How do you formulate a testable research question and a corresponding hypothesis in quantitative research?

How do you formulate a testable research question and a corresponding hypothesis in quantitative research?

• A. A good research question is broad and abstract.
• B. A hypothesis must be a narrative description.
• C. A good research question is informal and broad.
• D. A hypothesis is a clear, testable prediction about a relationship between defined variables.

Answer: (D)

In quantitative research, the testable question is sharpened into a precise, measurable focus, and the hypothesis turns that question into a clear, testable prediction about how defined variables relate. A good research question is specific enough to be operationalized—names the variables, the population, and how you’ll measure them—so you can collect data and assess relationships. The hypothesis then makes a declarative statement about the expected relationship between those variables, so it can be tested with data and statistical analysis. It should be explicit about the variables and the predicted direction (if you expect one), and it must be falsifiable. For example, you might predict that more study hours (independent variable) are associated with higher exam scores (dependent variable) among first-year students, with the relationship defined in a way you can measure and analyze. This combination—defined variables, measurable constructs, and a testable prediction—is what makes the hypothesis the best fit for guiding a quantitative study.

In quantitative research, the testable question is sharpened into a precise, measurable focus, and the hypothesis turns that question into a clear, testable prediction about how defined variables relate. A good research question is specific enough to be operationalized—names the variables, the population, and how you’ll measure them—so you can collect data and assess relationships. The hypothesis then makes a declarative statement about the expected relationship between those variables, so it can be tested with data and statistical analysis. It should be explicit about the variables and the predicted direction (if you expect one), and it must be falsifiable. For example, you might predict that more study hours (independent variable) are associated with higher exam scores (dependent variable) among first-year students, with the relationship defined in a way you can measure and analyze. This combination—defined variables, measurable constructs, and a testable prediction—is what makes the hypothesis the best fit for guiding a quantitative study.