Editing Factor analysis (section)

==In marketing==
The basic steps are:
* Identify the salient attributes consumers use to evaluate [[product (business)|products]] in this category.
* Use [[quantitative marketing research]] techniques (such as [[statistical survey|surveys]]) to collect data from a sample of potential [[customer]]s concerning their ratings of all the product attributes.
* Input the data into a statistical program and run the factor analysis procedure. The computer will yield a set of underlying attributes (or factors).
* Use these factors to construct [[perceptual mapping|perceptual maps]] and other [[positioning (marketing)|product positioning]] devices.

=== Information collection ===
The data collection stage is usually done by marketing research professionals. Survey questions ask the respondent to rate a product sample or descriptions of product concepts 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 studied. The same question is asked about all the products in the study. The data for multiple products is coded and input into a statistical program such as [[R (programming language)|R]], [[SPSS]], [[SAS System|SAS]], [[Stata]], [[STATISTICA]], JMP, and SYSTAT.

=== Analysis ===
The analysis will isolate the underlying factors that explain the data using a matrix of associations.<ref>Ritter, N. (2012). A comparison of distribution-free and non-distribution free methods in factor analysis. Paper presented at Southwestern Educational Research Association (SERA) Conference 2012, New Orleans, LA (ED529153).</ref> Factor analysis is an interdependence technique. The complete set of interdependent relationships is examined. There is no specification of 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 some attributes may be related to each other. 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''.

===Advantages===
* Both objective and subjective attributes can be used provided the subjective attributes can be converted into scores.
* Factor analysis can identify latent dimensions or constructs that direct analysis may not.
* It is easy and inexpensive.

===Disadvantages===
* Usefulness depends on the researchers' ability to collect a sufficient set of product attributes.  If important attributes are excluded or neglected, the value of the procedure is reduced.
* If sets of observed variables are highly similar to each other and distinct from other items, factor analysis will assign a single factor to them.  This may obscure factors that represent more interesting relationships. {{Clarify|date=May 2012}}
* Naming factors may require knowledge of theory because seemingly dissimilar attributes can correlate strongly for unknown reasons.