7.1: Correlational studies

Question 1

Experiments look for what?

Answer 1

Experiments look for a difference between 2 conditions of an IV

Question 2

Experiments look for a difference between 2 conditions of an IV, while correlational studies involve what?

Answer 2

Experiments look for a difference between 2 conditions of an IV, while correlational studies involve measuring the:
1. Strength
2. Direction of relationships
between co-variables

Question 3

Experiments look for a difference between 2 conditions of an IV, while correlational studies involve measuring the strength and direction of relationships between co-variables.
Example

Answer 3

For example, Holland’s (1967) study of the relationship between:

1. Locus of control
2. Obedience

Question 4

A positive correlation occurs where what?

Answer 4

A positive correlation occurs where one co-variable increases, as another co-variable increases

Question 5

A positive correlation occurs where one co-variable increases, as another co-variable increases.
Example

Answer 5

For example, ice cream sales increase as the temperature increases

Question 6

A negative correlation occurs where what?

Answer 6

A negative correlation occurs where one co-variable increases, while another co-variable decreases

Question 7

A negative correlation is where one co-variable increases, while another co-variable decreases.
Example

Answer 7

For example, raincoat sales decrease as sunny weather increases

Question 8

Co-variables

Answer 8

Co-variables are the variables investigated in a correlation

Question 9

Co-variables are the variables investigated in a correlation.
They are not referred to as the independent and dependent variables, because what?

Answer 9

The variables investigated in a correlation are not referred to as the independent and dependent variables, because the study is:

1. Investigating the relationship between them
2. Not trying to show a cause and effect relationship

Question 10

Scattergraphs are also known as what?

Answer 10

Scattergraphs are also known as scattergrams

Question 11

Scattergraphs

Answer 11

Scattergraphs are a type of graph used to display the extent to which 2 variables are correlated

Question 12

Advantages of correlational analysis:

1. Allows what to be made?

Answer 12

An advantage of correlational analysis is that it allows predictions to be made

Question 13

Advantages of correlational analysis:
1. Allows predictions to be made.
Predictions can be made from correlations, like predicting what?

Answer 13

Predictions can be made from correlations, like predicting the number of ice creams that will be sold on hot days

Question 14

Advantages of correlational analysis:
1. Allows predictions to be made.
Predictions can be made from correlations, like predicting the number of ice creams that will be sold on hot days.
2. Allows what?

Answer 14

An advantage of correlational analysis is that it allows quantification of relationships

Question 15

Advantages of correlational analysis:
1. Allows predictions to be made.
Predictions can be made from correlations, like predicting the number of ice creams that will be sold on hot days.
2. Allows quantification of relationships.
Correlations show what?

Answer 15

Correlations show the strength of relationship between 2 co-variables

Question 16

Advantages of correlational analysis:
1. Allows predictions to be made.
Predictions can be made from correlations, like predicting the number of ice creams that will be sold on hot days.
2. Allows quantification of relationships.
Correlations show the strength of relationship between 2 co-variables.
What does a correlation of +0.9 (90% similarity) indicate?

Answer 16

A correlation of +0.9 (90% similarity) indicates a high positive correlation

Question 17

Advantages of correlational analysis:
1. Allows predictions to be made.
Predictions can be made from correlations, like predicting the number of ice creams that will be sold on hot days.
2. Allows quantification of relationships.
Correlations show the strength of relationship between 2 co-variables.
What does a correlation of -0.1 (10% similarity) indicate?

Answer 17

A correlation of -0.1 (10% similarity) indicates a weak negative correlation

Question 18

Advantages of correlational analysis:
1. Allows predictions to be made.
Predictions can be made from correlations, like predicting the number of ice creams that will be sold on hot days.
2. Allows quantification of relationships.
Correlations show the strength of relationship between 2 co-variables.
A correlation of -0.1 (10% similarity) indicates a weak negative correlation.
3. No what?

Answer 18

An advantage of correlational analysis is no manipulation

Question 19

Advantages of correlational analysis:
1. Allows predictions to be made.
Predictions can be made from correlations, like predicting the number of ice creams that will be sold on hot days.
2. Allows quantification of relationships.
Correlations show the strength of relationship between 2 co-variables.
A correlation of -0.1 (10% similarity) indicates a weak negative correlation.
3. No manipulation.
Correlations do not require what?

Answer 19

Correlations do not require manipulation of variables

Question 20

Advantages of correlational analysis:
1. Allows predictions to be made.
Predictions can be made from correlations, like predicting the number of ice creams that will be sold on hot days.
2. Allows quantification of relationships.
Correlations show the strength of relationship between 2 co-variables.
A correlation of -0.1 (10% similarity) indicates a weak negative correlation.
3. No manipulation.
Correlations do not require manipulation of variables and so can be used where what?

Answer 20

Correlations:

1. Do not require manipulation of variables
2. So can be used where carrying out an experiment may be unethical

Question 21

Weaknesses of correlational analysis:

1. What problem?

Answer 21

A weakness of correlational analysis is a quantification problem

Question 22

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be what?

Answer 22

Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high

Question 23

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always what?

Answer 23

Correlations that:
1. Appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high
,while:
2. Seem high, for example +0.76, are not always statistically significant

Question 24

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always statistically significant.
2. C and e?

Answer 24

A weakness of correlational analysis is:

1. Cause
2. Effect

Question 25

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always statistically significant.
2. Cause and effect.
Correlations do not show causality, because what?

Answer 25

Correlations do not show causality, because they’re not done under controlled conditions

Question 26

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always statistically significant.
2. Cause and effect.
Correlations do not show causality, because they’re not done under controlled conditions.
Therefore, we cannot say what?

Answer 26

Therefore, we cannot say that one co-variable has caused the other

Question 27

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always statistically significant.
2. Cause and effect.
Correlations do not show causality, because they’re not done under controlled conditions.
Therefore, we cannot say that one co-variable has caused the other.
3. What relationships?

Answer 27

A weakness of correlational analysis is extraneous relationships

Question 28

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always statistically significant.
2. Cause and effect.
Correlations do not show causality, because they’re not done under controlled conditions.
Therefore, we cannot say that one co-variable has caused the other.
3. Extraneous relationships.
Other variables may do what?

Answer 28

Other variables may influence the co-variables

Question 29

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always statistically significant.
2. Cause and effect.
Correlations do not show causality, because they’re not done under controlled conditions.
Therefore, we cannot say that one co-variable has caused the other.
3. Extraneous relationships.
Other variables may influence the co-variables.
Example

Answer 29

For example:
1. Many holidays are taken in the summertime
2. People eat ice creams on holiday
,therefore, the variable ‘holiday’ is related to both temperature and ice cream sales

Question 30

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always statistically significant.
2. Cause and effect.
Correlations do not show causality, because they’re not done under controlled conditions.
Therefore, we cannot say that one co-variable has caused the other.
3. Extraneous relationships.
Other variables may influence the co-variables.
4. Only works for what?

Answer 30

A weakness of correlational analysis is that it only works for linear relationships

Question 31

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always statistically significant.
2. Cause and effect.
Correlations do not show causality, because they’re not done under controlled conditions.
Therefore, we cannot say that one co-variable has caused the other.
3. Extraneous relationships.
Other variables may influence the co-variables.
4. Only works for linear relationships.
Correlations only measure what?

Answer 31

Correlations only measure linear (straight-line) relationships

Question 32

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always statistically significant.
2. Cause and effect.
Correlations do not show causality, because they’re not done under controlled conditions.
Therefore, we cannot say that one co-variable has caused the other.
3. Extraneous relationships.
Other variables may influence the co-variables.
4. Only works for linear relationships.
Correlations only measure linear (straight-line) relationships.
Example

Answer 32

For example, correlations cannot show the relationship between:
1. Temperature
2. Aggression
,because it is curvilinear (not a straight line)

Question 33

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always statistically significant.
2. Cause and effect.
Correlations do not show causality, because they’re not done under controlled conditions.
Therefore, we cannot say that one co-variable has caused the other.
3. Extraneous relationships.
Other variables may influence the co-variables.
4. Only works for linear relationships.
Correlations only measure linear (straight-line) relationships.
For example, correlations cannot show the relationship between temperature and aggression, because it is curvilinear (not a straight line).
As temperature increases, what happens?

Answer 33

As temperature increases, aggression levels increase up to an optimum point

Question 34

Weaknesses of correlational analysis:
1. Quantification problem.
Correlations that appear low, for example +0.28, can sometimes be significant (meaningful) if the number of scores is high, while correlations that seem high, for example +0.76, are not always statistically significant.
2. Cause and effect.
Correlations do not show causality, because they’re not done under controlled conditions.
Therefore, we cannot say that one co-variable has caused the other.
3. Extraneous relationships.
Other variables may influence the co-variables.
4. Only works for linear relationships.
Correlations only measure linear (straight-line) relationships.
For example, correlations cannot show the relationship between temperature and aggression, because it is curvilinear (not a straight line).
As temperature increases, aggression levels increase up to an optimum point.
Then any further increase in temperature leads to what?

Answer 34

Then any further increase in temperature leads to a decline in aggression levels

Question 35

With correlations, why is there no IV or DV?

Answer 35

With correlations, there is no IV or DV, because:

1. No experiment has been conducted
2. The data has simply been compared and a relationship has become apparent

Question 36

Even when a relationship is shown between 2 co-variables, why do we need to be cautious about our conclusions?

Answer 36

Even when a relationship is shown between 2 co-variables, we need to be cautious about our conclusions, because:

1. No experiment has taken place
2. So we have not controlled extraneous variables

Question 37

What does the number +1 represent?

Answer 37

The number +1 represents a perfect positive correlation

Question 38

The number +1 (what )represents a perfect positive correlation?

Answer 38

The number +1 (maximum strength) represents a perfect positive correlation

Question 39

The number +1 (maximum strength) represents a perfect positive correlation.
What does 0 mean?

Answer 39

0 means no correlation

Question 40

The number +1 (maximum strength) represents a perfect positive correlation.
0 means no correlation.
The nearer the number is to +1 or -1, what?

Answer 40

The nearer the number is to:
1. +1
Or,
2. -1
,the stronger the correlation

Question 41

The number +1 (maximum strength) represents a perfect positive correlation.
0 means no correlation.
The nearer the number (what) is to +1 or -1, the stronger the correlation?

Answer 41

The nearer the number (the correlation coefficient) is to:
1. +1
Or,
2. -1
,the stronger the correlation

Question 42

Because experiments are about difference and correlations are about relationships, we write hypotheses for experiments and correlations differently.
In an experiment you might compare how confident tall people are compared to short people - to see what?

Answer 42

In an experiment you might compare how confident tall people are compared to short people - to see if there’s a difference in confidence

Question 43

Because experiments are about difference and correlations are about relationships, we write hypotheses for experiments and correlations differently.
In an experiment you might compare how confident tall people are compared to short people - to see if there’s a difference in confidence.
So the alternate hypothesis reflects this difference.
Example

Answer 43

For example, there will be a difference in how confident tall people are compared to short people

Question 44

Because experiments are about difference and correlations are about relationships, we write hypotheses for experiments and correlations differently.
In an experiment you might compare how confident tall people are compared to short people - to see if there’s a difference in confidence.
So the alternate hypothesis reflects this difference.
For example, there will be a difference in how confident tall people are compared to short people.
Correlations look for relationships between 2 variables.
The hypothesis reflects this.
Example

Answer 44

For example, there is a relationship between:

1. Height
2. Confidence

Question 45

In correlations, a non-directional hypothesis is a hypothesis that says that there will be a relationship between 2 variables, but what?

Answer 45

In correlations, a non-directional hypothesis is a hypothesis that:
1. Says that there will be a relationship between 2 variables
,but
2. Does not say what the direction of the relationship will be - it doesn’t say whether it will be a positive or negative correlation

Question 46

In correlations, a directional hypothesis is a hypothesis that says that there will be a relationship between 2 variables and does say what the direction of the relationship will be - it does say what?

Answer 46

In correlations, a directional hypothesis is a hypothesis that:

1. Says that there will be a relationship between 2 variables
2. Does say what the direction of the relationship will be - it does say whether the correlation will be positive or negative

Question 47

In correlations, what is the researcher looking at?

Answer 47

In correlations, the researcher is looking at relationships

Question 48

In correlations, the researcher is looking at relationships, but any extraneous variables are not controlled.
What does this mean?

Answer 48

This means that a third unknown variable could be causing the results

Question 49

In correlations, the researcher is looking at relationships, but any extraneous variables are not controlled.
This means that a third unknown variable could be causing the results.
Therefore, what?

Answer 49

Therefore, correlations cannot establish cause and effect relationships - they can only establish relationships