RESEARCH QUESTIONS

Understanding whether a research question is descriptive or explanatory is crucial as it significantly affects the research design and the information collected. Researchers must develop causal explanations when answering the 'why' questions. Causal explanations attempt to prove that a particular factor X, such as gender, affects a phenomenon Y, such as income level. While some causal explanations can be simple, others can be more complex.

When making predictions, researchers must distinguish between correlation and causation. It is a common mistake to assume that two events are causally related just because they occur together or one follows the other. The correlation is likely coincidental and does not indicate a causal relationship.

Distinguishing between causation and correlation is essential to accurately understanding prediction, causation, and explanation. It is important to note that an accurate prediction does not always require a causal relationship, and the ability to make a prediction does not necessarily prove a causal relationship. Confusing these concepts can lead to a lack of understanding and incorrect conclusions.

Recognizing the difference between correlation and causation is essential because we can observe correlation but directly observe causation. Therefore, we must infer the cause, making avoiding invalid inferences a primary goal of explanatory research design.

There are two approaches to causation: deterministic and probabilistic. In deterministic causation, variable X causes Y without exception if it reliably produces Y. This approach aims to establish causal laws, such as the rule that water boils at 100ºC.

However, most causal thinking in social sciences is probabilistic rather than deterministic. We can enhance probabilistic explanations by specifying the conditions under which one factor is more or less likely to affect another. However, we will never achieve complete or deterministic explanations. Two events are causally related because they occur together or follow the other. The correlation is likely coincidental and does not indicate a causal relationship.

Mistaking causation for correlation can lead to understanding prediction, causation, and explanation. Accurate prediction does not necessarily require a causal relationship, and the ability to predict does not prove causality.

Research objectives and questions can be single or multiple and can be covered synchronously  one at a time, or diachronically from the 1st to the 4th (Fig. 4).