General linear model describes the ability to handle which designs?

General linear model describes the ability to handle which designs?

• A. General linear model
• B. Generalized linear model
• C. Mixed effects model
• D. Structural equation model

Answer: (A)

The general linear model is the framework for analyzing designs where the outcome is continuous and is predicted by a linear combination of predictors. This includes simple and factorial ANOVA, ANCOVA, and multiple regression, where the effects are estimated as linear coefficients and the relationships are linear in those parameters. It assumes the residuals are normally distributed and independent, allowing straightforward hypothesis tests and confidence intervals for the effects. The other models operate in different realms. Generalized linear models extend this idea to non-normal outcomes by using a link function and a distribution from the exponential family, so they’re used for binary, count, or other non-continuous data. Mixed effects models add random effects to handle correlations or non-independence (such as repeated measures or hierarchical data). Structural equation models focus on latent variables and the relationships between observed and latent constructs, often involving measurement models and multiple equations.

The general linear model is the framework for analyzing designs where the outcome is continuous and is predicted by a linear combination of predictors. This includes simple and factorial ANOVA, ANCOVA, and multiple regression, where the effects are estimated as linear coefficients and the relationships are linear in those parameters. It assumes the residuals are normally distributed and independent, allowing straightforward hypothesis tests and confidence intervals for the effects.

The other models operate in different realms. Generalized linear models extend this idea to non-normal outcomes by using a link function and a distribution from the exponential family, so they’re used for binary, count, or other non-continuous data. Mixed effects models add random effects to handle correlations or non-independence (such as repeated measures or hierarchical data). Structural equation models focus on latent variables and the relationships between observed and latent constructs, often involving measurement models and multiple equations.