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Soland, James – Educational Measurement: Issues and Practice, 2023
Most individuals who take, interpret, design, or score tests are aware that examinees do not always provide full effort when responding to items. However, many such individuals are not aware of how pervasive the issue is, what its consequences are, and how to address it. In this digital ITEMS module, Dr. James Soland will help fill these gaps in…
Descriptors: Student Behavior, Tests, Scores, Incidence
Kim, Stella Y. – Educational Measurement: Issues and Practice, 2022
In this digital ITEMS module, Dr. Stella Kim provides an overview of multidimensional item response theory (MIRT) equating. Traditional unidimensional item response theory (IRT) equating methods impose the sometimes untenable restriction on data that only a single ability is assessed. This module discusses potential sources of multidimensionality…
Descriptors: Item Response Theory, Models, Equated Scores, Evaluation Methods
Bunch, Michael B. – Educational Measurement: Issues and Practice, 2020
In this digital ITEMS module, Dr. Michael Bunch provides an in-depth, step-by-step look at how standard setting is done. It does not focus on any specific procedure or methodology (e.g., modified Angoff, bookmark, and body of work) but on the practical tasks that must be completed for any standard setting activity. Dr. Bunch carries the…
Descriptors: Standard Setting, Cutting Scores, Scores, Reports
Student, Sanford R.; Gong, Brian – Educational Measurement: Issues and Practice, 2022
We address two persistent challenges in large-scale assessments of the Next Generation Science Standards: (a) the validity of score interpretations that target the standards broadly and (b) how to structure claims for assessments of this complex domain. The NGSS pose a particular challenge for specifying claims about students that evidence from…
Descriptors: Science Tests, Test Validity, Test Items, Test Construction
Cui, Zhongmin – Educational Measurement: Issues and Practice, 2021
Commonly used machine learning applications seem to relate to big data. This article provides a gentle review of machine learning and shows why machine learning can be applied to small data too. An example of applying machine learning to screen irregularity reports is presented. In the example, the support vector machine and multinomial naïve…
Descriptors: Artificial Intelligence, Man Machine Systems, Data, Bayesian Statistics
Sireci, Stephen G. – Educational Measurement: Issues and Practice, 2020
Educational tests are standardized so that all examinees are tested on the same material, under the same testing conditions, and with the same scoring protocols. This uniformity is designed to provide a level "playing field" for all examinees so that the test is "the same" for everyone. Thus, standardization is designed to…
Descriptors: Standards, Educational Assessment, Culture Fair Tests, Scoring
Leventhal, Brian; Ames, Allison – Educational Measurement: Issues and Practice, 2020
In this digital ITEMS module, Dr. Brian Leventhal and Dr. Allison Ames provide an overview of "Monte Carlo simulation studies" (MCSS) in "item response theory" (IRT). MCSS are utilized for a variety of reasons, one of the most compelling being that they can be used when analytic solutions are impractical or nonexistent because…
Descriptors: Item Response Theory, Monte Carlo Methods, Simulation, Test Items
Sinharay, Sandip – Educational Measurement: Issues and Practice, 2019
Test score users often demand the reporting of subscores due to their potential diagnostic, remedial, and instructional benefits. Therefore, there is substantial pressure on testing programs to report subscores. However, professional standards require that subscores have to satisfy minimum quality standards before they can be reported. In this…
Descriptors: Testing, Scores, Item Response Theory, Evaluation Methods
Klugman, Emma M.; Ho, Andrew D. – Educational Measurement: Issues and Practice, 2020
State testing programs regularly release previously administered test items to the public. We provide an open-source recipe for state, district, and school assessment coordinators to combine these items flexibly to produce scores linked to established state score scales. These would enable estimation of student score distributions and achievement…
Descriptors: Testing Programs, State Programs, Test Items, Scores
Sinharay, Sandip – Educational Measurement: Issues and Practice, 2018
The choice of anchor tests is crucial in applications of the nonequivalent groups with anchor test design of equating. Sinharay and Holland (2006, 2007) suggested "miditests," which are anchor tests that are content-representative and have the same mean item difficulty as the total test but have a smaller spread of item difficulties.…
Descriptors: Test Content, Difficulty Level, Test Items, Test Construction
Sinharay, Sandip; Haberman, Shelby; Boughton, Keith – Educational Measurement: Issues and Practice, 2015
Feinberg and Wainer (2014) provided a simple equation to approximate/predict a subscore's value. The purpose of this note is to point out that their equation is often inaccurate in that it does not always predict a subscore's value correctly. Therefore, the utility of their simple equation is not clear.
Descriptors: Equations (Mathematics), Scores, Prediction, Accuracy
Clauser, Amanda L.; Wainer, Howard – Educational Measurement: Issues and Practice, 2016
It is widely accepted dogma that consequential decisions are better made with multiple measures, because using but a single one is thought more likely to be laden with biases and errors that can be better controlled with a wider source of evidence for making judgments. Unfortunately, advocates of using multiple measures too rarely provide detailed…
Descriptors: Tests, Examiners, College Entrance Examinations, Measurement
Feinberg, Richard A.; Wainer, Howard – Educational Measurement: Issues and Practice, 2014
Subscores are often used to indicate test-takers' relative strengths and weaknesses and so help focus remediation. But a subscore is not worth reporting if it is too unreliable to believe or if it contains no information that is not already contained in the total score. It is possible, through the use of a simple linear equation provided in…
Descriptors: Scores, Equations (Mathematics), Prediction, Reliability
Lane, David; Oswald, Frederick L. – Educational Measurement: Issues and Practice, 2016
The educational literature, the popular press, and educated laypeople have all echoed a conclusion from the book "Academically Adrift" by Richard Arum and Josipa Roksa (which has now become received wisdom), namely, that 45% of college students showed no significant gains in critical thinking skills. Similar results were reported by…
Descriptors: College Students, Critical Thinking, Thinking Skills, Statistical Analysis
Kolen, Michael J.; Lee, Won-Chan – Educational Measurement: Issues and Practice, 2011
This paper illustrates that the psychometric properties of scores and scales that are used with mixed-format educational tests can impact the use and interpretation of the scores that are reported to examinees. Psychometric properties that include reliability and conditional standard errors of measurement are considered in this paper. The focus is…
Descriptors: Test Use, Test Format, Error of Measurement, Raw Scores