In an empirical sense, latent variables are not observable but are primarily supposed from other directly observable variables. Often, these variables happen to be mathematical replicas whose sole aim is to describe observable variables using thresholds set by latent variables. Computing these variables presents a scenario where aspects of physical reality that seem to conform to it revealing that there might be more than meets the eye. Nonetheless, a growing number of researchers prefer using it in reducing the dimensions of the data they are handling. It is for this reason that some researchers even go as far as defining it as a hypothetical variable consisting of constructs that are conjured up from individual’s imaginations (Montfort, 2010, p. 24). Therefore, it is true that we compute latent variables by combining the linear combination of multiple measured or observed variables.
Observable variables come in handy in helping us measure latent variables. Popular statistical methods such as the Structural Equation Modeling (SEM) and the Latent Class Analysis (LCA) are therefore used to present the inter-correlations that exist between these variables. On the other hand, cultural competence involves bringing together cultural variables that are distinct and elements of diversity that individuals may bring forth during interactions (Dreachslin, Gilbert, & Malone, 2013, p. 67).
The culture that a particular individual subscribes to might combine variables such as national origin, disability, gender expression and their age. Differences and similarities, therefore, become essential manifestations when dealing with cultural variables that are important in the event an intervention is required. It is for this reason that cultural competency is appropriate when dealing with demographical changes, removing disparities that exist in the health status of persons across a specific population meeting legislative mandates and bettering the quality of services provided.