Types of Research Designs – Correlational Studies
Summary
TLDRThis video script delves into correlational studies, a prevalent research design in psychology. It explains how these studies explore the relationship between two variables, such as marital satisfaction and parenting quality, or creativity and academic performance. The script clarifies the importance of interpreting correlation coefficients, emphasizing the magnitude and valence of correlations. It visually illustrates positive and negative correlations through scatterplots and cautions against the common mistake of assuming causation from correlation, a fallacy evident in sensational headlines. The script concludes by stressing the utility of correlations while advising skepticism and a search for alternative explanations.
Takeaways
- 🔍 Correlation studies are widely used in psychology to examine the relationship between two variables.
- 🤔 Research questions suitable for correlational research include those examining the link between marital satisfaction and parenting quality, or creativity and academic performance.
- 🧑🔬 Correlational studies often involve many participants and can be conducted through surveys or in a lab setting.
- 📊 The outcome of a correlational study is a correlation coefficient, which is quantified using Pearson's r-value.
- 📉 The magnitude of the correlation coefficient indicates the strength of the relationship, with values closer to 1 or -1 indicating stronger relationships.
- ➡️ Positive correlations mean that as one variable increases, the other tends to increase as well.
- ⬅️ Negative correlations indicate that as one variable increases, the other tends to decrease.
- 🔄 Zero correlation implies no relationship between the variables, often appearing as a random scatter of data points.
- 🚫 Correlation does not imply causation; seeing a correlation between two variables does not mean one causes the other.
- ❗️ Misinterpreting correlations as causation is known as the causation fallacy and is a common mistake in interpreting research findings.
- 📈 The next video will discuss experimental research designs, which allow for making causal inferences about the world.
Q & A
What is a correlational study in research?
-A correlational study is a type of research design used to examine the extent to which two variables are correlated with each other, meaning there is an association or relationship between them.
Why might a developmental psychologist be interested in a correlational study?
-A developmental psychologist might be interested in a correlational study to explore relationships between variables such as marital satisfaction and parenting quality or ability.
What are some real-world applications of correlational studies?
-Real-world applications of correlational studies include conducting surveys online to find relationships between variables like creativity and academic performance.
How does a correlational study typically differ from a case study in terms of participants?
-A correlational study typically involves a large number of participants, unlike a case study which focuses on an in-depth examination of a single individual or a small group.
What is the end result of a correlational study?
-The end result of a correlational study is a correlation, which is a measure of the strength and direction of a relationship between two variables.
Who invented the Pearson correlation coefficient?
-The Pearson correlation coefficient was invented by Karl Pearson.
What are the two important aspects to consider when interpreting a correlation coefficient?
-The two important aspects to consider when interpreting a correlation coefficient are the magnitude, which describes the strength of the relationship, and the valence, which indicates the direction or nature of the relationship.
What does a correlation close to zero indicate?
-A correlation close to zero indicates a weak relationship between the two variables, suggesting there is little to no association.
How is a positive correlation represented graphically?
-A positive correlation is represented graphically on a scatterplot by a line that goes from the bottom left to the top right, indicating that as one variable increases, the other also tends to increase.
What does a negative correlation imply?
-A negative correlation implies that as one variable increases, the other variable tends to decrease, indicating an inverse relationship between the two variables.
Why is it incorrect to assume causation from a correlation?
-It is incorrect to assume causation from a correlation because correlation only indicates a relationship between two variables, not that one variable causes the other to change. This mistake is known as the causation fallacy.
What is the difference between a positive, negative, and zero correlation?
-A positive correlation indicates that both variables increase or decrease together, a negative correlation indicates that as one variable increases, the other decreases, and a zero correlation indicates no discernible relationship between the variables.
Outlines
🔍 Introduction to Correlational Studies
This paragraph introduces the concept of correlational studies, a research design frequently used in psychology. It explains that the purpose of a correlational study is to examine the extent to which two variables are correlated, using interchangeable terms like 'association' or 'relationship'. The paragraph provides examples of research questions suitable for this design, such as the relationship between marital satisfaction and parenting quality, or creativity and academic performance. It also outlines the methodology of conducting such studies, which can involve surveys or lab-based assessments with multiple participants. The paragraph concludes with an introduction to Pearson's correlation coefficient, a statistical measure used to quantify the correlation between two variables. The focus is on interpreting the correlation coefficient, or r-value, which is crucial for understanding the strength and direction of the relationship between variables.
