# How Much is Enough?

Problem 1: When I define boundaries of grade levels, what confidence interval should I define around these boundaries? The person measures have standard errors of 0.25 - 0.3 logits, which are equivalent to about 3 to 5 marks on an 80 mark test. A 95% confidence interval around the boundary contains ±2 S.E.. This is about 1 to 1.2 logits wide, or 15 to 20 marks! This seems very high, and to make a nonsense of the grade boundaries, which are themselves about 1 to 1.5 logits apart. Have I got the wrong end of the stick?
Julian Williams, University of Manchester

Problem 2: We want to anchor the scale against last year's test results. In particular we want to define certain "grade boundaries" on the scale which will be equivalent to last year's grade boundaries. I propose to include a set of anchor items whose difficulty parameters are near the grade boundaries defined for last year's test. How many anchor items will I need?

Answer: Let's assume that last year's test was successfully calibrated, and so that you have "good" item difficulties for the anchor items and exact logit measures for the grade boundaries. Each ability measure on the 80 item test has an S.E. of about 0.3 logits on this year's test. But we want to translate these measures onto last year's frame of reference. This will introduce extra imprecision due to the equating. If the equating worked perfectly with an average p-value of .8 (80% success by sample on common items), the S.E. of the equating constant would be 1/(sample size * common items * .8 * (1-.8)). If the sample size is 1,000, and we want the S.E. of the equating constant to be negligible, e.g., 0.03 logits, i.e., 1/10th of the S.E. of the person measure, then you want 1/(.03*.03*1000*.8*.2) = 7 common items. In practice, anchor items are not perfectly efficient. Some may have become "new" items, and so will need to be unanchored. 10 anchor items permits a margin of error. Choose items spread fairly uniformly across the measurement segment of interest.

Problem 3: The goodness of fit usually accepted for the infit mean square is from 0.8 to 1.2. This a "rule of thumb": is there a scientific basis to this?

Answer: The values 0.8 to 1.2 are based on the analysis of well-behaved data from multiple-choice tests. Exact standard errors for fit statistics can be computed (see Wright & Masters, 1982, p. 100ff.), but they are of limited use. The measurement challenge is to extract as much useful information as possible from inevitably messy data. The focal question is not "do the data fit the model (perfectly)", but "do the data fit the model well enough to construct useful measures". This is easy to check. Peal off bad data in layers and compare the resulting measures with scatter plots. When the plots shows no meaningful change, the remaining data are good enough. Alternatively, start with a core of "good" data, and layer on successively more doubtful data until the measures start to degrade. A few analyses like this will give you better information for the crucial fit range for your data than can be provided by any statistical theory or expert pontification.

John M. Linacre

How much is enough? Linacre J.M., Williams J. … Rasch Measurement Transactions, 1998, 12:3 p. 653.

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

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