To my knowledge it was Paul Lazarsfeld who popularized the terminology of latent and manifest variables in measurement theory. The more interesting variables in our disciplines are always latent and we are never able to observe them in a direct way. Nobody has ever seen intelligence, knowledge of biology, or achievement motivation as such, and nobody ever will. What we have at hand is - at most - a collection of scattered behaviors or linguistic utterances expressing feelings or mental reactions to some situation, which in themselves are of no interest, but might be conceived of as allusions to the more interesting latent qualities we assume to be present.
However, simply because we talk about latent variables - and all textbooks in education or psychology are full of them - we may not assume that they exist, that they are just out there, and that we only have to collect data to measure them. Latent variables are no empirical quantities, but hypotheses about reality. The only way to get to know them is through a model that formulates hypothetical relations between the latent variables and certain observables. Social and behavioral scientists live in a hypothetical or latent space, and the only point of reference they have is a man-made model. We know that models can never be fully true, but that to some extent they are always idealizations, abstract schemes to represent reality.
At the recent AERA Annual Meeting in San Francisco a debate was held on "IRT in the 1990s: Which Models Work Best." I had the pleasure of participating as a discussant and enjoyed doing so - although I very much missed a tone of mutual respect among the participants. C'est le ton qui fait la musique! In the debate, I took the position that the first lesson to be learned from measurement theory is that we should be humble and not pretend to know more than we do. But I immediately added that the choice of model is as important as the choice of an Archimedean point, and that the Rasch model offers simplicity, interpretability, and plenty of statistical procedures with proven properties. Other models lack these features. I finally concluded that I might even use one of these other models, if I had to accept tests and data collected by others, but that I would do so suspicious of the results and knowing that a better world is possible.
I have been elected Chair of the SIG as successor to David Andrich, who finished his term at the Business Meeting in San Francisco. It is just fitting to thank him for the dedicated way in which he exercised his office. I am sure all of you will miss his insights as Chair, and so will I.
Latent variables, W van der Linden Rasch Measurement Transactions, 1992, 6:2 p. 213
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|
|in Spanish:||Análisis de Rasch para todos, Agustín Tristán||Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez|
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|Sept. 15-16, 2017, Fri.-Sat.||IOMC 2017: International Outcome Measurement Conference, Chicago, jampress.org/iomc2017.htm|
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