Basic Concept of Factor Analysis
Factor analysis is a statistical approach to reduce a large set of variables that are mostly correlated to each other to a small set of variables or factors. It is also used to explain the variables in the common underlying factors. (Hair et al, 1998) Malhotra, 2006 mentioned that factor analysis is also an interdependence technique that both dependent and independent variables are examined without making distinction between them
Conducting Factor Analysis
1. Formulate the problem
In this research, researcher’s objective is to determine the factors that influence customers’ satisfaction with their internet service provider in Malaysia such as Streamyx, Digi Broadband, Maxis Broadband, P1 and others (Malaysia Central, 2011). Mall intercept was used to interview a total of 30 respondents at Midvalley Megamall. Questionnaires were distributed and respondents are required to show their degree of agreement with the statements below whereby means very strongly disagree and means very strongly agrees:
Figure 1.1: Input in SPSS
2. Is the data appropriate?
a) The correlation matrix
Base on the data above, the correlation matrix was run to examine if the factor analysis is appropriate. Variables opt to be inter-related in order to be suitable to conduct a factor analysis. In other words, if all the variables have nothing in common, they can’t be analyzed into common factor. Hair et al, 1998 indicates that rule of thumb for factor analysis is a considerable correlation of 0.3. Field, 2009 has emphasized that if there is any value greater then 0.9, the variables may be omitted. According to the result, V3 (quality support), V5 (sincere interest in problem solving), V6 (prompt service), V7 (willingness to help), V8 (politeness) and V9 (knowledgeable) have high correlations about more than 50% (as highlighted in yellow) All the 5 variables may be inter-related under the same factor.
|Figure1. 2: Correlation Matrix | | | |Kaiser-Meyer-Olkin Measure of Sampling Adequacy. |.655 | | | | | |Bartlett's Test of Sphericity |Approx. Chi-Square |166.649 | | |df |36 | | |Sig. |.000 |
3. Method of Factor Analysis
After examining the suitableness to apply factor analysis in these data, right method of factor analysis would be selected. There are two approaches are the principal components analysis and common factor analysis. Malhotra 2006 indicates that principal components analysis takes into account the total variation of the data to generate the factor. In this research, principal component analysis is employed as the objective is to identify least number of factors to explain a maximum variance.
Figure 1.4 below shows the table of communalities before and after extraction. The initial assumption of principal component analysis is that all variance is common. Hence, the communalities equal to 1 before extraction. (Field, 2009) The extraction explains the common variance of the data which show researcher the relationship of the variables with each others. So for example, 72.3% of the variance related with V1 is common or shared. In this research, all 9 variables have accounted high value, hence, they fit well with the factor solution and...
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