Delements element type in data =

Delements= specifies the element identifier in the data file for the Labels= facet.

Format 1: Delements = type
Delements = N	all element identifiers in the data are element numbers in Labels=. Element identifiers like "1-4" are treated as ranges of elements: 1,2,3,4
Delements = L	all element identifiers in the data are element labels in Labels=. Element identifiers like "1-4" are treated as the labels of single elements: "1-4". Element numbers without labels in Labels= are matched on the numerical text of the numbers. Null elements can be matched on the Null element number or its element label.
Delements = NL Warning! This can cause unintended matches. It is safer to use Format 2.	all element identifiers in the data can be both element numbers and element labels in Labels=. Element numbers are matched first. If the element identifier in the data does not match any element number for the specified facet, the identifier is matched with the element labels. Element identifiers like "1-4" are treated as ranges of elements: 1,2,3,4
Delements = LN Warning! This can cause unintended matches. It is safer to use Format 2.	all element identifiers in the data can be both element numbers and element labels in Labels=. Element labels are matched first. If the element identifier in the data does not match any element label for the specified facet, the identifier is matched with the element numbers. Element identifiers like "1-4" are treated as the labels of single elements: "1-4". If the element label "1-4" is not found, then the element identifier 1-4 are treated as ranges of elements: 1,2,3,4

Format 2: Delements = Facet number + type, Facet number + type, ... Example: Delements = 1N, 2L, 3NL,4LN,0L
1N	facet 1 element identifiers in the data are element numbers in Labels=
2L	facet 2 element identifiers in the data are element labels in Labels=
3NL	facet 3 element identifiers in the data can be both element numbers and element labels in Labels=. Element numbers are matched first. If the element identifier does not match any element number for the specified facet, the identifier is matched with the element labels.
4LN	facet 4 element identifiers in the data can be both element numbers and element labels in Labels=. Element labels are matched first. If the element identifier in the data does not match any element label for the specified facet, the identifier is matched with the element numbers
0L	When a facet in the data is skipped with Entered=1,0,2, the element identifiers in the data for facet 0 may be numbers, labels or both. 0L specifies that they are labels so that element identifiers like "1-4" are to be treated as single elements, not ranges of elements.

Format 3: Delements = Facet number + type + label-matching location

Example: Delements = 3LS2W3

1NS2W3, (substring is ignored)

facet 1 element identifiers in the data are element numbers in Labels=

2LS2W3 or 2NLS2W3 or 2LNS2W3

facet 2 element identifiers in the data are in the element labels in Labels=. They are S2W3, starting in column 2 of the element label with a width of 3 columns.

Delements = L or N or NL or LN - Matching element identifiers in the data with Labels= element labels
Delements=	When matching an element identifier in the data with element numbers or labels in Labels=, the priority is:
N or NL or LN (if no element label match)	The identifier in the data is matched with the element number in the facet list	Facet 1: "7" in the data matches element number "7" so the element number for analysis is 7	Labels= 1, Grade level 7= Grade seven * Data= 7, x21, boy, run, M, 1	Facets = 5 Labels= 1, Grade level 7= Grade seven * 2, cohort 8= myx21pq * 3, gender facet 2 = boy: male student * 4, activity facet 4 = run * 5, school facet 8= Marymount * Data= 7, x21, boy, run, M, 1
2LS3E5 or 2LS3W3 or 2LNS3E5 or 2LNS3W3 or 2NLS3E5 or 2NLS3W3 (if no element number match)	The identifier is an exact match to the segment of an element label specified with S..W.. or S..E..	Facet 2: "x21" in the data matches "x21" in columns 3,4,5, so the element number is 8	Labels= 2, cohort 8= myx21pq * Data= 7, x21, boy, run, M, 1
L or LN or NL (if no element number match)	The identifier is an exact match to the part of an element label before : (a colon)	Facet 3: "boy" in the data matches "boy:". so the element number is 2	Labels= 3, gender facet 2 = boy: male student * Data= 7, x21, boy, run, M, 1
L or LN or NL (if no element number match)	The identifier is an exact match with an element label without a : (a colon)	Facet 4: "run" in the data matches "run:". so the element number is 4	Labels= 4, activity facet 4 = run * Data= 7, x21, boy, run, M, 1
L or LN or NL (if no element number match)	The identifier is a leading-character match with an element label that has no : (a colon)	Facet 5: "M" in the data matches the leading M in "Marymount", so the element number is 8	Labels= 5, school facet 8= Marymount * Data= 7, x21, boy, run, M, 1

Example 1: in the data, the facet 1 element identifiers are element labels, for facet 2, element numbers, and for facet 3 are element labels. The observations are 0-1 dichotomies:

Facets = 3

Delements = 1L, 2N, 3L

Labels =

1, Gender

1= Female

2= Male

2, Zip code

60601 = Central Chicago
60637 = Hype Park Chicago

...

3, Income

1 = Low

2 = Medium

3 = High

Data =

F 60637 2 0 ; Female, 60637 Zip code, Medium income, No

M 60601 3 1 ; Male, 60601 Zip code, High income, Yes

Example 2: chess tournament: paired comparisons with element labels. Facet 1 element identifiers are labels. Facet 2 element identifiers are numbers.

Facets = 3 ; player, player, round

Entered in data = 1,1, 2 ; the players are facet 1, round is facet 2

Models = ?,-?, ?, D

Delements = 1L, 2N ; element identifiers for facet 1 are element labels, for facet 2 are element numbers

Labels=

1, Players

1= John

2= Mary

3= ...

2 = Round

1= Monday morning

2= Monday afternoon

3= Monday evening

.....

Data =

John Mary 1 0 ; John played Mary on Monday morning. John lost.

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

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 Facets Tutorials - free	Many-Facet Rasch Measurement (Facets) - free, J.M. Linacre	Fairness, Justice and Language Assessment (Winsteps, Facets), McNamara, Knoch, Fan
Other Rasch-Related Resources: Rasch Measurement YouTube Channel
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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Delements element type in data = N

Questions, Suggestions? Want to update Winsteps or Facets? Please email Mike Linacre, author of Winsteps mike@winsteps.com