Although intraclass correlation coefficients (ICCs) are commonly used in behavioral measurement, psychometrics, and behavioral genetics, procedures available for forming inferences about ICCs are not widely known. Following a review of the distinction between various forms of the ICC, this article presents procedures available for calculating confidence intervals and conducting tests on ICCs developed using data from one-way and two-way random and mixed-effect analysis of variance models.

Some of the shortcomings in interpretability and generalizability of the effect size statistics currently available to researchers can be overcome by a statistic that expresses how often a score sampled from one distribution will be greater than a score sampled from another distribution. The statistic, the common language effect size indicator, is easily calculated from sample means and variances (or from proportions in the case of nominal-level data). It can be used for expressing the effect observed in both independent and related sample designs and in both 2-group and n-group designs. Empirical tests show it to be robust to violations of the normality assumption, particularly when the variances in the 2 parent distributions are equal.

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Data from Web-delivered experiments conducted in browsers by remote users of PsychExperiments, a public on-line psychology laboratory, reveal experiment effects that mirror lab-based findings, even for experiments that require nearly millisecond accuracy of displays and responses. Textbook results are obtained not just for within-subjects effects, but for between-subjects effects as well. These results suggest that existing technology is adequate to permit Web delivery of many cognitive and social psychological experiments and that the added noise created by having participants in different settings using different computers is easily compensated for by the sample sizes achievable with Web delivery.

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