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ITC Bulletin

Modern Facet Theory: Content Design and Measurement in Behavioral Research

Published Online:January 24, 2008https://doi.org/10.1027/1015-5759.14.2.160

Abstract

Facet Theory is a research strategy that integrates formal analysis of research contents (using the tool of the mapping sentence) with intrinsic data analysis (multivariate procedures such as Faceted Smallest Space Analysis (FSSA) and Multiple Scaling by Partial Order Scalogram Analysis by base Coordinates (POSAC)). The integration of content design and data analysis is argued to be essential for theory construction and meaningful measurement in the behavioral sciences that typically study complex systems with many, possibly infinitely many, variables. Mapping sentences permit the balanced selection of observational variables from the content universe studied. Focusing on observed variables (columns of the data matrix), FSSA creates a geometric representation of the concept-space and partitions it into regions, thus enabling the systematic investigation of the concept structure. Focusing on the observed individuals (rows of the data matrix), POSAC provides optimal measurement scales for the individuals' assessment. Traditional prediction models assume the existence of factors (underlying variables), by which both variables and individuals can be evaluated, which determine empirical observations. Modern facet theory shows that two kinds of factors are at work: facets (partitionings) of the concept-space, for evaluating variables; and scales (coordinates) of the measurement space, for evaluating individuals. The two factor types point to two roads for attaining the goal of prediction. Mathematical results in Multiple Scaling, and especially the introduction of the newly devised coefficient of structural similarity, contribute to the convergence of the two roads.

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