An introduction to the application of modern multivariate methods used in the social sciences, with particular focus on latent variable models for continuous observed variables, and their application ...

The treatment includes coverage of the multivariate and univariate approaches to repeated measures, MANOVA, ANOVA, multivariate regression, and univariate regression. Third, we describe the design of ...

Multivariate analysis is commonly used when we have more than one outcome variables for each observation. For instance, a survey of American adults’ physical and mental health might measure each ...

Enabling multivariate regression analysis is essential because researchers are typically interested in knowing how the probability of answering the sensitive question affirmatively varies as a ...

Conclusions: After adjustment for significant clinical factors in a multivariate regression analysis, consolidation treatment with BV significantly reduced the risk of treatment failure compared to ...

Multivariate analysis uses statistical tools such as multiple regression analysis, cluster analysis and conjoint analysis to determine the relationships between factors.

Even a change in the correlation of those parameters may indicate a problem. For that reason, multivariate monitoring, or multivariate statistics, is applied to these parameters. Multivariate analysis ...

Through logistic regression analysis, it was found that pain severity, walking ability, self-care ability, physical function, ...