# Factor analysis

**Factor analysis** is a statistical technique that originated in mathematical psychology. It is used in the social sciences and in

marketing,

product management,

operations research, and other applied sciences that deal with large quantities of data. The objective is to discover patterns among variations in the values of multiple variables. This is done by generating artificial dimensions (called factors) that correlate highly with the real variables.

The basic steps are:

### Information Collection

The data collection stage is usually done by marketing research professionals. Survey questions ask the respondant to rate a product from one to five (or 1 to 7, or 1 to 10) on a range of attributes. Anywhere from five to twenty attributes are chosen. They could include things like: ease of use, weight, accuracy, durability, colourfulness, price, or size. The attributes chosen will vary depending on the product being studed. The same question is asked about all the products in the study. The data for multiple products is codified and input into a statistical program such as SPSS or SAS.
### Analysis

The analysis will isolate the underlying factors that explain the data. Factor analysis is an interdependence technique. The complete set of interdependent relationships are examined. There is no specification of either dependent variables, independent variables, or causality. Factor analysis assumes that all the rating data on different attributes can be reduced down to a few important dimensions. This reduction is possible because the attributes are related. The rating given to any one attribute is partially the result of the influence of other attributes. The statistical algorithm deconstructs the rating (called a raw score) into its various components, and reconstructs the partial scores into underlying factor scores. The degree of correlation between the initial raw score and the final factor score is called a *factor loading*. There are two approaches to factor analysis: "principle components analysis" (the total variance in the data is considered); and "common factor analysis" (the common variance is considered).
### Advantages

- both objective and subjective attributes can be used
- it is fairly easy to do, inexpensive, and accurate
- it is based on direct inputs from customers
- there is flexibilty in naming and using dimensions

### Disadvantages

- usefulness depends on the researchers ability to develop a complete and accurate set of product attributes - If important attributes are missed the procedure is valueless.
- naming of the factors can be difficult - multiple attributes can be highly correlated with no appearent reason.
- factor analysis will always produce a pattern between variables, no matter how random.