Factor analysis
Factor analysis attempts to discover the nature of the constructs influencing a set of responses.
Both types of factor analyses are based on the Common Factor Model where model proposes that each observed response (measure 1 through measure 5) is influenced partially by underlying common factors (factor 1 and factor 2) and partially by underlying unique factors (E1 through E5). The strength of the link between each factor and each measure varies, such that a given factor influences some measures more than others.
Factor analysis helps you find these undetermined variables by looking at the variables you have actually collected.
An important element of the factor analysis output is the standardized factor score coefficients (Output-Table), which gives location of each product on each factor.
The vectors are obtained based on the amount of correlation the original attitudes possess with the factor scores (represented as factors). The direction of the vectors indicates the factor with which each attribute is associated, and the length of the vector indicates the strength of association. Thus, on the left map the “filling” attribute has little association with any factor, whereas on the right map the “filling” attribute is strongly associated with “refreshing” factor.Although a factor is not observable like the other original variables, it is still a variable. One output of most factor analysis programs is the values for each factor for all respondents. These values are termed factor scores and are shown above for three factors that were found to underlie the five input variables. Thus, each beverage has a factor score on each factor, in addition to the beverage’s rating on the original eight attributes. In subsequent stages while doing perceptual mapping this factor scores are used to position beverages in the perceptual map.
My learning from class:
Factor analysis is basically what is done when you have a large number of variables to work with. Such a large number of variables makes it very difficult to organize data, and analyze it. Thus, we can use factor analysis, to reduce the number of variables so that it becomes much easier to work.
Factor analysis uses correlation between variables to see which ones are related, and can be eliminated, without causing significant impact on the output of analysis.
For this purpose, we take a default eigen value >1 and use verimax rotation so that the first few variables have the maximum effect.
Using Rotated Component Matrix, we can very well determine which variables are to be combined into factors, and keeping a limit on the eigen values to be accepted, we can determine what percentage of the data is contained in the factors we are choosing. According to this, we can increase or decrease the number of factors to simplify our calculations.
We can look at the correlation between the variables in scatter plot, in which visually clustered patterns can be made out and combined.
The remaining factors can be combined to give more meaning to the analysis.
Regards
Titus Raju
Factor analysis attempts to discover the nature of the constructs influencing a set of responses.
Both types of factor analyses are based on the Common Factor Model where model proposes that each observed response (measure 1 through measure 5) is influenced partially by underlying common factors (factor 1 and factor 2) and partially by underlying unique factors (E1 through E5). The strength of the link between each factor and each measure varies, such that a given factor influences some measures more than others.
Factor analysis helps you find these undetermined variables by looking at the variables you have actually collected.
An important element of the factor analysis output is the standardized factor score coefficients (Output-Table), which gives location of each product on each factor.
The vectors are obtained based on the amount of correlation the original attitudes possess with the factor scores (represented as factors). The direction of the vectors indicates the factor with which each attribute is associated, and the length of the vector indicates the strength of association. Thus, on the left map the “filling” attribute has little association with any factor, whereas on the right map the “filling” attribute is strongly associated with “refreshing” factor.Although a factor is not observable like the other original variables, it is still a variable. One output of most factor analysis programs is the values for each factor for all respondents. These values are termed factor scores and are shown above for three factors that were found to underlie the five input variables. Thus, each beverage has a factor score on each factor, in addition to the beverage’s rating on the original eight attributes. In subsequent stages while doing perceptual mapping this factor scores are used to position beverages in the perceptual map.
My learning from class:
Factor analysis is basically what is done when you have a large number of variables to work with. Such a large number of variables makes it very difficult to organize data, and analyze it. Thus, we can use factor analysis, to reduce the number of variables so that it becomes much easier to work.
Factor analysis uses correlation between variables to see which ones are related, and can be eliminated, without causing significant impact on the output of analysis.
For this purpose, we take a default eigen value >1 and use verimax rotation so that the first few variables have the maximum effect.
Using Rotated Component Matrix, we can very well determine which variables are to be combined into factors, and keeping a limit on the eigen values to be accepted, we can determine what percentage of the data is contained in the factors we are choosing. According to this, we can increase or decrease the number of factors to simplify our calculations.
We can look at the correlation between the variables in scatter plot, in which visually clustered patterns can be made out and combined.
The remaining factors can be combined to give more meaning to the analysis.
Regards
Titus Raju
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