SE has been shown to predict task engagement and performance, and has become increasingly used within SLA, often as a variable within different motivational frameworks (Mills, 2014).
As a result, there has been relatively little work that has used uncertainty as a description of both stimulus (i.e., independent variable) AND response (i.e., dependent variable) characteristics within a unifying framework of uncertainty.
Viewed from a broader perspective, it seeks to examine and interpret the hypothesized relationships among these variables within a single framework.
Nevertheless, these constructs have been studied in parallel, and to the researchers' best knowledge, no empirical study to date has examined these variables within a single framework.
The variable was evaluated as a categorical variable within a logistic model framework.
Assuming a categorical confounding variable is observed, the author proposes weighted coefficients of correlation for continuous variables developed within a larger framework based on copulas.
This at first might seem redundant, but is needed for a variable to function within a two-factor framework.
Our evaluation of alternative approaches to imputation for limited-range variables was conducted within a simulation framework in which we fixed a particular complete dataset of interest and repeatedly set data to missing, comparing the results of MI-based inferences for two target parameters with the results from the complete data [ 20, 21].
The use of genetic algorithms (GAs) for structural optimisation is well established but little work has been reported on the inclusion of damage variables within an optimisation framework.
Moreover, using the simple IPW or the partitioned IPW as response variables, these methods can be expanded within a regression framework to control for covariates.
Within the latent variable modeling framework, several fit indices are usually examined.
