Correlation Research

Summary

Professor Doug Crawl from Northern Kentucky University discusses correlational research, which explores the association between two variables. He explains that correlation aids in prediction, using ACT scores and college performance as an example. Crawl clarifies that correlation values range from -1 to 1, indicating both the direction and strength of the relationship. He emphasizes that correlation does not imply causation, highlighting the importance of not confusing correlation with causative relationships. The lecture also touches on the direction problem and the third variable problem, using self-esteem and GPA, as well as ice cream sales and assaults, to illustrate these concepts.

Takeaways

• 🔍 Correlational research aims to discover if two variables are associated or related.
• 📊 A variable is a characteristic that varies among individuals, such as age or self-esteem levels.
• 🧐 Knowing the correlation between two variables can help in making predictions about one variable when the other is known.
• 📉 Correlations range from -1 to 1, with -1 indicating a perfect negative correlation, 1 indicating a perfect positive correlation, and 0 indicating no correlation.
• 🔼 Positive correlations suggest that as one variable increases, the other also increases, while negative correlations suggest an inverse relationship.
• 📈 The magnitude of the correlation coefficient indicates the strength of the relationship between the variables.
• 🚫 Correlation does not imply causation; just because two variables are correlated does not mean one causes the other.
• ❓ The direction problem in correlations arises when it's unclear whether variable A causes B or B causes A.
• 🔄 The third variable problem occurs when two variables are correlated not because they directly affect each other, but because they are both influenced by a third variable.
• 🧪 To determine causality, experiments are necessary rather than relying solely on correlational data.

Q & A

What is correlational research?

Correlational research is a type of study that seeks to discover if two variables are associated or related in some way.

What is a variable in the context of research?

A variable is a characteristic that everyone has but with different values among individuals, such as age or levels of self-esteem.

Why is it important to understand correlations?

Understanding correlations is important for prediction. If two variables are correlated, knowing one allows us to make an educated guess about the other.

Give an example of a correlation mentioned in the script.

An example of a correlation given in the script is between ACT scores and college performance.

What is the range of correlation coefficients?

Correlation coefficients range from -1 to 1, where -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and 0 indicates no correlation.

What does the direction of a correlation indicate?

The direction of a correlation indicates whether the variables increase or decrease together. A positive correlation means they rise and fall together, while a negative correlation means they have a reciprocal relationship.

What does the magnitude of a correlation tell us?

The magnitude of a correlation, ignoring the sign, tells us how strongly related the variables are and informs us about the confidence we can have in our predictions.

Why can't we infer causality from correlation?

We cannot infer causality from correlation because correlation only indicates an association between variables, not a cause-and-effect relationship.

What is the 'direction problem' in correlation studies?

The 'direction problem' refers to the issue of determining whether variable A causes variable B, variable B causes variable A, or if there is a bidirectional relationship.

What is the 'third variable problem' in correlation studies?

The 'third variable problem' occurs when two variables are correlated not because they directly cause each other, but because a third variable influences both of them.

What does the example of ice cream sales and assaults illustrate about correlations?

The example of ice cream sales and assaults illustrates that two variables can be correlated due to a third variable (heat), and this correlation does not imply causation between the two variables themselves.

Outlines

00:00

🔍 Understanding Correlational Research

Doug Craw, a professor at Northern Kentucky University, introduces correlational research, which aims to identify associations between two variables. Variables are characteristics that vary among individuals, such as age or self-esteem. The purpose of studying correlations is to make predictions; knowing one variable can help us estimate another. For instance, there's a correlation between ACT scores and college performance, suggesting that higher ACT scores are generally indicative of better college performance. However, this is not a perfect rule, as exceptions exist. Craw emphasizes that while correlations can range from -1 to 1, indicating the strength and direction of the relationship, they do not imply causation. A positive correlation means variables increase or decrease together, while a negative correlation implies an inverse relationship. The magnitude of the correlation indicates the strength of the relationship and the reliability of predictions based on it. Despite their usefulness, correlations must be interpreted with caution, as they do not provide insight into causality.

05:01

🤔 The Limitations of Correlations in Determining Causality

This paragraph delves into the limitations of using correlations to determine causality. Craw discusses how correlations can suggest a relationship between variables but do not prove that one causes the other. He uses the example of self-esteem and GPA, which may be correlated, but it's unclear whether self-esteem leads to higher GPA or vice versa. The 'direction problem' arises because it's challenging to determine the cause-and-effect relationship solely from correlation data. The 'third variable problem' further complicates matters, as two variables might be correlated not because they directly influence each other but because a third, unseen variable influences both. Craw gives the example of ice cream sales and assault rates, which correlate positively across months but are actually both influenced by temperature. This illustrates how correlations can be misleading without considering other potential factors. The paragraph concludes by stating that to understand causality, one must conduct experiments rather than relying solely on correlational data.

Keywords

💡Correlational Research

Correlational research is a method used in the field of psychology and other sciences to investigate the relationship between two variables. It aims to determine whether there is an association between them. In the context of the video, the professor explains that correlational research seeks to find out if two variables are related in some way, which is crucial for making predictions. For example, the professor discusses the correlation between ACT scores and college performance, indicating that a higher ACT score generally correlates with better college performance.

