Week 11 - correlation Flashcards

1
Q

What is correlation?

A

measures the relationship between two continuous/ numerical variables

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2
Q

What is a scatterplot?

A

displays scores of two variables

two values for each ppt plotted as a single point

1st step of correlation analysis

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3
Q

Use of scatterplots?

A

identify outliers

familiarise ourselves with data

identify initial relationships + distributions of data

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4
Q

Scatterplot: strength and direction of relationship between two variables

A

strength : closeness of the points to the line of best fit

direction : positive, negative, null

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5
Q

Positive correlation

A

As X increases, Y also increases

perfect +ve correlation: r = 1

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6
Q

Negative correlation

A

As X increases, Y decreases

Perfect -ve correlation: r = -1

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7
Q

Null correlation

A

When X increases, Y shows no constant change

null correlation: r = 0

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8
Q

What is pearson’s r?

A

A statistic which quantifies the linear relationship between two variables, ranging from 1- to 1

Its an effect size

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9
Q

Alternative name for pearson’s r?

Full name of pearson’s r?

A

correlation coefficient

pearson’s product-moment correlation coefficient

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10
Q

Describe the general rules for weak/ moderate/ strong correlation coefficient values

A

Weak -> r > 0.1

Moderate -> r > 0.3

Strong -> r > 0.5

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11
Q

What is covariance?

A

the extent to which two variables vary together

value can be -/+ - suggests direction of variance

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12
Q

Issue with covarience?

How to fix it

A

size of covariance is affected by the size of the variance of the two variables

makes comparing between different samples difficult

fix : replace N with SD
r = covariance / SD(x) x SD(y)

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13
Q

What is the standardised version of covariance?

A

Pearson’s r

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14
Q

What is null hypothesis

How to know if correlation is significant or not?

A

No correlation

To be significant, correlation needs to be bigger than critical value

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15
Q

Coefficient of determination

aka…?

Calculation?

A

AKA r squared

tells us how much variance in one variable is accounted for by the other variable

r^2
x 100 (rounded to 0 decimals)

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16
Q

Correlation vs causation

A

Correlation only infers relationships between variables, NOT causation

To infer causation we’d have to experimentally manipulate IV and measure effect on DV. Then we can say that changes in IV caused effects in DV.