Final answer:
Interpretation between interactions of the independent variables in a factorial design with four or more variables becomes more difficult, not simpler. Recruiting participants and controlling for all variables is more complex, with random assignment crucial for ensuring balance.
Step-by-step explanation:
When considering potential complications of a factorial design with four or more independent variables, the statement that interpretation between interactions of the independent variables becomes more difficult is true. As the number of independent variables increases, so does the complexity of the interactions that can occur among them. This complexity can make it more challenging to determine the individual and combined effects of each independent variable on the dependent variable. Additionally, the practicality of recruiting participants becomes more complex due to the need for a larger sample size that can adequately represent all combinations of independent variables. This also introduces more issues of control to consider, as each group must be exposed to a unique combination of independent variables while controlling for potential lurking variables. Random assignment of participants to treatment groups is a critical method to ensure that lurking variables are spread equally among the groups, maintaining the integrity of the experimental manipulation. A control group is necessary to compare results with the experimental groups where the independent variables are manipulated, allowing for a cause-and-effect connection between the explanatory and the response variables to be established. If properly executed, when different outcomes in the response variable are measured, they can be attributed to the different treatments.