💡Variables

In research, a variable is a characteristic that can take on different values across individuals. The video script uses age and self-esteem as examples, where everyone has an age and a level of self-esteem, but these values vary from person to person. Variables are central to correlational research as they are the elements being analyzed for potential relationships or associations.

💡Prediction

Prediction in the context of the video refers to the ability to make an educated guess about the value of one variable based on the knowledge of another variable with which it is correlated. The professor emphasizes the importance of correlations for prediction, as knowing one variable can provide insights into the likely value of another. This is exemplified by the relationship between ACT scores and college performance.

💡Correlation Coefficient

The correlation coefficient is a statistical measure that ranges from -1 to 1, indicating the strength and direction of a relationship between two variables. In the script, the professor clarifies that the coefficient must be a number within this range, and it cannot be a value like '227' or 'a platypus', which would be incorrect. The coefficient's value indicates how closely the variables are related and in which direction the relationship goes.

💡Positive Correlation

A positive correlation implies that as one variable increases, the other variable also increases. The video uses the example of studying hours and exam performance, suggesting that more hours of studying are likely associated with better exam performance. This concept is important in understanding how changes in one variable can be associated with changes in another.

💡Negative Correlation

A negative correlation is present when one variable increases while the other decreases. The script gives the example of classes skipped and exam performance, indicating that as the number of classes skipped increases, exam performance tends to decrease. This shows an inverse relationship between the two variables.

💡Magnitude

Magnitude in the context of correlation refers to the strength of the relationship between two variables, disregarding the direction. The professor explains that a high magnitude (e.g., +0.8 or -0.8) indicates a strong relationship, while a low magnitude (e.g., +0.1 or -0.1) suggests a weak relationship. This is important for understanding the reliability of predictions based on the correlation.

💡Causation

Causation refers to a cause-and-effect relationship between variables, where a change in one variable leads to a change in another. The video script emphasizes that correlation does not imply causation. Even if two variables are correlated, it does not necessarily mean that one causes the changes in the other. This is a critical concept in research as it highlights the limitations of correlational studies in determining causality.

💡Direction Problem

The direction problem in correlational research is the challenge of determining whether variable A causes changes in variable B, or if it's the other way around, or if there is a reciprocal relationship. The script uses the example of self-esteem and GPA, where it's unclear whether higher self-esteem leads to better GPA or if a higher GPA leads to increased self-esteem.

💡Third Variable Problem

The third variable problem occurs when two variables are correlated not because one causes the other, but because both are influenced by a third, unseen variable. The professor gives the example of ice cream sales and assaults being correlated, but both are actually influenced by temperature. This problem illustrates the complexity of interpreting correlations and the potential for misleading conclusions if not properly considered.

Highlights

Correlational research aims to discover if two variables are associated or related.

A variable is a characteristic that everyone has but with different values, such as age or self-esteem.

Correlations are used for prediction, allowing educated guesses about one variable based on knowledge of another.

ACT scores and college performance are an example of correlated variables.

Correlations range from -1 to 1, indicating the strength and direction of the relationship between variables.

A positive correlation indicates variables rise and fall together, while a negative correlation suggests a reciprocal relationship.

The magnitude of correlation, ignoring the sign, indicates the strength of the relationship.

Highly correlated variables allow for more confident predictions, whereas weak correlations provide less certainty.

A correlation of zero indicates no relationship between variables.

Correlation does not imply causation; knowing two variables are correlated does not reveal the causal relationship.

The direction problem in correlations arises when it's unclear whether one variable causes the other or vice versa.

The third variable problem suggests that two correlated variables might be related due to a third, underlying variable causing both.

An example of the third variable problem is the correlation between ice cream sales and assaults, which both peak in the summer due to heat.

To determine causality, an experiment is needed rather than relying solely on correlational data.

