Measuring for large effect sizes is a great decision made by researchers. Large effect sizes can be detected with smaller sample sizes and always lead to increased statistical power. When measuring for large effect sizes in the relative sense, it may be a good idea to overestimate the variance of the effect size so that more observations will be collected.
Large effect sizes can be detected using all three scales of measurement: Categorical, ordinal, and continuous. Categorical and ordinal level measurement often decreases statistical power. Yet, if large effect sizes are hypothesized with categorical and ordinal outcomes, they will be much easier to detect. Large continuous effect sizes are the optimal combination of effect size and scale of measurement.
Large effect sizes can be detected using all three scales of measurement: Categorical, ordinal, and continuous. Categorical and ordinal level measurement often decreases statistical power. Yet, if large effect sizes are hypothesized with categorical and ordinal outcomes, they will be much easier to detect. Large continuous effect sizes are the optimal combination of effect size and scale of measurement.
Large effect sizes increase statistical power and decrease the needed sample size.
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