What do modified/natural/quasi-experiments have in common with correlational and experimental designs?

What do modified/natural/quasi-experiments have in common with correlational and experimental designs?

• A. They examine cause-and-effect relationships, include independent and dependent variables, and compare groups without random assignment.
• B. They always use random assignment.
• C. They ignore independent and dependent variables.
• D. They cannot compare groups.

Answer: (A)

These designs share a focus on examining how variables relate to each other across groups without using random assignment. In modified, natural, and quasi-experiments, researchers work with pre-existing groups and compare outcomes to see whether a variable functions like a cause and another as the effect, even though participants aren’t randomly assigned to conditions. They still frame the situation in terms of a variable that is treated as the possible influence (an independent-variable likeness) and an outcome variable (a dependent-variable likeness), to assess how groups differ or how changes unfold. Correlational designs fit into this by probing relationships between variables to see how they move together, rather than manipulating them directly. The shared thread is the non-randomized comparison of groups or conditions to understand how variables relate, which is also the sense in which causal ideas are explored, though the strength of causal inference is strongest in true experiments. Options that claim random assignment is always used, or that these designs ignore variables or cannot compare groups, don’t fit. Random assignment is not a feature of quasi-experiments, and correlational designs do not manipulate variables in the way true experiments do, though they still examine relationships and often involve comparing groups or levels.

These designs share a focus on examining how variables relate to each other across groups without using random assignment. In modified, natural, and quasi-experiments, researchers work with pre-existing groups and compare outcomes to see whether a variable functions like a cause and another as the effect, even though participants aren’t randomly assigned to conditions. They still frame the situation in terms of a variable that is treated as the possible influence (an independent-variable likeness) and an outcome variable (a dependent-variable likeness), to assess how groups differ or how changes unfold.

Correlational designs fit into this by probing relationships between variables to see how they move together, rather than manipulating them directly. The shared thread is the non-randomized comparison of groups or conditions to understand how variables relate, which is also the sense in which causal ideas are explored, though the strength of causal inference is strongest in true experiments.

Options that claim random assignment is always used, or that these designs ignore variables or cannot compare groups, don’t fit. Random assignment is not a feature of quasi-experiments, and correlational designs do not manipulate variables in the way true experiments do, though they still examine relationships and often involve comparing groups or levels.