# PSY230H5 Lecture Notes - Lecture 2: Shyness, Convergent Validity, Discriminant Validity

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28 Aug 2016
School
Department
Course
Lecture 2
Sept,14,2015
Fundamentals II: Causality and Validity
Relevance of this Lecture
This lecture explains the relation between correlations and causality.
This lecture explains the distinction between personality measures and personality
constructs (validity).
Iclicker question:
Correlation doesn’t prove causality!
A = true, B = false
The Bermuda Triangle of Causality
Correlation does not prove causality
r = p1 + p2 + p3 *p4
too many unknowns!
Unless we have done an experiment we cannot claim causality
Correlation doesn’t prove causality but that doesn’t mean that we can’t use correlations to
see if something is causal
Often it is not feasible (ethically immoral) to conduct experiments on personality so we
have to use correlations to conduct research
Correlations can occur for a number of reasons but there is a limited number of reasons
that can be the causal variable
Using a curve arrow between the two variables just means they are related it doesn’t say
that they are directly related
There are three reasons why two variables related:
o1) A is a cause of B eg. variation of height causes weight
o2) B causes A (both #1 and #2 can occur at the same time)
o3) another variable can cause the two variables to relate to each other
R=p1+p2+p3*p4
Height and weight
R=.5
There is a third variable that is causing the two variables to relate to each other
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Height and hair length
Hair length and weight have a -0.2 correlation (p1)
oMen have a shorter hair and have more weight and women have more hair and
have less weight
oGender is the third variable
oGender and hair length have a -0.6 correlation (p3)
There are more women in the class than males
This correlation may vary depending on the culture
There are more women in the class than males
Gender and weight have a 0.3 correlation (p2)
.3 x-.65=.195 which is p1(-.2) : shows that p2 x p3=p1
Reliability
Meaning of shyness varies from person to person
Shyness in the first week and shyness in the second week are a 0.7 correlation
Shyness and our memory of shyness last week has a 0.8 correlation
What we want u to forget ur response last week so that we can make sure that ur shyness
this week and last week is related by a third variable
When p3 and p4 are the same then there is only one unknown
The reliable variance explained 70% of variance in the first shyness test – means that we
have a stable sense of our shyness between the two weeks, we have a strong sense of self
When there is a reliability of 1 then p1 and p2 are directly relative
Reliability
Reliability coefficients assume that measurement errors are independent.
If u measure the same thing again it should be the same – reliability
Error at time 1 is unrelated to error at time 2.
Uncorrelated errors are called random errors.
If error variance is random, reliability coefficients estimate the amount of reliable
variance that is not due to random measurement error.
Whenever we do not find a perfect correlation we have to wonder why the data has
changed
Reliability =reliability x reliability^2
Two Methods to Estimate Reliability
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Internal Consistency: Internal consistency can be measured by assigning half of the items
of a scale to one variable, and the other half to another variable, and then compute the
correlation between the two variables (split-half reliability).
oAsk the same question but worded differently
oAre you reserved vs. are you shy have a 0.5 correlation
Retest Correlation: Measuring the same items twice over a relatively brief period of time
(e.g., one week apart).
oProblem- we have to assume that things didn’t change between the two times
o
Problems
Internal Consistency:
- can be influenced by responses to previous items
- can be influenced by shared error variance
(e.g., something that happened that day)
Retest Correlations:
- can still be influenced by memory of previous response
- the true score could have changed over time
Important Implications
Measurement error lowers the ‘true’ correlations between two variables.
As a rule of thumb, observed effect sizes underestimate true effects.
For example, a correlation of .2 (r2 = 4%) could be a true correlation of .29 (r2 = 8%) if
the reliability of the two measures were .70.
Measurement error should be reduced whenever possible and your report must account
for the measurement error in your results
Validity
Is the measure that you are using the correct measure to test your construct
The relationship between a measure of height and the construct of height
Definition:
A measure measures the intended construct.
Examples:
A ruler is a valid measure of height.
A scale is a valid measure of weight.
The Relationship between a Measure of Height and the Construct of Height
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