DISCRIMINATION= report item discrimination = No

Rasch models assert that items exhibit the model-specified item discrimination. Empirically, however, item discriminations vary. During the estimation phase of Winsteps, all item discriminations are asserted to be equal, of value 1.0, and to fit the Rasch model. But empirical item discriminations never are exactly equal, so Winsteps can also report an estimate of those discriminations post-hoc (as a type of fit statistic). The amount of the departure of a discrimination from 1.0 is an indication of the degree to which that item misfits the Rasch model.


DISCRIM=NO Do not report an estimate of the empirical item discrimination.


DISCRIM=YES Report an estimate of the empirical item discrimination in the IFILE= and Tables 6.1, 10.1, etc.


An estimated discrimination of 1.0 accords with Rasch model expectations for an item of this difficulty. A value greater than 1 means that the item discriminates between high and low performers more than expected for an item of this difficulty. A value less than 1 means that the item discriminates between high and low performers less than expected for an item of this difficulty. In general, the geometric mean of the estimated discriminations approximates 1.0, the Rasch item discrimination.


Rasch analysis requires items which provide indication of relative performance along the latent variable. It is this information which is used to construct measures. From a Rasch perspective, over-discriminating items are tending to act like switches, not measuring devices. Under-discriminating items are tending neither to stratify nor to measure.


Over-discrimination is thought to be beneficial in many raw-score and IRT item analyses. High discrimination usually corresponds to low MNSQ values, and low discrimination with high MNSQ values. In Classical Test Theory, Guttman Analysis and much of Item Response Theory, the ideal item acts like a switch. High performers pass, low performers fail. This is perfect discrimination, and is ideal for sample stratification, but such an item provides no information about the relative performance of low performers, or the relative performers of high performers.


Winsteps reports an approximation to what the discrimination parameter value would have been in a 2-PL IRT program, e.g., BILOG for MCQ, or PARSCALE for partial credit items. IRT programs artificially constrain discrimination values in order to make them estimable, so Winsteps discrimination estimates tend to be wider than 2-PL estimates. For the lower asymptote, see ASYMPTOTE=.


The algebraic representation of the discrimination and lower asymptote estimate by Winsteps are similar to 2-PL/3-PL IRT, but the estimation method is different, because Winsteps does not change the difficulties and abilities from their 1-PL values. Consequently, in Winsteps, discrimination and asymptotes are indexes, not parameters as they are in 2-PL/3-PL.


A Rasch-Andrich threshold discrimination is also reported, see Table 3.2.




|ENTRY    RAW                        |   INFIT  |  OUTFIT  |SCORE|ESTIM|                        |



|    23     40     74    2.19     .21|2.42   6.3|4.13   8.9|  .00|  .09| WATCH A RAT            |

|    17     93     74     .16     .19| .65  -2.7| .59  -2.5|  .70| 1.20| WATCH WHAT ANIMALS EAT |

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Rasch Publications
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 with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments, George Engelhard, Jr. & Stefanie Wind 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
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Coming Rasch-related Events
April 10-12, 2018, Tues.-Thurs. Rasch Conference: IOMW, New York, NY, www.iomw.org
April 13-17, 2018, Fri.-Tues. AERA, New York, NY, www.aera.net
May 22 - 24, 2018, Tues.-Thur. EALTA 2018 pre-conference workshop (Introduction to Rasch measurement using WINSTEPS and FACETS, Thomas Eckes & Frank Weiss-Motz), https://ealta2018.testdaf.de
May 25 - June 22, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
June 27 - 29, 2018, Wed.-Fri. Measurement at the Crossroads: History, philosophy and sociology of measurement, Paris, France., https://measurement2018.sciencesconf.org
June 29 - July 27, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com
July 25 - July 27, 2018, Wed.-Fri. Pacific-Rim Objective Measurement Symposium (PROMS), (Preconference workshops July 23-24, 2018) Fudan University, Shanghai, China "Applying Rasch Measurement in Language Assessment and across the Human Sciences" www.promsociety.org
Aug. 10 - Sept. 7, 2018, Fri.-Fri. On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com
Sept. 3 - 6, 2018, Mon.-Thurs. IMEKO World Congress, Belfast, Northern Ireland www.imeko2018.org
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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