Explain the difference between quantitative and qualitative data with examples.

Quantitative and qualitative data are two types of data that researchers collect in their studies. They differ in what they communicate and how they are collected.

1. Quantitative Data: This type of data is numerical. It is used to quantify the problem being studied. It can be counted, measured, and expressed using numbers. Quantitative data can be used to construct graphs and tables of raw data. It's often collected through methods like online surveys, face-to-face interviews, telephone surveys, longitudinal studies, website interceptors, and online polls.

Example: If you conduct a survey to find out how many people like a particular brand of soda, the data you collect would be quantitative. The data might show that 200 people like Brand A, 150 people like Brand B, and 50 people like Brand C.

2. Qualitative Data: This type of data is non-numerical. It is used to understand concepts, thoughts or experiences. It can't be measured in the traditional sense. Instead, it is categorized based on properties, attributes, labels, and other identifiers. Qualitative data is often collected through methods like individual interviews, focus groups, observations, action research, immersion, or case studies.

Example: If you conduct a focus group to find out why people like a particular brand of soda, the data you collect would be qualitative. The data might show that people like Brand A because it's less sweet, people like Brand B because it's fizzy, and people like Brand C because it has a unique flavor.

In summary, quantitative data provides the what, while qualitative data provides the why and how.