For polytomies, see www.rasch.org/rmt/rmt122q.htm
Once item difficulties (criterion-referenced or norm-referenced) have been carefully calibrated and the measurement system constructed, we can administer some or all of the calibrated items to further examinees and measure them based on the pre-calibrated item difficulties. The approach here obtains the maximum-likelihood estimates using Newton-Raphson iteration.
1) Collect observed responses by person n to the desired subset of calibrated items.
There are L observed dichotomous responses to L of the calibrated items taken by this person, with R correct answers and W incorrect.
If R = 0, then put R = 0.5, W = L-0.5
If W = 0, then put R = L-0.5, W = 0.5
Check that R+W = L.
2) Each item, i, has a calibration Ui in user-scaled units. If not already in logits, convert this to logits Di.
3) For person n's L observed responses on L items, compute the average item difficulty Dmean and the item sample variance, V:
Dmean = ( Σ Di )/L for i=1,L
V = (Σ (Di - Dmean)² ) / (L-1) for i=1,L
4) An initial estimate of person n's ability M is the PROX estimate:
M = Dmean + (√(1 + V/2.9))*loge(R/W)
alternatively, M = any convenient value
5) Compute expected score and variance for M:
For each item i of difficulty Di, the probability of person n's success on item i = Pi = 1 / ( 1 + e (Di - M) )
where e = 2.7183
person n's total raw score = Score = Σ( Pi ) for i=1,L
the model variance of person n's raw score = Variance = Σ( Pi (1 - Pi) ) for i=1,L
6) Obtain a better estimate M' of the measure M:
If, after the first iteration, the estimates overshoot (diverge, so that the changes in the estimates become bigger, not smaller),
abs(M' - M) > abs(M'' - M)
then multiply the divider by 2 and set its minimum value at 1.0:
Variance divider = max(variance*2, 1.0)
Do not change an estimate by more than one logit from its value in the previous iteration.
M' = max(min(M+1,M'),M-1)
7) If abs(M' - M) > 0.01 logits, then set M'' = M and M = M' and go to (5).
8) Set M = M', and report this final ability estimate with standard error = sqrt(1/Variance). Convert measure and standard error back to scaled U units for reporting.
Note: Summary statistics for the final person measures may not match directly-estimated person distributional parameters - but, since the persons are often regarded as "incidental" parameters, no one seems too much concerned.
For explanation, see Wright B.D., Douglas G.A. 1975. Best Test and Self-Tailored Testing. Research Memorandum #19. Chicago: MESA Press.
This estimation is implemented in Mark Moulton's Excel Spreadsheet.
For an explanation of WLE, see RMT (2009), 23:1, 1188-9
Warm's bias correction is applied to each MLE estimate, M, to produce a Warm's Mean Likelihood Estimate (WLE), MWLE, which is almost always closer to the mean item difficulty than M.
person n's WLE estimate = MWLE = M + ( J / ( 2 * I2 ) )
where, for dichotomous Rasch items,
J = Σ ( Pi (1-Pi ) (1-2Pi) ) summed over i = 1,L
I = Σ ( Pi (1-Pi ) )
Estimating Rasch (person, ability, theta) measures with known dichotomous item difficulties: Anchored Maximum Likelihood Estimation (AMLE). Wright B.D., Douglas G.A. Rasch Measurement Transactions, 1996, 10:2 p.499
|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|
|Feb. 28 - June 18, 2022, Mon.-Sat.||On-line course: Introduction to Classical and Rasch Measurement Theories (D. Andrich, I. Marais, RUMM), The Psychometric Laboratory at UWA, Australia|
|Feb. 28 - June 18, 2022, Mon.-Sat.||On-line course: Advanced Course in Rasch Measurement Theory (D. Andrich, I. Marais, RUMM), The Psychometric Laboratory at UWA, Australia|
|May 20 - June 17, 2022, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|June 24 - July 22, 2022, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com|
|Aug. 12 - Sept. 9, 2022, Fri.-Fri.||On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com|
|Oct. 7 - Nov. 4, 2022, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|June 23 - July 21, 2023, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com|
|Aug. 11 - Sept. 8, 2023, Fri.-Fri.||On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com|
The URL of this page is www.rasch.org/rmt/rmt102t.htm