Understanding Mixed Effects Models
Qn1. A mixed model is “mixed” because it contains both between-subjects and within-subjects factors.
Qn2. Which of the following best describes fixed effects?
Fixed effects are manipulated factors whose levels are sampled randomly from a larger population of interest Fixed effects are random factors whose chosen levels are of explicit interest. Fixed effects are random factors whose levels are sampled randomly from a larger population of interest None of the above
Qn3. Random effects are called “random” in part because their levels are randomly sampled form a larger population about which wish to generalize
Qn4. Linear mixed models (LMMS) can handle Poisson response distributions.
Qn5. Which is not an advantage of a linear mixed model (LMM)
The ability to handle within-subjects factors The ability to handle unbalanced designs The ability to handle missing data The ability to handle non-normal response distributions The ability to handle violations of sphericity
Qn6. Linear mixed models (LMMs) produce small residual degrees of freedom.
Qn7. Nesting is useful when the levels of a factor are not meaningful when pooled across all levels of the other factors.
Qn8. Nesting is necessary when we wish to calculate the means and variances of a nested factor’s levels only within the levels of the other factors, that is, the nesting factors.
Qn9, Linear mixed models (LMMs) generalize the linear model (LM) to non-normal response
Qn10. Generalized linear mixed models (GLMMs) generalized the linear mixed model (LMM) to non-normal response distributions.
Qn11. Why are planned pairwise comparisons important? (Mark all that apply)
Planned pairwise comparisons enable experimenters to communicate more effectively within the public Planned pairwise comparisons force the experiment to consider his or her hypotheses before the data arrives to prevent revisions. Planned pairwise comparisons should be based on a priori hypotheses and therefore prevent “fishing expeditions” for significant p-values Planned pairwise comparisons ensure that research funds are only used for anticipated purposes Planned pairwise comparisons guarantee that significant differences, if they exist, will be found eventually
Qn12. Generalized linear mixed models (GLMMs) are capable of handling repeated measures factors via random effects and non-normal response distributions
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