one-step (concurrent) equating of General Medical Knowledge, L Shen
Done by using 2647 items and 5886 persons to equate three medical certification examinations measuring basic science knowledge, clinical science knowledge, and clinical practice related knowledge.
Factors influencing form-development items, G Kramer
Data for 50 items from 6062 Dental Admission Test examinees were used to study 11 design characteristics that contribute to the difficulty of form-development items. Rasch item difficulties were regressed on the 11 characteristics to estimate the variance explained by each characteristic. The importance of complexity was confirmed, especially for irregular items.
Review and decision confidence on CAT, G Stone & M Lunz
The effect of allowing computer-adaptive test examinees to review items and alter responses on ability estimates, decision confidence, and pass-fail decisions is explored. Ability measures before and after review were highly correlated. Review did not interact with specific ability groupings. The validity benefits of review outweigh psychometric concerns.
CAT with successive intervals, W Koch & B Dodd
The Rasch "successive intervals" model, proposed by Jurgen Rost following Thurstone, was applied to CAT-administered Likert items. The properties of the model with different item pool sizes, methods of item selection and sizes of item dispersion parameters are reported.
Assessing rater behavior, W Zhu, C Ennis, A Chen
298 physical education teachers rated each item of an inventory assessing physical education value. A 6 facet model for the ratings was used to analyze these data. Although rater group membership did not affect model-data fit, its impact on item ratings was detected.
Levels of constructivism among math teachers, L Roberts
A Teacher Belief Scale was administered to "Beyond Activities Project" exemplary program elementary school teachers and a control group to assess project teachers' levels of constructivism. The project teachers were more constructivist.
Quick norms, R Schumacker
Rasch quick norming, a method that overcomes the dependency of "true score" norming on a particular set of items and sample of examinees, is applied to simulated data sets of varying test length, sample size and distribution.
DIF detection stability, C Parshall, R Smith, J Kromrey
A Rasch procedure for detecting biased items is investigated with Monte Carlo reorganization of real data. Reported are: 1) sensitivity and stability of a Rasch DIF statistic, 2) comparison of results with different numbers of replications, 3) comparison with a Mantel- Haenszel study.
Detecting item bias with separate calibration and between-fit, R Smith
The separate calibration t-test approach is compared with the common calibration between-fit approach to detecting item bias. Detection of non-existing bias and failure to detect existing bias are examined for different sample sizes, bias sizes, number of biased items, and ability differences between reference and focus groups.
Understanding performance assessments, D Kenyon
Audience members will rate ESL speakers led by Charles Stansfield. Robert Hess will discuss writing assessment. Carol Myford and Robert Mislevy will discuss art portfolio assessment. Dorry Kenyon will then present a facet analysis of the audience's ratings.
Scoring Model for partial credit data, P Pedler
The probability function of the Scoring Model for polytomous items is generalized from the Rasch dichotomous model. Parameter estimation from real data is illustrated.
Measuring change with graphical techniques, B Sheridan & B Hands
A study investigated change in attitudes of teachers exposed to a novel teaching strategy. Simple graphical techniques are used to assess the quality of the variable and the precision of measurement. This technique detects local interactions and changes in measures at group and individual level.
Computerized clinical simulation testing (CST), A Bersky
A CST examinee works on a simulated nursing case entering actions at any time and in any sequence. The actions are scored for problem solving and decision making competence. The concurrent validity of this CST is investigated by comparisons with examinee performance on an MCQ licensure examination.
Generalizability Theory, G Boodoo, L Bachman, G Marcoulides
Performance appraisal is an important practice in education. The criteria for assessing performance are ratings. Human judgement, however, is fallible. It is an obligation of evaluators to provide evidence of the psychometric quality of the ratings they use. This presentation introduces generalizability (G) theory as an approach to the assessment of the quality of ratings, and exemplifies it with performance assessment data. The basic concepts of G theory will be reviewed. These will be followed by an analysis of actual performance data. It is hoped that an understandable picture of G theory will enable this technique to contribute to future performance evaluations.
