the first step in analytics or statistics is to have a good look at
your data and before you begin try to understand what kind of variable you're
working with and based on the type of variable you will decide what kind of
analytics could be performed with it let's have a look at various different
types of data that we encounter and is commonly used in our daily lives the
most common one would be a cross sectional data which is basically
looking at a measurement taken at one point in time sensors in a given year is
a cross-section of the society as students evaluate course an instructor
that becomes cross-section at any given point compared to the cross-sectional
data we can have panel or cross-sectional panel data which is
essentially asking the same group of individuals the same questions
repeatedly over time so you may pick a group of people prosecute it as a panel
and then ask the same questions once every year over a given period of time
the time series data is rather different you're looking at a particular phenomena
such as unemployment rate and then you measure it every month and then display
that data or analyze that data which is repeated measurements on the same
phenomena over time so you may have monthly data going back to 1940s or your
climate data going back to hundreds of years so based on the type of data
cross-sectional panels time series we will pick appropriate two statistical
tools to deal with them if your dataset has only one variable which is called a
univariate data set and if you have multiple variables in your data set then
it's a multivariate data set let us now look at variable types and start with
categorical or nominal variables let's consider home ownership for instance one
can either own a home or rent a home and knowing that there are only two
categories here owning and renting and that is a categorical variable the
tenure status of an individual is essentially a categorical variable in
this particular case because you only have two choices on a rent it's a
binomial variable consider travel choices you can go to work by driving or
by someone Drive you there so you're a passenger you can take off in transit or
you can walk or bike so in this particular case you have four choices
more than two so we call it multinomial so both binomial and multinomial
variables are part of categorical variables you cannot have any
quantitative relationships among categories and for these types of
variables averages are usually meaningless so if you have a mode of
travel and there are four categories in average category would mean absolutely
nothing of use a particular type of categorical variable is ordinal data
where data are ranked or ordered in some particular fashion so for instance
number of cars owned by household a household may have zero car one car two
cars three or more cars and that essentially is an ordinal data where
zero represents a zero and zero cannot record it as one and one cannot be coded
as zero so the order in which variable has been reported matters categories can
be compared with one another and you still cannot use regular statistics the
differences are also meaningless in this particular case another type of data is
called ratio data which is the data set that have a natural zero so example
sales dollars length of a distance or weight of an object these are all
examples of ratio data SPSS calls its scale data and I often would use the
term continuous data or continuous variable so a variable such as distance
from point A to B could be 8 kilometers 8.5 kilometers to miles and the variable
is continuous and it's in zero makes some logical sense in this particular
variable so for instance you say I zero dollars and zero means something
here it's a strongest form of measurement and you can compute ratios
and differences and another type of variable is internal data or interval
variables that are ordered and characterized by specific measure of
distance between observations and it may not have a natural zero so temperature
is a good example and when you say that it's zero degrees Celsius it does not
mean that there is no temperature it's freezing but it is measuring something
that exists and so ratios are also meaningless so for example if someone
say would you know the temperature in some African country is 50 degrees and
compared to somewhere tropical where it was 25 degrees it doesn't mean that the
temperatures in the in the African desert is two times what twice as hot as
it is in the tropics but we can say that there's a difference of 25 degrees
between the two places