The point-biserial correlation is a commonly used measure of effect size in two-group designs. New estimators of point-biserial correlation are derived from different forms of a standardized mean difference. Point-biserial correlations are defined for designs with either fixed or random group sample sizes and can accommodate unequal variances. Confidence intervals and standard errors for the point-biserial correlation estimators are derived from the sampling distributions for pooled-variance and separate-variance versions of a standardized mean difference. The proposed point-biserial confidence intervals can be used to conduct directional two-sided tests, equivalence tests, directional non-equivalence tests, and non-inferiority tests. A confidence interval for an average point-biserial correlation in meta-analysis applications performs substantially better than the currently used methods. Sample size formulas for estimating a point-biserial correlation with desired precision and testing a point-biserial correlation with desired power are proposed. R functions are provided that can be used to compute the proposed confidence intervals and sample size formulas.

The limitations of Cohen’s κ are reviewed and an alternative G-index is recommended for assessing nominal-scale agreement. Maximum likelihood estimates, standard errors, and confidence intervals for a two-rater G-index are derived for one-group and two-group designs. A new G-index of agreement for multirater designs is proposed. Statistical inference methods for some important special cases of the multirater design also are derived. G-index meta-analysis methods are proposed and can be used to combine and compare agreement across two or more populations. Closed-form sample-size formulas to achieve desired confidence interval precision are proposed for two-rater and multirater designs. R functions are given for all results.

Two interval estimation methods for a general linear function of binomial proportions have been proposed. One method [Zou GY, Huang W, Zhang X. A note on confidence interval estimation for a linear function of binomial proportions. Comput Statist Data Anal. 2009;53:1080–1085] combines Wilson interval estimates of individual proportions, and the other method [Price RM, Bonett DG. An improved confidence interval for a linear function of binomial proportions. Comput Statist Data Anal. 2004;45:449–456] uses an adjusted Wald interval. Both methods are appropriate in varying coefficient meta-analysis models where the risk differences are allowed to vary across studies. The two methods were compared in a simulation study under realistic meta-analysis conditions and the adjusted Wald method was found to have the best performance characteristics.

Asymptotically optimal correlation structure methods with binary data can break down in small samples. A new correlation structure methodology based on a recently developed odds-ratio (OR) approximation to the tetrachoric correlation coefficient is proposed as an alternative to the LPB approach proposed by Lee et al. (1995). Unweighted least squares (ULS) estimation with robust standard errors and generalized least squares (GLS) estimation methods were compared. Confidence intervals and tests for individual model parameters exhibited the best performance using the OR approach with ULS estimation. The goodness-of-fit chi-square test exhibited the best Type I error control using the LPB approach with ULS estimation.