Measurement of variables has a pervasive impact on all aspects of research
The way that researchers measure for variables has a drastic impact on their ability to detect precise and accurate treatment effects. While categorical and ordinal variables are important and prevalent in empirical research, researchers must always be cognizant of the increased sample size needed to detect clinically meaningful treatment effects measured at these levels.
So, if at all possible, try to measure for predictor, confounding, and outcome variables at a continuous level because it leads to increased statistical power, increased precision and accuracy, and decreased sample size.
Click on the Scales of Measurement button to learn about categorical, nominal, ordinal, interval, ratio, count, and continuous level measurement.
Click on the Types of Variables button to learn about demographic, independent, control, dependent, predictor, confounding, and outcome variables.
Scales of measurement and types of variables
Precision and accuracy in measurement
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