﻿ Elements in Labels=

Elements in Labels=

3. Element Identification

 3A. Element number = 1 to 2147483646, 3B. Element label, 3C. Measure value, 3D. Group number, 1 to 32767 0 group-anchoring 3E. Weight, 3F. Target element number

3A. Element numbers

Assign each element within each facet its own integer element number in the range 1 to 2147483646 for each facet. Numbers may be skipped over or omitted, and listed in any order. If the same element number is listed twice in Labels= then the details of the element are combined.. The maximum number of elements across all facets is 100,000,000.

Thus,

each judge must be assigned a unique element number in the range 1 to 2147483646.

each person must be assigned a unique element number in the range 1 to 2147483646.

each item must be assigned a unique element number in the range 1 to 2147483646.

The ranges of judge, person, and item numbers may overlap, so that judge 1 may rate person 1 on item 1.

Your elements may already labeled with numbers that are too big, such as social security numbers, or with non-numeric names, such as Mary. These elements must be re-identified with unique element numbers in the range 1 - 2147483646. Facform can assist with this. It can be downloaded from www.winsteps.com/facets.htm.

Element 0 is the null element, indicating that the facet does not apply to the observation. But 0 can be used as a regular element number if Keepasnull= has been used to set a different element number as the null element.

If a desired element number is too big (such as a Social Security number), then use it as an element label with a shorter element number. The element identifier in the data can be the element label: Delements= NL

Observations in Data= referencing elements not included in the Labels= list are omitted from Facets analysis. You can deliberately omit an element (and its observations) from an analysis by typing a ";" before its element number, turning that specification line into a comment.

3B.  Element labels

Each element number can have an element label (including the Null= element). They are shown in Table 7 The element labels can be used as element identifiers in the data (with Delements=) or as references to the elements in other facets (with Dvalues=).

How to construct a list containing all facets and elements:

b) List for yourself all the facets, in whatever order you like:

e.g., raters, persons, items

c) For each facet, the facet number and its identifying label are entered first.

Labels=

1, Judges ; the rater facet

d) For that facet, the element numbers and their labels are entered, in any order.

Labels=

1, Judges ; the rater facet

10 = Rehnquist

1 = John Jay

123456789 = Jose ; this is Jose

If the element numbers are in a range, which share features, then they can be summarized:

Labels=

1, Judges ; the rater facet

1-100 = Males

101-200 = Females
Specifying long ranges containing many unused number will slow down Facets, e.g,,
1 - 5000000 = Males ; when there are only 10 males
Use the Excel "unique records" feature to list the numbers used

If the labels are to be used for reference by Dvalues=, then the code letter can be indicated by ":".

Dvalues=

3, 1, 2, 1 ; element number for facet 3 in the data is in the for element label for facet 1 at column 2 with a length of 1.

*

Labels=

1, Persons

1, 3M Fred ; M is used by Dvalues=

2, 2F Mary ; F is used by Dvalues=

3, 2M Jose ; M is used by Dvalues=

...

*

; either

3, Gender ; Sex

1, F: Girls ; F prior to : is matched to F of Mary

2, M: Boys ; M prior to : is matched to M of Fred and Jose

*

; or

3, Gender

1, Female ; F is matched to F of Mary

2, Male  ; M is matched to M of Fred and Jose

*

e) An "*" marks the end of each facet's elements.

Labels=

1, Judges ; the rater facet

10 = Rehnquist

1 = John Jay

*

f) For each further facet, repeat c) through e).

Labels=

1, Judges ; the rater facet

10 = Rehnquist

1 = John Jay

*

2, Persons

Example 1:

Labels= ; specifying that facet/element details follow:

1,Judges ; This labels the first facet

1=John Jay ; Judge no. 1

2=Roy Beam ; Judge no. 2

; Judge 3 to be omitted

4=Wapner ; Judge 4

| ; the other judges go here

15=Scalia ; Judge no. 15

* ; an asterisk terminates the elements for this facet

2,Persons ; facet 2

1=Ben ; first person

2=7021596 ; person 2 as a telephone number

|

*

3,Items ; facet 3

1= 2+2 ; the first item addition

2= Tennis ; second item

3= Attitude ; third item

4 ; the fourth item has no label

* ; end of labels for last facet also ends Labels=

When there is no description, Facets uses the facet or element number instead, e.g.,

Labels=

1, Rater

4  ; this means 4=4

*

g) Big element identification numbers are no problem.

1) There must be no more that 4,000,000 element numbers.

