So, what statistical analyses are required
for your bachelor's dissertation? Obviously,
different universities and faculties would
have different requirements. I did a quick
Google search and here's what I found.
At the university of Groningen's faculty of
psychology, they would advise for a bachelor's
thesis, recommended analyses would include
things like Chi sq, t test, regression; more
difficult and advanced analyses include things
like factor analysis, multi-level analysis,
and even structural equation models. These
requirements are in fact rather demanding for a bachelor's thesis.
Another example I found through my Google
search is a bit less demanding. At the faculty
of journalism of the university of Florida,
they require things like: descriptive analysis,
ANOVAs, crosstabs, and correlations. These
are less complex statistical procedures.
Now, if you are watching this video, you're
probably a student here at hotel school Leeuwarden.
Let's take a look at what we require.
In our undergrad stats course, essentially
we talk about 5 main things -- descriptive
analysis, t test, correlation, Chi sq, and
linear regression. So, for your bachelor's
thesis, you are supposed to use a combination
of a few of these 5 things. You don't have
to do all of them, but you should do 2 or
3 of those 5 things, depending on what your
research questions or hypotheses are.
So, in this video, I'm going to quickly run
through these 5 statistical procedures for
those of you who are busy with your bachelor's
research.
Now, before we jump into SPSS, I can imagine
some might argue and complain that this video
is an oversimplification of stats, to which
I would say -- yes, it is. This video is just
a quick general guide, it's not meant to replace
your stats course.
Okay, let's go through the 5 things one by
one, starting with descriptive measures. In
our stats course, we talk about mode, median,
mean, range, and SD. The most frequently used
ones are obviously the mean and the SD, so
let's take a look at these two. Let's say
we've collected a bunch of data and we want
to describe what our sample is like in terms
of their age and their 'power distance orientation',
we should calculate and report mean and SD
on these variables. To do so, we go to Analyze,
descriptives, descriptives. we choose our
target variables and run the analysis. In
the output table, we get the mean and the
SD on these variables. In this way, we can
describe the central tendencies and dispersions
of our variables. We can see that mean age
of this sample is something like 19.31 with
a SD of 1.79, power distance orientation,
mean of 3.87 with a SD of 1.04 on a 7 point
Likert scale. So, these basic descriptive
statistical measures can be used if your research
question is a descriptive kind of question.
In other words, if your RQ asks -- what is
the general level of power distance orientation
among employees of this hotel? Because this
question is calling for a descriptive kind
of answer, we can answer it by using descriptive
stats.
Now we are going to move on to t tests, which
compare means among two groups. Let's say
I am interested in whether or not males and
females in this sample differ in their power
distance orientation, to find out, we run
an independent samples t test. In other words,
if my research question is asking about potential
differences between two groups, we can test
that with a t test. We go to analyze, compare
means, independent samples t test. We put
power distance here and gender here, gender
is coded 0 and 1. In the output, we can see
that in this sample, males appear to have
a slightly greater power distance orientation
than females. But is this difference significant?
Let's see -- F is significant, which means
the variances in the two sub groups are not
equal, so we take the second t value here,
and we can see the t is associated with a
p that is greater than .05, which means it
is not a significant difference. So, males
and females do not significantly differ in
their power distance orientation. That's about
it. Oh, let me add, in this sample, we looked
at males vs. females in their power distance,
but for your research, you can compare any
two groups -- business travelers vs. leisure
travelers, Americans vs. Chinese, control
group vs. experimental group, so on and so
forth.
All right, next up -- correlation. Bivariate
correlations identify the relationship between
two scale variables. If my research question
is asking about the relationship between two
things, I can answer that RQ with a correlation
analysis. We go to analyze, correlate, bivariate,
let's just throw in student age and part time
job experience. We can see that the Pearson
correlation coefficient is .28, and it is
sig. at the .01 level. So, there is a positive
relationship between student age and part
time job experience, which makes sense, the
older a student is, the more part time job
experience they have. So, that's bivariate
correlation -- or correlation between two
variables.
Next we go to Chi sq. Chi sq. tests the relationship
between two categorical variables. Say, I
am interested in knowing whether the gender
of the student is related to their preferred
sports. To find out, we go to analyze, descriptives,
crosstabs, gender here, sport here, we choose
Chi sq, as well as percentages. We can see
in the output tables that the Chi sq coefficient
is indeed significant, looking at the frequency
table here we can see that in this sample,
males seem to prefer soccer more while females
appear to enjoy swimming more. In other words,
in this sample, there is indeed a relationship
between gender and preferred sports.
Finally, let's do a regression. Regression
analyses would be used for causal or predictive
questions. If your RQ asks about how one thing
influences something else, then consider a
regression. For example, if I want to know
how advertisement spending influences the
sales of CDs, I can 
run a regression. we go to analyze, regression,
linear, I put advertisement spending as the
IV and the CD sales as
the DV. In the output tables, we can see that
the model is a good one, the F is sig., furthermore,
we can see that advertisement positively and
significantly predicts sales of CDs.
So, that's it. These are the five statistical
methods we teach in the undergrad program
here at the hotel school. Obviously, in this
video, I only talked about how to run the
analyses in SPSS, I didn't speak about how
to report stats in the dissertation. I have
a video here about how to report correlation,
so check it out. Something else I would like
to talk about maybe in a future video is how
to make illustrations for different analyses.
For instance, when it comes to correlation,
we would normally make a scatterplot; when
it comes to descriptive measures, we can make
bar charts or pie charts. I will try to make
video about that in the future, so make sure
you subscribe and check back later.
All right, thanks so much for watching, please
like and sub, good luck with your dissertation,
I'll see you next time.
