Extracting factors to investigate Consumer attitudes Using SPSS

1. Extracting factors to investigate Consumer attitudes Using SPSS
Dr. Kalyan Sengupta*, Prof Atish Chattopadhyay*
Introduction
A consumer survey was carried out to measure relative importanceof attributes the
consumers attach to various attitudinal variables, while purchasing branded bakery
products from bakery chains of the city of Kolkata. A sample of 287 consumers were
contacted at purchase points and asked about nine different attributes influencing the
choice of specific brand. The factors listed on the questionnaire were: convenience,
décor and ambience of shop, service, food quality and taste, availability of a wide variety,
brand reputation, value for money, freshness and portion size.
It was interesting to assess how much weight the consumers put onto each of these nine
factors in a five point Likert Scale measuring 1 for absolutely no value and 5 for
extremely high value. The respondents constituted a good representation of buyers with
different age, income, and gender groups.
Initially a number of Chi-Square tests were performed to examine dependency of each
attitude factors with respect to different demographic variables, namely age, income and
gender using SPSS program. It was found out from the test attitudes data that the factors
were independent of the said demographic variables. This may be interpreted as the
measured attributes influence consumers of different groups more or less in the same
manner.
Factor Analysis for further understanding of attitudes
Factor Analysis is a Statistical Model which uncovers the latent structure of a set of
variables, by reducing the number of variables and clubbing them into a smaller number
* Faculty Members, ICFAI Business School, Kolkata

2. of factors. SPSS has several methods or models for extracting factors from a set of
variables. Principal Component Analysis (PCA) method was chosen for the purpose.
This method seeks a liner combination of variables in a way to explain maximum
variance. Once one factor is extracted, the model searches for another linear combination
to explain maximum amount of remaining variance and the iterative process continues
until some tolerance limit is come across.
Use of SPSS
In our case of attitude data, SPSS extracted four factor components, explaining a total of
60.5 percent variance in the variables, after a factor rotation (varimax) treatment. The
rotated component matrix is illustrated in the figure 1. From the correlation matrix, it
was possible to interpret those four dimensions in the following way:
• Factor Component 1: Reflects onto some trendy attitude of the consumers, including
the attitude variables like shop décor, brand name, etc.
• Factor Component 2: Reflects onto some economic value related attitude related to
the variables indicating good value for money, portion size, freshness of the product.
• Factors Component 3: Reflects onto service and quality related attitudes.
• Factor Component 4 reflects onto convenience (low access time) related attitude
variable.
Figure.1: Rotated Component Matrix for Factor Analysis
Component
1 2 3 4
Factors-convenient location 0.295 0.103 0.136 0.748
Factors-shop décor & 0.801 -6.286E-02 8.847E-02 7.352E-02
ambience
Factors-service 0.170 0.147 0.748 8.180E-02
Factors-food quality/taste -6.252E-02 -3.095E-02 0.836 -4.723E-02
Factors-availability of a 0.375 0.122 0.101 -0.697
wide variety

3. Factors-brand 0.712 0.166 -5.643E-03 -5.091E-02
name/reputation
Factors-value for money 4.099E-02 0.691 -0.137 0.223
Factors-freshness -9.536E-02 0.574 0.144 -0.199
Factors-portion size 0.238 0.758 0.129 -1.696E-02
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
The components may be viewed from the factor component plots to locate their positions
in the multidimensional space. Such an example plot is illustrated in the figure 2.
Component Plot in Rotated Space
1.0
faporsiz favalmon
fafresh
0.5
Component 2
faavail fabrname
faserv faconloc
0.0
fafoodqu fadecamb
-0.5
-1.0
-1.0
-1.0 -0.5
-0.5 0.0
0.0 0.5
0.5 1.0 1.0 nt 3
Compone
nt 1 pone
Com
Figure 2

4. The component score coefficient matrix, which could be used to compute factor scores of
the individual respondent from the original variables, is displayed in figure 3. These
individual scores may be automatically computed by SPSS and saved in the spread sheet.
Figure 3: Component Score Coefficient Matrix
Component
1 2 3 4
Factors-convenient location 0.176 0.014 0.070 0.646
Factors-shop décor & 0.581 -0.172 -0.009 0.055
ambience
Factors-service 0.023 0.024 0.546 0.071
Factors-food quality/taste -0.127 -0.077 0.651 -0.036
Factors-availability of a 0.252 0.032 0.026 -0.610
wide variety
Factors-brand 0.494 0.016 -0.091 -0.054
name/reputation
Factors-value for money -0.061 0.505 -0.162 0.186
Factors-freshness -0.167 0.423 0.076 -0.177
Factors-portion size 0.048 0.507 0.016 -0.024
Extraction Method: Principal Component Analysis . Rotation Method: Varimax with
Kaiser Normalization. Component Scores.
The individual factor scores were used for further causal analyses of the data. Thus
factoranalysis is mainly a data reduction technique with which the analyst can reduce the
variable space, which can ease up the computations forcomplex analytical models.
Key to the variable names in the Factor Analysis
Attribute Name Variable
Factors-convenient location faconloc
Factors-shop décor & ambience fadecamb
Factors-service faserv
Factors-food quality/taste fafoodqu
Factors-availability of a wide variety favail

5. Factors-brand name/reputation fabrname
Factors-value for money favalmon
Factors-freshness fafresh
Factors-portion size faporsiz