correlation - university of michigan dearbornacfoos/courses/381/05... · 2018-01-30 · •pearson...
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Correlation
Experimental Psychology
Arlo Clark-Foos
Preview
• Standardized Scores (z scores)
• Correlation
– Characteristics of
– Relationship to Cause-Effect
– Partial Correlations
• Psychometrics
• Reliability
• Validity
Las Cucarachas
• How heavy are these cockroaches?
– 8.0 drachms
– .25 pound
– 98 grams
– Need to put these on the same scale!
– Not always this easy (these were all measures of weight)
(14.17 grams)
(113.40 grams)
(98 grams)
Equal Footing
• Different variables, often different scales
• Need standardization
– z score (z is italicized)
• the number of standard deviations a particular score is
from the mean
• Why is this useful?
• z distribution
– Scores vs. Distributions
z Distributions
Mean = 0, SD = 1 (Always!)…why?
Raw Score z Score
• Hypothetical Test Scores
– Mean = 70, SD = 10, you scored an 85.
Raw Score z Score
• NEED: Population parameters for the
mean (μ) and standard deviation (σ)
Xz
Changing Gears:
Correlations
What is a correlation?
A general term to describe the association (or
relation) between two variables
– co-relation
Correlation Coefficient
• A statistic that quantifies the relation
between two variables
• Pearson (r) correlation coefficient
– Measures the strength of a linear
relationship
Correlation Coefficient
• Characteristics
1. Can be positive or negative
2. Ranges from -1.00 to 1.00
3. Strength (magnitude) of correlation is
indicated by size of resulting value, not
the sign.
Positive Correlation
r > 0
Positive Correlations
• Positive correlation between
smoking and lung cancer
• Positive correlation between SAT
scores and high school grades
• Positive correlation between the
amount of diet soda you drink and
your weight
Negative Correlation
r < 0
Negative Correlations
• Negative correlation between
education and years in jail
• Negative correlation between
amount of time a baby is held
and how much she cries
• Negative correlation between
amount of TV watched and
grades
No Correlation
r = 0
Strength of Correlations
Strong, positive Weak/moderate, positive
Cohen (1988)
• How strong is your correlation?
Correlation & Causation
• True: After WWII, there was a
strong positive correlation
between the birth rate and
the number of storks in
Copenhagen.
– Why?
– Increasing population
• More buildings– Increasing numbers of storks
• More baby makers– Increasing numbers of babies
Illusory Correlations
Capricorn: Some rather boring and mundane tasks, perhaps
involving paperwork, could take up much of your time today,
Capricorn. You could get easily distracted and be tempted to
set it aside and do something more interesting, but don't fall
into this trap. You'll want your recent pattern of success to
continue, and so it's best to get the boring stuff out of the
way and then move on to what's exciting. Hang in there!
Pisces: Some paperwork involving business enterprises could be
executed today, Pisces, possibly in your home. This might be
a new and unexpected development. It could have you feeling
a bit disoriented, but try to pull yourself together and make
the most of it. Whatever the opportunity, all signs say that it
might prove quite successful, so don't let it pass you by. Go
with the flow and see where it takes you. You could be
pleasantly surprised!
Correlation is NOT Causation
• Third Variables
Karl Pearson
• Pearson correlation coefficient
– r (sample) or ρ (population)
– Let’s use attendance and exam grade as
an example…
N
zzr
yx
X Y
X Y
X M Y Mr
SS SS
or
Correlation Example
Method 1: Correlation with Standard Scores
N
zzr
yx
Correlation with z
1. Plot the raw scores for each variable on a
scatter plot to see if there might be a
linear relationship
– If so, proceed with calculating the Pearson
correlation coefficient.
2. Create z scores corresponding to each value
– Do this for both variables of interest for each
person, keeping pairs of scores together
Xz
Scatterplot
Absences
0 2 4 6 8 10
Mean E
xam
Gra
de
40
50
60
70
80
90
100
110
Level the Field
1. Plot the raw scores for each variable on a
scatter plot to see if there might be a
linear relationship
– If so, proceed with calculating the Pearson
correlation coefficient.
2. Create z scores corresponding to each value
– Do this for both variables of interest for each
person, keeping pairs of scores together
Xz
Correlation Example
Xz
Correlation Example
Xz
Correlation Example
3. Pearson Correlation Coefficient
– Calculate the cross-products
• Multiple each pair of z scores together
4. Find the average of all of the cross-
products• This is your Pearson correlation coefficient
Cross-products
Correlation Example
3. Pearson Correlation Coefficient
– Calculate the cross-products
• Multiple each pair of z scores together
4. Find the average of all of the cross-
products• This is your Pearson correlation coefficient
Cross-products
∑(Cross Products) / Number of Pairs =
Pearson Correlation Coefficient
r = -.85
Method 2: Correlation with Deviations and Sums of Squares
X Y
X Y
X M Y Mr
SS SS
Please Use:
Same Data, New Method
Correlation with z
1. Plot the raw scores for each variable on a
scatter plot to see if there might be a
linear relationship
– If so, proceed with calculating the Pearson
correlation coefficient.
