Ludlow and O'Leary (1999) state: "Missing data occur for a variety of reasons. Particularly problematic are those items an individual skipped or could not reach in time."
They go on to discuss four strategies:
(1) Omitted and not-reached items treated as not administered. This strategy may encourage students to respond only to items they are absolutely sure they will get correct.
(2) Omitted and not-reached items treated as incorrect. This encourages guessing and penalizes slow, careful work.
(3) Omitted items treated as incorrect and not-reached items treated as not administered. This encourages "guess rather than omit", but then "stop when it looks like the test is getting too hard."
(4) First, students are instructed to attempt every question. Then, for item calibration, omitted items treated as incorrect and not-reached items treated as not administered. The items are now anchored, and for person calibration, omitted and not-reached items treated as incorrect. This was adopted for TIMSS.
[This compromise pleases no one, but appears to match American testwiseness more closely than it does the caution expected in traditionalist cultures.]
Ludlow and O'Leary conclude: "The notion of a single, best approach exists in theory only, and suggested courses of action must be evaluated on a number of levels. ... Common sense and reasonable explanations still must govern decisions regarding how test data are analyzed and reported."
Ludlow, L.H., & O'Leary, M. (1999) Scoring omitted and not- reached items: practical data analysis implications. Educational and Psychological Measurement, 59, 4, 615- 603.
Note: though choices as to how to handle missing data must be made by all analysts, it is a testimony to the fundamental strength of Rasch that the precise choice usually has little impact on the measurement framework. The NFER-Nelson plot below, produced independently using their own data, indicates that their item calibrations are essentially equivalent under any reasonable "missing data" strategy.
What to Do about Missing Data? Ludlow, L., & O'Leary, M. Rasch Measurement Transactions, 2000, 14:2 p.751
Please help with Standard Dataset 4: Andrich Rating Scale Model
|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|
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