2) element numbers must be in the range: 0 - 2,147,438,646

If your element identifiers are outside that range, please use those element identifiers as element labels, not numbers

3) elements can be identified by element numbers or element labels in the data file.

4) if you don't put the element identifiers in Labels=, then Facets will construct a list of elements in its Output Table 2. You can paste this list into Labels=

Example 2:  student IDs are numbers like 9999999999999999999 and you want to use them in the data file

Step 1) Construct the data file with student IDs as element identifiers for Facet 1

Step 2) In your Facets specification file:

Delements= 1L  ; element identifiers in the data file are element labels

Labels=

1=Students

; no element identifiers here for the Students

*

2=Items

1=What is the capital of Germany?

......

Step 3) Analyze your data file with Facets. Output Table 2 will contain a list of element numbers and labels:

List of unspecified elements. Please copy and paste into your specification file, where needed

Labels=Nobuild ; to suppress this list

1, Students,  ; facet 1

1 = 210100002

2 = 210100008

3 = 210100013

4 = 210100025

5 = 210100026

....

Step 4) Copy and paste these element numbers and labels into your Facets specification file:

Delements= 1L  ; element identifiers in the data file are element labels

Labels=

1=Students

1 = 210100002

2 = 210100008

3 = 210100013

4 = 210100025

5 = 210100026

....

*

2=Items

1=What is the capital of Germany?

......

Step 5) Do your Facets analysis

3C. Establish pre-set measure values for starting values or anchor values.

Ignore this for initial analyses.

When logit anchor values or starting values are to be provided, a comma followed by a third value is appended after each element label. These values have one of three meanings:

1) Starting values (the standard when values are provided)

this is useful for speeding up analyses when you have a good idea of what some of the measures will be. These values will not be changed during the PROX phase of the estimation. Whenever the element label is followed by a logit value, this is used as the starting estimate for that element in the analysis.

Labels=

1,Persons ; no code after facet label

23=Joe,2.3 ; means Estimation starts with Joe at 2.3 logit

2) Anchor values: A and D

Use this when you know what you want some of the measures to be from another analysis or from an item bank. Elements representing demographic facets such as gender can be anchored at 0 logits. This excludes them from estimation, but includes them for fit statistics and bias analysis.

When the facet label is followed by ",A", then the measure of each element in that facet is anchored (fixed) at the value following its label, whenever such a value is provided.

When the facet label is followed by ",D", then the measure of each element in that facet is anchored (fixed) at the Umean= value. The anchor value following its label (if any) is ignored.

Labels=

2=Persons,A ; anchoring wanted

1=Ben ; no value - anchoring does not apply

23=Joe,2.3 ; Joe anchored at 2.3 for the entire analysis

When Umean= is used, then the anchor values must align with it:

Umean=50,10 ; User mean is 50, user scaling is 10 per logit.

2=Persons,A ; anchoring wanted

1=Ben ; no value - anchoring does not apply

23=Joe, 73 ; Joe anchored at 50 + 10*2.3 for the entire analysis

This feature is useful for dummy facets removing classification elements from the measurement. These may be used to partition fit, detect bias or select rating scale (or partial credit) models.

Labels=

2=Classifier,A ; anchoring wanted

1=Type A,0 ; anchor at 0, so doesn't affect measurement

2=Type B,0 ; anchor at 0, so doesn't affect measurement

or, and easier,

Labels=

2=Classifier,D ; dummy-facet anchoring wanted

1=Type A ; no anchor value required

2=Type B ; no anchor value required

When Umean= is used, use the Umean= value for these "dummy" elements

Umean=50,10 ; User mean is 50, user scaling is 10 per logit.

Labels=

2=Classifier,D ; dummy-facet anchoring wanted

1=Type A ; no anchor value required

2=Type B ; no anchor value required

3) Group-anchoring: G (all elements, including extreme elements) and X (exclude extreme elements)

Use this to equate by groups of elements, e.g., to maintain the same average severity of a group judges from one test analysis to the next, or to anchor the average difficulty calibration of a set of 6th grade math items. An easy way to specify a particular group mean is to give all group elements that same value.

