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Statistics as a Tool in Scientific Research:
Assessing the Relationship Between 2 Numerical Variables With Correlation
A. What a Correlation Is
Statistical test: Pearson correlation coefficient (r)
Pearsons r is Used For:
Analyzing the strength of the linear relationship between two numerical variables in order to answer research questions where scientists want to know whether or how strongly two variables are related to each other, but they do not have experimental control over those variables and rely on already existing conditions, e.g., Is there a relationship between TV viewing and obesity? Between age and severity of flu symptoms in adults? Between tire pressure and gas mileage? Between the amount of pressure and compression of insulation?
The nature of the research question is about the association between two variables, not about whether one causes the other. Is there an association between X and Y? As X increases, what does Y do? What is the type and strength of linear relationship between X and Y?
Correlation describes and quantifies the systematic linear relation between variables, but correlation ( causation.
Pearsons r is Used When: Both variables are numerical (interval or ratio) and the research question is about the type and strength of relation (not about causality)
Other Correlation Coefficients
There are many different correlation coefficients that are used for different types of variables, such as:
Point biserial (rPB): Used when one variable is nominal and has two levels (e.g., gender [male/female], type of car [gas-powered, hybrid]) and one variable is numerical (e.g., reaction time; miles per gallon)
Spearman rank order (rS): Used for ordinal data or numerical data that are not normally distributed or linear
Calculators for these and others are available at: http://faculty.vassar.edu/lowry/VassarStats.html
This handout focuses on the most commonly used correlation coefficient (Pearsons r or more commonly known just as r)
B. What a Correlation Tells You
The value of Pearsons r tells you about the type and strength of the relationship
Direction/type:
Positive: As X increased, Y increased; as X decreased, Y decreased
Negative: As X increased, Y decreased; as X decreased, Y increased
No systematic relation between X and Y: High values of X are associated with both high & low values of Y; Low values of X are associated with both high & low values of Y
Curvilinear: Not straight
U: As X increased, Y decreased up to a point then increased
Inverted U: As X increased, Y increased up to a point, then decreased
Magnitude/strength: r values range from (1 to +1; the size of the r value tells you about the strength of the relationship. r values close to zero = weak or no correlation; r values closer to +1 or -1 = stronger correlation (the absolute value of r determines its strength; the sign indicates whether it is positive or negative -- that is as X increased, does Y increased (positive) or decreased (negative)?
|r| Conclusion About Relationship
0 to .20 negligible to weak
.20 to .40 weak to moderate
.40 to .60 moderate to strong
.60 to 1.0 strong
C. Hypothesis Testing With Correlations
Correlations are descriptive: You can describe the type and strength of the linear relationship between two variables by examining the r value
But you can also test hypotheses because r values are associated with p values that reflect the probability that the obtained relationship is real or is more likely just due to chance
The key research question in a correlational design is: Is there a real linear relationship between the two variables or is the obtained pattern no different what we would expect just by chance?
Null hypothesis (H0): The population correlation coefficient is zero; the relationship we see is just due to chance
Research hypothesis (HA): The population correlation coefficient is not zero; the relationship is most likely real and is not due to chance
D. What Do We Mean by Just Due to Chance?
p value = probability of results being due to chance
When the p value is high (p > .05), the obtained relationship is probably due to chance: .99 .75 .55 .25 .15 .10 .07
When the p value is low (p < .05), the obtained relationship is probably NOT due to chance and more likely reflects a real relationship: .04 .03 .02 .01 .001
In science, a p value of .05 is a conventionally accepted cutoff point for saying when a result is more likely due to chance or more likely due to a real effect
Not significant = the obtained relationship is probably due to chance; the relationship observed does not appear to really differ from what would be expected based on chance; p > .05
Statistically significant = the obtained relationship is probably NOT due to chance and is likely a real linear relationship between the two variables; p < .05
E. Finding Pearsons r Using Excel
Step 1: Make a scatterplot of the data. If there is a linear trend, go to Step 2
In Excel the column containing the explanatory variable (X) needs to be listed directly to the left of the column containing the response variable (Y). Highlight both the X and Y values. Click on the Chart Wizard on the tool bar (the icon that looks like a bar chart) or select Insert/Chart on the menu bar and hit enter. Choose the XY(Scatter) chart type, hit Next.
