Factor analysis is a statistical technique that is used to determine the extent to which a group of measures share common variance. Factor analysis is sometimes termed a "data reduction" technique because the method is frequently used to extract a few underlying components (or factors) from a large initial set of observed variables. It is extensively used in psychological research concerned with the construction of scales intended to measure attitudes, perceptions, motivations, and so forth. Business-related applications are numerous and examples include the development of scales used to measure customer satisfaction with products and employee work attitudes. Factor analysis, however, has applicability outside of the realm of psychological research. It may be used, for example, by financial analysts to identify groups of stocks whose prices fluctuate in similar ways. Factor analysis often plays a crucial role in establishing the validity of employment tests and performance appraisal methods, thus helping a firm defend itself against employment discrimination charges.
There are many different methods of factor analysis and the underlying mathematical theory is quite complex. The basic elements of factor analysis, though, are relatively simple to understand. An example of the use of factor analysis might involve research designed to construct a scale of employee job satisfaction. Initially, a researcher or consultant may assemble a large set of questionnaire items that seem to be related to job satisfaction. These items will generally be presented to subjects along with some type of numeric or verbal scale.
Factor analysis includes both component analysis and common factor analysis. More than other statistical techniques, factor analysis has suffered from confusion concerning its very purpose.
Disadvantages of Factor Analysis
1. Factor analysis is a statistical method for attempting to find what are known as latent variables when you have data on a great many questions. Latent variables are things that cannot be directly measured. For example, most aspects of personality are latent. Personality researchers often ask a sample of people a lot of questions that they think are related to personality, and then carry out factor analysis to determine what latent factors exist.
2. The factors that appear can only come from the answers to the questions you ask. If you do not ask about sleep habits, for example, then no factor related to sleep habits will appear. On the other hand, if you ask only about sleep habits, then nothing else can appear. Selecting a good set of questions is complicated, and different researchers will choose different sets of questions. Random Data Gives Factors
3. If you generate a lot of random numbers, a factor analysis may still find apparent structure in the data. It is difficult to tell if the factors that emerge reflect the data or are simply part of the power of factor analysis to find patterns. It Is Hard to Decide How Many Factors to Include.
4. One task of the factor analyst is deciding how many factors to keep. There are a variety of methods for determining this, and there is little agreement as to which is best. Interpretation of the Meaning of the Factors Is Subjective.
5. Factor analysis can tell you which variables in your dataset "go together" in ways that aren't always obvious. But interpreting what those sets of variables actually represent is up to the analyst, and reasonable people can disagree.
Submitted By:
Bipin Easow
Roll No:12094
Group 12
No comments:
Post a Comment