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Part IV Significantly Different: Using Inferential Statistics

Part IV Significantly Different: Using Inferential Statistics. Chapter 15     Cousins or Just Good Friends? Testing Relationships Using the Correlation Coefficient. The Correlation Coefficient. Remember…correlations examine the relationship between variables,

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Part IV Significantly Different: Using Inferential Statistics

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  1. Part IVSignificantly Different:Using Inferential Statistics Chapter 15     Cousins or Just Good Friends? Testing Relationships Using the Correlation Coefficient

  2. The Correlation Coefficient • Remember…correlations examine the relationship between variables, • They do not attempt to determine causation • Examine the “strength” of the relationship • Range from -1 to +1 • Direct relationships • Positive correlations (0 to 1) • Indirect relationships • Negative correlations (0 to -1)

  3. Path to Wisdom & Knowledge

  4. Computing the Test Statistic • Use the Pearson formula H0: ρxy= 0 H1: rxy ≠ 0

  5. So How Do I Interpret… • r(27) = .393, p < .05? • r is the test statistic • 27 is the degrees of freedom • .393 is the obtained value • p < .05 is the probability

  6. Causes and Associations (Again!) • Just because two variables are related has no bearing on whether there is a causal relationship. • Example: • The number of storks and number of births in an area • Two variables may be correlated because they share something in common…but just because there is an “association” does not mean there is “causation.”

  7. Significance Versus Meaningfulness (Again, Again!!) • Even if a correlation is significant, it doesn’t mean that the amount of variance accounted for is meaningful. • Example • Correlation of .393 , p < .05 • Variance accounted for: .154 or 15.4% • 84.6% remaining (coefficient of alienation)!!! • Need higher r and p < .05 statistical significance

  8. Using the Computer • SPSS and the Correlation

  9. SPSS Output • What does it all mean?

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