Constructed measures and causal inference: towards a new model of measurement for psychosocial constructs

TL;DR

This paper critiques traditional measurement models for psychosocial constructs, proposing a new causal interpretation and model to improve measure construction and understanding of their associations with outcomes.

Contribution

It introduces a novel causal framework applicable to both reflective and formative models, addressing their limitations and guiding better measure development.

Findings

Empirical implications of existing models are often violated by data.

Reflective models assume unidimensional causally efficacious constituents.

A new causal interpretation enhances measure construction and analysis.

Abstract

Psychosocial constructs can only be assessed indirectly, and measures are typically formed by a combination of indicators that are thought to relate to the construct. Reflective and formative measurement models offer different conceptualizations of the relation between the indicators and what is sometimes conceived of as a univariate latent variable supposed to correspond in some way to the construct. It is argued that the empirical implications of reflective and formative models will often be violated by data since the causally relevant constituents will generally be multivariate, not univariate. These empirical implications can be formally tested but factor analysis is not adequate to do so. It is argued that formative models misconstrue the relationship between the constructed measures and the underlying reality by which causal processes operate, but that reflective models misconstrue the nature of the underlying reality itself by typically presuming that the constituents of it that are causally efficacious are unidimensional. The ensuing problems arising from these misconstruals are discussed. A causal interpretation is proposed of associations between constructed measures and various outcomes that is applicable to both reflective and formative models and is applicable even if the usual assumptions of these models are violated. An outline for a new model of the process of measure construction is put forward. Discussion is given to the practical implications of these observations and proposals for the provision of definitions, the selection of items, item-by-item analyses, the construction of measures, and the interpretation of the associations of these measures with subsequent outcomes.