Take the quiz test your understanding of the key concepts covered in the chapter. Try testing yourself before you read the chapter to see where your strengths and weaknesses are, then test yourself again once you’ve read the chapter to see how well you’ve understood.

1. What is the basic assumption of multilevel modeling?

That the dependent variable is continuous

The expected value of Y can be modeled by a combination of unknown parameters

That the units of analysis are measured over time

A unit at the lowest level is nested into a higher level unit(s)

Answer:

d. A unit at the lowest level is nested into a higher level unit(s)

2. What is a level?

A level is a given variable chosen from your theoretical approach

A level is a variable that identifies units sampled from a population

A level can be any categorical variable in your dataset

A level can be any continuous variable in your dataset

Answer:

b. A level is a variable that identifies units sampled from a population

3. How can we calculate how much of the variation in Y is situated at each level?

By dividing the log likelihood on the number of units in the respective levels

By running your full model and then calculating the intraclass correlation coefficient

By dividing the level-1 residual on each of the higher-level residuals

By running an empty model and then calculating the intraclass correlation coefficient

Answer:

d. By running an empty model and then calculating the intraclass correlation coefficient

4. Which of the following are advantages of multilevel modeling?

It is the statistical method that comes closest to experiments in establishing causality

It takes into account the problem of dependency among observations

It allows us to model the influence of variables from all levels on our Y

It allows us to model risk-development over time of an event taking place

Answer:

b. It takes into account the problem of dependency among observations

c. It allows us to model the influence of variables from all levels on our Y

5. What is a random coefficient (slope) model?

A multilevel model that allows both the intercept and coefficient(s) to vary

A multilevel model that allows the intercept to vary

A multilevel model with a fixed intercept and fixed coefficients

A multilevel model with statistical interaction

Answer:

a. A multilevel model that allows both the intercept and coefficient(s) to vary

6. What is a cross-level interaction term?

A variable made by multiplying two variables situated at level-1

A variable made by multiplying two variables situated at level-2

A variable made up by log transforming any X-variable

A variable made by multiplying two variables situated at different levels

Answer:

d. A variable made by multiplying two variables situated at different levels

7. How many levels is it possible to model through multilevel modeling?

1

2

3

There is no theoretical limit

Answer:

d. There is no theoretical limit

8. How many identifier variables is required to run a three-level model?

1

2

3

4

Answer:

b. 2

9. When is it appropriate to use a cross-classified multilevel model?

When it is unclear which level-1 units belong to the different higher level units

When level-1 units can be members of more than one higher-level unit at the same time

When the level-1 units can only be nested into level-3 units

If your regular model fails to converge

Answer:

b. When level-1 units can be members of more than one higher-level unit at the same time