Positively-oriented facet = 1

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

This command specifies which facets are positively oriented. The standard in Facets is for only the first facet to be measured positively, so consult the table below to understand how to interpret the Rasch measures which are output by Facets. The facet orientations specified in your command file are indicated in the output on the all-facet ruler (Table 6.0) with a + or - sign at the top of the ruler. (This section incorporates text from William Bonk.)

 

If both Positive= and Negative= are specified, only the first one encountered is actioned.

 

Example 1: Positive=1 (Only facet 1 is positively-oriented. Facets uses this as standard)

Persons (facet 1) respond to items (facet 2) which are scored by raters using rating scales (facet 3). Persons are measured positively, i.e., a high ability measure means the raw score was high, a low measure means the raw score was low. On the other hand, items are measured negatively so the higher the Rasch measure, the greater difficulty of the item; so are raters, such that the higher the Rasch measure, the greater severity of the rater. This is a convention in educational measurement.

 

Facet

Interpretation

Examinee (positive)

Higher Rasch measures mean greater ability

Test items (negative)

Higher Rasch measures mean items are harder

Survey items (negative)

Higher Rasch measures mean items are more difficult to endorse

Raters (negative)

For examinees: Higher Rasch measures mean raters are more severe

For standard-setting: Higher Rasch measures mean raters are less demanding

Prompt (negative)

Higher Rasch measures mean prompts are harder (tend to yield lower raw scores)

 

Example 2: Positive=1,2,3 (All facets are positively-oriented)

Another method is to orient all facets positively. For instance, Patients (facet 1) are rated on tasks (facet 2) by raters (facet 3). All facets are measured positively, so that higher Rasch measures mean that patients scored higher, that tasks were easier, and that raters were more lenient. This is the convention in health care measurement.

Positive=1,2,3

 

Facet

Interpretation

Examinee (positive)

Higher Rasch measures mean greater ability

Test items (positive)

Higher Rasch measures mean items are easier

Survey items (positive)

Higher Rasch measures mean items are easier to endorse

Raters (positive)

For examinees: Higher Rasch measures mean raters are more lenient

For standard-setting: Higher Rasch measures mean raters are more demanding

Prompt (positive)

Higher Rasch measures mean prompts are easier (tend to yield higher raw scores)

 

Positive= specifies which facets are to be measured positively, i.e., higher score means higher logit value. The other facets will be negatively oriented. Positively-oriented facets are indicated by "+" in Table 6.0. Positive-orientation and Negative-orientation" are complex. We must answer:

 

For a particular facet,

Do increasing raw scores indicate more or less of what we are looking for?

counting right answers = more capability

counting mistakes = less capability

longer times in performing a task = less capability

higher Likert rating = more agreement or less agreement (depending how the categories are numbered).

higher p-value = more easiness for items

higher ratings = more leniency for raters

etc.

 

For a particular facet,

Do we want higher Rasch measures to indicate higher or lower levels of what we are looking for:

more ability as in educational testing

more disability as in some medical applications

more leniency for raters

more severity for raters

more difficulty (to perform, to answer correctly, to agree with, etc.) for items

more easiness (to perform, to answer correctly, to agree with, etc.) for items

or

etc.

 

Then, for a particular facet,

if higher raw scores align with what we want higher Rasch measures to indicate, then the facet must be coded as "Positive=",

but if lower raw scores align with what we want higher Rasch measures to indicate, then the facet must be omitted from "Positive=".

 

 

Orientation of facet

Meaning of raw scores

Positive

Negative

Examinees: higher raw score indicate higher capability

Higher Rasch measures mean greater ability

Higher Rasch measures mean greater disability (not recommended because tends to be misunderstood)

Items: higher p-values or average ratings indicate greater easiness

Higher Rasch measures mean greater item easiness

Higher Rasch measures mean greater item difficulty

Raters: higher average ratings indicate greater leniency for examinees or more demanding for standard setting

Higher Rasch measures mean greater rater leniency or more demanding for standard setting

Higher Rasch measures mean greater rater severity or less demanding for standard setting

Prompt: higher average raw scores indicate greater easiness

Higher Rasch measures mean greater prompt easiness

Higher Rasch measures mean greater prompt difficulty

 

Example 3: The usual convention in educational testing is that only those facets corresponding to the objects of measurement, typically persons, are measured positively (higher score means higher ability measure), but the agents of measurement, items, tasks and judges are measured negatively (lower score means higher difficulty measure or higher severity measure). For instance, Persons (facet 1) take items (facet 2) with some items rated by raters (facet 3). Persons are to be measured positively, i.e., higher score corresponds to higher ability measure. Items are to be measured negatively (lower score corresponds to higher difficulty measure) and raters are to be measured negatively (lower score corresponds to higher severity).

Positive=1

 

Example 4: In Example 3, the raters (facet 3) are to be reported by leniency (higher score means higher leniency measure), not by severity (lower score means higher severity measure).

Positive=1,3

 

Example 4: The usual convention in healthcare is that all facets corresponding are measured positively: higher score means higher ability measure, higher easiness measure or higher leniency measure. For instance, Patients (facet 1) are rated on items (facet 2) by raters (facet 3). All facets are to be measured positively, i.e., higher score corresponds to higher measure.

Positive=1,2,3

 

Example 5: Both facet 2, boys, and facet 4, girls, are to be reported as positively oriented (higher scores = higher measures). All other facets negatively (higher scores = lower measures):

Positive=2,4

 

Example 6: No facets are to be positively oriented. For all facets, higher scores mean less of what we are looking for.

Positive=


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 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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