Alright welcome back everybody. In this
video we're going to cover some of the
basic elements of the scientific method
as well as some classic research methods
that are found in not just the social
sciences but science in general. And the
important thing to consider is that the
statistical analyses you will learn this
semester are found all over in terms of
the scientific method. And so
understanding the different types of
research methods as well as how the
process of the scientific method works
is going to allow you to better
implement the statistics in this course,
as well as once again being a better
consumer of statistical information. So
it all begins with the scientific method
which is a systematic process of
gathering evidence before making
decisions. At least that's how I think
about it so the idea is we are, you know,
asked to make decisions all the time and
the best thing we can do is you know use
our rational brain which means, you know,
relying on evidence as opposed to
intuition, because sometimes intuition is
wrong you know what we're often
surprised that the world doesn't always
work the way we expected it to. So the
scientific method is arguably unbiased
or at least it attempts to be unbiased.
It tries to gather evidence in a very
deliberate and systematic manner so that
we make the most reliable and valid
decisions about the world. So we'll get
more specific in a moment but let's
start off with some key terms.
The first is “theory” and theory is
basically an organized understanding of
some phenomena. So my theory might be
that sleep is important for getting good
grades in school. You know I think that
students who get better sleep are going
to have better grades. Now in order for
me to gather evidence, in
favor or against this theory I need to
test that theory. And that's where a
hypothesis comes in. A “hypothesis” is a
more specific and testable form of my
theory. So a hypothesis might be if I
have two groups of students and one
group sleeps more than eight hours a
night and the other group sleeps less
than eight hours a night there would be
a difference on GPA between these two
groups. So it's not a perfect study but
the idea is I've taken my theory which
is general and abstract, simply saying
sleep is related to grades, and I've made
it much more specific and testable now.
When it comes to testing hypotheses this
is where we can look at three classic
research designs or research methods. And
this is the basis of any
science. So it all starts off with
“description” which is the starting point
to any science. So if I don't know enough
information about a topic, the first
thing you want to do is describe as much
as you can. So making observations,
interviewing people, giving people
surveys; descriptive research is great
because it's simply a method of
gathering information about some topic.
So for example, maybe Pierce College does
a descriptive study where they simply
count the number of Pierce students who
drive to campus, and so they want to know
for example, do we have enough
parking spaces (which probably they don’t)
but the idea is they're simply
describing a population of people which
is how many people drive to campus. It's
not wildly complex, but it's important
and it's a way of
gathering important information. Now
typically more advanced research
involves “correlational” or “experimental”
research and so let's talk about that.
The basic takeaway that I want to cover
is what each are defined as, but
then more importantly, sort of the
strengths and weaknesses of each. So
correlation is where you measure two
variables to see if they have a
relationship with each other. And so for
example, let's look at this article right
here, the title of this article is “Study
reveals correlation between exercise and
good grades” and so this is some
journalist’s report from some study
conducted in Colorado where they found
that students who exercised more tended
to have better grades. So essentially
they found a relationship between those
two variables, but the important concept
to consider is that correlation does not
imply causation!
So just because students who exercised
more got better grades, it does not mean
that exercise is causing better grades.
And we'll talk about why in a moment, but
essentially what they found was simply
that those students who reported higher
amounts of exercise also reported higher
grades and vice-versa… lower exercise…
those students tended to report lower
grades. But the key idea here is that
it's a tendency; it's a pattern; and it is
not necessarily a causal relationship. So
it's possible for example that students
who have a conscientious personality, who
have a really motivated
personality are the types of students
who are going to exercise, and those
conscientious people are also more
likely to study and therefore get good
grades. So we call this the “third
variable problem” … the idea that in a
correlational study just because two
variables
are correlated you can't rule out the
fact that some third variable might
actually be causing each of those two to
change. So the advantage of a
correlational study, as you're going to
see later this semester when you
actually conduct correlational analyses,
the advantage is it's rather simple
because it only involves measurement, but
the major disadvantage is you cannot
talk about causal relationships (i.e., does X
cause Y?). If you want to look at causal
relationships you must conduct an
experiment. An “experiment” is the gold
standard because it allows you to look
at cause and effect. So let's look at
an example of an experiment.
Imagine a researcher wants to compare
two teaching methods to see how it
affects students grades, and so the first
teaching method is just a lecture only
and the second teaching method is a
lecture with a lab component. The
idea is that you have two groups of
people, one group receives the first
treatment (the lecture only) and the
second group receives the second
treatment (the lecture and the lab), and if
there's a difference in
their grades you have some reason to
believe that it was the differences in
how they were treated that caused the
differences in their grades. For
example, here what we see is that
students who had the
lecture and the lab component they also
had higher grades
compared to students who only had the
lecture only. So it's reasonable to
expect that it was the different
teaching method that caused an outcome
difference in grades. But, we have some
further clarifications to make. So you
have some key terms here the first is
the “independent variable” and the second is
the “confounding variable” and the third
is the “dependent variable.” Let's define
each. The independent variable is the
predictor
variable; it's the one that the
researcher manipulates. In other words,
the researcher makes it different in one
group compared to another. So the
independent variable in this study was
the teaching method. One group received a
lecture only and the other group
received a lecture with a lab; that's the
independent variable manipulated between
two groups. The dependent variable… let's
go to the bottom… the dependent variable
is the outcome. This is the effect, this
is the idea that we think the
independent variable might cause a
difference in (the dependent variable). The
dependent variable in this study is
grades, and what we're trying to see is,
is the dependent variable dependent on
the independent variable? Now finally, in
order for us to claim that the
independent variable causes a change in
the dependent variable, the only thing
that can be different between your two
groups is the independent variable.