– Caveat: Not always necessary (e.g., SPSS, SAS)
2. Numerator: Create deviations (Score –
Mean) for each variable, keeping pairs
together
Scatterplot
Absences
0 2 4 6 8 10
Mean E
xam
Gra
de
40
50
60
70
80
90
100
110
Correlation
1. Plot the raw scores for each variable on a
scatter plot to see if there might be a
linear relationship
– If so, proceed with calculating the Pearson
correlation coefficient.
2. Numerator: Create deviations (Score –
Mean) for each variable, keeping pairs
together
Same Data, New Method
X Y
X Y
X M Y Mr
SS SS
Numerator: Deviations &
Cross Products
X Y
X Y
X M Y Mr
SS SS
Correlation Example
3. Denominator: Create Sums of Squares
(Sum of all Squared Deviations) for
each variable
4. Denominator: Multiply your Sums of
Squares
5. Take the Square Root of that product
6. Divide Numerator by Denominator to
get your Correlation Coefficient
X Y
X Y
X M Y Mr
SS SS
3. Denominator: Create
Sums of Squares
X Y
X Y
X M Y Mr
SS SS
Correlation Example
3. Denominator: Create Sums of Squares
(Sum of all Squared Deviations) for
each variable
4. Denominator: Multiply your Sums of
Squares
5. Take the Square Root of that product
6. Divide Numerator by Denominator to
get your Correlation Coefficient
X Y
X Y
X M Y Mr
SS SS
Correlation Example
4. Denominator: Multiply your Sums of Squares
SSX = ∑(X - Mx2) = 56.4
SSY = ∑(Y - MY2) = 2262
(SSX)(SSY) = (56.4)(2252) = 12576.8
5. Take the Square Root of that product
12576.8 = 357.18
X Y
X Y
X M Y Mr
SS SS
Correlation Example
3. Denominator: Create Sums of Squares
(Sum of all Squared Deviations) for
each variable
4. Denominator: Multiply your Sums of
Squares
5. Take the Square Root of that product
6. Divide Numerator by Denominator to
get your Correlation Coefficient
X Y
X Y
X M Y Mr
SS SS
6. Find Correlation
Coefficient (r)
• Divide Numerator by Denominator to get your
Correlation Coefficient
𝑟 =−304
357.18= −.851
• r = -.85
Absences
0 2 4 6 8 10
Mean
Exam
Gra
de
40
50
60
70
80
90
100
110
X Y
X Y
X M Y Mr
SS SS
Cautions and Issues Related to
Correlations
Correlation Example
• There appears to be a negative correlation
between attendance and exam grades
• What else could contribute to this?
Correlation is NOT Causation
• Restriction of Range
r = .05
Correlation is NOT Causation
• Restriction of Range
r = .56
Correlation is NOT Causation
• Outlier Effects
r = .39
Correlation is NOT Causation
• Outlier Effects
r = -.135
Applications of Correlational
Techniques
Some examples
of Reliability
• Test-Retest
– Determines whether scale being used
provides consistent information every time
it is given
• Split-Half Reliability
– Measures internal consistency of a test or
scale by typically correlating the odd-
numbered items with the even-numbered
items
– Coefficient/Cronbach’s alpha α)
• Calculated by taking the average of all
possible split-half correlations
Some examples of
Validity
• Criterion-related
– The scale of measurement of interest is
correlated with a criterion, which is some
external standard (e.g., an observable behavior)
– Postdictive/Predictive
• The scale or measurement of interest is
correlated with a criterion measured in the past
or future
– Concurrent
• The scale or measurement of interest is
correlated with a criterion measured at the same
time
Partial Correlation
• A technique that quantifies the
degree of association between two
variables, after statistically
removing the influence of a third
variable by holding it constant
Psychometrics
• Branch of statistics used in the development of
tests and measures and/or the evaluation of
statistical data on the behavior of animals
(typically humans)
– Examples
• Test for cultural biases in SAT/ACT/GRE
• Identify high-achieving employees
• Analyze online behavioral patterns, biostatistics, and much
much more
• “critical shortage” (NYT, Herszenhorn, 2006)
Review
• Correlation
– Characteristics of
– Relationship to Cause-Effect
– Partial Correlations
• Standardized Scores (z scores)
• Psychometrics
• Reliability
• Validity
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