## linear mixed-models coursera quiz

## Understanding Mixed Effects Models

Qn1. A mixed model is “mixed” because it contains both between-subjects and **within-subjects factors**.

```
True
False
```

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

```
True
False
```

Qn4. Linear mixed models (LMMS) can handle Poisson response distributions.

`True `

False

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.

```
True
False
```

Qn7. Nesting is useful when the levels of a factor are not meaningful when pooled across all levels of the other factors.

```
True
False
```

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.

```
True
False
```

Qn9, Linear mixed models (LMMs) generalize the linear model (LM) to non-normal response

```
True
False
```

Qn10. **Generalized linear mixed models** (GLMMs) generalized the linear mixed model (LMM) to non-normal response distributions.

```
True
False
```

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

```
True
False
```

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