Step 2: Get the Pearson Correlation Coefficient (r).
To find the correlation between the data in cells C1:C26 and D1:D26, click in an empty cell and type:
=correl(C1:C26, D1:D26)
When to Calculate Pearsons r: Both variables are numerical (interval or ratio) and the research question is about the type and strength of relation (not about causality)
Need:
Pearson correlation coefficient (r)
Number of pairs of observations (N)
Note: It doesnt matter which variable you call X or Y when calculating r
To run: Open Excel file SFE Statistical Tests and go to page called Linear Correlation Test. Enter the correlation coefficient (r) and number of pairs of observations (N)
Output: Computer calculates the p value
F. Finding Pearsons r Using SPSS
Step 1: Make a scatterplot of the data. If there is a linear trend, go to Step 2
Graphs ( Scatter/Dot ( Simple Scatter and click Define. Move the Y and X variables over and click Ok.
When to Calculate Pearsons r: Both variables are numerical (interval or ratio) and the research question is about the type and strength of relation (not about causality)
To run: Get the Pearson Correlation Coefficient (r) and test for significance
Analyze ( Correlate ( Bivariate; Move the X and Y variables over. Check Pearson Correlation and Two-tailed test of signficance. Then click Ok.
Output: Computer calculates r and the p value
Correlations
SurfaceAreaToVolumeRatioDrugReleaseRateSurfaceAreaToVolumeRatioPearson Correlation1.989(**) Sig. (2-tailed) .000 N66DrugReleaseRatePearson Correlation.989(**)1 Sig. (2-tailed).000 N66** Correlation is significant at the 0.01 level (2-tailed).
Report as: r = .989, p < .001 (Significant)
G. Reporting Results of Pearsons r Correlation
If the relationship was significant (p < .05)
(a) Say: A [say something about size: weak, moderate, strong] [say direction: positive/negative] correlation was found between [X] and [Y] that was statistically significant, r = .xx, p = .xx
(b) Describe the relation: Thus as X increased, Y [increased/decreased]
Note: If the r value is positive, then as X increased, Y increased; if r is negative, then as X increased, Y decreased
e.g., A strong positive correlation was found between surface area to volume ratio and the drug release rate that was statistically significant, r = .99, p = .0001. As the surface area to volume ratio increased, the drug release rate increased.
If the relationship was not significant (p > .05)
Say: No statistically significant correlation between [X] and [Y] was found, r = .xx, p = .xx. Thus as X increased, Y neither increased nor decreased systematically.
e.g., No statistically significant correlation between oxide thickness and deposit time was found,
r = .002, p = .99. Thus as oxide thickness increased, deposit time neither increased nor decreased systematically.
Other Things to Keep in Mind When Reporting Correlations
An association does not imply causation! e.g., Average life expectancy and the average number of TVs per household are highly correlated, but you cant increase life expectancy by increasing the number of TVs. They are related, but it isnt a cause-and-effect relationship.
r is unitless, it is NOT a proportion or percentage. Never read r = .15 as 15%! Say r = point one five.
You can only use the word significant only when you mean it (i.e., the probability the results are due to chance is less than 5%)
Do not use the word significant with adjectives (i.e., it is a mistake to think one test can be more significant or less significant than another). Significant is a cutoff that is either met or not met -- Just like you are either found guilty or not guilty, pregnant or not pregnant. There are no gradients. Lower p values = less likelihood result is due to chance, not more significant
H. What About R2?
Often in correlational research the value R2 is reported.