When the facet label is followed by ",G" or ",X", groups of elements are anchored so that their mean is fixed, though they float individually relative to that mean. "G" includes all measurable elements, extreme and not extreme. "X" excludes measures corresponding to extreme scores from the group-anchor computation. This is how "G" behaved in earlier version of Facets. If several groups are to be defined, enter each element's group number as a fourth entry:

2,Raters, G  ; Raters are facet 2

1=Ben  ; no value, normal estimation, does not belong to a group

2=Mike, 2.7,1 ; Mike contributes 2.7 logits to group 1

3=Mary, 3.3,2 ; Mary contributes 3.3 logits to group 2

5=Anne,-1.7,1 ; Anne contributes -1.7 logits to group 1

6=Joel, 0.5,2 ; Joe contributes 0.5 logits to group 2

7=Fred,  ,2 ; Fred is reported in group 2, but he does not participate in the group-anchoring.

; The double comma, ", ,", omits the anchor value.

8=Kent, 3.1 ; No group number, so 3.1 is a starting value

9=Irma,-2.8,0 ; Group 0 means "anchor at this value"

10=Jim, 1.3,0 ; Group 0 means "anchor at this value"

11=Abe, 1.5,3 ; Only one in a group-anchor, so treated as if anchored, "A", but reported as a group

*

Mike, Anne and Joe are in group 1, so their individual estimated measures will alter, but their mean measure will be fixed at (2.0 -1.0 + 0.5)/3 = 0.5 logits. Mary is the only one anchored in group 2, so her measure will be fixed at 3.0.

An easy way to specify a particular group mean is to give all group elements that same value. In this example we want Groups 1 and 2 both to have average measures of 0.

Labels=

2=Persons,G ; group-anchoring wanted

1=Ben,0,1 ; Ben is in group 1

2=Mike,0,1 ; Mike is in group 1

3=Mary,0,2 ; Mary is in group 2

5=Anne,0,1 ; Anne is in group 1

6=Joe,0,1 ; Joe is in group 1

7=Fred,0,2 ; Fred is in group 2

Group 0 is for assigning standard anchor values. It acts as a group of 1 element for anchoring.

Labels=

2=Persons,G ; group-anchoring wanted

1=Ben,0,1 ; Ben is in group 1

2=Mike,0,1 ; Mike is in group 1

3=Mary,1,0 ; Mary is in group 0 - so is anchored at 1.0

4=John,2,0 ; John is in group 0 - so is anchored at 2.0

See Nested Designs for more examples.

3D. Grouping

Grouping of elements is controlled by a fourth parameter at the element level. This is used for reporting as well as group-anchoring. Thus, to report people classified into two groups, males and females:

Labels=

2 = Person ; No anchoring specified, so values (if any) will be starting estimates. We could also specify ",A" (anchoring) or ",G" (group-anchoring).

1 = Ben, , 1 ; in group 1, note use of double commas, ",,"

2 = Mike, , 1 ; in group 1

3 = May, 3.4, 2 ; in group 2, with a starting logit value of 3.4

5 = Anne, , 2 ; in group 2

6 = Harold, ,1 ; in group 1

Group number in element label: Group numbers can be numeric (1,2,3,..) or can specify a number in the element label: \$(starting position)W(width).

1 = 1 Ben,  ,\$1W1  ; the group number will be the first character of "1 Ben" = 1.

This can be conveniently applied to all the elements of a facet:

Labels=

2, Person

1, 123 Ben

2, 235 Mary

3, 123 George

....

1003, 347 Jose

1-1003, , , \$1W3 ; the group number for all Persons is in the first 3 columns of the person label.

Table 7 contains:

1) all elements of each facet with summary statistics for the facet.

2) each group of elements within each facet with summary statistics for the group.

Grouping simplifies analysis of subsets of elements in a facet. Boys and girls can be separately listed. The mean measures of the two groups can be directly compared. Grouping of test items by strand, and arranging the elements by measure, aids in verifying the construct validity of each strand. It also makes the strand information more available for use in curriculum and assessment decision-making.

3E. Element weighting: default 1.0

Observations specified for this element are weighted with this element weight multiplied by model weights and observation weights. See Weighting the data. If you want to report an element, but not allow it to influence other estimates, then give it a very small weight.

3F. Target element number: default none.

Two or more elements with different element numbers can be reassigned to the same element number. The observations with the element number of this specification are reassigned to the element with the Target element number. Duplicate element numbers can be combined. For instance:

Labels=

1 = Task A, , , , 4  ; observations with element 1 are reassigned to element 4 for analysis.

.....

4 = Task AB ; observations for element 1 are included with observations for with element 4

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Rating Scale Analysis, Wright & Masters Introduction to Rasch Measurement, E. Smith & R. Smith Introduction to Many-Facet Rasch Measurement, Thomas Eckes Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments, George Engelhard, Jr. & Stefanie Wind Statistical Analyses for Language Testers, Rita Green
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