This analytic tool provides a GLS-based within-study meta-analytic method to combine multiple correlation coefficients from a single study—accounting for the statistical dependency between them to produce a single pooled correlation estimate. It uses the generalized least squares (GLS) framework, weighting each correlation by its precision and accounting for their inter-correlations via a covariance matrix.

This analytic tool provides a series of useful descriptive statistics for evaluating a given data variable including (but not limited to): number of valid values, percentage of missing values, number of unique values, proportions of binary variables, min/max values, arithmetic mean, standard deviation, variance, standard error, 95% confidence interval around the mean, skewness, kurtosis, and the D'Agostino-Pearson K^2 Test for Normality.

A step-by-step guide to using the area of resilience to stress event (ARSE) method of quantifying the resilience process using the ‘arse’ R package. This guide is a companion to a paper introducing the ARSE method (Ratcliff, Mahoney-Nair, & Goldstein, 2019).

This code will help provide a colorful correlation plot displaying the Pearson correlation coefficient, a 95% confidence interval (CI) around the correlation coefficient, the p-value, and a indicator of effect size magnitude following Cohen's (1988) suggested small, medium, and large effect size thresholds.