Umean (user mean and scale and decimal places) = 0, 1, 2

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

Improving Communication:

The standard unit of measurement is the logit (log-odds unit). These measurement units are inconvenient for reporting results to examinees, parents, administrators etc. They can be linearly rescaled to make the reported results more useful to the user. The standard origin (mean) is the mean of the item difficulties, the standard scaling is 1, i.e., 1 reported unit per logit. These values may be changed by

Umean = user origin value, user units per logit, decimal places to report

also, for convenience, these can be specified separately

Uscale = user units per logit

Udecimals = user decimal places

 

Anchoring and Umean=

It is always safest to include the Umean= (mean),(logit scaling) of the analysis that produced the anchor values.

Umean=(umean of anchoring analysis) is needed so that Facets knows what is the baseline for Fair average=zero or if an unanchored facet is to be centered or Dummy anchoring (,D) is used for a facet.

 

Example 1: A useful transformation is to take the high and low measures on the vertical rulers in Table 6.0 or Table 7 and assign that range to 0-100. Then linearly transform all other measures.

Suppose the highest measure is +8.0 logits and the lowest measure is -7.0 logits on Table 6.0 of an analysis. This gives a range of 15 logits, i.e., 100/15 = 6.67 points per logit.

For reporting purposes, transform all measures by Measure* 6.67 + 46.7 and report with 1 decimal place.
Umean = 7, 6.67, 1
A measure of +2.0 logits becomes 2+*6.67 + 46.7 = 60.0 user-scaled points
Standard errors become (Standard error logits) * 6.67.
A standard error of .03 logits becomes (.03)*6.67 = .2 user-scaled points

 

Example 2: Set the origin of the measurement scale at 50 units, with 10 units per logit, and report 0 decimal places. This usually gives a measure range somewhat like 0-100.

Umean = 50, 10, 0

 

Example 3: From an earlier run, the lowest person measure is -6.23 logits, and highest 7.45 logits. Rescale so that the person measure range is 0 - 100.

The new scale is 100 units. The old range was 6.23 + 7.45 = 13.68 logits, so scaling factor = 100 / 13.68 = 7.31 units per logit.

The new origin is offset 6.23 logits = 6.23*7.31 units = 45.54 units

Umean = 45.54, 7.31 ; the standard number of decimal places, 2, is reported.

 

Example 4: You wish to treat an element as a dummy. It is used for selection, fit analysis and bias detection only.

If Umean = 0 (the standard)
Labels=
4=dummy facet,A
1=dummy element,0 ; anchor this at the umean= value
....
*

 

If Umean = 50 (user setting)
Labels=
4=dummy facet,A
1=dummy element,50 ; anchor this at the umean= value
...
*

 

Example 5:  -5 to +3 is to be reported as 0 to 25.

Usually the "previous Umean=" is zero.

The computation generally is:

Existing range of measures =  -5 to +3  (or whatever is in your Facets report)

Desired range = 0 to 25

Then:

Scaling = (25 - 0) / (3 - -5)  = 25/8 = 3.125

Umean= is given by equivalent values: 25 matches 3

New value = old value * scaling + Umean

25 = 3.125 * 3 + Umean

Umean = 15.625

So that the Facets specification is:

Umean = 15.625,  3.125

Checking this for the 0 point:

0 = -5*3.125 + 15.625

 

Example 6: You want a meaningful baseline for classrooms or Grade-levels across schools. Prof. Everett Smith suggests: determining the classroom means first. Then using the mean and SD of those means, determine UMEAN and USCALE so that the new metric would be 100/10 for the classroom means.


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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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
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