Table 3 Iteration report for the main analysis

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

Convergence= and Iterations= control the number of iterations performed. Write=Yes writes the details reported on screen into Table 2. The number of iterations required depends on how difficult it is to obtain good estimates from the data. Many iterations may be required if

 

1) there is a poor fit of the data to the Rasch model.

2) the element parameter distribution is badly skewed or multi-modal.

3) there are rarely observed response categories.

4) exceedingly precise Convergence= criteria have been specified.

5) the data matrix is composed of disjoint subsets of observations, e.g., boys rated by Judge A, but girls by Judge B. When this is detected, a warning message is displayed.

 

 

Table 3. Iteration Report.

-------------------------------------------------------------

| Iteration      Max. Score Residual      Max. Logit Change |

|             Elements    %  Categories   Elements    Steps |

-------------------------------------------------------------

| PROX   1                                   .7405          |

| JMLE   2     26.6978  22.2    29.9588      .3374    .9902 |

| JMLE   3     22.4284  13.4    27.3399     -.1155   1.0049 |

| JMLE   4     11.0189   6.6    22.0935     -.0514    .9957 |

| JMLE   5     -3.5380  -2.9     9.5224     -.0304   -.7481 |

| JMLE   6     -5.7141  -4.8     2.6727     -.0620   -.2008 |

| JMLE   7     -4.5692  -3.4     1.8210      .0501   -.0892 |

| JMLE   8     -3.5327  -2.5     1.4746      .0393   -.0599 |

| JMLE   9     -2.7709  -1.9     1.2091      .0314   -.0529 |

| JMLE  10     -2.1980  -1.4      .9963      .0255   -.0446 |

| JMLE  11     -1.7600  -1.1      .8245      .0209   -.0371 |

| JMLE  12     -1.4209   -.9      .6847      .0172   -.0309 |

| JMLE  13     -1.1553   -.7      .5703      .0143   -.0258 |

| JMLE  14      -.9451   -.6      .4761      .0119   -.0215 |

| JMLE  15      -.7771   -.5      .3982      .0099   -.0180 |

| JMLE  16      -.6418   -.4      .3336      .0083   -.0151 |

| JMLE  17      -.5319   -.3      .2798      .0069   -.0127 |

| JMLE  18      -.4422   -.3      .2349      .0058   -.0106 |

| JMLE  19      -.3685   -.2      .1974      .0049   -.0089 |

-------------------------------------------------------------

Subset connection O.K.

 

Iteration counts the number of times the data has been read.

PROX is the "normal approximation algorithm" to obtain approximate estimates speedily. Steps are not estimated during PROX.

JMLE is joint (unconditional) maximum likelihood estimation to obtain precise estimates.

Facets generally produces its results with high precision. This precision is rarely needed in practice before the final runs. There are several ways to lower the precision of the results. Most immediately, Ctrl+F forces Facets to move into the reporting phase at the end of the current iteration through the data. Other specifications include Iterations= and Convergence=. Inspection of the iterations, Table 3 of the output, indicates when the changes per iteration are too small to have any important meaning at the current stage of your analysis. Here this happens after just 4 iterations.

 

Max. Score Residual

Elements: the largest difference (residual), in score points, between the observed and expected score corresponding to any element's parameter estimate. 1.0 is the smallest observable (i.e., in the data) difference with the standard model weighting of 1.

%: the largest residual as a percent of the (maximum possible score - minimum possible score) for any element.

Categories: the largest difference between the observed and expected counts of occurrence corresponding to any category of a rating scale (or partial credit). 1.0 is the smallest observable difference with the standard model weighting of 1.

 

Recount required

when this appears, it means that the scores corresponding to some element parameters had extreme values (either 0 or the maximum possible). These parameters are dropped from estimation, forcing a recount of the marginal scores of the other elements.

 

Max. Logit Change

Elements: the largest change, in logits, between any element parameter estimate this iteration and its estimate the previous iteration. Starting estimates are either 0.0 logits, or the values given in the specification file.

Categories: the largest change, in logits, between any step parameter estimate this iteration and its estimate the previous iteration. Starting estimates are either 0.0 logits, or the values given in the specification file.

 

After the first few iterations, both "Max. Score Residual" and "Max. Logit Change" should steadily reduce in absolute size, i.e., draw closer to zero. There may be occasional perturbations due to unusual data. If the iterative procedure seems to have reached a plateau, you may force termination by pressing the Ctrl+"S" keys simultaneously.

 

The more detailed iteration report, which appears on your screen, can be recorded in your output file with a "Write=Yes" specification

 

Subset connection O.K.

Facets has verified that all measures can be estimated in one, unambiguous frame of reference. Warning messages here require investigation.


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
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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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