## How do you conduct a factor analysis?

As a data analyst, the goal of a factor analysis is to reduce the number of variables to explain and to interpret the results. This can be accomplished in two steps: factor extraction….Running a PCA with 2 components in SPSS.

Component Matrixa | ||
---|---|---|

Item | Component | |

4 | 0.720 | 0.119 |

5 | 0.650 | 0.096 |

6 | 0.572 | 0.185 |

**What type of data is used in factor analysis?**

Factor analysis is designed for interval data, although it can also be used for ordinal data (e.g. scores assigned to Likert scales). The variables used in factor analysis should be linearly related to each other. This can be checked by looking at scatterplots of pairs of variables.

### How do you prepare a questionnaire for a factor analysis?

After designing an initial/proposed model, select items (statements) carefully you think of measuring each factor. (Existing literature can help you a lot in this matter). The number of items should be 4 or more for each factor and items should be reflective to each other for a single factor.

**What is factor analysis example?**

Factor analysis is used to identify “factors” that explain a variety of results on different tests. For example, intelligence research found that people who get a high score on a test of verbal ability are also good on other tests that require verbal abilities.

## What are the two main forms of factor analysis?

There are two types of factor analyses, exploratory and confirmatory. Exploratory factor analysis (EFA) is method to explore the underlying structure of a set of observed variables, and is a crucial step in the scale development process.

**What is simple structure in factor analysis?**

Simple structure is pattern of results such that each variable loads highly onto one and only one factor. Factor analysis is based on the correlation matrix of the variables involved, and correlations usually need a large sample size before they stabilize.

### How do you write a factor analysis result?

In the results, explain the criteria and process used for deciding how many factors and which items were selected. Clearly explain which items were removed and why, plus the number of factors extracted and the rationale for key decisions.

**How do you deal with cross loading in factor analysis?**

The solution is to try different rotation methods to eliminate any cross-loadings and thus define a simpler structure. If the cross-loadings persist, it becomes a candidate for deletion. Another approach is to examine each variable’s communality to assess whether the variables meet acceptable levels of explanation.