policy makers

Using generalizability (G-) theory and rater interviews as research methods, this study examined the impact of the current scoring system of the CET-4 (College English Test Band 4, a high-stakes national standardized EFL assessment in China) writing on its score variability and reliability. One hundred and twenty CET-4 essays written by 60 non-English major undergraduate students at one Chinese university were scored holistically by 35 experienced CET-4 raters using the authentic CET-4 scoring rubric. Ten purposively selected raters were further interviewed for their views on how the current scoring system could impact its score variability and reliability. The G-theory results indicated that the current single-task and single-rater holistic scoring system would not be able to yield acceptable generalizability and dependability coefficients. The rater interview results supported the quantitative findings. Important implications for the CET-4 writing assessment policy in China are discussed.     

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.     

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.     

Classical,Generalizability, and Multifaceted Rasch Detection of Interrater Variability in Large,Sparse Data Sets

Classical test theory (CTT), generalizability theory (GT), and multi-faceted Rasch model (MFRM) approaches to detecting and correcting for rater variability were compared. Each of 4,930 students' responses on an English examination was graded on 9 scales by 3 raters drawn from a pool of 70. CTT and MFRM indicated substantial variation among raters; the MFRM analysis identified far more raters as different than the CTT analysis did. In contrast, the GT rater variance component and the Rasch histograms suggested little rater variation. CTT and MFRM correction procedures both produced different scores for more than 50% of the examinees, but 75% of the examinees received identical results after each correction. The demonstrated value of a correction for systems of well-trained multiple graders has implications for all systems in which subjective scoring is used.     

A Latent Class Signal Detection Model for Rater Scoring with Ordered Perceptual Distributions

In signal detection rater models for constructed response (CR) scoring, it is assumed that raters discriminate equally well between different latent classes defined by the scoring rubric. An extended model that relaxes this assumption is introduced; the model recognizes that a rater may not discriminate equally well between some of the scoring classes. The extension recognizes a different type of rater effect and is shown to offer useful tests and diagnostic plots of the equal discrimination assumption, along with ways to assess rater accuracy and various rater effects. The approach is illustrated with an application to a large‐scale language test.     

Statistically Comparing the Performance of Multiple Automated Raters Across Multiple Items

Automated scoring systems are typically evaluated by comparing the performance of a single automated rater item-by-item to human raters. This presents a challenge when the performance of multiple raters needs to be compared across multiple items. Rankings could depend on specifics of the ranking procedure; observed differences could be due to random sampling of items and/or responses in the validation sets. Any statistical hypothesis test of the differences in rankings needs to be appropriate for use with rater statistics and adjust for multiple comparisons. This study considered different statistical methods to evaluate differences in performance across multiple raters and items. These methods are illustrated leveraging data from the 2012 Automated Scoring Assessment Prize competitions. Using average rankings to test for significant differences in performance between automated and human raters, findings show that most automated raters did not perform statistically significantly different from human-to-human inter-rater agreement for essays but they did perform differently on short-answer items. Differences in average rankings between most automated raters were not statistically significant, even when their observed performance differed substantially.     

The Stability of Rater Severity in Large-Scale Assessment Programs

The purpose of this study was to investigate the stability of rater severity over an extended rating period. Multifaceted Rasch analysis was applied to ratings of 16 raters on writing performances of 8, 285 elementary school students. Each performance was rated by two trained raters over a period of seven rating days. Performances rated on the first day were re-rated at the end of the rating period. Statistically significant differences between raters were found within each day and in all days combined. Daily estimates of the relative severity of individual raters were found to differ significantly from single, on-average estimates for the whole rating period. For 10 raters, severity estimates on the last day were significantly different from estimates on the first day. These fndings cast doubt on the practice of using a single calibration of rater severity as the basis for adjustment of person measures.     

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.     

Modeling Rater Response Processes in Evaluating Score Meaning

Rater‐mediated assessments require the evaluation of the accuracy and consistency of the inferences made by the raters to ensure the validity of score interpretations and uses. Modeling rater response processes allows for a better understanding of how raters map their representations of the examinee performance to their representation of the scoring criteria. Validity of score meaning is affected by the accuracy of raters' representations of examinee performance and the scoring criteria, and the accuracy of the mapping process. Methodological advances and applications that model rater response processes, rater accuracy, and rater consistency inform the design, scoring, interpretations, and uses of rater‐mediated assessments.