R2 = Proportion of variability of Y accounted for by X and the linear model
For example, if R2 = .64 then we can say:
64% of the variance in the Y scores can be predicted from Ys (linear) relation with X
Predictions are 64% more accurate using the linear regression equation to make predictions (Y) than when we use MY to make predictions
R2 is literally the correlation coefficient, r, times itself.
R2 = (r)2; 0 (( R2 ( 1
r = Sqrt(R2) or r = ( Sqrt(R2); The correlation (r) has the same sign as the slope of the least squares line
Low r ( low R2 ( little improvement over MY
High r ( high R2 ( more accurate predictions using Y
I. What is Linear Regression?
Linear regression is used for:
Making predictions of Variable Y based on knowing the value of Variable X
You can predict one variable from the other if there is a strong correlation. Stronger correlation = better prediction
Low or no correlation: Best prediction of Y is the mean of Y (knowing X doesnt add anything)
High correlation: Best prediction of Y is based on knowing X
It is important to make a scatterplot of the data (see above under correlation) to see if a linear model is appropriate and/or the best model!
Terminology
X variable: predictor (independent variable, explanatory variable, what we know)
Y variable: criterion (dependent variable, response variable, what we want to predict)
Regression line: Best fitting straight line that summarizes a linear relation; Comprised of the predicted values of Y (denoted by Y )
What Do We Mean by the Best Fit Line?
Error or residual = observed Y predicted Y = Y Y
The least-squares line is the best fit line that minimizes the sum of the square errors/residuals
Used to predict Y using a single predictor X and a linear model
Y = bX + a
Y = predicted y-value
X = known x-value
b = slope (ratio of how much Y changes relative to a change in one unit of X; same sign as the correlation)
a = y-intercept or the point where line crosses y-axis (where X=0)
J. Running Linear Regression Using Excel
Linear Regression: When you want to predict a numerical variable Y from another numerical variable X
Need:
Enter X and Y data in Excel
Be sure that X column is directly to the left of the Y column
To get linear model: (a) Make a scatterplot of Y vs. X. (b) Click on the Chart and choose Chart/Add Trendline. (c) Choose the option tab and select Display equation
Output: Least squares line
Example X = Surface Area to Volume Ratio (mm2/mm3)
Y = Drug Release Rate (%released)
The least squares line is
Y = 36.92X + 7.21
If surface area to volume ratio is 1, what is predicted drug release rate?
Y = 36.92(1) + 7.21 = 44.13% released
If the actual drug release rate was 40%, what is the residual?
Residual = Y Y = 40 44.13 = (4.13%
By how much can you expect the drug release rate to increase or decrease if you increase the surface
area to volume ratio by 1 mm2/mm3?
This is just the slope, so you would expect the drug release rate to increase by 36.92%
K. Running Linear Regression Using SPSS
Linear Regression: When you want to predict a numerical variable Y from another numerical variable X
Need: Enter X and Y data in SPSS
To get linear model: Analyze ( Regression ( Linear; Move the Y variable to Dependent and the X variable to Independent; Click Ok.
Output: Least squares line
Coefficients(a)
Model Unstandardized CoefficientsStandardized CoefficientstSig. BStd. ErrorBeta 1(Constant)7.2092.584 2.790.049 SurfaceAreaToVolumeRatio35.9162.697.98913.317.000a Dependent Variable: DrugReleaseRate
So, the linear model is: Y = 7.209 + 35.916X; Y = Drug Release Rate, X = Surface Area to Volume Ratio
L. Things to Keep in Mind When Using Regression to Make Predictions
You cant extrapolate outside the given domain of the Xs. You can only make predictions in the range of the given data.
Be careful of the effect of potential outliers
Association does not mean causation!
