Let's analyze the given assertion and reason statements about experimental research and causality.

Understanding Experimental Research

Experimental research is a quantitative research method where researchers manipulate one or more variables to see their effect on another variable. It is designed to establish cause-and-effect relationships.

• Independent Variable (IV): This is the variable that is manipulated or changed by the researcher. It is the presumed cause.
• Dependent Variable (DV): This is the variable that is measured to see if it changes as a result of the independent variable manipulation. It is the presumed effect.

Analyzing Assertion A

Assertion A: In experimental research, the effect of independent variable is examined on the dependent variable.

This statement accurately describes a fundamental aspect of experimental research. The core purpose is to observe how changes in the independent variable lead to changes in the dependent variable. For example, if a researcher studies the effect of a new teaching method (IV) on student test scores (DV), they are examining the effect of the independent variable on the dependent variable.

Thus, Assertion A is true.

Analyzing Reason R

Reason R: Causal relation between two variables can be established only by the experimental method.

Reason R claims that causality can *only* be established through the experimental method. Experimental research is widely considered the most robust method for establishing causal relationships because it allows for manipulation of the independent variable, control of confounding variables, and random assignment of participants to conditions. While other methods (like correlational studies) can identify associations, they cannot definitively prove cause and effect due to the possibility of lurking variables or reverse causality.

While some might argue about nuances in fields outside typical lab experiments, within the standard framework of research methodology focused on rigorous causal inference between two variables, the experimental method is indeed the primary and most reliable way to establish causality. Therefore, Reason R is largely considered true in this context.

Evaluating the Relationship between A and R

We have determined that both Assertion A and Reason R are true.

Now, let's consider if Reason R is the correct explanation for Assertion A. Assertion A states *what* is done in experimental research (examining the effect of IV on DV). Reason R states *why* the experimental method is important (it establishes causal relations). Reason R explains the *significance* or the *goal* behind using the experimental method (establishing causality), which makes examining the effect of the independent variable on the dependent variable crucial. However, Reason R doesn't explain *how* the examination happens or *why* examining the effect of IV on DV is the specific mechanism for establishing causality. It states the outcome (causality established) is linked to the method, but doesn't explain the process A describes.

Therefore, while both statements are true and related to experimental research, Reason R does not serve as a direct explanation of *why* the effect of the independent variable is examined on the dependent variable in the way Assertion A describes the action.

Conclusion on Assertion and Reason

Based on the analysis:

• Assertion A is true.
• Reason R is true.
• Reason R is not the correct explanation for Assertion A.

This aligns with the option stating that both A and R are true, but R is not the correct explanation of A.

Choosing the Correct Option

Given that A and R are true, and R is not the correct explanation of A, the correct option is the one that reflects this finding.

The correct answer option states: Both A and R are true but R is NOT the correct explanation of A.

Revision Table: Experimental Research Basics

Additional Information: Beyond Correlation

It is important to distinguish between correlation and causation. Correlation means two variables are related or move together (e.g., as one increases, the other tends to increase or decrease). However, correlation does not prove that one variable causes the other. There might be a third variable influencing both, or the relationship could be coincidental.

Experimental research addresses this by actively manipulating the independent variable under controlled conditions and randomly assigning participants. This control and manipulation are key to isolating the effect of the independent variable and confidently inferring a causal link to the dependent variable.

While observational studies, surveys, and correlational studies are valuable for identifying relationships and making predictions, they are generally insufficient to definitively establish cause and effect on their own. The experimental method, with its control and manipulation, provides the strongest evidence for causality in research.