Gwyneth Boodoo: Explanation of G theory, Universe of observations, Random & fixed facets, Variance components, G-studies
Lyle Bachman: D-studies, Absolute & relative error, Norm and criterion dependability indicators, "What if?" projections
George A. Marcoulides: Design optimization, Estimation of variance components, Multivariate G theory
Many-facet Rasch measurement, J Linacre, M Lunz, C Myford
The Rasch approach to judged tests will be explained. The inevitably non-linear ordinal ratings are used to construct linear examinee measures adjusted for the difficulty of the specific items and the severity of the specific judges each examinee encounters. Success of construction is evaluated through the meaning (construct validity of the variable definition), consistency (quality-control fit) and utility ("reliability" separation of performance spread vs. replication-dependent precision) of the measures. The Rasch approach features practical, flexible, minimum-effort judging plans with predictable characteristics. Since judge severity is measured and removed, rather than included as a source of "error" variance, judge training emphasizes rater self-consistency and a shared understanding of the judging task across raters, rather than rating uniformity across judges.
Michael Linacre: Explanation of Rasch approach, Rating scales, Judge training (intra-rater consistency), Precision, quality and sample size
Mary Lunz: Judging plans, Feedback to judges, Judge effect on raw scores
Carol Myford: Variable definition, Judge expertise
Rasch SIG brief Abstracts for AERA 1993 Rasch Measurement Transactions, 1993, 6:4 p. 252-3
Please help with Standard Dataset 4: Andrich Rating Scale Model
|Rasch Measurement Transactions (free, online)||Rasch Measurement research papers (free, online)||Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch||Applying the Rasch Model 3rd. Ed., Bond & Fox||Best Test Design, Wright & Stone|
|Rating Scale Analysis, Wright & Masters||Introduction to Rasch Measurement, E. Smith & R. Smith||Introduction to Many-Facet Rasch Measurement, Thomas Eckes||Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr.||Statistical Analyses for Language Testers, Rita Green|
|Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar||Journal of Applied Measurement||Rasch models for measurement, David Andrich||Constructing Measures, Mark Wilson||Rasch Analysis in the Human Sciences, Boone, Stave, Yale|
|in Spanish:||Análisis de Rasch para todos, Agustín Tristán||Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez|
|Forum||Rasch Measurement Forum to discuss any Rasch-related topic|
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|Coming Rasch-related Events|
|July 31 - Aug. 3, 2017, Mon.-Thurs.||Joint IMEKO TC1-TC7-TC13 Symposium 2017: Measurement Science challenges in Natural and Social Sciences, Rio de Janeiro, Brazil, imeko-tc7-rio.org.br|
|Aug. 7-9, 2017, Mon-Wed.||In-person workshop and research coloquium: Effect size of family and school indexes in writing competence using TERCE data (C. Pardo, A. Atorressi, Winsteps), Bariloche Argentina. Carlos Pardo, Universidad Catòlica de Colombia|
|Aug. 7-9, 2017, Mon-Wed.||PROMS 2017: Pacific Rim Objective Measurement Symposium, Sabah, Borneo, Malaysia, proms.promsociety.org/2017/|
|Aug. 10, 2017, Thurs.||In-person Winsteps Training Workshop (M. Linacre, Winsteps), Sydney, Australia. www.winsteps.com/sydneyws.htm|
|Aug. 11 - Sept. 8, 2017, Fri.-Fri.||On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com|
|Aug. 18-21, 2017, Fri.-Mon.||IACAT 2017: International Association for Computerized Adaptive Testing, Niigata, Japan, iacat.org|
|Sept. 15-16, 2017, Fri.-Sat.||IOMC 2017: International Outcome Measurement Conference, Chicago, jampress.org/iomc2017.htm|
|Oct. 13 - Nov. 10, 2017, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|Oct. 25-27, 2017, Wed.-Fri.||In-person workshop: Applying the Rasch Model hands-on introductory workshop, Melbourne, Australia (T. Bond, B&FSteps), Announcement|
|Jan. 5 - Feb. 2, 2018, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|Jan. 10-16, 2018, Wed.-Tues.||In-person workshop: Advanced Course in Rasch Measurement Theory and the application of RUMM2030, Perth, Australia (D. Andrich), Announcement|
|Jan. 17-19, 2018, Wed.-Fri.||Rasch Conference: Seventh International Conference on Probabilistic Models for Measurement, Matilda Bay Club, Perth, Australia, Website|
|April 13-17, 2018, Fri.-Tues.||AERA, New York, NY, www.aera.net|
|May 25 - June 22, 2018, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|June 29 - July 27, 2018, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com|
|Aug. 10 - Sept. 7, 2018, Fri.-Fri.||On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com|
|Oct. 12 - Nov. 9, 2018, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
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