M. Hypothesis Test for Simple Linear Regression
Assumed: Y = ( + (X+ ( where ( is normally distributed with mean 0 and variance (2
The F test allows a scientist to determine whether their research hypothesis is supported
Null hypothesis H0:
There is not a linear relationship between X and Y
There is no correlation between X and Y
( = 0
Research hypothesis HA:
X is linearly related to Y
The correlation between X and Y is different from 0
( ( 0
Examples:
Null hypothesis: Pain level is not linearly related to weight
Research hypothesis: Pain level is linearly related to weight
Null hypothesis: There is no linear relationship between age and heart rate
Research hypothesis: There is a linear relationship between age and heart rate
Sources of Variation: SSTotal = SSRegression + SSResidual
((Yi MY)2 = ((Yi MY)2 + ((Yi Yi)2
SSTotal = Total Sums of Squares = How much do all the individual scores differ from the grand mean
SSRegression = Regression Sums of Squares = How much do all the predicted values differ from the grand mean
SSResidual = Residual Sums of Squares = How much do the individual scores differ from the predicted values
Each F test has certain values for degrees of freedom (df), which is based on the sample size (N) and number of conditions, and the F value will be associated with a particular p value
SPSS calculates these numbers.
Summary Table for Simple Linear Regression
SourceSum of Squares (SS)df Mean
Square (MS)FRegression((Yi MY)2 1 SSRegression
dfRegression MSRegression
MSResidual Residual (Error)((Yi Yi)2 N - 2 SSResidual
dfResidual Total((Yi MY)2 N - 1
N= # samples
To report, use the format: F(dfRegression, dfResidual) = x.xx, p _____.
A test for simple linear regression gives you an F ratio
The bigger the F value, the less likely the relationship between X and Y is just due to chance
The bigger the F value, the more likely the relationship between X and Y is not just due to chance and is due to a real relationship
So big values of F will be associated with small p values that indicate the linear relationship is significant (p < .05)
Little values of F (i.e., close to 1) will be associated with larger p values that indicate the linear relationship is not significant (p > .05)
Based on p value, determine whether you have evidence to conclude the relationship was probably real or was probably due to chance: Is the research hypothesis supported?
p < .05: Significant
Reject null hypothesis and support research hypothesis (the relationship was probably real; X is linearly related to Y)
p > .05: Not significant
Retain null hypothesis and reject research hypothesis (any relationship was probably due to chance; X is not linearly related to Y)
N. Running the F Test for Simple Linear Regression in SPSS
Linear Regression: When you want to test whether there is a linear relationship between two numerical variables (X and Y)
Need: Enter X and Y data in SPSS
To get linear model: Analyze ( Regression ( Linear;
Move the Y variable to Dependent and the X variable to Independent; Click Ok.
Output: F and p-value. (Sig = p-value)
ANOVA(b)
Model Sum of SquaresdfMean SquareFSig.1Regression718.6241718.624177.335.000(a) Residual16.20944.052 Total734.8335 a Predictors: (Constant), SurfaceAreaToVolume
b Dependent Variable: DrugReleaseRate
Report as: F(1,4) = 177.34, p < .001 (Significant)
O. Reporting the Results of the F Test for Simple Linear Regression in SPSS
Step 1: Write a sentence that clearly indicates what statistical analysis you used
An F test was used to determine if there was a linear relationship between [X] and [Y].
Or, an F test was used to determine if [X] is linearly related to [Y].
Examples:
An F test was used to determine if pain level is linearly related to weight.
An F test was used to determine if there is a linear relationship between age and heart rate.
Step 2: Report whether the linear relationship was significant or not
The linear relationship between [X] and [Y] was significant [or not significant], F(dfRegression, dfResidual) = X.XX [fill in F], p = xxxx.
Examples:
The linear relationship between pain level and weight was significant, F(2, 40) = 7.31, p = .01.
The linear relationship between age and heart rate was not significant, F(2, 120) = 2.35, p = .10.
P. Assumptions of the F Test for Simple Linear Regression
Linearity
Is a linear model appropriate? Is a linear model the best model?
Look at a scatterplot to look for an overall trend.
Randomness and Normality
Are the residuals independent and normally distributed?
The points should be randomly scattered about the regression line. There should be no patterns in the residual plot.
Homoscedasticity
Is the variability about the regression line constant?
Does the model fit consistently well for all Xs?
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