Beyond the factor indeterminacy problem using genome-wide association data

Latent factors, such as general intelligence, depression and risk tolerance, are invoked in nearly all social science research where a construct is measured via aggregation of symptoms, question responses or other measurements. Because latent factors cannot be directly observed, they are inferred by fitting a specific model to empirical patterns of correlations among measured variables. A long-standing critique of latent factor theories is that the correlations used to infer latent factors can be produced by alternative data-generating mechanisms that do not include latent factors. This is referred to as the factor indeterminacy problem. Researchers have recently begun to overcome this problem by using information on the associations between individual genetic variants and measured variables. We review historical work on the factor indeterminacy problem and describe recent efforts in genomics to rigorously test the validity of latent factors, advancing the understanding of behavioural science constructs. The authors address the central criticism of latent variable models in behavioural science, which is that a wide range of causal models may account for the observed data (the factor indeterminacy problem). They review how researchers have recently started using genome-wide data to provide a source of additional information to help to overcome the factor indeterminacy problem by decomposing the genome into a set of uncorrelated units.