1. Data are presumed valid and useful, so that subsequent analysis will be productive and results informative. While effort is usually given to careful collection of data, systematic checking for data quality is often omitted. When quality control is skimped, results are suspect.
2. Collecting data asserts that what is utilized has already passed scrutiny. It suggests that data is already in a pristine, though unsubstantiated, state. Is this presuming too much? Does collecting data provide what is required or does it open the door to contamination? Examine the word "collecting". It means gathering together or assembling. Does this define the quality needed for data? Hardly. This process is questionable.
3. Manufacturing data describes the process we require. We do not "find" useful data by beach-combing. Data do not exist in nature. They are manufactured for the occasion. To suggest otherwise implies that data can be found laying around in their natural state, waiting to be picked up by those who stumble upon them. Data are fictitious, standing for instances of information in the solution to a problem, produced from a sample, and inferred to a population. While everyone realizes we cannot know the parameters, few would want to doubt the statistics computed from their own data. Yet we should be doubtful, and the only way to assuage that doubt is scrupulously to address the manufacturing.
6. The production of data requires scrutiny. We have an idea, then an intention. We plan and carry out a strategy. We choose the specifications and manufacture according to our intentions. The process of data manufacture should be no less rigorous than subsequent data analysis. Quality control over production is absolutely necessary. We should keep records on data manufacturing and monitor the process with control charts. Rasch measurement strategies have always stressed data scrutiny. The examination of item and persons records, fit analyses and data plots are some of the tools by which Rasch measurement monitors the quality of manufactured data.
Mark H. Stone
Adler School of Professional Psychology
Data: Collecting or manufacturing? Stone M.H. Rasch Measurement Transactions, 1996, 10:3 p. 517.
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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