The Logic of Latent Variable Analysis as Validity Evidence in Psychological Measurement

Validity is the most important characteristic of tests and social science researchers have a general consensus of opinion that the trustworthiness of any substantive research depends on the validity of the instruments employed to gather the data.

It is a common practice among psychologists and educationalists to provide validity evidence for their instruments by fitting a latent trait model such as exploratory and confirmatory factor analysis or the Rasch model. However, there has been little discussion on the rationale behind model fitting and its use as validity evidence. The purpose of this paper is to answer the question: why the fit of data to a latent trait model counts as validity evidence for a test?

To answer this question latent trait theory and validity concept as delineated by Borsboom and his colleagues in a number of publications between 2003 to 2013 is reviewed.

Validating psychological tests employing latent trait models rests on the assumption of conditional independence. If this assumption holds it means that there is a ‘common cause’ underlying the co-variation among the test items, which hopefully is our intended construct.

Providing validity evidence by fitting latent trait models is logistically easy and straightforward. However, it is of paramount importance that researchers appreciate what they do and imply about their measures when they demonstrate that their data fit a model. This helps them to avoid unforeseen pitfalls and draw logical conclusions.