The reason for standardizing the infit and outfit mean square statistics is to allow their statistical significance, or p-values, to be more conveniently represented. A familiar scale to use for this purpose is the Z-scale, or standard normal scale. Most of us are familiar enough with this scale that we don't even need to look up the p-value of 1.96. And we know that a Z-score over 2.0 is "statistically significant." In contrast, one does not immediately know the statistical significance of variables from other commonly-used reference distributions, such as the chi-square distribution. The distribution changes with its degrees of freedom!
A general formula for converting a variable, X, to the standard normal variate, Z, is:
(1) |
Now one may be certain that Z(X) has a mean of 0 and a variance of 1, but unless X is normally distributed to begin with, the p-values of Z(X) in a standard normal distribution do not necessarily agree with the p-values of X in its own distribution. For instance, a "normally distributed" variable has no skew, but chi-square distributions are skewed.
Wilson & Hilferty (1931) found a way to transform a chi-square
variable to the Z-scale so that
their p-values closely approximated. Since chi-square distributions
are skewed, the transformation has an extra layer of complexity.
The first step in the transformation is to transform the chi-square
statistic to a more normally-distributed variable. They showed that
the pth root of a chi-square variable divided by its degrees of
freedom, n, is approximately normally distributed and that
if | (2) | |
then | (3) | |
and | (4) |
Wilson & Hilferty chose p=3 (the cube root) for their transformation. The second step in the transformation is to substitute the results of Equations (2) through (4) into Equation (1). The complete transformation in terms of a chi-square variable, Y, with degrees of freedom, n, is:
(5) |
Notice that Equation (5) has the basic form of a normalizing transformation, but is actually a normalizing transformation of a transformation! The p-values of W(Y) are very close to those of a standard normal variable, as desired. That is, if Z is a standard normal variable, P(Z < W(y)) approx. = P(Y < y). So W(Y) approximates a t statistic.
The expectation of a chi-square variable, Y, is its degrees of freedom n. So the expectation of Y/n is 1. Let's call this v_{i}. The model variance of Y is 2n. So the variance of Y/n is 2/n, let's call this q_{i}^{2}. Substituting in (5) and simplifying, we can see that (5) parallels the formula for the standardized weighted mean square at the bottom of Table 5.4a in Rating Scale Analysis (Wright & Masters, 1982, p. 100):
(6) |
In RSA, the residuals comprising the v_{i} have been weighted, embodying an unstated assumption that the distributional characteristics of weighted and unweighted mean-squares are the same. The unweighted form, which matches (5) exactly, substitutes u_{i} for v_{i} and the unweighted mean-square variance for the weighted one. Since the actual degrees of freedom for residual chi-squares are difficult to compute, RSA estimates them from the model distributions of the observations.
Matthew Schulz
Wilson, E. B., & Hilferty, M. M. (1931). The distribution of chi-square. Proceedings of the National Academy of Sciences of the United States of America, 17, 684-688. water.usgs.gov/osw/bulletin17b/Wilson_Hilferty_1931.pdf
Standardization of mean-squares. Schulz, M. … 16:2 p.879
Standardization of mean-squares. Schulz, M. … Rasch Measurement Transactions, 2002, 16:2 p.879
Please help with Standard Dataset 4: Andrich Rating Scale Model
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: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr. | 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 |
Forum | Rasch Measurement Forum to discuss any Rasch-related topic |
Go to Top of Page
Go to index of all Rasch Measurement Transactions
AERA members: Join the Rasch Measurement SIG and receive the printed version of RMT
Some back issues of RMT are available as bound volumes
Subscribe to Journal of Applied Measurement
Go to Institute for Objective Measurement Home Page. The Rasch Measurement SIG (AERA) thanks the Institute for Objective Measurement for inviting the publication of Rasch Measurement Transactions on the Institute's website, www.rasch.org.
Coming Rasch-related Events | |
---|---|
May 26 - June 23, 2017, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
June 30 - July 29, 2017, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com |
July 31 - Aug. 3, 2017, Mon.-Thurs. | Joint IMEKO TC1-TC7-TC13 Symposium 2017: Measurement Science challenges in Natural and Social Sciences, Rio de Janeiro, Brazil, imeko-tc7-rio.org.br |
Aug. 7-9, 2017, Mon-Wed. | In-person workshop and research coloquium: Effect size of family and school indexes in writing competence using TERCE data (C. Pardo, A. Atorressi, Winsteps), Bariloche Argentina. Carlos Pardo, Universidad Catòlica de Colombia |
Aug. 7-9, 2017, Mon-Wed. | PROMS 2017: Pacific Rim Objective Measurement Symposium, Sabah, Borneo, Malaysia, proms.promsociety.org/2017/ |
Aug. 10, 2017, Thurs. | In-person Winsteps Training Workshop (M. Linacre, Winsteps), Sydney, Australia. www.winsteps.com/sydneyws.htm |
Aug. 11 - Sept. 8, 2017, Fri.-Fri. | On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com |
Aug. 18-21, 2017, Fri.-Mon. | IACAT 2017: International Association for Computerized Adaptive Testing, Niigata, Japan, iacat.org |
Sept. 15-16, 2017, Fri.-Sat. | IOMC 2017: International Outcome Measurement Conference, Chicago, jampress.org/iomc2017.htm |
Oct. 13 - Nov. 10, 2017, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
Jan. 5 - Feb. 2, 2018, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
Jan. 10-16, 2018, Wed.-Tues. | In-person workshop: Advanced Course in Rasch Measurement Theory and the application of RUMM2030, Perth, Australia (D. Andrich), Announcement |
Jan. 17-19, 2018, Wed.-Fri. | Rasch Conference: Seventh International Conference on Probabilistic Models for Measurement, Matilda Bay Club, Perth, Australia, Website |
April 13-17, 2018, Fri.-Tues. | AERA, New York, NY, www.aera.net |
May 25 - June 22, 2018, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
June 29 - July 27, 2018, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com |
Aug. 10 - Sept. 7, 2018, Fri.-Fri. | On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com |
Oct. 12 - Nov. 9, 2018, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
The HTML to add "Coming Rasch-related Events" to your webpage is: <script type="text/javascript" src="http://www.rasch.org/events.txt"></script> |
The URL of this page is www.rasch.org/rmt/rmt162g.htm
Website: www.rasch.org/rmt/contents.htm