Question 1

What is the basic assumption of multilevel modelling?&#10;A) The dependent variable is continuous&#10;B) The expected value of Y can be modelled by a combination of unknown parameters&#10;C) The units of analysis are measured over time&#10;D) A unit at the lowest level is nested into a higher level of unit(s)

Accepted Answer

Multilevel modelling assumes that data is hierarchically structured, meaning that lower-level units are nested within higher-level units, allowing for the analysis of data with multiple levels of influence.

Question 2

What is a level?&#10;A) A level is a given variable chosen from your theoretical approach&#10;B) A level is a variable that identifies units sampled from a population&#10;C) A level can be any categorical variable in your dataset&#10;D) A level can be any continuous variable in your dataset

Accepted Answer

B

Question 3

How can we calculate how much of the variation in Y is situated at each level?&#10;A) By dividing the log likelihood on the number of units in the respective levels&#10;B) By running your full model and then calculating the intraclass correlation coefficient&#10;C) By dividing the level-1 residual on each of the higher-level residuals&#10;D) By running an empty model and then calculating the intraclass correlation coefficient

Accepted Answer

The intraclass correlation coefficient (ICC) is calculated from an empty model to determine the proportion of variance at each level in a multilevel model.

Question 4

Which of the following are advantages of multilevel modelling?&#10;A) It is the statistical method that comes closest to experiments in establishing causality&#10;B) It takes into account the problem of dependency among observations&#10;C) It allows us to model the influence of variables from all levels on our Y&#10;D) It allows us to model risk development over time of an event taking place

Accepted Answer

Multilevel modelling accounts for the dependency among observations by considering the hierarchical structure of the data (B). It also allows for the inclusion of variables from different levels of the hierarchy to influence the outcome variable (C).

Question 5

What is a random coefficient (slope) model?&#10;A) A multilevel model that allows both the intercept and coefficient(s) to vary&#10;B) A multilevel model that allows the intercept to vary&#10;C) A multilevel model with a fixed intercept and fixed coefficients&#10;D) A multilevel model with statistical interaction

Accepted Answer

A random coefficient (slope) model is a type of multilevel model that allows both the intercept and the coefficients (slopes) to vary across different levels or groups.

Question 6

What is a cross-level interaction term?&#10;A) A variable made by multiplying two variables situated at level-1&#10;B) A variable made by multiplying two variables situated at level-2&#10;C) A variable made up by log transforming any X-variable&#10;D) A variable made by multiplying two variables situated at different levels

Accepted Answer

A cross-level interaction term is created by multiplying variables from different levels, such as a level-1 variable with a level-2 variable, in multilevel modeling.

Question 7

How many levels is it possible to model through multilevel modelling?&#10;A) 1&#10;B) 2&#10;C) 3&#10;D) There is no theoretical limit

Accepted Answer

Multilevel modeling can theoretically accommodate any number of levels, limited only by the complexity of the data and computational resources.

Question 8

How many identifier variables are required to run a three-level model?&#10;A) 1&#10;B) 2&#10;C) 3&#10;D) 4

Accepted Answer

A three-level model requires two identifier variables: one for the second level and one for the third level, to distinguish between the different levels of hierarchy in the data.

Question 9

When is it appropriate to use a cross-classified multilevel model?&#10;A) When it is unclear which level-1 units belong to the different higher-level units&#10;B) When level-1 units can be members of more than one higher-level unit at the same time&#10;C) When the level-1 units can only be nested into level-3 units&#10;D) If your regular model fails to converge

Accepted Answer

Cross-classified multilevel models are appropriate when level-1 units can belong to more than one higher-level unit simultaneously, such as students attending multiple schools.

Deck 10: Multilevel Analysis