Q9 Correlation Flashcards
1
Q
Correlation
A
A relationship between two measures that change together
2
Q
“Relationship”
A
- May or not be causal
- Correlation does not imply causation
2
Q
“Measures”
A
In data, measures are known as variables
2
Q
Ways data can “change together”
A
- Increase together
- Decrease together
- Go in opposite directions
3
Q
Correlations are crucial to . . .
A
Science
4
Q
Plausible alternative explanation
A
A rival rationale that might better explain an observed relationship, or a third variable that could be impacting the correlation
5
Q
Variable
A
A unit of measurement that has the potential to vary or change
6
Q
Four types of variables
A
- Nominal
- Ordinal
- Interval
- Ratio
7
Q
Nominal
A
- Means name, like “nombre” in Spanish
- Gives a name to things without any order or rank
8
Q
Ordinal
A
- A nominal variable whose measurements have an order or rank
- The gaps between each rank may not be equal
8
Q
Interval
A
- Ordinal variables where the gaps between each rank are equal
- “Interval” refers to the size of the units
- No true zero
9
Q
Ratio
A
- Measures equal units, such as elapsed time
- Has a true zero
- Zero could also mean the absence of a measurement, like weight
10
Q
Type of variable: Religions
A
Nominal
11
Q
Type of variable: Letter grades
A
Ordinal
12
Q
Type of variable: A temperature scale of Fahrenheit
A
Interval
13
Q
Type of variable: A clock
A
Interval
14
Q
Type of variable: Stopwatch
A
Ratio
15
Q
Categorical variables
A
- Nominal & Ordinal
- Coarse measure
- Less sensitive to statistics, but can still detect correlation
15
Q
Type of variable: Money
A
Ratio
16
Q
Continuous variables
A
- Interval & Ratio
- Refined measure (specific)
- More sensitive to statistics, making it easier to detect correlation
17
Q
X-axis
A
Independent variable
18
Q
Y-axis
A
Dependent variable
19
Q
Positive correlation
A
Two measures increase together or decrease together
20
Q
Negative correlation
A
As one measure increases, the other decreases
21
What do positive and negative correlations have in common?
They are causations
22
A correlation can be a causation if:
- The change is unidirectional
- The change is direct, without a plausible alternative explanation
23
Unidirectional example with height and weight
- Height causes weight
- Weight does not cause height
24
Direct change without a plausible alternative explanation example with height and weight
While factors like diet also cause weight, the influence of height is direct
25
Causal relationships and correlations
Every causal relationship is a correlation, but not every correlation is a causation
26
Association
- A mutual relationship between two variables that can either be causal, or only a correlation
- Either variable can be the independent variable
27
Correlation between pay and gender
- Complicated because people and the soceieties we exist in are complicated
- So many variables involves, so causality is hard to pinpoint
28
Correlation between ice cream and swimsuits sales
- Plausible alternative explanation: summer
29
Correlation between having a strong interest in the arts and living in the city
- Association
- You could be interest in arts because you live in the city
- Or you could move to the city because you're interested in arts
30
Correlation between people who watch crime shows and having mean world syndrome
- Association
- People could have mean world syndrome because they watch crime shows
- Or people could watch crime shows because they have mean world syndrome
31
32
How can researchers determine if correlations are meaningful?
Statistics
32
Statistical significance tests
Determine if a relationship among variables exists beyond chance, with some allowable error
32
Spurious relationship
A correlation of unrelated variables, or a coincidence
32
What does "beyond chance" mean?
- p < .05 refers to the amount of error allowed
- p is probability
- .05 is 5%
- Less than 5% chance correlation in error
33
Relationship between a Gator victory and your lucky socks
Spurious relationship
34
Testing a correlation while testing a second one
Test the control, which could be a placebo, another treatment, or no treatment at all
35
Experiment
Comparing correlation for two or more conditions
36
Good experiment
- Participants must be assigned the conditions at random (random assignment)
- Participants cannot know what condition they're in, and neither can the practitioners (double-blidn study)
- These factors reduce bias
37
Randomized controlled trial
Tests a treatment against a control with participants assigned at random, and if possible, is double-blind
38
If the treatment is stronger than the control . . .
The treatment is causal
39
Split testing in media
Compares two messages, A and B
40
Sending ad tweets two weeks apart using different wording for each
- Split testing
- Online analytics determine what generates more engagement
