Missing and sparse data considerations: Sample size and Category counts |
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Here are remarks from the Abstract of an instructive paper::
"Previous research includes frequent admonitions regarding the importance of establishing connectivity in data collection designs prior to the application of Rasch models. ... A simulation design whose characteristics were intended to reflect practical large-scale assessment networks with sparse connections were used to consider the influence of locations on the latent variable, model–data fit, and sample size within linking sets on the stability and model–data fit of estimates. Results suggested that parameter estimates for examinee and task facets are quite robust to modifications in the size, model–data fit, and latent-variable location of the link. Parameter estimates for the rater, while still quite robust, are more sensitive to reductions in link size."
The Stabilizing Influences of Linking Set Size and Model–Data Fit in Sparse Rater-Mediated Assessment Networks.
Stefanie A. Wind, Eli Jones. Educational and Psychological Measurement. 2018. Volume: 78 issue: 4, page(s): 679-707.
For codes for missing: Missing score=
Sample size and Category Counts: please see "Sample Size and Item Calibration [or Person Measure] Stability"
For statistically robust estimates (= estimates likely to be observed next time, at least approximately), we usually need at least 10 observations of each category, but this is not required, and often not achieved, even in large datasets.
If your purpose is exploratory or to report on a specific dataset, e.g., to give feedback to raters, then any number of observations in a category is good. If your purpose is to establish norms for a population, i.e., to make generalizations about a population from this dataset, then at least 10 observations in each category is recommended.
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