Convergence (criteria to end iteration) = .5, .01, 0, 0

This is for 32-bit Facets 3.87. Here is Help for 64-bit Facets 4

This sets the convergence criteria for the iterative joint JMLE (unconditional UCON) maximum likelihood estimation procedure (also the Facets implementation of PMLE). Both criteria (of size and change) must be satisfied for iteration to cease automatically. Select "Finish Iterating" from Files pull-down menu to override automatic operation. You may be able to speed up convergence by using the "Estimation" pull-down menu, and requesting Bigger changes.

 

The four criteria are:

 

element: maximum residual

default: 0.5

the maximum size of the marginal score-point residual (i.e., difference between observed and expected "total" raw score after omission of extreme scores) for any element. The standard convergence value is 0.5 score points, half the smallest observable difference between raw scores.

element: maximum logit change

default: 0.01

the maximum size of the largest logit change in any estimated measure for an element during the previous iteration (regardless of Umean=) . The standard convergence value is .01 logits, the smallest useful or printable difference.

rating-scale category: maximum residual

default: 0 (ignored: element maximum residual used instead)

the maximum size of the largest marginal score point residual (i.e., difference between observed and expected "total" raw score) for any category.

Andrich threshold: maximum logit change

default: 0 (ignored: element: maximum logit change used instead)

the maximum size of the largest logit change in any estimated measure for a Rasch-Andrich threshold (step calibration) during this iteration.

If a criterion value is not specified, then its value is not changed.

If a criterion value is set to 0, then that criterion is ignored.

 

Example 1: In some situations, a pass-fail or other "high stakes" decision may hinge upon a difference of hundredths of a logit between a person measure and a criterion measure. For the final, decisive analysis, set the convergence criteria very tightly, e.g.,

Iterations=0 ; unlimited number of iterations

Convergence=.01, .0001 ; exaggerated accuracy: .01 score points and .0001 logits

Be prepared to let your computer run a long time!

 

Example 2: You want convergence to occur when no marginal score point residual (e.g., difference between any element's observed and expected raw score) is greater than 1.0 score points, and the default logit change is left at its default value:

Convergence = 1.0 ; 1.0 score points and .01 logits (the default). Category defaults are unchanged at 0 and so are ignored.

 

Example 3: You want to apply the convergence criterion applied in Facets 3.38.

Convergence = 0.5, .01, 0.5, 0 ; category and item residuals apply

 

Example 4: You want to match the convergence criterion applied in Facets 3.22, the last DOS version.

Convergence = .01, .001 ; run apparently tighter convergence criteria

 

Example 5: Convergence = 0.1, 0.001, 0.5, 0.01

Element measure: we want John's (and all elements) expected score to be within 0.1 of his observed score, and his measure not to change by more than 0.001 logits each iteration.

Category: we want category 1 (and 2, 3, 4) to have an expected frequency count within 0.5 of its observed frequency count, and the Rasch-Andrich threshold between categories 1 and 2 (also 2 and 3, 3 and 4) not to change by more than 0.01 logits each iteration.

 

 

 


Help for Facets Rasch Measurement and Rasch Analysis Software: www.winsteps.com Author: John Michael Linacre.
 

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Rasch Books and Publications
Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, 2nd Edn, 2024 George Engelhard, Jr. & Jue Wang Applying the Rasch Model (Winsteps, Facets) 4th Ed., Bond, Yan, Heene Advances in Rasch Analyses in the Human Sciences (Winsteps, Facets) 1st Ed., Boone, Staver Advances in Applications of Rasch Measurement in Science Education, X. Liu & W. J. Boone Rasch Analysis in the Human Sciences (Winsteps) Boone, Staver, Yale
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes Statistical Analyses for Language Testers (Facets), Rita Green Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments (Facets), George Engelhard, Jr. & Stefanie Wind Aplicação do Modelo de Rasch (Português), de Bond, Trevor G., Fox, Christine M Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
Exploring Rating Scale Functioning for Survey Research (R, Facets), Stefanie Wind Rasch Measurement: Applications, Khine Winsteps Tutorials - free
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Many-Facet Rasch Measurement (Facets) - free, J.M. Linacre Fairness, Justice and Language Assessment (Winsteps, Facets), McNamara, Knoch, Fan
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Rasch Measurement Transactions & Rasch Measurement research papers - free An Introduction to the Rasch Model with Examples in R (eRm, etc.), Debelak, Strobl, Zeigenfuse Rasch Measurement Theory Analysis in R, Wind, Hua Applying the Rasch Model in Social Sciences Using R, Lamprianou El modelo métrico de Rasch: Fundamentación, implementación e interpretación de la medida en ciencias sociales (Spanish Edition), Manuel González-Montesinos M.
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch Rasch Models for Measurement, David Andrich Constructing Measures, Mark Wilson Best Test Design - free, Wright & Stone
Rating Scale Analysis - free, Wright & Masters
Virtual Standard Setting: Setting Cut Scores, Charalambos Kollias Diseño de Mejores Pruebas - free, Spanish Best Test Design A Course in Rasch Measurement Theory, Andrich, Marais Rasch Models in Health, Christensen, Kreiner, Mesba Multivariate and Mixture Distribution Rasch Models, von Davier, Carstensen
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