RALV (Relationships Among Latent Variables) makes possible the implementation of stable and distortion-free causal analyses (structural equation models).
FEATURES IN BRIEF
- Analyzes effective relationships between latent variables
- Convenient graphical model construction
- Ability to play through many model variants quickly and securely without long familiarization or specialized methodological knowledge
- Orthogonalization as an effective solution for the avoidance of distortions in estimated path coefficients due to multicollinearity
- Uses the information from missing values to enhance the quality of the results
Structural Equation Modeling is used to inspect causal relationships between variables. The objective is to investigate hypothetical structures of correlations and influences. As with regression analysis, the analysis looks for the amount and direction of influence of one or more so-called exogenous variables on one or more endogenous variables.
As with factor analysis, the assumption is made that the attribute to be investigated cannot be observed directly, but rather “latently” represents the basis for the observed behavior, opinion or attitude expressed by the respondent. These latter observations serve as indicators for the level of the underlying basic attributes.
Structural equation models are initially based around a hypothesis stating which attributes are influenced by which variables. In this manner, complex covariance structures can be set up, whereby attributes can simultaneously be independent (influencing) and dependent (influenced by others). Structural equation models check this hypothesis by measuring the degree of the impact of the independent variables and determining the goodness of fit of such a model.
Structural equation models cannot detect improperly assumed directions of relationship. The direction of the influence, or in other words, the question of whether feature A influences feature B or vice versa, must be established through preliminary theoretical consideration.
In order to be able to interpret the coefficients properly, the influencing variables of an endogenous variable should be mutually independent.
With RALV (Relationships Among Latent Variables), IfaD provides a tool for the realization of causal analyses, which like the classic structural equation modeling methods provides confirmatory analysis of linear relationships between latent variables.