## Shiny practice items 'Statistics and business analytics', module 10

This app allows you to practice various aspects covered in this module.

Please make sure that you have fully completed the following tasks before continuing with this app:

1. Read the module's learning plan

2. Watch the topic videos and review the lecture notes

3. Work through the SPSS How to guide

The module has the following objectives:

1. Explain the logic behind factor analysis

2. Decide on how many factors underlay the original variables

3. Use rotation to facilitate factor interpretation

4. Asses the goodness of fit of a factor solution

5. Perform a factor analysis with SPSS

Please proceed with the practice items by clicking on the links in the top bar of this app. It is not necessary to complete these items in order. We encourage you to work together with a classmate!

There are three types of practice items: theory ('TH'), SPSS ('SP') and multiple choice ('MC').

Thus, you should complete these practice items before joining the lab meeting corresponding to this module.

MBA program
Peter Ebbes

#### Question 1

Before you get started with any fancy-pancy data analytic approach, it is always a good idea to imagine how the data table would need to look like. Write down on scratch paper, how the data table could look like for this context.

#### Purpose

##### Test your knowledge about the subjects of this module. Let's do it!

1. A researcher fits a regression with a large set of independent variables. But he is quite nervous as almost none of the independent variables is significant. That is disturbing because his manager paid a lot of money to collect the data through a survey. He is afraid she will be quite unhappy with these findings. You to the rescue! Which technique do you recommend him to use?

2. Suppose the (rotated) factor loading of a certain variable (say) V10 on a factor is -0.9. Which expression is true?

3. Which of the following expressions about factor analysis is not true?

4. Communalities in factor analysis capture ...

5. Consider the fancy word 'Eigenvalue'. What does it mean in the context of factor analysis?