How many participants do you need for a correlational study?
When a study’s aim is to investigate a correlational relationship, however, we recommend sampling between 500 and 1,000 people. More participants in a study will always be better, but these numbers are a useful rule of thumb for researchers seeking to find out how many participants they need to sample.
What is a good sample correlation?
The possible range of values for the correlation coefficient is -1.0 to 1.0. In other words, the values cannot exceed 1.0 or be less than -1.0. A correlation of -1.0 indicates a perfect negative correlation, and a correlation of 1.0 indicates a perfect positive correlation.
How to compare the Pearson and Spearman correlations?
Comparing the Pearson and Spearman Correlation Coefficients Across Distributions and Sample Sizes: A Tutorial Using Simulations and Empirical Data Joost C. F. de Winter Delft University of Technology Samuel D. Gosling University of Texas at Austin and University of Melbourne Jeff Potter Atof Inc., Cambridge, Massachusetts
Which is the correct Pearson’s your correlation coefficient?
I would like to know if it is statistically correct to compare Pearson’s R correlation coefficients calculated from samples of different size. For example I have a sample of 160 observations that yield R=0.5 with the experimental values. After some filtering I end up with 130 observations and I get R=0.6.
What are the effects of sample size on correlation coefficients?
This post illustrates two important effects of sample size on the estimation of correlation coefficients: lower sample sizes are associated with increased variability and lower probability of replication.
When to use percentile bootstrap to compare Pearson correlations?
According to Wilcox (2009), a percentile bootstrap can be used to compare Pearson’s correlations, leading to satisfactory proportions of false positives. If instead of Fisher’s z we use a percentile bootstrap to compare Pearson’s correlations when g = h =0.2, we get the following results: