Chapter 12: Correlation Flashcards
What is correlation:
A: Correlation is a measure of causation, indicating the direct influence of one variable on another in a statistical analysis.
B: Correlation is a common statistical technique for
describing associations or trends between two
variables
C: Correlation is exclusively used to determine the strength of relationships between multiple variables within a dataset, disregarding any trends or associations between just two variables.
B: Correlation is a common statistical technique for
describing associations or trends between two
variables
The use of “correlation” conveys the idea that two
events tend to happen together (if A happens, then B happens).
Pearson’s correlation is the most common correlation in psychology research, when should you use it?
A: Correlation is used to evaluate an association
between two categorical variables
B: Correlation is used to evaluate an association
between two numeric variables
C: Correlation is used to evaluate an association
between a categorical variable and a
numeric variable (e.g., treatment vs. control
group comparison).
B: Correlation is used to evaluate an association
between two NUMERIC variables
> The SAMPLE Pearson’s correlation is denoted as r
REVIEW: Which test is used to measure the relationship between one categorical variable and one numeric variable? Select all that apply:
A. ANOVA
B. Independent-sample t-test
C. Correlation
A. ANOVA
B. Independent-sample t-test
True or false: Scale scores are numerical variables?
A: True
B: False
A: True
How many scores does correlation require from each participant:
A: 1
B: 2
C: It varies from study to study
B: 2
> AND both must be numeric!
What is a scatterplot?
A: A scatterplot is a graphical representation used exclusively for categorical data, where each category is represented by a distinct shape or symbol on a coordinate system.
B: Scatterplots are only applicable when there is a perfect linear relationship between variables, and they cannot be used to visualize any other type of association or pattern.
C: A scatterplot helps visualize a correlation.
One variable will be on the horizontal
axis (x-axis) and the other variable will be on the
vertical axis (y-axis). A dot denotes the location of each score pair in a coordinate system.
C: A scatterplot helps visualize a correlation.
One variable will be on the horizontal
axis (x-axis) and the other variable will be on the
vertical axis (y-axis). A dot denotes the location of each score pair in a coordinate system.
Anatomy Of A Scatterplot - ADD THIS SLIDE TO YOUR CHEAT SHEET (SLIDE 11)
Anatomy Of A Scatterplot - ADD THIS SLIDE TO YOUR CHEAT SHEET (SLIDE 11)
What are the characteristics of a correlation:
A:
1. Correlations have the same direction and strength
2. The direction can be positive or negative
3. The strength of a correlation can range from
weak (or nonexistent) to strong (or perfect)
B:
1. Correlations have the same direction but differ in strength
2. The direction is almost always positive
3. The strength of a correlation can range from
weak (or nonexistent) to strong (or perfect)
C:
1. Correlations differ in direction and strength
2. The direction can be positive or negative
3. The strength of a correlation can range from
weak (or nonexistent) to strong (or perfect)
C:
1. Correlations differ in direction and strength
2. The direction can be positive or negative
3. The strength of a correlation can range from
weak (or nonexistent) to strong (or perfect)
Describe a positive correlation:
A: Scores on the two variables tend to move in the
same direction or follow the same trend (a high score on one variable tends to be associated with a high score on the other variable, and a low score tends to be paired with a low score).
B: In a positive correlation, scores on the two variables move in opposite directions, meaning a high score on one variable is associated with a low score on the other variable.
C: A positive correlation indicates that there is no discernible relationship between the two variables, and scores on one variable do not influence or predict scores on the other variable.
A: Scores on the two variables tend to move in the
same direction or follow the same trend (a high score on one variable tends to be associated with a high score on the other variable, and a low score tends to be paired with a low score).
FOR EXAMPLE:
More time studying = higher exam grade
Less time studying = lower exam grade
SHE MIGHT ALSO SAY:
A high score on the independent variable tends to be associated with a high score on the dependent variable
> You’ll see more dots in the green areas of the “Anatomy Of A Scatterplot” image on slide 11
> If you think it will be helpful, add the image from slide 26 to your cheat sheet!!!
Describe a negative correlation:
A: In a negative correlation, scores on the two variables move in the same direction, meaning a high score on one variable is associated with a high score on the other variable.
B: A negative correlation implies that there is no relationship between the two variables, and the scores on one variable have no impact on or connection to the scores on the other variable.
C: Scores on the two variables tend to move in the
opposite direction or follow the opposite trend (a high score on one variable tends to be associated with a low score on the other variable (and vice versa).
C: Scores on the two variables tend to move in the
opposite direction or follow the opposite trend (a high score on one variable tends to be associated with a low score on the other variable (and vice versa).
FOR EXAMPLE:
More unhealthy food = less healthy
Less unhealthy food = more healthy
SHE MIGHT ALSO SAY:
A high score on the independent variable tends to be associated with a low score on the dependent variable
> You’ll see more dots in the red areas of the “Anatomy Of A Scatterplot” image on slide 11
> If you think it will be helpful, add the image from slide 28 to your cheat sheet!!!
