Scales of measurement
There are three primary scales of measurement in applied statistical analysis
Ordinal variables provide a sense of order and most often are used in applied research as Likert-type scales. They can provide a measure of distance, but not magnitude. Non-parametric statistics are used with ordinal outcomes.
The final and most powerful scale of measurement is continuous. Continuous level measurement possesses a "true zero," meaning that it can provide a measure of both distance and magnitude. Continuous level measurement provides the most precise and accurate level of measurement for an outcome or variable. In applied research, interval, ratio, and count variables are treated the same as continuous variables. Parametric statistics are used with continuous outcomes.
The way that researchers measure for their predictor and outcome variables in terms of scale of measurement has a drastic impact on statistical power, or the ability to detect significant treatment effects. Categorical and ordinal scales of measurement decrease statistical power due to limited precision and accuracy in measurement. Continuous measurement possesses a "true zero" that allows for both distance and magnitude to be detected, leading to more precision and accuracy when measuring for variables or outcomes. Continuous level measurement will always increase statistical power due to increased precision and accuracy in measurement.
There are three primary scales of measurement: Categorical, ordinal, and continuous
More scales of measurement
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