1. Factor Analysis :
Factor analysis aims to describe a large number of variables or questions by only using a
reduced set of underlying variables, called factors. It explains a pattern of similarity between
observed variables. Questions which belong to one factor are highly correlated with each
other.
There are two types of factor analysis: exploratory and confirmatory. Exploratory factor
analysis is driven by the data, i.e. the data determines the factors. Confirmatory factor
analysis, used in structural equation modelling, tests and confirms hypotheses.
Factor analysis is often used in customer satisfaction studies to identify underlying service
dimensions, and in profiling studies to determine core attitudes.
T TEST :
A t-test is any statistical hypothesis test in which the test statistic follows a Student's t
distribution if the null hypothesis is supported. It can be used to determine if two sets of data
are significantly different from each other, and is most commonly applied when the test
statistic would follow a normal distribution if the value of a scaling term in the test statistic
were known. When the scaling term is unknown and is replaced by an estimate based on the
data, the test statistic (under certain conditions) follows a Student's t distribution.