so far we've been talking about research
design and then data collection in this
video I want to focus on analyzing the
responses to your questionnaires this is
one of the harder talks to give simply
because there is just too much detail
and variation to discuss in a short
video what I decided to provide is the
steps that I take when analyzing my own
questionnaires however before I begin it
would be useful to remind you of a few
terms we use when talking about
questionnaires questions can be divided
into three types this is sometimes
called level measurement firstly we have
category type questions which are also
known as nominal questions these are
when participants select from a list of
categories for their response such as
male or female or they may include
ethnic origin secondly we have ordinal
type questions these are similar to
category questions but instead of the
categories being independent there is
some sort of order between them if we
ask people to indicate their age in
categories this is an ordinal type
question thirdly we have continuous
questions these are any question that
can be answered by a number it could be
an open-ended question asking
participants to tell you how many times
they attended lectures or how often they
used a VLE or it could involve asking
them to rate the importance or intensity
of some experience the reasons I
reminded you of these types of questions
is that our choice of question dictates
the types of analysis we can use
therefore it is important to get our
questionnaires correct in the first
place the first step in my data analysis
is to code my questionnaire this means
looking at each question and allocating
a number for each possible response for
example on a category type question
about gender
I would allocate a one for males and two
for females if the question is about age
and its measured in categories I would
allocate a one
for the 15 the 20 year old age group a 2
for the 21 the 25 age group and so on
the next step is to actually transfer
the information from my questionnaires
into a spreadsheet or a statistical
package such as spss once the questions
have been entered I need to come up with
a strategy for my analysis ideally this
should be linked to my research
questions if you were planning on doing
a quantitative study then your research
questions should reflect this the first
stage for me is to summarize and
describe the responses to each of the
questions in turn for category type
questions we count or determine the
frequency of a response for example I
would find the frequency or the
occurrence of the males and the females
this is usually reported using
percentages and by using bar charts and
pie charts ordinal questions such as the
responses to various age categories are
reported in the same manner when it
comes to describing and summarizing
continuous type questions we use
measures of central tendency in
day-to-day language this is called the
average however in research language we
refer to the mean median and mode so if
I have asked people about how often they
use a VLE the mean of the group could be
four point six times a week
what is also useful to report is the
dispersion or the distribution of the
responses one way is to use the range
that's the difference between the
smallest and the largest responses a
more rigorous approach would be to use
standard deviation this is less swayed
by extreme cases we call all of these
statistics univariate that is they look
at one question or variable at a time
the next step in my description of my
questionnaire data is to do a bivariate
analysis this involves looking at pairs
of questions and seeing how they
interact or how are they different a
cross tabulation lets me look at the
association between two category type
questions
for example gender and age as you can
see a cross tabulation involves firstly
dividing my questionnaire up into males
and females and then dividing up the
males into the various age categories
and then dividing up the females into
the various age categories as well a
stacked bar chart presents this quite
well remember all this is doing is
summarizing and describing the responses
you may also want to do a bivariate
analysis involving a category question
and a continuous question let's say
gender is my category a question and the
use of a VLE is my continuous question
in statistical terms this is simply a
comparison of means what happens is that
we divide our questionnaires into two
piles male and female then we calculate
the mean and standard deviation of the
VLE question for just the males then we
calculate the mean and standard
deviation for the females and then we
compare them a clustered bar chart
presents this well remember once again
all we are doing is summarizing and
describing the responses at this stage
if we have two continuous type questions
and we want to present their
relationship we could use a scatter plot
a scatter plot simply plots each
person's response to each of the
questions you can see here how we have
plotted the use of VLE against the level
of competence with i.t a positive
relationship would mean as one increases
so does the other a negative
relationship would mean as one increases
the other decreases a point to remember
is that we don't know which one causes
the other
just that they look related so far all I
have spoken about is descriptive
statistical techniques if we want to
raise the rigor of our interpretation we
need to use inferential statistics
inferential statistics are used as a
basis for making predictions based on
information obtained
small-group inferential statistics help
us determine whether the results
produced by our research could or could
not have been achieved by chance if the
results could have been achieved by
chance then we say they are not
significant if the results could not
have been achieved by chance then they
are said to be significant in the social
sciences and education we usually set
the level competence at 95% that means
when we say something is significant we
are 95% confident in our analysis there
are a variety of inferential statistic
tests I will just talk briefly about the
main ones that you will encounter so
let's go back to my category type
question if I want to see if one of the
categories is statistically larger than
the others I would use a one-way
chi-squared test if I use my first
example males and females
I would ask were there more male
responses than female responses a
one-way chi-squared test would tell us
whether the difference between the sizes
of the two groups is just down the
chance or was it really significant okay
let's move on if we look at my bivariate
examples let's look at my two category
type questions gender and age the cross
tabulation from before gave me this
table so the question is is the
difference I can see in these groups
down the chance or are they significant
a two-way chi-squared test is the best
to use another one of the bivariate
tests from before looked at a category
type question and a continuous type
question here is the comparison of the
means from before I can see that the
mean of the males is higher than the
females so the question is is the
difference just down the chance or is it
significant we use a t-test to determine
this if we have more than two groups in
our category question we would use an an
over finally we have two continuous
questions this is the scatter plot from
before
you can see here how we have plotted the
use of ele against the level of
competence with IT I wanted to determine
whether there is a relationship between
these variables so I use a correlation
once again it tells me whether the
relationship is down to chance or is it
significant so just to summarize we look
for associations between category type
questions using a one-way and two-way
chi-squared we look for differences
between subgroups on continuous type
questions using a t-test or a nova and
we look for relationships between
continuous type questions using
correlations I know this is a very quick
overview if you're going to use these
techniques in your dissertation then
there are some online tutorials on the
website to help and there are several
very good textbooks in the reading list
