If a researcher wanted to evaluate differences between treatment and control groups, which would be group or condition and would have two levels: treatment and control?

In the context of evaluating differences between treatment and control groups, the term 'independent variable' refers to a factor that is manipulated by the researcher to observe its effect on the outcome. In this case, the treatment versus control designation creates an independent variable with two distinct levels: the treatment group receives the intervention being tested, while the control group does not. This setup allows researchers to directly assess the impact of the treatment by comparing the outcomes of the two groups.

The independent variable is crucial in experimental design as it serves as the basis for determining whether any observed effect on the dependent variable (the outcome being measured) is attributable to the manipulation of the independent variable. In this instance, there is a clear distinction between the conditions under which the participants are placed, thereby allowing for an analysis of their responses based on the level of the independent variable.

In contrast, dependent variables refer to the outcomes that are being measured, while ratio and interval variables pertain to the types of data measurement scales used to assess the outcomes. Thus, when discussing the groups or conditions that specifically delineate treatment from control, identifying it as an independent variable is essential to the research framework.