t-Test for Zero Correlation: Interval Estimation - Quick Tab
Noncentrality Interval Estimation and the Evaluation of Statistical Models
Select the Quick tab of the t-Test for Zero Correlation: Interval Estimation dialog box to access options to calculate exact and approximate confidence intervals for the population correlation. These confidence intervals will commonly be employed in situations where the t-test on a single correlation is calculated. The exact confidence interval can, in fact, be used in lieu of the (two-tailed) hypothesis test procedure, since it excludes zero if and only if the hypothesis test (at the same level of type I error) fails to reject.
- Observed R
- In the Observed R box, enter the value of the observed correlation. If you are attempting to calculate the confidence interval, based on a reported value of a t-statistic or its probability level, you can reconstruct the value of r, using the probability distribution calculator.
- Sample Size (N)
- In the Sample Size (N) box, enter the sample size (N) for the experimental group used in the study.
- Conf. Level.
- In the Conf. Level box, enter the confidence level for the confidence interval calculation.
- Computational Algorithm
- Most programs that include methods for power analysis of tests on correlations do not provide exact probability calculations. The Power Analysis module allows you to choose from three algorithms:
- Exact
- Select the Exact option button to obtain exact calculations. These calculations take substantially longer to compute than the approximations given below, but the calculation is not prohibitively time-consuming for the sample sizes typically employed in research studies.
- Fisher Z Refined
- Select the Fisher Z Refined option button to employ an approximation based on the Fisher transformation, but using refined series approximation formulae for the mean and variance (Fouladi, 1991).
- Fisher Z Crude
- Select the Fisher Z Crude option button to use the traditional (somewhat crude) approximation, based on the Fisher transformation, assuming that the mean of the Fisher transform of r is the Fisher transform of ρ, and that the variance of the Fisher transform of r is 1/(N - 3).
Copyright © 2021. Cloud Software Group, Inc. All Rights Reserved.