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Abstract
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OBJECTIVES
Due to time and financial limitations, most randomized controlled trials (RCTs) are conducted employing non-random sampling techniques. Although valuable, when the unique characteristics of a non-random sample unknowingly interact with the treatment, the results of the RCT could become biased. Nevertheless, the amount of bias remains unexamined.
Methods
The current study evaluated if non-random sampling techniques could bias the slope coefficients of an RCT when an interaction exists between the treatment and a characteristic in the population using two simulation analyses.
Results
The results suggested that the sampling distributions of slope coefficients from an RCT -- across random specifications -- expand drastically when (1) an interaction between the treatment and a characteristic in the population exists and (2) the non-random sample has unique scores on that characteristic.
Conclusions
Considering these findings, four recommendations are made for scholars currently or intending to conduct a RCT employing non-random sampling techniques.
Language: en |