Transcripts

my name is Doug crawl I'm a professor in

the department of psychological science

at Northern Kentucky University and I'm

going to talk to you about correlational

research correlational research seeks to

discover if two variables are associated

or related in some way now a variable is

a characteristic that everyone has but

different people have different values

so for example everyone has an age but

different people have different ages

everyone has a level of self-esteem but

different people have different levels

of

self-esteem now one of the main reasons

that we want to know about correlations

is for prediction that if two variables

are correlated then that means knowing

one allows us to take an educated guess

about what the other one is likely to be

for example something like ACT scores

and college performance there's a

correlation between ACT scores and

college performance so if we know a

person's ACT score that gives us a clue

about what the person's College

performance it's likely to be now it's

not perfect sometimes people may have a

relatively low ACT score and end up

doing brilliantly in college and

sometimes people have a really high ACT

score and end up doing not so well in

college but as a general rule ACT score

tells us something about what their

college performance is likely to be all

right well correlations range from one

to minus one got to be a number

somewhere in that range so if we

calculate the correlation it turns out

to be you know uh 227 or a platypus or

by low cell High then something's wrong

because it can't be that it's got to be

a number somewhere from one to minus one

now we could break that down into a

couple of aspects one is the direction

is it a positive number or a negative

number a positive correlation like

positive. 53 or positive. 27 that

indicates that the variables rise and

fall together so as one variable

increases then the other variable also

increases if one of them is falling then

the other one uh tends to be low as well

so for example something like uh hour

studying and exam performance we would

expect that uh as the number of ours

studied goes up that exam performance

would also tend to go up if uh hour

studying is low then exam performance

would also tend to be low on the other

hand a negative correlation means that

they have a reciprocal relationship

whereas one of them increases the other

one tends to fall or if one of them is

low then the other one tends to be high

maybe something like uh classes skipped

and exam performance we would expect

that as the number of classes skipped

goes up that exam performance would tend

to be lower to the degree that people

don't skip classes then we would expect

exam performance to be higher so that's

the direction but then there's also the

magnitude that is how strongly uh

related are they ignoring the sign so

two variables could be very strongly

correlated like positive point8 or

negative .8 or they could be only weekly

correlated like positive .1 or NE .1 and

the magnitude kind of is it tells us

something about how strongly related

they are and so it tells us something

about how confident we can be in our

prediction if two things are highly

correlated The Knowing one really tells

us a lot about what the other one is

likely to be if they're only weakly

correlated then it tells us relatively

little about what the other one is

likely to be and of course if they're

correlated zero then it tells us nothing

about what the other one is likely to

be but probably the most important thing

to keep in mind with regard to

correlations is that correlation is not

causation that is if we know that two

variables are correlated that doesn't

tell us anything about what the causal

relationship between those variables

might be uh so for example let's suppose

we do a study here at NKU and uh we

discover that self-esteem and GPA are

positively correlated maybe positive. 35

and so if you as you might recall that

means that as one of them Rises the

other one tends to rise if one of them

is low than the other one tends to be

low and let's suppose someone hears

about that and maybe writes an article

in a newspaper somewhere and says study

reveals that self-esteem and GPA are

positively correlated therefore if we

want people's grades to be higher what

we need to do is build up everyone's

level of self-esteem now that would work

if the reason that GPA and self-esteem

are correlated is that esteem is driving

GPA then that would mean that any

changes that occur on self-esteem those

should cause changes to occur on GPA

but that might not necessarily be the

reason that they're correlated there's

at least a couple of problems that we

need to address uh one of them is the

direction problem the direction problem

is that it could be that self-esteem is

causing GPA but it could also be that

GPA is causing self-esteem and if that

second one is true then that would mean

that raising self-esteem won't do

anything to GPA because GPA is the one

causing self-esteem and uh we really

have no way of determining which of

those two things might be it could be

either one now some of you might be

thinking that sometimes we could

probably be fairly confident in ruling

out one of those two directions so for

example before we knew that smoking

caused lung cancer we probably knew that

they were correlated so you can imagine

a a researcher sitting there thinking

about this like let's see H smoking and

lung cancer are positively correlated

could it be that getting lung cancer

causes people to start smoking and

doesn't seem very likely and so it's

probably the other way and that sort of

reasoning of if we could just eliminate

One Direction then we would know that it

was the other one that sort of reasoning

would work if this was the only problem

but it's not the only problem we also

have the third variable problem that is

if two variables are correlated it could

be that neither one causes the other but

rather they're correlated because some

third variable causes both so for

example with regard to self-esteem and

GPA maybe uh good nutrition

tends to cause people to feel good about

themselves have higher esteem and also

tends to cause them to have higher

grades except it might not be nutrition

maybe it's U getting enough sleep maybe

when people get enough sleep then that

tends to cause them to have higher

esteem and also to have higher grades

except it might not be that maybe it's a

a supportive family to the degree that

people have a supportive family maybe

that causes them to have a higher esteem

and also higher grades and of course we

could you know sit here all day trying

to think about different things it could

possibly be and there's really no way to

know let me give you my favorite example

of the third variable problem I read

this years ago I'm not sure exactly

where it was but apparently ice cream

sales vary according to the month of the

year they tend to be relatively low in

January and then they go up and up and

up and they tend to Peak somewhere

around August and then they tend to

decline and be relatively low again in

December same sort of thing for assaults

like aggravated assaults tend to be

relatively low in January and then they

go up and up and up Peak somewhere

around August and then they tend to

decline and be relatively low in

December and so as a result those two

things are positively correlated they

tend to rise and fall together now can

imagine someone thinking about the

direction of that they might think let's

see is it the case that when people uh

beat somebody up they think you know it

would be a great way to top off this

evening big bowl of ice

cream I don't think that's it maybe it's

that when they uh eat a big bowl of ice

cream then they get that brain freeze

thing and that makes them mad and so

they decide to haul up and punch someone

in the nose no that doesn't make sense

to me either and you could probably

guess what the third variable probably

is it's probably heat that to the degree

that it gets hot then that tends to

cause ice cream sales to go up that also

tends to cause assaults to go up as well

so with a correlation we can't tell

anything about causality and yet we

often want to know something about cause

and so correlations are not going to

help us if we want to know about cause

we're going to have to do an experiment

o