FACTORS RESPONSIBLE WHILE MAKING A DRIVING SCHOOL SELECTION
Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. Factor analysis is often used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables
Analysis: Looking at the mean, we can conclude that Skilled Trainer is the most important variable that influences customers to choose a driving school. It has the highest mean of 4.10.
Kaiser-Meyer-Olkin (KMO) Test is a measure of how suited your data is for Factor Analysis. The test measures sampling adequacy for each variable in the model and for the complete model. The statistic is a measure of the proportion of variance among variables that might be common variance. The lower the proportion, the more suited your data is to Factor Analysis.
KMO returns values between 0 and 1. A rule of thumb for interpreting the statistic:
· KMO values between 0.8 and 1 indicate the sampling is adequate.
· KMO values less than 0.6 indicate the sampling is not adequate and that remedial action should be taken. Some authors put this value at 0.5, so use your own judgment for values between 0.5 and 0.6.
· KMO Values close to zero means that there are large partial correlations compared to the sum of correlations. In other words, there are widespread correlations which are a large problem for factor analysis.
From the table we can see that KMO value is 0.892, therefore, it can be easily concluded that the data collected is very well suited for conducting factor analysis and the sample is adequate enough.
Analysis: The table of communalities shows how...