Pearson’s correlation quantifies the magnitude
of a correlation on a _____ to ______ scale:
A: 1, -1
B: Linear
C: 0, 1
C: 0, 1
> POSITIVE correlations range between 0 (no
correlation) and 1 (perfect positive correlation)
> NEGATIVE correlations range between 0 (no
correlation) and -1 (perfect negative correlation)
Based on the scatterplot from slide 31, is the correlation positive or negative?
A. Positive
B. Negative
A. Positive
True or false: Pearson’s correlation is an effect size. We don’t need to standardize it?
A: True
B: False
A: True
> It’s like Cohen’s D effect size in that respect
> There is no absolute value for Pearson’s correlation
> The range can only be between -1 and 1
> If the value given is exactly on the threshold (.30 for example) go with the larger value (so .30 would be moderate not small).
> When talking about the MAGNITUDE use the absolute value otherwise don’t use the absolute value (the signs only tell you the direction).
!!!ADD SLIDE 34 TO YOUR CHEAT SHEET!!!!
IF YOU’D LIKE TO HAVE SOME VISUAL EXAMPLES OF NEGLIGIBLE-LARGE CORRELATIONS YOU CAN ADD SLIDES 35-39!
IF YOU’D LIKE TO HAVE SOME VISUAL EXAMPLES OF NEGLIGIBLE-LARGE CORRELATIONS YOU CAN ADD SLIDES 35-39!
What values represent Pearson correlation effect size represents a perfect correlation?
A: 1
B: 1.00, -1.00
C: 0
B: 1.00, -1.00
> THERE’S AN IMAGE OF THIS ON SLIDE 39 IF YOU THINK IT WILL BE HELPFUL FOR YOU!
Based on the scatterplot on slide 40, how strong is the correlation?
A. Negligible
B. Small
C. Moderate
D. Large
E. It is hard to tell
A. Negligible
B. Small
After calculating the correlation (represented at “Pearson’s r” on slide 45), how strong is the correlation?
A. Negligible
B. Small
C. Moderate
D. Large
E. It is hard to tell
B. Small
LOOK AT SLIDE 45, IF YOU SEE A LINE (—) ON JAMOVI IT MEANS THERE IS NO CORRELATION BETWEEN THE TWO VARIABLES.
IF YOU SEE AN EMPTY SPACE IT MEANS THAT THE VALUE HAS ALREADY BEEN CALCULATED SOMEWHERE ELSE IN THE CHART!
LOOK AT SLIDE 45, IF YOU SEE A LINE (—) ON JAMOVI IT MEANS THERE IS NO CORRELATION BETWEEN THE TWO VARIABLES.
IF YOU SEE AN EMPTY SPACE IT MEANS THAT THE VALUE HAS ALREADY BEEN CALCULATED SOMEWHERE ELSE IN THE CHART!
Correlation Interpretation
PUT THIS ON YOUR CHEAT SHEET!!!
The sign of the correlation is positive, meaning that higher levels of narcissism are associated with more selfie posts.
The magnitude (absolute value) of the correlation (.141) is consistent with what psychologists consider a small association.
> Always look for two things in an interpretation:
1. The sign
2. The magnitude (absolute value)
> The sign tells direction / absolute value tells you the magnitude
PUT THIS ON YOUR CHEAT SHEET!!!
Here is an incorrect correlation interpretation
PUT THIS ON YOUR CHEAT SHEET!!!
Pearson’s r is not a percentage, it is incorrect to say that there is a 30% relationship, the dependent variable can be predicted with 30% accuracy, etc.
Correlations are not on a ratio scale, so a correlation of .30 is not twice as strong as .15
> No equal distance property
PUT THIS ON YOUR CHEAT SHEET!!!
True or false: A zero correlation implies a lack of
association.
A: True
B: False
B: False
A zero correlation may or may not imply a lack of
association. Pearson’s r cannot quantify nonlinear relations.
Correlation, Prediction & Causation:
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Correlations make it possible to use the value of one variable to predict the value of another.
For example, self-objectification and narcissism predicted time spent on SNSs; fingers stained yellow
predicts the future development of lung cancer.
Any type of correlation can be used to make a prediction. However, correlation does not imply causation (the underlying cause of the relationship), nor does it tell us why two variables are correlated.
> Basically, you can only say that something “predicts” something but never that it’s the “cause” of something.
> Correlation only tells you something about the relationships between two variables
> With this technique you can say that mathematically, A can predict B, and B can predict A
> The underlying reason for a correlation does not matter in making a prediction. As long as the correlation is stable–lasting into the future–one can use it to make predictions.
> A causal conclusion is more difficult than coming up with a prediction and a prediction has nothing to do with a causal conclusion.
PUT THIS ON YOUR CHEAT SHEET!
Suppose we find a positive correlation between marital satisfaction and social support from friends and family. If we know someone’s marital satisfaction, can we predict their social support level?
A. Yes
B. No
A. Yes
Suppose we find a positive correlation between marital
satisfaction and social support from friends and family. What can we conclude?
A. A higher marital satisfaction leads to higher social support
B. A higher social support level leads to higher marital
satisfaction
C. We lack knowledge about the underlying causal
relationship
C. We lack knowledge about the underlying causal
relationship
> “Leads to” means “causes” and we can NEVER say “causes.”