📊 Understanding Correlation Coefficients
This paragraph delves into the interpretation of correlation coefficients, emphasizing the importance of understanding their magnitude and valence. The magnitude indicates the strength of the relationship, with values closer to 1 or -1 representing stronger relationships, while values close to zero indicate weaker relationships. The valence, or direction of the relationship, can be positive, negative, or zero, indicating whether the variables increase or decrease together, or show no relationship at all. The paragraph uses visual examples, such as scatterplots, to illustrate positive and negative correlations. Positive correlations are shown where an increase in one variable is associated with an increase in the other, exemplified by the relationship between height and weight. Negative correlations are demonstrated with the relationship between hours of sleep and tiredness, where more sleep is associated with less tiredness. The paragraph also cautions against the common mistake of inferring causation from correlation, a fallacy known as the 'causation fallacy'. It provides examples of headlines that incorrectly imply causation from correlated data, highlighting the need for skepticism and consideration of alternative explanations when interpreting correlations.
Mindmap
Keywords
💡Correlation
💡Correlational Study
💡Association
💡Relationship
💡Marital Satisfaction
💡Parenting Quality
💡Creativity
💡Academic Performance
💡Pearson's Correlation
💡Correlation Coefficient
💡Causation Fallacy
Highlights
Correlational studies are used to examine the extent to which two variables are correlated.
The terms 'association' and 'relationship' are used interchangeably with 'correlation'.
A correlational study might investigate if higher marital satisfaction leads to better parenting.
Another example is whether creativity is linked to better academic performance.
Correlational research can be conducted with many participants in a lab setting.
Participants might be assessed for creativity and asked about their GPA to find a correlation.
The result of a correlational study is a correlation coefficient, often measured by Pearson's r.
Correlation coefficients, or r-values, are crucial for understanding relationships between variables.
Correlations must be between -1 and 1, with values closer to 1 indicating stronger relationships.
A positive correlation indicates that as one variable increases, the other tends to increase as well.
A negative correlation suggests that as one variable increases, the other tends to decrease.
A zero correlation implies no relationship between the variables.
Scatterplots are used to visually represent the data in correlational studies.
Positive correlations are shown on a scatterplot as points that rise from left to right.
Negative correlations are depicted as points that fall from left to right on a scatterplot.
Zero correlations appear as a scatterplot with no clear pattern or trend.
Correlation does not imply causation; one must be cautious not to assume causation from correlation alone.
Misinterpreting correlation as causation is known as the causation fallacy.
Examples of causation fallacy can be found in sensationalist headlines that incorrectly link two variables.
It's important to consider alternative explanations when interpreting correlations.
Upcoming videos will discuss experimental designs that allow for causal inferences.
Transcripts
in this video we're going to talk about
one of the most widely used research
designs particularly in the field of
psychology correlational studies in a
correlational study you simply examine
the extent to which two variables are
correlated with each other there's a lot
of interchangeable words we can use here
whether there's an association between
the two variables whether there's a
relationship between the two variables
but all of these terms simply reflect a
correlation let's start by basically
going over a couple examples of a few
research questions someone might have
that would be appropriate to address
using a correlational research design
first of all are people who have higher
marital satisfaction better parents this
is something for example that a
developmental psychologist might be
interested in but this is great for a
correlational design because we have two
different variables that we want to know
is there a relationship between these
two marital satisfaction and parenting
quality or ability here's another
example do people who are more creative
perform better in school so here we're
looking for a correlation between
creativity and academic performance so
what might this look like in the real
world well there's a lot of ways you can
do a correlational research study you
can do it online via surveys but let's
go over an example of bringing
participants into the lab usually this
is very unlike a case study in that you
will have lots of different participants
so you might have for example a hundred
and fifty participants that all come
into the lab at different times and you
sit them down for like a 30 minute study
okay and in this study for this research
question you might assess their
creativity somehow and then you might
ask them what was your GPA in school to
assess academic performance and the end
result of a correlational study is
always going to be a correlation which
we measure or sort of quantify using
Pearson's are named after the person who
invented it Karl Pearson and I'm going
to focus for the rest of this video or
at least the majority of it on
interpreting a correlation coefficient
interpreting an r-value because you're
so likely
see correlations both throughout your
academic career or if you're past that
point throughout life in general these
are such widely used measures that it's
really important to be able to look at a
correlation and understand what it's
telling you because it does convey a lot
of information so there's two important
things to know or to look at when you're
basically interpreting a correlation
coefficient and r-value the first thing
to pay attention to is the magnitude
which describes the strength of the
relationship so it's important to know
that correlations must always be between
negative 1 and positive 1 these are
numeric values and if you see a
correlation of 36.2 you know something
went horribly wrong and you probably
shouldn't trust that researcher at all
correlations must always be between
negative 1 and positive 1 and here's how
you can assess the magnitude from the
value correlations that are closer to an
absolute value of 1 represent stronger
relationships so if you see a
correlation of you know 0.9 or negative
0.8 both of those represent very strong
relationships between the two variables
in contrast if you see correlations
close to zero for example point zero six
or negative point zero seven those are
