Deck 21: Multilevel Linear Models

Question 1

How can we overcome the problem identified in the previous question?

A) Interclass correlation
B) Intraclass correction
C) Interclass correction
D) Intraclass correlation

Answer 1

Intraclass correlation

Question 2

The data analyst decided to run a five parameter multi-level linear model, using the same outcome variable Satisfaction, which measured guest satisfaction on a ten-point scale. He now had five parameters in his model: Satisfactionx (guest' satisfaction with the hotel chain prior to them staying, measured on a ten-point scale); Sex (guests' gender); Age (guests' ages); Hotel (the 8 hotels in the chain); and Duration (measuring duration of each guest's stay in days). The log-likelihood for the five-parameter model was 1689. How does this compare to his previous model?

A) The five parameter model is no better a fit than the previous one
B) The previous model was a better fit
C) The five parameter model is a better fit
D) Both models are flawed

Answer 2

The five parameter model is a better fit

Question 3

The data analyst wanted to finalise his model for guest satisfaction with the introduction of one last parameter, Reason (a dummy variable of guest reason for staying at the hotel, coded 0 for holiday stay and 1 for business stay). How might we categorise this parameter within this model?

A) A random variable
B) An interaction
C) An interval
D) An error

Answer 3

An interaction

Question 4

The data analyst felt more confident and decided to introduce a new parameter, Hotel (the 8 hotels in the chain). This parameter is what sort of variable within the model?

A) Contextual
B) Random
C) Simple
D) Complex

Question 5

What assumptions apply to multilevel linear models?

A) Linearity, normality, and homoscedasticity
B) Linearity, independence of errors, and homoscedasticity
C) Independence of errors, homoscedasticity and normality
D) Normality, Linearity and heterogeneity

Question 6

A data analyst for a small chain of boutique hotels was interested in what shaped guest satisfaction with their hotel stays. He decided to build a multi-level linear model. Because he had not run one before he decided to build the model in stages, starting with a basic linear model. His outcome variable was Satisfaction, which measured guest satisfaction on a ten-point scale. His initial parameters were Satisfactionx (guests' satisfaction with the hotel chain prior to them staying, measured on a ten-point scale) and Duration (measuring duration of each guest's stay in days). He ran the model and found that Satisfaction2 had a p = 0.05 (b = -1.69) and Duration had a p = 0.01 (b = 0.665). How would you interpret this?

A) Guest satisfaction is not significantly associated with duration of stay
B) Guest satisfaction is significantly associated with duration of stay
C) Guest satisfaction significantly improves with duration of stay
D) Guest satisfaction significantly declines with duration of stay

Question 7

What assumption of linear models does multilevel linear modelling violate?

A) Independence of errors
B) Linearity
C) Normality
D) Probability

Question 8

The HR manager conducted her evaluation of staff performance across the ten stores. How many levels of analysis does her study have?

A) Two (staff member and store)
B) Three (staff member, department and store)
C) Six (Household, Food, Technology, Women's Clothes, Men's Clothes and Children's Clothes departments)
D) Ten (ten stores in the chain)

Question 9

What is hierarchical data?

A) Data in which scores are multi-linear
B) Data in which scores are contextualised
C) Data in which scores are nested within contexts
D) Data in which scores are linear

Question 10

The data analyst conducted his evaluation of user satisfaction across the five clinics. What is the Level 1 variable in his study?

A) Specialist software
B) Clinic
C) User satisfaction
D) Software user

Question 11

The data analyst found that the introduction of 'Hotel' to his model altered the log-likelihood from 1798 (the previous model) to 1786. How would you interpret this?

A) The model containing 'Hotel' makes no difference to the model
B) The model containing 'Hotel' is a better fit
C) The model containing 'Hotel' is a poor fit
D) The model containing 'Hotel' is a moderate fit

Question 12

What are growth models?

A) Multilevel linear models where changes in an outcome variable over time are modelled
B) Linear models where changes in an outcome variable over time are modelled
C) Multilevel linear models where changes in an outcome do not vary over time
D) Multilevel linear models where changes in contextual variables over time are modelled.

Question 13

As a business development manager for a car rental company, you are attempting to conduct a multilevel model of performance of sales people within and across four car showrooms in two different regions, but you keep facing the problem of multicollinearity. How might you solve this?

A) Grand mean centring
B) Group mean centring
C) Multilevel centring
D) Random centring

Question 14

A data analyst for a software design company was interested in evaluating user satisfaction with a new specialist software for use in medical settings. He designed a multilevel linear model of software users' evaluations within five clinics. What is the contextual variable in his study?

A) Software user
B) User satisfaction
C) Clinic
D) Specialist software

Question 15

The HR manager of a chain of department stores wants to evaluate staff performance across the chain. There are ten stores in the chain and each store has six departments (Household, Food, Technology, Women's Clothes, Men's Clothes and Children's Clothes). She is interested in the performance of staff within and across departments and stores in the chain. What sort of analysis would be most appropriate?

A) Multilevel linear
B) Multiple Linear
C) Multinominal Linear
D) Multivariate Linear