PSY230H5 Lecture Notes - Lecture 2: Internal Consistency, Explained Variation, Effect Size

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26 Oct 2017
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Lecture 2
Conclusion
correlations reflect the direction and the strength of the relation between
two variables
correlations vary from -1 to +1
correlations depend on the sample (E.g higher in a sample of actresses
than normal women)
a measure of the strength of a correlation is the amount of explained
variance (2), which ranges from 0 to 1 (%)
Effect size
there are multiple ways to quantify the size of an effect
the binomial effect size display
W- W+ W- W+
H- 50 50 H- 75 25
H+ 50 50 H+ 25 75
r = .00 r = .50
positive correlation in height
take the correlation coefficient .50 and split it in half and it becomes .25,
bc you get an extra .25, you now get a .75
cohen proposed the following verbal labels for different correlations:
r r2 verbal label
.10 1% small
.30 9% moderate
.50 25% large
even if a correlation is small, it still matters as it means many other
factors are important
Reliability
the correlation between two assessments of the same construct with the
same method
1. asking about the same construct with many similar questions or
include many similar items on a multiple choice test (internal
consistency)
2. repeating the same measure twice some time apart (Retest-reliability)
Example: the satisfaction with life scale (SWLS)
1. in most ways my life is close to ideal
2. I am satisfied with my life
1= strongly disagree and 5= strongly agree
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)
Reliability of the SWLS
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