Factor Analysis is used to study the patterns of relationship among many dependent variables, with the goal of discovering something about the nature of the independent variables that affect them. Factor analysis is used to reduce the no. of variables and group them. Basically there are two types of factor analysis:
Confirmatory factor analysis – when we have certain ideas but want to get confirm through factor analysis
Exploratory factor analysis – when we don’t know how to group the variables.
Thus factor analysis attempts to identify underlying variables or factors that explain of that explain the pattern of correlations within a set of observed variables. . Factor analysis can also be used to generate hypotheses regarding causal mechanisms or to screen variables for subsequent analysis.
The variables should be quantitative at the interval or ratio level. Categorical data (such as religion or country of origin) are not suitable for factor analysis. Data for which Pearson correlation coefficients can sensibly be calculated should be suitable for factor analysis.
Examples of factor analysis problems:
Suppose each of 500 people, who are all familiar with different kinds of automobiles, rates each of 20 automobile models on the question, "How much would you like to own that kind of automobile?" We could usefully ask about the number of dimensions on which the ratings differ. A one-factor theory would conceive that people simply give the highest ratings to the most expensive models. A two-factor theory would hypothesize that some people are most attracted to sporty models while others are most attracted to luxurious models. Three-factor and four-factor theories might add safety and reliability.
Suppose we have a pile of 100 statements that can be used to describe a product X. We could just present a ranked list of statements or we could Factor Analyse it and determine what underlining factors really exist that explains as much of those 100 statements in as few factors as possible. It is easier to describe a product in 5-6 factors rather than 100.
Factor analysis is extensively used in Operational research. In Total Quality Management, FA can be used to identify various dominant factors in TQM implementation in an organization. Factor Analysis is also used in supply chain integration. It helps in identifying various emerging patterns of Supply chain by reducing various research variables to a few principal components. E.g Agile, Lean and Traditional.
Reference: http://www.psych.cornell.edu/Darlington/factor.htm
Posted by: Siddhartha Sabale
Operations _Group 2
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