However, we have a confounding variable
in this study. So the first group
received their teaching from professor
Smith, and the second group received
their teaching from professor Jones. The
problem to consider is we don't know why
the second group had higher grades than
the first group, because there's two
possibilities. Possibility number one is
that they received the lecture with the
lab component. It's possible that the lab
component sort of adds to their
understanding of the course and
increases their grades, but the second
possibility is that there's something
about professor Jones. Perhaps professor
Jones is a better teacher than professor
Smith, and therefore we don't know what
caused the higher grades. The fact
that there's different professors
is known as a confounding variable. The
difference in the professors ‘confounds’
the researcher’s ability to understand
cause and effect.
Essentially, you don't want
confounding variables in your study, you
want the independent
variable to be the only variable that is
different between your two groups.
Otherwise, you cannot discuss cause and
effect. Finally one thing to consider
about the experiment is that if it's the
only way to discuss cause and
effect, why would anyone ever do a
correlational study? And there's two
primary reasons. The first is ethics. It's
not always ethical to conduct an
experiment. For example, if I wanted to
know, does homelessness cause mental
disorder? The way I would do that
experiment is to take a group of
individuals and have one group become
homeless and the other group not become
homeless to see if there's a difference
in their outcomes (in terms of mental
health). But clearly it's unethical to ask
people to become homeless for the sake
of research, so instead I would have to
do a correlational study. I would have to
see if, for example, do homeless people
tend to have higher amounts of mental
illness compared to those who are not
homeless? But because I did a
correlational study I cannot necessarily
talk about cause and effect. So the
downside to an experiment is that you
can't always conduct an experiment
ethically. The second is that experiments
are more time consuming and they
often cost more money because
you're actually setting up various
conditions and that takes more time and
often more effort and more money. So as a
result, correlational studies are often
used simply because they're more
practical, but experiments are the gold
standard because you can only talk about
cause and effect through an experiment.
The last thing I want to talk about in
this video is the concept of “statistical
significance” versus “sampling error.” So
let's look at this slide here. We have a
population of first grade students who
are split into two groups. Sample A and
sample B; and what's happening here is
that each group is receiving a different
teaching method.
And so we want to know, did the
differences in their teaching method
affect the differences in their grades?
And so if you look at step two you can
see that on average, sample A scored
higher. Their average was 76 compared to
sample B which was an average of 71.
Now obviously 76 is a higher number than
71, but what's important to think about
(and this is something that is really an
important takeaway for most of the class)
and recall the question is why is there a
difference in those two numbers… so 76 is
obviously higher than 71… and there's two
possible reasons for that. The first is
that there's a statistically significant
effect brought about by the differences
in teaching method. Or, put differently,
teaching method A is simply better than
teaching method B, and therefore students
who were in Group A got higher grades
because the teaching method was better
than those in teaching method B. So we
call that a statistically significant
effect, and what that means is that if
you were to take another group of
students, a different group of students,
and divide them into two groups (A and B)
again you would expect group A to score
higher than group B because it is a
statistically significant effect in the
sense that method A is better than
method B. The second possibility is that
there is no difference between method A
and method B but instead the reason
group A scored higher was because of
sampling error. And so think about this,
it's possible that through random
selection, Group A happened to have
students who are simply smarter than
those in Group B and therefore the
reason sample A scored higher on average
than sample B had nothing to do with the
fact that teaching method A was
different than teaching method B. It
simply had to do with the fact that
students in Group A were smarter to
begin with and therefore we don't know.
Why is 76 higher than 71?
Why did group A score higher on average
than Group B? Two possibilities.
Statistical significance, in other words
there's a meaningful difference between
Group A and Group B, the teaching
methods, or there is no meaningful
difference, and
it's simple sampling error. So how do you
decide which of those two possibilities
is actually the case? That's what this
class is all about. We are going to learn
statistical analyses that help us
understand which of those two outcomes
is more likely and that's how we figure
out lots of different things in our
society. So for one, medication, you know
why do we take medication as opposed to
not taking medication? Well arguably
researchers have evaluated the effects
of medication using statistics and
determined that they are effective and
we will actually talk about how that
works.
We'll talk about, for example, let's
talk about politics. So exit polls, we can
determine whether or not
candidate A or candidate B is going to
win the election based on a sample of
voters. So these research methods,
they're found in all areas of science
and we're going to touch upon them in
this course so that we can learn how the
scientific method applies to various
social sciences, and of course how
statistical analyses are involved.
