All right, this is module one, lesson four,
part two; we’re going to continue our discussion
of qualitative data and we’re going to pick
up and talk about the ways that you can display
qualitative data.
We’re going to focus on two different types
of graphs.
They are a bar chart and a pie chart.
You’re not going to be asked to actually
create these graphs using the raw data, but
you will be asked to interpret information
provided by the graphs.
In today’s society you can probably find
a free website – Microsoft Excel, Microsoft
Access, which would actually create these
graphs for you.
Rarely do researchers create these graphs
by hand anymore.
If you’ll recall, in part one we created
a categorical frequency distribution which
allowed us to organize the raw data collected
about the county of residence for students
in spring 2016 at Georgia Highlands, which
I have displayed here on the screen.
We can use this frequency distribution to
kind of give us a jumpstart for the two different
graphs.
The first graph focuses on the third column
of the frequency distribution, which is the
frequency.
With a bar graph the height or the length
of the bar represents the frequency, and you’ll
see a graph – a bar graph there, on the
left-hand side of the screen, that I generated
through Microsoft Excel, that shows the county
of residence for GHC students spring 2016.
I emphasize the frequency by putting the actual
numeric value at the top of each bar so that
the reader or the researcher doesn’t have
to estimate or guess what the value was.
Notice that there are gaps between the bars
because each category in a – with qualitative
data is considered a standalone; there’s
no overlap between the different categories.
Any type graph should always have a title
with lots of detail.
You should also indicate what information
is given on the horizontal axis and what information
is given on the vertical axis.
A pie chart uses the relative frequency, which
is the fourth column of the categorical frequency
distribution, and each slice or piece of the
pie represents that relative frequency or
that percentage.
Pie charts are very useful if we want to compare
more than one category to another, so we can
compare category against category.
We can also compare a category to the entire
data set as well, so we can see that relationship.
Again, it’s important that you give the
graph a title with details.
Also, you want to label each individual piece
or each individual slice of the pie, ideally
with the category name and the corresponding
percentage or relative frequency.
Let’s think about how we can analyze qualitative
data.
Once you have the data organized into a categorical
frequency distribution, we can use that to
create either a bar graph or a pie chart,
depending upon the information we need to
communicate.
But ultimately our goal is to answer the research
question.
So what kind of analysis can we do?
What can we learn from the sample about the
population?
With qualitative data the type of analysis
we can do is very limited because we have
words and phrases.
Since we don’t have numbers, we’re not
able to perform any mathematical calculations.
So there are basically three things that researchers
can determine whenever they’re dealing with
qualitative data.
They can determine the most popular or the
most common, which category occurred the most
– statistically that’s called the mode
– which category occurred the least, and
then if need be they can provide rankings
with that sense of order from lowest to highest
or highest to lowest.
So thinking about the example we’ve been
working on, the most popular county of residence
is Bartow, based on the sample; therefore
we can conclude or infer that the most popular
county of residence for all GHC students in
spring 2016 is Bartow.
The least popular county is Gordon, based
on the sample; therefore we can conclude or
infer that the least popular county of residence
for all GHC students in spring 2016 is Gordon.
And then we can look at the rankings.
We already know that the most popular was
Bartow, second was Cobb, third is Paulding,
fourth is Cherokee, and again – the least
popular is Gordon.
All right, let’s walk through another example
together, okay, real quick, so you’ll have
another one to refer back to.
Beginning with the question – what is the
most popular type of computer sold at a local
retail store – we need to think about what
our variable is; what information are we going
to collect?
We need to collect information about the type
of computer sold to a customer at a local
retail store, and the options at the store
are – a desktop; laptop; notebook; or tablet.
Again, hopefully it’s obvious that those
are words or phrases, which means we are collecting
qualitative data, which is neither discrete
nor continuous and is also nominal.
Again, a desktop is not necessarily better
than a laptop.
The population is all computers sold to customers
at a local retail store.
The sample was collected using sales records
from the past year.
A random starting point was selected and every
seventh record examined.
This is a systematic sampling method, which
is one that’s very easy to use.
It is random because we chose a random starting
point and it is representative of the sales
at the store.
So as we went through we recorded what type
of computer was sold on every seventh record,
and this is the list that we obtained.
Again, there’s no rhyme or reason to the
list; so we need to organize it.
So I want you to try this on your own.
I’ve given you the setup to construct a
frequency distribution.
So pause the video and take a minute and go
through the raw data and complete the table.
When you’re finished, you should have these
results – 11 desktops; 23 laptops; 9 notebooks;
and 7 tablets.
Okay, let’s analyze it.
Based on our categorical frequency distribution
- The most popular type of computer sold at
a local retail store is a laptop.
The least popular type of computer is a tablet.
If computer types were ranked most popular
to least popular, the rankings would be laptop
first, desktop second, notebook third, and
tablet fourth.
Again, think about how a retail store might
use the information.
If they know the most popular type of computer
is a laptop, then they may want to stock more
laptops in the store.
If they know a tablet is not very popular,
maybe they don’t stock as many tablets,
to kind of keep down their overhead.
So statistical analysis of qualitative data
can be very useful to, or be very useful in
the real world.
