Scientists draw useful ideas - make up variables - from the careful observation and recording of experience. These ideas are necessarily simplified in order to be useful for their particular purposes. Because purposes vary, there can be different variables to explain similar experiences.
Ideas come to be regarded as true when they prove useful in predicting the future. After supposing a variable, the scientist attempts to establish its definition by collecting, validating and calibrating observations that provide information about it. When observations can be specified and validated, the scientist has established an "operational definition" of the variable. Then general principles can be formulated and probable results predicted.
In order to extract information about variables from observations, the supposed relationship between observation and variable must be specified explicitly. This enables inferences to be made about the variable. Values on the variable become free of the particular observations made. When the observations are pertinent, and inferences are drawn from them correctly, there is nothing more to know about the observation. The inferences are enough.
Observation and measurement models connect the observations to the variable. Models also provide means for assessing the validity of measures. Models enable us to recognize surprising observations and unpredicted results. Quality control through fit analysis compares predicted and observed outcomes. Every observation has two parts: the part explained by a model and the part unexplained. The main concern in fit analysis is not to quantify the distribution of the unexplained residuals when a model "holds" in some statistical sense, but rather to detect and diagnose unexpected residual patterns. These fit diagnoses yield insight into the utility and potential of a model.
MESA Psychometric Laboratory
University of Chicago
Ideas and Observations. S. Chae. Rasch Measurement Transactions, 1992, 6:1, 206
|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|
|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/rmt61h.htm