One of the advantages of MANOVA is that it may be able to detect combined differences

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Select either “True” or “False” for questions 1 through 4. 

1. If  and  , then A+B does not exist.

2. An identity matrix is always a symmetric matrix.

3. Multivariate Analysis of Variance (MANOVA) is an extension of analysis of variance with more than one independent variable.

4. One of the advantages of MANOVA is that it may be able to detect combined differences in the dependent variables not found by examining the dependent variables independently of one another.

[For questions 5-8]

A psychologist Lucy was interested in gauging the success of a mood manipulation during one of her experiments. She had three groups of participants who underwent different types of mood induction: disgust mood induction, negative mood induction and positive mood induction. After the mood induction, participants were asked to endorse nine statements relating to their mood on a 100 point scale from 1—disagree to 100—agree: (1) When you’re smiling the whole world smiles with you, (2) I love the pretty flowers, (3) I could never touch a dead body, (4) I would never eat cat food, (5) If someone served me monkey brain soup I would vomit, (6) I feel fed up, (7) Bodily fluids are nasty, (8) I could not drink from a glass that I’d used to catch a spider, (9) I am a worthless piece of scum.

This psychologist performed a MANOVA to see if the mood inductions had an effect on responses to these 9 items 

Use the following outputs to answer the questions 5 through 8.

 

[For question 9]

The psychologist Lucy wondered if the differences among three groups found in the previous analysis was contributed to the participant’s mood status before the induction started. She had the measure of the mood status before the experiment (pre-mood) and decided to use this continuous variable as a covariate to run an MANCOVA to test the same research question as stated earlier.

Answer question 9.

[For question 10]

A researcher was interested in knowing whether students with different socio-economic status (CATses: low/median/high) shared the same profile of the achievement scores on four different subjects (Subject: Reading/Math/Science/History). He conducted a profile analysis to compare the profiles of the achievement across three socio-economic status groups.  The outputs are as follows.

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