Difference between Random Selection and Random Assignment
People commonly confuse or use random selection and random assignment interchangeably, though the terms refer to entirely different processes. It refers to how researchers select sample members (study participants) from the population for inclusion in the study. Assignment is an aspect of experimental design where researchers randomly assign study participants to the treatment or control group.
Selection requires using a form of random sampling, such as stratified random sampling, where researchers sort the population into groups and randomly choose sample members. Random sampling is a probability method that uses the laws of probability to select a sample, allowing inferences to the population and forming the basis of statistical tests of significance.
Random assignment takes place following the selection of participants for the study. In a true experiment, researchers randomly assign all study participants to receive the treatment (stimulus or intervention) or act as a control (not receiving the treatment). Although random assignment is simple (e.g., flipping a coin), it can be challenging to implement outside of controlled laboratory conditions.
A study can use both, only one, or neither. Here are some examples to illustrate each situation:
A researcher gets a list of all students enrolled at a particular school (the population). Using a random number generator, the researcher selects 100 students from the school to participate in the study (the random sample). They randomly choose 50 students for the intervention and assign 50 to the control group. This design uses both random selection and random assignment.
A study with random assignment could have the principal select students and randomly assign them to treatment and control groups. In this design, the researcher could conclude the intervention’s effect but couldn’t infer if it applies to the population.
A study using random selection could assign one grade to the intervention and another to the control group. Data could infer the school population, but without random assignment, the intervention’s effect cannot be concluded.
Random selection is essential to external validity, as it allows researchers to generalize study results to the larger population. Random assignment is crucial for internal validity, enabling the researcher to make causal claims about the treatment’s effect. Nonrandom assignment can lead to non-equivalent groups, where treatment effects may stem from pre-existing differences. Random selection and assignment serve different purposes, and strong research design uses both to ensure validity.
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