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

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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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