Introduction

A common problem in the analysis of human growth data is to relate biometric variables to genetic and/or environmental or demographic factors. Quite often we refer to techniques such as multiple regression or principal components analysis. Structural equation modelling is a technique that combines the benefits of both approaches. While in principal components analysis all variables score on each factor (component or latent variable), in structural equation modelling, the investigator can decide about the set of variables that will explain a specific latent variable. The investigator also decides on which paths of relationship between observed and latent variables should be investigated by the model and which ones should not. The procedure consists of an explorative phase, essentially based on principal components analysis of the data, allowing identification of the structure of the latent variables or constructs that, at biological level, are best able to explain the various interrelationships. The second phase consists of testing several possible models and gradually coming to an optimal solution that can explain the interrelationships between the explanatory variables and the dependent variables.

From the late 1980s on, structural equations with latent variables, or so-called LISREL models, became very popular in social sciences. There are two reasons for this increased attention. The capability to include latent variables (or concepts) in the models is a major step forward compared to models where only manifest variables (observed measures or items) can be used.