Table 11 Responses used to calculate bias estimates

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

Table 11 summarizes the response modeling in the bias analysis

 

Bias/Interaction: 1. Student, 2. Format

+--------------------------------------------------+

| Cat  Score  Exp.  Resd StRes|                    |

|-----------------------------+--------------------|

| 9.60  9.60  9.60   .01  .01 | Mean (Cnt: 296)    |

| 1.34  1.34  1.30   .31  .53 | S.D. (Population)  |

| 1.34  1.34  1.30   .31  .53 | S.D. (Sample)      |

+--------------------------------------------------+

Count of measurable responses = 296.00

Raw-score variance of observations      = 1.80 100.00%

Variance explained by Rasch measures    = 1.53  85.00%

Variance of residuals                   = 0.27  15.00%

Variance explained by bias/interactions = 0.18  10.00%

Variance remaining in residuals         = 0.09   5.00%

 

Identification

Meaning

Cat

Observed value of the category in the data file

Score

Value of category in this analysis. This is less than Cat. if there are structural zeroes.

Exp.

Expected score based on current measures

Resd

Residual, the score difference between Score and Exp.

StRes

The residual divided by its standard error. The Rasch model is now over-parameterized, so the standardized residuals are expected to have an S.D. less than 1.0.

Mean

average of the observations

Cnt:

Count is the number of observations

S.D. (Population)

standard deviation treating this sample as the entire population

S.D. (Sample)

standard deviation treating this sample as a sample from the population. It is larger than S.D. (Population).

 

Count of measurable responses

the number of ratings used in estimating the bias terms. This may be only a small fraction of the entire data set.

Raw-score variance of observations

square of S.D. (Population) of Score in Table 11

Variance explained by Rasch measures

from Table 5

Variance of residuals

from Table 5

Variance explained by bias/interactions

Variance of Residuals - Variance remaining in residuals

Variance remaining in residuals

square of S.D. (Population) of Resd in Table 11


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
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