Final answer:
In between-subject designs, ensure explanatory and response variables are clearly defined, participants are randomly assigned, lurking variables are controlled for, and consider using blinding to minimize bias.
Step-by-step explanation:
When designing an experiment, specifically a between-subject design, it's vital to observe several factors to ensure the validity and reliability of the results. The explanatory variable, also known as the independent variable, is the one that is manipulated by the researcher to observe its effect on the response variable, or dependent variable. The treatments refer to the different conditions or interventions that the experimental groups are subjected to.
Participant selection should be made with care to avoid bias, and ideally, participants should be randomly assigned to the treatment groups. This randomization helps to prevent pre-existing differences among participants from affecting the results. For example, dividing participants randomly into two groups where one drives without distraction and the other texts while driving seems reasonable for studying the impact of texting on driving performance. However, ethical considerations must be taken into account when exposing participants to potentially harmful situations.
Several lurking variables could interfere with the study, such as participants' driving experience, stress levels, or familiarity with the test vehicle. These variables should be controlled for or at least acknowledged when interpreting the results. Blinding can be implemented to reduce bias, where participants or researchers (or both) are unaware of who is assigned to which treatment group. In a single-blind study, the participants, for example, do not know whether they are in the control or experimental group. This practice minimizes the power of suggestion and expectancy that could influence the outcome.