Which of the following are common quantitative research methods used to quantify responses and test hypotheses with numerical data?

• A. Case studies and ethnography
• B. Time-series analysis and regression
• C. Interviews and ethnography
• D. Surveys, experiments, analytics

Answer: (D)

The main idea is using methods that collect numerical information and allow you to test ideas with statistical reasoning. Surveys, experiments, and analytics fit this best because they are designed to produce numerical data you can analyze to draw conclusions.

Surveys gather standardized responses from participants, often using scales that yield numbers. This makes it straightforward to quantify attitudes, preferences, or behaviors and apply statistical tests to see if patterns hold across groups or conditions.

Experiments involve deliberately manipulating a variable and observing the effect on another variable under controlled conditions. The outcomes are numerical, enabling precise comparisons and hypothesis testing about cause-and-effect relationships.

Analytics refers to processing and interpreting data from various sources to produce metrics, trends, and insights. This typically results in numerical outcomes that can be used to evaluate hypotheses and performance.

Case studies and ethnography are primarily qualitative, emphasizing deep, contextual understanding through observations and descriptions rather than numerical measurement. Interviews and ethnography similarly lean toward qualitative data, though interviews can be structured to gather quantitative data, the overall emphasis here is on qualitative insight. Time-series analysis and regression are powerful numerical analysis techniques, but they are methods for analyzing data rather than foundational data-collection approaches used to quantify responses and test hypotheses.