﻿ Facets requires specifications and data

# Facets requires specifications and data

Facets requires specifications and data. The data are qualitative observations. These are numbered in ascending sequence along the latent variable. For instance:

 Qualitatively-ordered observations must be exclusive, extensive, exhaustive Numerical (scored, coded) observations: higher number = more of the latent variable must be 0 or positive integers Yes-No 1-0 Correct-Incorrect 1-0 None-Some-All 0-1-2 Likert scale: Strongly Disagree-Disagree-Neutral- Agree-Strongly Agree 1-2-3-4-5 Essay rating 1-2-3-4-5-6-7-8-9 Attitude survey 1-2-3-4-5-6-7-8-9-10 Visual-analog scale or Percent 0-1-2-3-4-....-96-97-98-99-100

The specifications define how these data are modeled to be the outcome of a linear combination of elements. Each element belongs to a facet. All facets and elements are numbered. For instance:

 Facet (number) Elements (numbers) Examinees (1) Albert (1), Beryl (2), Claudia (3), ... Raters (2) Prof. Cilic (1), Dr. Nadal (2), Ing. Laver (3), ... Items (3) Item 1 (1), Item 2 (2), Item 3 (3), ... Time-points (4) Admission (1), Discharge (2)

The measurement model becomes:

Examinee ability + Rater leniency - Item difficulty - Time-point challenge Observation

a) Specifications are instructions on how to analyze the data. They are in the Facets specification file. They look like:

Specification = value

Most specifications have standard default values, suitable for most analyses.

Example 1: an excerpt from a list of specifications:

Facets = 4   ; facets are 1=examinees, 2=raters, 3=items, 4=time-point

Positive = 1, 2  ; examinees are able, raters are lenient

Noncenter = 1   ; examinee measures float relative to raters, items, time-points

b) The data are the qualitative observations. Their location is specified by Data=. These can be in the same file as the specifications or in separate files. Each data point, response, observation or rating is analyzed as the interaction of one (or more) elements from each of the specified facets.

Example 2: an excerpt of Facets data:

Examinee 23 is rated by rater 4 on item 6 at time-point 2 with a rating of 7

Data=

23, 4, 6, 2, 7

Example 3: Here is a typical data file, after data collection:

123 A 25467 ; Examinee 123 is rated by judge A on items 1-5 with ratings 2, 5, 4, 6, 7

452 C 34245 ; Examinee 452, judge C, ratings 3, 4, 2, 4, 5

312 B 34452 ; Examinee 312, judge B, ratings 3, 4, 4, 5, 2

123 B 24443 ; Examinee 123, judge B, ratings 2, 4, 4, 4, 3

The Facets specification and data file:

 Specification Explanation Title = Sample Analysis The title of the analysis appears on every output report. There are 3 facets: examinees, judges, items. Model = ?,?,?,R The facets measurement model is: any examinee (?,) can combine with any rater (?,) and with any item (?,) to produce a rating (R) Center the elements all the facets except facet 1. This established the zero-point (local origin) of the measurement frame-of-reference. Positive = 1, 2 Facets with "higher score = higher measure" (ability, leniency, easiness) are positive. Facets with "higher score = lower measure" (disability, severity, difficulty) are negative. Umean = 0, 1 The user-defined mean for the measures is zero. The user-defined scaling is 1. The reported measures will be in logits. Labels = The list of labels (identifiers) follows: 1,Examinees 123 =  452 = 312 = * Facet 1 is the Examinees. 123 is the first examinee element. 453 is the second examinee 312 is another examinee * is the end of Facet 1 2,Judges 1 = A 2 = B 3 = C * Facets 2 is the Judges A is the label for the first judge, with element number 1. 3,Items 1-5 * Facet 3 is the Items. The item element numbers are 1, 2, 3, 4, 5 Data= The data follows, or the data can be in a separate file: Data = filename.txt 123, 1, 1-5, 2, 5, 4, 6, 7 (comma-separated) Examinee 123 is rated by judge 1 (A) on items 1-5 with ratings 2, 5, 4, 6, 7 452 3 1-5 3 4 2 4 5 (blank separated) Examinee 452, judge 3 (C), items 1-5, ratings 3, 4, 2, 4, 5 312→2→1-5→3→4→4→5→2 (→tab separated) Examinee 312, judge2 (B), items 1-5, ratings 3, 4, 4, 5, 2 123, B, 1-5, 2, 4, 4, 4, 3 Element labels are also allowed in the data. They will be automatically replaced by their element numbers ... more data

Look for a problem like yours in our examples. Then copy-and-edit it!

You can also use the template, Excel, SAS, SPSS, STATA and Facform.

End of Introductory Overview: Now start on your own! Run one of the Example files: how about the Diving Competition?

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

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Rasch Publications
Rasch Measurement Transactions (free, online) Rasch Measurement research papers (free, online) Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch Applying the Rasch Model 3rd. Ed., Bond & Fox Best Test Design, Wright & Stone
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
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Journal of Applied Measurement Rasch models for measurement, David Andrich Constructing Measures, Mark Wilson Rasch Analysis in the Human Sciences, Boone, Stave, Yale
in Spanish: Análisis de Rasch para todos, Agustín Tristán Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez
Winsteps Tutorials Facets Tutorials Rasch Discussion Groups

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