1. SI0030 Social Research Methods Week 6 Luke Sloan
Quantitative Data Analysis II: Correlation and Simple Linear Regression
SI0030
Social Research Methods
Week 6
Luke Sloan

2. Introduction Last Week – Recap Correlation How To Draw A Line
Simple Linear Regression
Summary

3. Last Week - Recap Hypotheses Probability & Significance (p=<0.05)
Chi-square test for two categorical variables
t-test for one categorical and one interval variables
What about a test for two interval variables?...

4. Correlation I
Calculates the strength and direction of a linear relationship between two interval variables
e.g. is there a relationship between age and income?
Measured using the Pearson correlation coefficient (r)
Data must be normally distributed (check with a histogram)
If not normally distributed use Spearman’s Rank Order Correlation (rho) - consult Pallant (2005:297)

5. Correlation II ‘r’ can take any value from +1 to -1
+/- indicates whether the relationship is positive or negative
+1 or -1 is a perfect linear relationship, but usually it is not this clear cut
Rule of thumb:
+/- 0.7 = a strong linear relationship
+/- 0.5 = a good linear relationship
+/- 0.3 = a linear relationship
Below +/- 0.3 = weak linear relationship
0 = no linear relationship
Alternatively:
+/ to 0.29 = weak
+/ to 0.49 = medium
+/ to strong

6. Positive relationship Negative relationship
Correlation III
Positive relationship
No relationship
Negative relationship
Positive
Relationship No
Relationship
Negative
Relationship
Formulate hypotheses and use scatter plots!

7. Correlation IV
H1 = There is a relationship between Age and the number of years a candidate has been a member of a political party
H0 = There is no relationship between Age and the number of years a candidate has been a member of a political party
What do you think?

8. Is this normal? Just to prove a point…
Correlation V
Is this normal? Just to prove a point…

9. Correlation VI
Perfect correlation against itself (obviously!) and number of cases in analysis
Correlations
What was your age last birthday
Number of years a party member
Pearson Correlation 1
.425**
Sig. (2-tailed)
.000 N
4481
1874
1936
**. Correlation is significant at the 0.01 level (2-tailed).
Significance for correlation is problematic (highly dependent on sample size) – report p-value but ignore level of significance
Pearson’s Correlation Coefficient is r=0.43 – medium/good positive linear relationship

10. Correlation VII
Don’t forget to refute or accept the null hypothesis and discuss the relationship
Correlation is not causation!
The relationship between the number of years a candidate has been a member of a party and candidate age was explored using Pearson’s correlation coefficient. Both variables were confirmed to have normal distributions [?] and a scatter plot revealed a linear relationship. There was a medium-strength, positive relationship between the two variables (r=0.43, n=4481, p<0.05)... [go on to explain the relationship in detail]

11. The line of best fit is a predictive – it is the regression line!
How To Draw A Line I
Correlation is indicative of a relationship, but it does not allow us to quantify it
What if we wanted to explain how an increase in age leads to an increase in years of party membership?
What if we wanted to predict years of party membership based only on age?
The line of best fit is a predictive – it is the regression line!

12. How To Draw A Line II
The regression line allows us to predict any given value of y when we know x
i.e. if we know the age of a candidate we can predict how long they are likely to have been a member of a political party
Another (more useful!) example would be years in education and income
Using a regression line we can predict someone’s income based on the number of years they have been in education
Assumes a causal relationship – that income is ‘caused’ by years in education

13. How To Draw A Line III y = a + b x
But… we don’t simply look very closely at the line and the axis of the scatter plot because the regression line can be written as an equation:
y = a + b x
‘y’ represents the dependent variable (what we are trying to predict) e.g. income
‘a’ represents the intercept(where the regression line crosses the vertical ‘y’ axis) aka the constant
‘b’ represents the slope of the line (the association between ‘y’ & ‘x’) e.g. how income changes in relation to education
‘x’ represents the independent variable (what we are using to predict ‘y’) e.g. years in education

14. How To Draw A Line IV y axis x axis y = 0 + 2x y = 0 + 1x y = 0 + 0.5x
What about…
y = x
y = 1 + 1x
x axis

15. Simple Linear Regression
If we know the slope (b) and the intercept (a), for any given value of ‘x’ we can predict ‘y’
EXAMPLE: predicting income (y) in thousands (£) from years in education (x)
Preconditions:
Equations:
Intercept (a) = 4
y = a + bx
Or…
Slope (b) = 1.5
Income = intercept + (slope*years in education)
For someone with 10 years of education
Or…
Income = 4 + (1.5*10) = 19 (£19,000)

16. Simple Linear Regression II
Assumptions
Interval level data
Linearity between ‘x’ and ‘y’
Outliers (check scatter plot)
Sample size = 100+?
R2 measure of ‘model fit’
Literally the Pearson’s correlation coefficient squared
R2 tells us how much of the variance in the dependent variable is explained by the independent variable e.g. how much of the variance in income can be explained by age
Expressed as a percentage (1.0 = 100%, 0.5 = 50% etc)

17. Simple Linear Regression III
H0 = There is no relationship between Age and the number of years a candidate has been a member of a political party
H1 = There is a relationship between Age and the number of years a candidate has been a member of a political party
H2 = As the age of a candidate increases, so will the number of years that they have been a party member
‘Years as Party Member’ = intercept + (slope * ’Age’)

18. Simple Linear Regression IV
Pearson’s correlation coefficient (same value!)
18% of variance in party membership (y) explained by age (x)
Model Summary
Model R
R Square
Adjusted R Square
Std. Error of the Estimate 1
.425a
.181
.180
11.995
a. Predictors: (Constant), What was your age last birthday
This tests the hypothesis that the model is a better predictor of party membership than if we simply used the mean value of party membership
p<0.05 so the regression model is a significantly better predictor than the mean value
ANOVAb
Model
Sum of Squares df
Mean Square F
Sig. 1
Regression
.000a
Residual
1872
Total
1873
a. Predictors: (Constant), What was your age last birthday
b. Dependent Variable: Number of years a party member

19. Simple Linear Regression V
y = a + b x
p<0.05 so ‘Age’ has a significant effect on ‘Party Membership’
This is the intercept (a)
This is the slope (b)
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients t
Sig. B
Std. Error
Beta 1
(Constant)
-6.899
1.156
-5.966
.000
What was your age last birthday
.418
.021
.425
20.327
a. Dependent Variable: Number of years a party member
A one unit increase in age will result in an increase in party membership of 0.42
‘Party Membership’ = (0.42 * ’Age’)
Or…

20. Simple Linear Regression VI
… and this is what we saw in the original scatter plot!
The ‘regression line’ will intercept the verticle (y) axis at -6.9
The ‘regression line’ rises by 0.42 on the verticle axis (y) for every one unit increase on the horizontal axis (x)
The R2 value is low because of the fanning effect (remember the histograms!)

21. Summary
How to describe and quantify the relationship between two interval variables
Correlation – the strength and direction of the association
Regression – the causal and quantified effect of an independent on a dependent variable