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  1. Introduction
  2. Graphing Distributions
  3. Summarizing Distributions
  4. Describing Bivariate Data
  5. Probability
  6. Research Design
  7. Normal Distribution
  8. Advanced Graphs
  9. Sampling Distributions

  10. Estimation
    1. Contents
      Standard
    2. Introduction
      Standard
         Video
    3. Degrees of Freedom
      Standard
         Video
    4. Characteristics of Estimators
      Standard
         Video
    5. Bias and Variability Simulation
      Standard
    6. Confidence Intervals
      Standard
         Video
    7. Confidence Intervals Intro
      Standard
         Video
    8. Confidence Interval for Mean
      Standard
         Video
    9. t distribution
      Standard
         Video
    10. Confidence Interval Simulation
      Standard
    11. Difference between Means
      Standard
         Video
    12. Correlation
      Standard  
    13. Proportion
      Standard
         Video
    14. Statistical Literacy
      Standard
    15. Exercises
      Standard

  11. Logic of Hypothesis Testing
  12. Tests of Means
  13. Power
  14. Regression
  15. Analysis of Variance
  16. Transformations
  17. Chi Square
  18. Distribution Free Tests
  19. Effect Size
  20. Case Studies
  21. Calculators
  22. Glossary
 

Correlation

Author(s)

David M. Lane

Prerequisites

Values of the Pearson Correlation, Sampling Distribution of Pearson's r, Confidence Intervals

Learning Objectives
  1. State the standard error of z'
  2. Compute a confidence interval on ρ

The computation of a confidence interval on the population value of Pearson's correlation (ρ) is complicated by the fact that the sampling distribution of r is not normally distributed. The solution lies with Fisher's z' transformation described in the section on the sampling distribution of Pearson's r. The steps in computing a confidence interval for ρ are:

    1. Convert r to z'
    2. Compute a confidence interval in terms of z'
    3. Convert the confidence interval back to r.

Let's take the data from the case study Animal Research as an example. In this study, students were asked to rate the degree to which they thought animal research is wrong and the degree to which they thought it is necessary. As you might have expected, there was a negative relationship between these two variables: the more that students thought animal research is wrong, the less they thought it is necessary. The correlation based on 34 observations is -0.654. The problem is to compute a 95% confidence interval on ρ based on this r of -0.654.

The conversion of r to z' can be done using a calculator. This calculator shows that the z' associated with an r of -0.654 is -0.78.

The sampling distribution of z' is approximately normally distributed and has a standard error of

For this example, N = 34 and therefore the standard error is 0.180. The Z for a 95% confidence interval (Z.95) is 1.96, as can be found using the normal distribution calculator (setting the shaded area to .95 and clicking on the "Between" button). The confidence interval is therefore computed as:

Lower limit = -0.775 - (1.96)(0.18) = -1.13
Upper limit = -0.775 + (1.96)(0.18) = -0.43

The final step is to convert the endpoints of the interval back to r using a calculator. The r associated with a z' of -1.13 is -0.81 and the r associated with a z' of -0.43 is -0.40. Therefore, the population correlation (ρ) is likely to be between -0.81 and -0.40. The 95% confidence interval is:

-0.81 ≤ ρ ≤ -0.40

To calculate the 99% confidence interval, you use the Z for a 99% confidence interval of 2.58 as follows:

Lower limit = -0.775 - (2.58)(0.18) = -1.24
Upper limit = -0.775 + (2.58)(0.18) = -0.32

Converting back to r, the confidence interval is:

-0.84 ≤ ρ ≤ -0.31

Naturally, the 99% confidence interval is wider than the 95% confidence interval.

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