bivariate correlation lesson 11. measuring relationships n correlation l degree relationship b/n 2...
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Measuring Relationships
Correlation degree relationship b/n 2 variables linear predictive relationship
Covariance If X changes, does Y change also? e.g., height (X) and weight (Y) ~
Covariance Variance
How much do scores (Xi) vary from mean? (standard deviation)2
Covariance How much do scores (Xi, Yi) from their
means
1
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N
XXs i
1
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N
XXXX ii
1
))((),cov(
N
YYXXyx ii
Covariance: Problem How to interpret size
Different scales of measurement Standardization
like in z scores Divide by standard deviation Gets rid of units
Correlation coefficient (r)
YX
ii
YX ssN
YYXX
ss
YXr
)1(
))((),cov(
Pearson Correlation Coefficient Both variables quantitative (interval/ratio) Values of r
between -1 and +1 0 = no relationship Parameter = ρ (rho)
Types of correlations Positive: change in same direction
X then Y; or X then Y Negative: change in opposite direction
X then Y; or X then Y ~
Correlation & Graphs Scatter Diagrams Also called scatter plots
1 variable: Y axis; other X axis plot point at intersection of values look for trends
e.g., height vs shoe size ~
Height
Shoe size
6 7 8 9 10 11 12
60
66
72
78
84
Slope & value of r
Determines sign positive or
negative From lower left to
upper right positive ~
Weight
Chin ups
3 6 9 12 15 18 21
100
150
200
250
300
Slope & value of r
From upper left to lower right negative ~
Width & value of r Magnitude of r
draw imaginary ellipse around most points Narrow: r near -1 or +1
strong relationship between variables straight line: perfect relationship (1 or -1)
Wide: r near 0 weak relationship between variables ~
Width & value of r
Weight
Chin ups
3 6 9 12 15 18 21
100
150
200
250
300
Strong negative relationship
r near -1
Weight
Chin ups
3 6 9 12 15 18 21
100
150
200
250
300
Weak relationship
r near 0
Strength of Correlation R2
Coefficient of Determination Proportion of variance in X
explained by relationship with Y Example: IQ and gray matter volume
r = .25 (statisically significant) R2 = .0625 Approximately 6% of differences in
IQ explained by relationship to gray matter volume ~
Guidelines for interpreting strength of correlation
Table 5.2 Interpreting a correlation coefficient
Size of Correlation (r) General coefficient interpretation
.8 to 1.0 Very strong relationship
.6 to .8 Strong relationship
.4 to .6 Moderate relationship
.2 to .4 Weak relationship
.0 to .2 Weak to no relationship
*The same guidelines apply for negative values of r
*from Statistics for People Who (Think They) Hate Statistics: Excel 2007 Edition By Neil J. Salkind
Factors that affect size of r
Nonlinear relationships Pearson’s r does not
detect more complex relationships
r near 0 ~
Peeps (Y)
Stress (X)
Height
Shoe size
6 7 8 9 10 11 12
60
66
72
78
84
Factors that affect size of r Range restriction
eliminate values from 1 or both variable
r is reduced e.g. eliminate
people under 72 inches ~
Hypothesis Test for r
H0: ρ = 0 rho = parameter
H1: ρ ≠ 0
ρCV
df = n – 2 Table: Critical values of ρ PASW output gives sig.
Example: n = 30; df=28; nondirectional ρCV = + .361 decision: r = .285 ? r = -.38 ? ~
Using Pearson r Reliability
Inter-rater reliability Validity of a measure
ACT scores and college success? Also GPA, dean’s list, graduation rate,
dropout rate Effect size
Alternative to Cohen’s d ~
Evaluating Effect Size
Cohen’s d
Small: d = 0.2
Medium: d = 0.5
Large: d = 0.8
Note: Why no zero before decimal for r ?
Pearson’s r
r = ± .1
r = ± .3
r = ±.5 ~
Correlation and Causation
Causation requires correlation, but... Correlation does not imply causation!
The 3d variable problem Some unkown variable affects both e.g. # of household appliances
negatively correlated with family size Direction of causality
Like psychology get good grades Or vice versa ~
Point-biserial Correlation
One variable dichotomous Only two values e.g., Sex: male & female
PASW/SPSS Same as for Pearson’s r ~
Correlation: NonParametric
Spearman’s rs
Ordinal Non-normal interval/ratio
Kendall’s Tau Large # tied ranks Or small data sets Maybe better choice than Spearman’s ~
Correlation: SPSS Data entry
1 column per variable Menus
Analyze Correlate Bivariate Dialog box
Select variables Choose correlation type 1- or 2-tailed test of significance ~
Guidelines1. No zero before decimal point
2. Round to 2 decimal places
3. significance: 1- or 2-tailed test
4. Use correct symbol for correlation type
5. Report significance level
There was a significant relationship between the number of commercials watch and the amount of candy purchased, r = +.87, p (one-tailed) < .05.
Creativity was negatively correlated with how well people did in the World’s Biggest Liar Contest, rS = -.37, p (two-tailed) = .001.
Reporting Correlation Coefficients
Correlation: ExampleCorrelations
WorkHours ExCurrHours
WorkHours Pearson Correlation 1 -.313
Sig. (2-tailed) .081
N 32 32
ExCurrHours Pearson Correlation -.313 1
Sig. (2-tailed) .081
N 32 32
Correlation: Example
Analysis using the Pearson’s r correlation indicated that the there was moderately strong negative relationship between the number of work hours and the number of hours spent on extracurricular activities, but the relationship was not statistically significant, r = -.31, p (two-tailed) = .08. The R2 = .097, indicating that the relationship accounts for approximately 9.7% of the variance in the number of hours spent in each activity.