Structural equation model framework

Why does Ki use structural equation model framework?

This technique is ideal for describing unmeasured variables, such as pathogen transmission, with available quantitative measures like handwashing practices and household toilet access. These measured predictor variables serve to enumerate the association between an unmeasured variable and a causal outcome of interest. HBGDki can leverage its vast data resources and this methodology to confirm and explore causal pathways using measured predictors and assumed pathways to determine their influence on healthy growth and development.


SEM is not a single technique, but a general framework that integrates several multivariate techniques into a single model.[1]

SEM is defined as a “path analysis using latent variables.”[1]

Path analysis, or a structural model, is a visual representation of the model, including regression equations between measured variables (see specific notation and example in Figure 1) and a specified causal order.

Latent variables are theoretical constructs that are not directly measured, such as dietary intake.[2]


SEM provides confirmatory (hypothesis testing) or explanatory (hypothesis generating) analyses.

Confirmatory factor analysis models are imposed on the data and aim to estimate parameters and assess fit of the model to the data.[2]

Statistical methods[3]

FIGURE 2. Example variance‑ covariance matrix

Advantages of SEM

Disadvantages of SEM

FIGURE 1. Notation for a Path Diagram & an Example Path Diagram. The example diagram explores the association of the latent variable dietary intake on growth.
  1. Sturgis P. Structural Equation Modeling: What is it and what can we use it for? (part 1 of 3) [Online]. 2016. Available from: Accessed 26 Nov 2017.
  2. Hox J, Bechger T. An Introduction to Structural Equation Modeling. Family Science Review.11:354-73.
  3. Sturgis P. Key ideas, terms & concepts in Structural Equation Modeling (part 2 of 3) [Online]. 2016. Available from: Accessed 26 Nov 2017.