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The broad purpose of conducting a factor analysis is of reducing the number of variables so that we are able to establish relationships and patterns amongst them and understand them.
[spss output 35 - Correlation Matrix]
In constructing this factorial analysis, we assume all values are greater than 0.3, which effectively falls into the acceptable range of mean.
The KMO index usually ranges from 0 to 1 and shows that a value closer to 1 is of higher suitability when conducting a factorial analysis. As our value is of 0.748, we consider it suitable for the factor analysis as it is a reasonable value.
When looking at the Bartlett's test, we are able to identify patterned relationships if the correlation matrix is an identity matrix.
The p-value we observe in this test is of 0.000. As it should be higher than a significance level of 0.5, we assume the factor analysis is not suitable.
[spss output 36 - KMO and Bartlett's test]
[spss output 37 - total variance explained]
Using the Kaiser’s criterion, we analyze 6 factors. Moreover, these 6 factors explains 70.1% of total variance.
[spss output 38 - initial Eigenvalues]
[spss output 39 - scree plot]
In the observed scree plot, we pay critical attention to the Eigenvalues greater than 1, this being mostly on the left side of the plot, and this is according to the Kaiser's criterion which is based on the cumulative percentage of the variance in this scale.
As we observe two breaks, we are able to identify the points above it, which show to be a value of 4 without taking into consideration the levels of infliction, which would add to a total of 6.
Taking these 2 criteria into consideration, we should retain 6 factors, also considering the dimension of the sample used.
When subjecting an analysis to residuals, it must be known that it represents the differences between correlation based on the already observed data. This footnote summary discloses that there are 17 residuals greater than 0.05, being 11%, and as it is less than 50% consequently we consider it a good fit for analysis.
As there are 6 factors, we classify them and name them therefore identifying them into common themes they might fall into.
Factor 1 : Association - We name this factor accordingly because of its emotional features a consumer might relate it to such as brand image and the emotional relationship, with the brand which would probably help consumers identify with the brand a situate it in the market its made available in.
Factor 2: Attributes - This is because the factor names certain features the content of beer has such as calories, gas, but furthermore goes onto including advertisement and the brands involvement with events and causes, and consequently also relates to the attributes of the brand on a 'personal level' and causes it stands for.
Factor 3: Characteristics - As this factor relates to the characteristics of the beer content, and moreover how much consumers should pay for it to benefit from all characteristics, foam and freshness. It could also be classified into the first impression consumers have of the beer.
Factor 4: Nutritional information - As consumers are able to be aware of the levels of alcohol being ingested.
Factor 5: Promotion - As packaging plays a role in the image of the product and how consumers receive it which consequently also affects their level of satisfaction.
Factor 6: Palliative aspects - As it takes into consideration acidity of beer and body of beer, meaning at a first sip, consumers embrace this factor instantly.
[spss output 40 - Rotated Factor Matrix]
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