Ideas and Observations

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.

Sunhee Chae
MESA Psychometric Laboratory
University of Chicago

Ideas and Observations. S. Chae. … Rasch Measurement Transactions, 1992, 6:1, 206

Rasch Books and Publications
Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, 2nd Edn. George Engelhard, Jr. & Jue Wang	Applying the Rasch Model (Winsteps, Facets) 4th Ed., Bond, Yan, Heene	Advances in Rasch Analyses in the Human Sciences (Winsteps, Facets) 1st Ed., Boone, Staver	Advances in Applications of Rasch Measurement in Science Education, X. Liu & W. J. Boone	Rasch Analysis in the Human Sciences (Winsteps) Boone, Staver, Yale
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes	Statistical Analyses for Language Testers (Facets), Rita Green	Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments (Facets), George Engelhard, Jr. & Stefanie Wind	Aplicação do Modelo de Rasch (Português), de Bond, Trevor G., Fox, Christine M	Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
Exploring Rating Scale Functioning for Survey Research (R, Facets), Stefanie Wind	Rasch Measurement: Applications, Khine	Winsteps Tutorials - free Facets Tutorials - free	Many-Facet Rasch Measurement (Facets) - free, J.M. Linacre	Fairness, Justice and Language Assessment (Winsteps, Facets), McNamara, Knoch, Fan
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Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar	Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch	Rasch Models for Measurement, David Andrich	Constructing Measures, Mark Wilson	Best Test Design - free, Wright & Stone Rating Scale Analysis - free, Wright & Masters
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