both examples of really weak
correlations because they're close to
zero so that's the magnitude of the
relationship really easy to tell that
right off the bat the second thing to
pay attention to is the valence meaning
the charge of the relationship which is
basically just a fancy way of saying the
nature of the relationship what is the
relationship between these two variables
looks like is it positive is it negative
or is it zero and that's what I'm going
to focus on next let's talk about each
intern starting with positive
correlations a positive correlation is
one in which as one variable changes the
other variable tends to change in the
same direction so the two variables are
working together let's take a look at
that graphically what you're seeing here
is a scatterplot it's a type of graph a
visual representation of data in which
each dot on the graph is a single
participant and we have two different
variables we're looking at here we're
looking at weight and we're looking at
height and notice as one variable
changes the other tends to change in the
same direction as height tends to
increase weight tends to increase as
well think about it this makes a lot of
sense people who are really short for
example tend to weigh less whereas
people who are taller tend to weigh more
probably because they're taller now I
will note that you're always gonna have
some exceptions to this rule
correlations describe the relationship
between two variables among you know
lots of different people but there are
always gonna be some exceptions so you
might have somebody who's really short
and heavy you might have someone else
who's really tall but you know thin and
doesn't weigh too much and that's
perfectly fine but the positive
correlation describes the relationship
between the two variables in general
overall across everybody so here this is
a great way to tell what type of
relationship you're looking at on a
graph by trying to draw a line that best
fits the data so here we have a line
that starts from the bottom left and
goes up to the top right and that's an
easy way to tell that this is a positive
correlation all right let's talk for a
minute about negative correlations
because this is where I tend to see the
most mistakes on exams and things like
that and just generally the most
misunderstanding negative correlations
are correlations in which as one
variable changes the other variable
tends to change in the opposite
direction so here the two variables are
sort of working against each other
indirectly in opposite directions so
let's see that visually as well as we
did before
so what you're looking at here is the
correlation the scatterplot the
relationship between hours of sleep and
tiredness well as you would expect as
hours of sleep tends to increase how
tired somebody is tends to decrease and
we can see that here people who got lots
of sleep the night before report being
not very tired whereas people who got
very little sleep the night before tend
to report being very tired which makes a
lot of sense now if we're sort of
graphing the line of best fit between
these two variables you'll see that the
line goes from the top left down to the
bottom right which is a clear giveaway
this is a negative correlation all right
last but not least let's talk about the
zero correlation this correlation is the
easiest to understand because it simply
means no relationship so if you're
looking at the value of the correlation
it's just going to be something close to
zero if you're looking at the
correlation visually on a scatterplot
it's just gonna look like a big blob of
dots where you can't really draw a clear
line that best fits the data there's
really not a great way to do that now
what you're looking at here by the way
is the correlation between the number of
hours of sleep participants got the
night before and their shoe size and
this makes sense that it's a zero
correlation because we have no reason to
predict that people who have bigger feet
for example might sleep better it's just
going to be a zero correlation no
relationship now correlational studies
are awesome and they're really useful
they tell you whether there are
relationships between variables in the
world great information to have but
remember as we talked about in a
previous video when we learned about the
six principles of scientific thinking
correlation does not imply causation
just because you see a correlation
between two variables this doesn't
necessarily mean that those two
variables are causally linked assuming
that that's the case mistakenly is known
as the causation fallacy and this
happens all the time for example here
are some actual newspaper headlines that
make this mistake that make this
causation fallacy
now I'll mention that some of these are
still out there others have been
retracted because they just made such an
egregious mistake and lots of people
found out and it wasn't very pleasant
but all of these headlines are basically
the mistake of taking a correlation and
inferring causation from it here's one
example low self-esteem shrinks brain
all right through all of these I
encourage you to think about alternative
explanations because it's probably not
the case that having low self-esteem
literally makes your brain smaller over
time it might be something else for
example perhaps people who tend to be
taller have more self-esteem and they
probably have bigger brains as a result
just because they're taller and they're
in a bigger body now I'll mention by the
way if you're short don't feel bad
because it's much more important to have
lots of fold
in your brain for intelligence rather
than just the overall size of your brain
otherwise Wales would be the smartest
beings on the planet and they're
probably not but in any case here are
some other examples housework cuts
breast cancer risk certainly no ulterior
motives there right wearing a helmet
puts cyclists at risk suggests research
so on this basis should you decide if
you're gonna cycle not to wear a helmet
no there's an alternative explanation
it's probably the case that cyclists who
are wearing helmets feel more confident
to ride in the middle of the street and
to take riskier moves
whereas cyclists who don't have a helmet
are probably playing it safe because
they know they're under geared here are
two more winning the World Cup lowers
heart attack deaths and finally my
personal favorite eating fish prevents
crime all right so if you want to be
safe and not engaged in any criminal
activity definitely eat a lot of fish
okay all right so in any case
correlations are useful but make sure to
take them with a grain of salt now in
our next video we're going to talk about
the first research design that finally
allows us to make causal inferences
about the world experiments
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