Exam 8: Path Analysis

Misspecification, a limitation encountered with path analysis occurs when:

Path analysis is a technique that is exploratory in nature.

A curved line with two arrowheads in a path diagram represents an indirect effect.

An indirect effect in path analysis occurs when a variable affects an endogenous variable through its effect on some other variable, known as:

Causal modeling techniques examine whether:

A path diagram is absolutely necessary for causal modeling analysis.

If it is determined that a model does not fit the data, consideration should be given to retaining included paths and incorporating excluded paths.

Path analysis builds on the simpler version of causal modeling by modeling both direct and indirect causal effects among variables.

In presenting path analysis results, one must only describe how reproduced correlations were consistent with empirical correlations.

When a model has many reproduced correlations that significantly differ from the empirical data, the model is said to be consistent with empirical data.

In the specific language of causal modeling, the variable that is being explained by the model is referred to as the exogenous variable.

The simplest version of the causal modeling technique is one in which:

Path analysis begins with the researcher developing a diagram with arrows connecting variables and depicting cause-and-effect.

The assumed causal model, when stated as an equation, is often referred to as a structural equation and is typically stated in its unstandardized form.

A latent variable is a variable that cannot actually be measured but can only be approximated with actual measures.

One commonly used approach to determining the reproduced correlations between two variables in path analysis involves the identification of all legitimate paths between the variables in the model in a process referred to as path tracing or path composition.

Another assumption of path analysis is that the IVs are measured with error.

If several reproduced correlations differ from empirical correlations by more than .05, then the model is consistent with the empirical data.

One of the assumptions of path analysis is that the errors are correlated with the independent variables.

In structural equation modeling, a diagram is set up such that latent variables are combinations of observable, measurable variables.